Contents
1

2

3

4

5

Introduction ..........................................................................................................1
1.1

Probability: the likelihood of future tectonic impacts on a repository ........................... 1

1.2

Using the ITM Methodology ......................................................................................... 2

1.3

References for Section 1.............................................................................................. 4

The ITM Methodology Road Map ........................................................................5
2.1

Outline of the ITM Methodology ................................................................................... 5

2.2

The Steps in the ITM Methodology .............................................................................. 7

2.3

References for Section 2............................................................................................ 25

The Geology and Tectonics of Kyushu. Part 1: Tectonic Setting and
Evolution from 150 Ma to 15 Ma........................................................................27
3.1

Kyushu Basement Terranes....................................................................................... 29

3.2

Tectonic History of Kyushu Region ............................................................................ 29

3.3

Summary .................................................................................................................... 30

3.4

References for Section 3............................................................................................ 31

The Geology and Tectonics of Kyushu. Part 2: Tectonic Evolution ofsouthwest Japan since 15 Ma ...........................................................................34
4.1

15 Ma to 10 Ma .......................................................................................................... 34

4.2

10 Ma to 6 Ma ............................................................................................................ 35

4.3

6 Ma to 2 Ma .............................................................................................................. 36

4.4

2 Ma to Present .......................................................................................................... 37

4.5

Present Day Tectonic Situation.................................................................................. 40

4.6

References for Section 4............................................................................................ 42

Volcanic History of Kyushu...............................................................................44
5.1

Dating and Classifying Volcanoes.............................................................................. 44

5.2

The Kyushu Volcano Dataset..................................................................................... 44

5.3

Introduction to the Three Volcanic Regions ............................................................... 45

5.3.1

Types of volcanism and occurrence throughout Kyushu .................................... 46

5.3.2

Geochemistry of the volcanic rocks in Kyushu ................................................... 46

5.4

Volcanic history of Kyushu since 15 Ma..................................................................... 48

5.4.1

15-10 Ma ............................................................................................................. 48

5.4.2

10-6 Ma ............................................................................................................... 49

5.4.3

6-2 Ma ................................................................................................................. 50

5.4.4

2-1 Ma ................................................................................................................. 52

5.4.5

1-0.3 Ma .............................................................................................................. 54

5.4.6

0.3-0.01 Ma ......................................................................................................... 56

5.4.7

Active Volcanism (Holocene) .............................................................................. 58

5.5

Future volcanism in Kyushu ....................................................................................... 61

5.6

References for Section 5............................................................................................ 61i6Discrimination of Tectonic Domains and Selection of Example Locations..64
6.1

7

Definition of tectonic domains .................................................................................... 64

6.1.1

The Back-arc domain .......................................................................................... 65

6.1.2

The Extensional Arc domain ............................................................................... 66

6.1.3

The Southern Arc domain ................................................................................... 67

6.1.4

The Forearc domain ............................................................................................ 68

6.2

Selection of example locations in Kyushu for more detailed evaluation .................... 68

6.3

References for Section 6............................................................................................ 70

Volcano-tectonic Interactions in Kyushu and Implications for Future
Tectonic and Volcanic Evolution ......................................................................71
7.1

Interplay Between Volcanism and Faulting ................................................................ 71

7.2

Large-scale Plate Tectonic Influences ....................................................................... 72

7.2.1

8

9

Key tectonic elements that influence faulting...................................................... 74

7.3

Evolution of Volcanism and Tectonism over the Next 1 Ma....................................... 77

7.4

Implications for Tectonic/Volcanic Hazard Evaluation ............................................... 78

7.5

References for Section 7............................................................................................ 79

Strain Rates from Surface Deformation ...........................................................80
8.1

Active fault datasets ................................................................................................... 80

8.2

Vertical deformation datasets..................................................................................... 83

8.3

Active Fault Strain Rate Calculations and Logic Tree................................................ 85

8.4

Active Fault Strain Map and Example Location Calculations..................................... 86

8.5

References for Section 8............................................................................................ 89

Strain Rates from GPS.......................................................................................90
9.1

GPS Data Used.......................................................................................................... 90

9.2

Approach Used to Model GPS Data in Kyushu ......................................................... 92

9.3

Elastic Block Model Set-up for Kyushu ...................................................................... 94

9.4

Block Modelling Results for the Best-Fitting Tectonic Model ..................................... 98

9.5

Upper Plate Strains in Kyushu ................................................................................. 100

9.6

Tectonic implications ................................................................................................ 102

9.6.1 Is left-lateral shear in southern Kyushu caused by collision with the KyushuPalau Ridge? ................................................................................................................. 103
9.7

Probabilistic GPS Strain Estimates .......................................................................... 106

9.7.1

Weighted average of GPS strain models from logic tree .................................. 106

9.7.2

Histograms of GPS strain values at selected locations in Kyushu ................... 107

9.8

Notes and Caveats................................................................................................... 113

9.9

References for Section 9.......................................................................................... 114

10 Strain Rates from Seismicity...........................................................................117
10.1 Seismicity of Kyushu ................................................................................................ 117
10.2 Methodology............................................................................................................. 118
10.3 Results ..................................................................................................................... 119

ii

10.4 Discussion ................................................................................................................ 122
10.5 Conclusions.............................................................................................................. 124
10.6 References for Section 10........................................................................................ 124

11 Nature and Spatial Probability of Future Rock Deformation in Each Domainusing Example Locations ................................................................................126
11.1 Back-arc domain ...................................................................................................... 129
11.2 Extensional domain .................................................................................................. 130
11.3 Forearc domain ........................................................................................................ 130
11.4 Southern Arc domain................................................................................................ 131

12 Probability of Future Volcanism .....................................................................132
12.1 A Methodology for Assessing Long-term Hazard Due to Monogenetic Volcanism . 132
12.1.1 Background to an Overall Methodology ............................................................ 133
12.1.2 Methods of Estimating Spatial Density.............................................................. 133
12.2 Testing Models with Data from the Abu volcanic field, SW Honshu ........................ 136
12.2.1 The Abu Monogenetic Volcano Group .............................................................. 136
12.2.2 Determination of volcanic centres in the Abu Monogenetic Volcano Group..... 139
12.2.3 Spatial density and temporal recurrence rate ................................................... 139
12.2.4 Seismic tomography.......................................................................................... 141
12.2.5 Discussion ......................................................................................................... 143
12.3 Introduction to the Comprehensive Evaluation of Kyushu ....................................... 144
12.4 Assessing volcanism on a regional scale................................................................. 145
12.5 Assessing volcanism within tectonic domains ......................................................... 146
12.6 Assessing volcanic hazard on the scale of individual volcanic systems.................. 150
12.7 Event Magnitude and Probability Estimates ............................................................ 152
12.7.1 Event definition.................................................................................................. 153
12.7.2 Recurrence Rate of Caldera Events ................................................................. 153
12.7.3 Spatial Density of Caldera Events..................................................................... 154
12.7.4 Calculation of probability ................................................................................... 154
12.8 Estimation of volcanic hazard using a Cox process with a multivariate potential .... 157
12.8.1 Conceptualisation.............................................................................................. 157
12.8.2 Model development ........................................................................................... 157
12.8.3 Application at regional scale to Kyushu ............................................................ 160
12.8.4 Application to Kyushu Tectonic Domains.......................................................... 163
12.8.5 Conclusions and perspectives .......................................................................... 167
12.9

References for Section 12................................................................................. 168

13 Comparison of Rock Deformation Strain and Volcanic Hazard Results for
Kyushu ..............................................................................................................169
13.1 Normalised rock deformation strain rates ................................................................ 169
13.2 Normalised volcanic hazard ..................................................................................... 173
13.3 Comparison and combination of rock deformation strain and volcanic hazards ..... 174iii

13.4 Volcanic hazard and surface deformation................................................................ 176
13.5 Volcanic hazard and GPS-derived strain ................................................................. 177
13.6 Volcanic hazard and seismic strain.......................................................................... 178
13.7 Combination of volcanic hazard and all rock deformation strain estimates ............. 178
13.8 Discussion ................................................................................................................ 179
13.9 References for Section 13........................................................................................ 180

14 Conclusions......................................................................................................182
14.1 Performance of the ITM Methodology in Kyushu..................................................... 182
14.2 Limitations of the Current Version of the Methodology ............................................ 183
14.3 Limitations of the Kyushu Dataset............................................................................ 183
14.4 Opportunities for Further Methodology Development .............................................. 184
14.5 Refining the Methodology for Downscaling to Sub-regional or Site Scale............... 184
14.6 Refining the Methodology for the 100 k years to 1 million years period .................. 186

iv

1 Introduction
NUMO is responsible for the siting, development and operation of one or more deep
geological repositories for the disposal of high level waste (HLW) and transuranic wastes(TRU) in Japan. The process is expected to take at least 15 years to reach the point of
repository construction. During the period before this, NUMO will need to evaluate sites thatemerge from the ‘volunteer process’ (whereby local communities have been invited tovolunteer to be considered as potential hosts for the repository) and select a preferred site.
This evaluation will involve initially surface based and then underground site characterisation
work. Underground characterisation work will only take place at the preferred site. Prior to thesurface based investigations, volunteer sites will have first had to pass a test of generalsuitability and NUMO will then have carried out a detailed, literature-based preliminary
evaluation of suitability, prior to accepting them as ‘Preliminary Investigation Areas’ (PIAs).
Because Japan lies in such a tectonically active region of the world on the Pacific rim (the socalled ‘ring of fire’), a key aspect of all these steps is consideration of the susceptibility of asite to future tectonic activity and tectonically driven processes and events.
In particular, the potential for volcanic and rock deformation impacts on a repository siteneeds to be considered at each stage of NUMO’s siting programme. Whilst the nationwide
evaluation factors for qualification (EFQs) for PIA acceptance are designed to remove clearly
unsuitable sites from consideration, they cannot guarantee that, over the next tens of
thousands of years, the risks of tectonic hazard for a chosen PIA will be acceptable. This isbecause large parts of Japan that are potentially suitable for siting are directly affected tovarying extents by rock deformation, the peripheral impacts of volcanic activity or the
possibility of new magma intrusion or volcanic activity. The EFQs were only intended as
preliminary screening guidelines to prevent obviously poor candidates entering the siting
process.
Consequently, an integration of additional and more refined techniques is required to evaluatesites that pass the EFQ test, so that NUMO can have a clear idea of the likelihood andpotential impacts of tectonic events and processes at each PIA. The ITM project was
designed to provide NUMO with such a methodology, based upon state-of-the-art approaches
used internationally, developed and extended for the specific purposes of NUMO and thespecific conditions of Japan: hereafter, we refer to it as the ‘ITM Methodology’.

1.1

Probability: the likelihood of future tectonic impacts on a repository

During the course of the ITM project, both NUMO and the Japanese regulatory agencies wereconsidering how best to handle the evaluation of low probability, disruptive events (e.g.
volcanic intrusion, fault rupture) and deformation processes that are discontinuous in time andmagnitude in response to continuous regional strain, when carrying out safety assessments ofgeological repositories for radioactive wastes. Essentially, two approaches have been
adopted internationally to address this situation:1

To calculate the health risk1 to people in the future by combining the probability of a
disruptive event occurring with its radiological consequences in terms of releases
from a repository: simply, risk = probability x consequence. With this approach,regulatory standards or targets can be defined in terms of risk to an individual.



To consider the impacts of a disruptive event and calculate the radiological doses2 topeople in the future and then, separately, to discuss the likelihood that this might
happen (the so-called ‘disaggregated’ approach). With this approach, separate
regulatory targets for radiation doses might be set for events (or scenarios) with

Health risk is normally defined as the risk of death or serious genetic effects.

2

Of course, a radiological dose can also be expressed in terms of health risk, by applying accepted dose-to-risk
conversion factors.

1different degrees of likelihood (often expressed qualitatively; e.g. ‘likely’, ‘less likely’,‘highly unlikely’).
In either approach, an appreciation of probability is essential: in the first ‘risk approach’ asound quantitative estimate will provide more confident estimation of risk; in the second,some form of quantification of ‘likelihood’ is needed to decide which category to place anevent or scenario into.
The ITM methodology that has been developed in this Project is centred on a probabilistic
approach. A probabilistic approach is seen by the ITM group as the only realistic means ofquantitatively addressing the uncertainties in assessing possible hazards when there ismarked variability in the spatial distribution, the timing, the intensity and the style of the
volcanic and deformational events and processes being evaluated (for convenience, in thisreport, we frequently group these together within the general term ‘tectonic events andprocesses’). The probabilistic approach being developed is based upon and strongly
supported by deterministic models of the underlying tectonic processes that lead to magma
intrusion, volcanism and rock deformation.
NUMO is developing both the probabilistic ITM methodology and other, independent,
deterministic approaches, in parallel projects that will eventually be deployed at volunteer
sites when they arise. The weight that will be given to deterministic and probabilistic
evaluation results will depend, to some extent, on the nature and the geographical location ofthese sites. It will also be influenced by the direction in which regulatory development moveover the next few years. As noted above, however, some quantification or estimation ofprobabilities is required in any reasonable approach to safety evaluation and regulation. For
the specific tectonic circumstances of Japan, this is especially relevant and important.

1.2

Using the ITM Methodology

The probabilistic ITM methodology will be used at three important stages of NUMO’s siting
programme:


SITING STAGE 1: during the literature survey (LS) stage when potential PIAs arebeing assessed. The ITM methodology will use currently available information toallow comparison of sites in terms of confidence that they are likely to prove
acceptable with respect to tectonic impacts.



SITING STAGE 2: during the planning of the PIA site investigations, to identify
geoscientific information requirements that will be needed to refine the Stage 1
analysis.



SITING STAGE 3: at the point where PIAs are being evaluated and compared inorder to select a preferred site (or sites) for detailed investigation (as DIAs).

The ITM project is mainly concerned with Stages 1 and 2. As will be seen from this report, themethodology is currently focussed on evaluating comparative hazards of small (25 km2) areas
within a regional or sub-regional context of 100,000 to 10,000 km2 (see Figure 1.1). This ispartly because the project originally developed to compare several possible alternative
volunteer sites that might arise within a region. However, it is clear that regional to subregional scale assessment of tectonic hazard will also be required even for single sites.
Application of the methodology in Siting Stage 3 is several years into the future and it isexpected that it will be most efficient to carry out any necessary updates/refinements on aregion-specific basis during the PIA investigations when NUMO has narrowed down to agroup of sites. The ITM project has involved methodology development and testing only anddoes not include actual deployment for volunteer sites/regions.

2• Comparing multiple siting options• Overall tectonic context for a single site
Detailed site specifichazard assessment

100,000 km2
Regional Scale

TohokuCase
Studye.g. Tohoku andKyushu Case Study
Areas

Regional
evaluations are
required toassess tectonic
hazards ateither a single
site or multiple
sites

1000 km2(c. 30 x 30 km)

Site Scale
10,000 km2
KyushuCase
Study

Sub-regional Scalee.g. Kyushu Case Study
Tectonic Domains
Regional tectonic context
for a single site

Figure 1.1: The ITM Methodology is applied at the regional to sub-regional scale, whether comparing
alternative sites or assessing the tectonic hazard for a single site.

The overall structure of the ITM methodology is described in Section 2 of this report andconsists of:

assembling nationally available data and alternative models of the nature, causes andlocations of tectonic processes and events;using probabilistic techniques to evaluate the likelihood and scale of future tectonic
processes and events, shown as a function of their type and geographicaldistribution;feeding information on these potential likelihoods and impacts to NUMO’s
performance assessment team so that feedback can be provided on repository
performance under tectonic stress;

providing clearly justified and traceable input to decision-making on consequent site
suitability.

For convenience, the methodology for rock deformation and volcanic hazards assessment
has been applied as two parallel tasks. This recognises the fact that, although the concept ofeach approach as shown above is similar, in some parts of the methodology they differ
significantly in detail. Consequently, it was found that two teams with different specialities(structural, geophysics and tectonics specialists; volcanologists) worked efficiently in parallel.
However, it is most important that, if carried out in this manner in future, the two ‘discipline’
teams integrate their work frequently, as there are clear overlaps in the processes being
evaluated (e.g. magma intrusion has an impact on rock stress regimes and vice versa).
NUMO will need to ensure that such integration is carried out effectively when themethodology is applied to ‘real’ sites.
The methodology was first tested during its development by means of a Case Study of the
Tohoku region of northern Honshu (Chapman et al., 2009). The first Case Study looked into
the varied strain response of the crustal plate to subduction of the Pacific Ocean plate (thekey current tectonic driver for much of Japan) and the mechanisms that underlie the apparent
clustering of Quaternary volcanoes in much of Honshu.

3The present report covers the final results of a second Case Study designed to further thedevelopment and testing of the methodology and look at a tectonically quite different setting inJapan. This Kyushu Case Study was developed specifically to look at the more complex (thanTohoku) interrelationship of rock deformation and volcanism in a region characterised bythree or four domains responding to identifiably different tectonic mechanisms. In contrast to
Tohoku, it is also an area with significant monogenetic volcanism. During the transition fromthe Tohoku to the Kyushu Case Studies, the ITM Methodology had also been updated andimproved.
This report is a compilation of material provided by research groups from Japan, New
Zealand, Switzerland, the United Kingdom and the United States.

1.3

References for Section 1

Chapman, N., M. Apted, J. Beavan, K. Berryman, M. Cloos, C. Connor, L. Connor, O. Jaquet,
N. Litchfield, S. Mahony, W. Smith, S. Sparks, M. Stirling, P. Villamor and L. Wallace(2009). Development of Methodologies for the Identification of Volcanic and Tectonic
Hazards to Potential HLW Repository Sites in Japan: The Tohoku Case Study. Nuclear
Waste Management Organisation of Japan, Tokyo. Technical Report: NUMO-TR-08-03.
135 pps.

4

2 The ITM Methodology Road Map
This Section provides an outline of the overall concept and methodology that has beendeveloped by ITM in the form of a ‘road map’. The methodology development is essentiallycomplete and this road map represents a description and checklist for application to volunteer
sites by NUMO staff and contractors in Siting Stages 1 and 2.
As discussed in Section 1, the methodology should be regarded as a living entity, as it must
respond to new scientific knowledge and techniques that will arise over the next few years.
Consequently, it will have to be revisited from time to time. This is especially true with respect
to application in Siting Stage 3 (at the point where PIAs are being evaluated and compared inorder to select a preferred site, or sites, for detailed investigation as DIAs), as this may be fiveor more years into the future.

2.1

Outline of the ITM Methodology

The overall structure of the ITM methodology consists of:

assembling nationally available data and alternative models of the nature, causes andlocations of tectonic processes and events;using probabilistic techniques to evaluate the likelihood and scale of future tectonic
processes and events, shown as a function of their type and geographicaldistribution;feeding information on these potential likelihoods and impacts to NUMO’s
performance assessment team so that feedback can be provided on repository
performance under tectonic stress;

providing clearly justified and traceable input to decision-making on consequent site
suitability.

A probabilistic approach has been selected for the ITM methodology as it is seen as the only
realistic means of addressing the uncertainties in predicting possible hazards when there issuch variability in the spatial distribution, the timing, the intensity and the style of the volcanicand deformational events and processes being evaluated.
Naturally, the probabilistic approach being developed is based upon and strongly supported
by deterministic models of the underlying tectonic processes that lead to magma intrusion,
volcanism and rock deformation.
For convenience, the methodology for rock deformation and volcanic hazards assessment
has been applied as two parallel tasks. This recognises the fact that, although the concept ofeach approach as shown above is similar, in some parts of the methodology they differ
significantly in detail. Consequently, it was found that two teams with different specialities(structural, geophysics and tectonics specialists; volcanologists) worked efficiently in parallel.
However, it is most important that, if carried out in this way in the future, the two teams
integrate their work frequently, as there are clear overlaps in the processes being evaluated(e.g. magma intrusion impacts on rock stress regimes and vice versa). NUMO will need toensure that such integration is carried out effectively when the methodology is applied to ‘real’sites.
The broad structure presented above is shown in more detail in the top-level ‘road-map’ inFigure 2.1. It comprises a series of eight Steps, which are described in Section 2,
distinguishing between the ‘rock deformation’ approach and the ‘volcanic’ approach, where
they involve significantly different activities.

5

Figure 2.1: The Steps in the ITM methodology, shown as a top-level road-map.

There is considerable depth and detail to the application of the ITM methodology in each of
the Steps. This road-map description is intended to provide a simplified overview. The
complete detail of what is involved in each analysis is described in the two project Case Study
reports.

6

2.2

The Steps in the ITM Methodology

STEP 1: Assembling the Data
Step 1.1: Define the region of interest: The region of interest should be defined. For
deployment of the ITM methodology in Siting Stage 1 (the LS stage), we expect that theregion of interest identified around a site, or group of nearby sites, will be ~104 - 105 km2. The
larger area is more appropriate when comparing the situation of several siting options. This isbased upon our experience in the Case Study areas. Regions need to be large enough bothto contain a statistically large enough number of features that are manifestations of theprocesses being evaluated (e.g. volcanic edifices) and to contain a good spread of datapoints for modelling these processes (e.g. GPS stations). They should also be internallyconsistent, at a rough approximation, with respect to tectonic regime. For example, in the
Kyushu Case Study, it was found that the large overall region considered (approximately
100,000 km2) had to be broken down into several intermediate blocks representing
tectonically ‘coherent domains’, ranging in size from c. 20,000 to c. 50,000 km2. A region of
104 km2 is at the lowest end of the size range that is likely to be useable and the aim should
be to have a region of at least some tens of thousands of square kilometres to ensure that thestatistical approach adopted is meaningful.
For deployment at a single site scale (Stage 3: not considered in depth in the ITM project), themethodology is likely to require downscaling to 104 km2 or perhaps 103 km2, the scaledepending upon the complexity of the tectonic setting. This has not been done in the Case
Studies and would require further test application and probably some modification of themethodology. The regional scale deployment of the methodology described below is based
upon estimating probabilities within 5 x 5 km areas. For site scale deployment, a finer grid will
be required.
Step 1.2: Data gathering: Following definition of the region of interest, relevant data are
obtained from the literature to constrain possible models of magma intrusion and rockdeformation. The principal source data at Siting Stage 1 are likely to comprise a limited
number of national databases, including the following:

geological maps;



uplift and subsidence data;

topographic maps (onshore and offshore);gravity and magnetic maps;



volcanic edifices/features location, nature and age (Catalogue of Quaternary
Volcanoes);national onshore and offshore active fault map;recorded distribution of seismic events (locations, magnitudes, depths);

velocity field measurements derived from GPS for all monitoring stations.

Additional geological and geophysical information, such as geological maps, heat flow dataand interpretations of the geological, structural and tectonic histories of the region, areneeded to support the development of conceptual models of the processes of interest (inStep 3). There may also be relevant scientific publications on specific volcanoes or tectonic
features, and more generic research publications on relevant processes. This additional
information should be augmented by discussions with academic and other researchorganisations in Japan. Such discussions will be an extremely important part of ensuring boththe currency of the models used and the involvement of the national (and international)
geosciences community.
When the methodology is applied later in the siting programme, for example, to evaluate
tectonic hazard in more detail at a site already identified as a PIA, the integration of LS datafrom existing catalogues and databases with site investigation data gathered both in the PIA
7

area and in the area of interest around it, will be an important activity. This will need to bedone carefully and consistently, because the probabilistic evaluations may be being used at
this stage to support quantitative estimates of radiological impacts.
STEP 2: Sorting the Data and Identifying Alternative Conceptual Models
Step 2.1: Data Sifting and Ground-truth: The data need to be evaluated and sifted. The
databases used have not been gathered for the specific purposes of the ITM methodology, sothey are not necessarily organised in an appropriate fashion and they may not contain data in
the form in which they will be used in Steps 3 and 4. The data also need to be evaluated forreliability and consistency. In cases where inconsistent or anomalous data are identified, thisneeds to be taken into account in the assessment of uncertainty. Such issues have beenfound to be the case for the Catalogue of Quaternary Volcanoes, where mapped volcanicfeatures include a wide range of different structures with differing significance in terms ofprocesses of magma generation and intrusion. Consequently, this Step requires a close
evaluation of what the data actually represent and whether datasets are internally consistentand of uniform quality. It may be necessary to develop derivative datasets that reduce orremove anomalies and inconsistencies, but it is important that such changes are transparent
and carefully documented.
It will be essential to obtain ‘ground truth’ on observations included in some of the databases(but not all: e.g. GPS strain data). For the highest level of confidence in the statisticalmethods used in Step 4, ‘ground truthing’ is likely to involve a significant effort in a minimal
resurveying of the region of interest (e.g. visits to all volcanic structures to ensure that theircorrect classification is known). NUMO needs to take this into account in its planning: eventhough field visits to prospective PIAs may be excluded at Siting Stage 1 (LS), regional
observations by specialists (even ‘drive-by’ observations) would likely be most valuable.
An equivalent example for the rock deformation datasets is the requirement to remove thelarge-scale, elastic subduction-related interseismic overprint from the GPS velocity field. Only
the residual strain is considered to be the ‘signal’ of interest, as it is related to faulting in thecrustal rocks of the Japanese islands. It is therefore of use in developing maps that show
differential strain for ‘cells’ across the region of interest that can be linked to localised rockdeformation processes. The ITM methodology uses 5 x 5 km cells, as these 25 km2 blocks
represent a representative area to contain a repository that is likely to have an approximate
footprint of 10 km2, plus space around its margins, where there may be additional accessworks, for example. Clearly, other sizes of cell could be used in an analysis.
Step 2.2: Underlying Conceptual Models: Underlying conceptual geological models need tobe identified (or developed) to explain the distribution of features (volcanic edifices) anddifferent styles of rock deformation. These conceptual models have been developed during
the ITM work to date. In simple terms, the models are as follows:

Distribution of polygenetic volcanoes: during the Quaternary, and for the next tensof thousands of years (up to ~100 ka) this is controlled by variable magma generation
potential in the mantle and crust overlying the subducting oceanic plates. The originof this variability is not fully understood but is manifest in some structure to the
distribution of these volcanoes; for example, ‘clusters’ of volcanoes with intervening‘gaps’ in some areas. The distribution of clusters and gaps is correlated to varying
extents to gravity, basement topography and the seismic tomographic structure of thecrust and mantle wedge beneath the Japanese archipelago. There is also evidence
for temporal variations in volcanism that need to be taken into account in theassessment of future volcanism, which is also not fully understood.

Distribution of monogenetic volcanoes: this aspect of the ITM methodology will
not be fully developed until the Western Japan – Kyushu Case Study is completed.
One possibility is that locations of monogenetic volcanoes are influenced by local
stress variations related to structure. Section 3 of this report discusses progress todate: to date, a volume-predictable model appears to work best, and we havedeveloped a model to suggest that in compressional tectonic settings with constant8

magma supply, monogenetic volcanism should be generally volume-predictable. In
other words, the exact timing of future eruptions is uncertain, but in a given timeinterval a given volume of magma should erupt.


Regional and Local Strain Budgets: the upper crustal rocks of the Japanesearchipelago are undergoing progressive deformation as a result of horizontal andvertical strain responses to dynamic plate tectonic forces. The amount and style ofdeformation varies from region to region depending on the geological formations
present and the location with respect to the major plate boundaries and other largescale deformation zone features, such as major (>100 km long) strike-slip fault zones(including the distance from such boundaries/features). The deformation can varyfrom compression to extension and subsidence to uplift. The resultant strain is
manifest as faulting (episodic movement along Quaternary active faults), folding anddistributed deformation. The overall strain ‘budget’ for an area is accessible using a
range of indicators, each of which characterises different strain manifestations: GPSdata on relative surface movements; coseismic movements, accessible through theseismological database and surface uplift/subsidence rate (which can be combinedwith other indicators to give an estimate of surface deformation, or ‘tilt’).

There are also more local controls on strain and stress related to geological and structural
heterogeneities. There may be local feedbacks between faulting, deeper ductile deformationand magmatism that results in local departures from regional variations related to plate scale
processes.
Following Step 2, the volcanic and rock deformation analyses take separate paths, defined
here as Steps 3a to 4a for the magma intrusion analysis and Steps 3b to 4b for the rockdeformation analysis.
It is expected that there would be some feedback to Step 2 from Step 3b. There will be arequirement to update the conceptual models after strain rates are estimated. Feedback
between modelling and data are an important aspect of ensuring that alternative models are
not overlooked.
STEP 3a: Classifying and Grouping Magma Intrusion Features
For the evaluation of possible future magma intrusion in areas that have not been affected byintrusion in the last ~2 Ma, it is first essential to classify the indicators of past intrusion(starting with the mapped volcanic features in the Quaternary catalogue).
Depending on the area being considered, different means of classification may be reasonable
and the first step is to carry out a ‘classification analysis of events’ to determine how best
to group the intrusion indicators. No internationally accepted scheme of volcano classification
exists, so the ITM methodology developed a cladistic approach for the Tohoku Case Study(Chapman et al., 2009) as well as exploring alternative ways of defining volcanic events. In
the Kyushu Case Study, where the tectonic environment and styles of intrusion were more
complex, classification needed to consider groupings of intrusions based on geochemicalaffinities of magmas and correlations with tectonic structures that control the spatial
occurrence of some groups of intrusion. In this case, there is an even stronger need to ensurethat volcanic assessment is integrated with the rock deformation and tectonic structuralassessment.
Step 3a.1: Classification Analysis:
Several approaches are possible to assign intrusion events to genetically related groups thatcan be treated in a consistent manner statistically (i.e. ‘event definition’). The use of expert
elicitation will greatly improve confidence in the classification schemes developed and,
although the method was not trialled during the ITM project, logic trees represent a wellestablished approach to depicting the results of expert elicitation.

9

A cladistic approach has been developed to facilitate this classification, which is based onstandard taxonomic approaches used for species classification in the biological sciences andfor which analysis software is readily available. The methodology is described more fully in an
ITM project publication (Hone et al., 2007). In simple terms, it involves defining characteristic
properties (such as size, morphology, age, chemical composition, intrusive or eruption style)to each mapped feature and then using a software analysis package (PAUP) to assess all
possible ways of grouping the features using these characteristics. The simplest possiblegroupings that explain the most characteristics are selected (the parsimony principle, or
Ockham’s razor). The different groups are called clades, although we use the term
‘Alternative Groups’ for simplicity and because techniques other than cladistic analysis can beused to form alternative groups. In the Tohoku Case Study, the cladistic method was found towork well for the polygenetic volcanoes and to provide a deeper understanding of thestrengths and limitations of the volcano database.
Step 3a.2. Database analysis. The database of volcanic features is then analysed to identify
alternative data bases, which can be used in the probabilistic analysis. This step involves
using the clade groups and field data to verify alternative groupings of volcanoes and volcanicfeatures.
Step 3a.3: Time-series analysis: It is then necessary to carry out a time series analysis ofeach alternative database to assess whether they display different periodicity (dormancy andactivity) and whether this periodicity is structured (i.e. related to eruption history, rather thanbeing random), which is used in Step 4a.
Step 3a.4: Sensitivity analysis: It is also important to test the sensitivity of the groupings to
the size of the region considered (by extending or reducing the area the number of edifices
included is increased or reduced and the statistical groupings may change) and by adding
new ‘synthetic’ volcanoes: a large change in group characteristics could indicate instability inany model invoked to explain the distribution of volcanoes.
The sub-steps in Step 3a can thus be represented as shown in Figure 2.2.
Define discriminating
characteristics of volcanicfeatures appropriate for theregion under evaluation

Assess regional
geological model
for clustering andcorrelations uponwhich to basesensitivity test

Define each mappedfeature in term of thecharacteristics

Run PAUP on full set ofvolcanic features

Sensitivity test byadding or removing
volcanic featuresand re-running
PAUP

Alternative Groups(e.g. as clades)put members of each group intime series & send toprobability mapping in Step 4a

iterate as needed

Figure 2.2: Flowchart showing sub-steps used in Step 3a.

STEP 3b: Developing Strain Models and Estimating Strain Rates for EachFor rock deformation, the objective is to calculate strain rates across the region of interest
using independent data sources that reflect widely different time averaging: GPS derived
10

strain (years), surface deformation (tens of thousands of years) and seismic strains from theseismic moment of earthquakes (centuries). These can then be compared. In Step 4b theseare presented as strain maps.
Each data source may indicate strains that are the result of one or more processes, for eachof which there may be alternative tectonic models and interpretations (e.g. boundaries ofregions that can be defined as discrete tectonic blocks, dips of major fault zones, amount,degree and depth of subduction coupling). The way that strain is calculated from the raw data
will need to account for the relative contributions of these different processes, factoring in theinherent uncertainty introduced by having alternative conceptual models. The contribution
from different processes will thus have to be estimated by expert judgement, depending on
the degree of belief in the importance of different processes/mechanisms (essentially,reflecting the alternative conceptualisations of what is driving rock deformation in the region).
Step 3b.1: Defining tectonic blocks: A first sub-step in Step 3b is to consider whether theregion of interest (or an even larger area if appropriate) can conveniently be divided into
stable rock blocks that behave internally in a relatively homogeneous way or respond in asimilar way to external, large scale tectonic driving processes. This assessment forms thebasis for the subsequent development of strain models, and there may be alternative ways ofdefining such blocks, which affects the number of models developed.
Step 3b.2: Assembling alternative conceptual models in a Logic Tree: The approach
adopted in the ITM methodology is to use Logic Trees to bring together all alternative
conceptual models identified; an example of which is shown in Figure 2.3.

A

A1
A2
A3
A4

B

C

Figure 2.3: Example of part of a Logic Tree used in Step 3b to calculate strains from GPS data for the
Tohoku Case Study area (Chapman et al., 2009).

As can be seen in Figure 2.3, the Logic Tree is constructed by asking questions of the form:How many alternative explanations (models) could describe strain in this block?’: theanswer requires a certain number of starting nodes to be established in the tree (A, B,
and C in Figure 2.3).

11



If Model A is correct, what are the alternative ways of describing its impact on
deformation?’: the answer produces branches from the node for Model A (A1, A2, A3
and A4 in Figure 2.3).

Continuing in this fashion a tree is generated that incorporates as many alternative
conceptualisations of deformation mechanisms and associated uncertainties as are deemedfeasible. Following down any one branch to the end defines how strain will need to be
calculated for that particular set of model assumptions.
Use of expert judgement: Expert judgement, elicited from a group of experts in Japanese
tectonics at a workshop, is factored into the construction of the tree to ensure that it issufficiently comprehensive of alternative models. The experts then contribute by agreeing
weightings for each branch (expressing their degree of belief in the validity of each alternative
conceptualisation).
Each strain indicator requires its own logic tree, in order to calculate strain rates. In summary,the three indicators used to date to estimate strain as follows:•

Surface deformation and active faults = mm/km/a strain. The period over which thisindicator has ‘recorded’ strain is ~10,000s years.

•

Gradients in GPS velocity = mm/km/a strain. The period over which this indicator has‘recorded’ strain is ~10s years.

•

Recorded earthquakes = seismic moment + Kostrov equation3 = strain. The period
over which this indicator has ‘recorded’ strain is ~100s years.

Despite the fact that these indicators record strain over many orders of magnitude of time, theprocesses that they are recording are widely considered to have been stable in magnitude
and direction for about 100,000 years.
Site 4 - GPS

Site 4 - SEIS
0.4

0.4

0.35
0.3

0.3

Weighted Frequency

0.25
0.2
0.15
0.1
0.05

0.25
0.2
0.15
0.1
0.05

0

89

85

81

77

73

69

65

61

57

53

49

45

41

37

33

29

25

21

9

17

5

1

1
5
9
13
17
21
25
29
33
37
41
45
49
53
57
61
65
69
73
77
81
85
89

0

Nstrain/yr

13

Weighted Frequency

0.35

NStrain/yr

Site 4 - Tilt
0.4

Tohoku Site 4

0.35

1

0.25

SEIS

89

85

81

77

73

69

TILT
65

0.4

61

37

33

29

25

21

9

17

13

5

1

0

GPS

0.6
57

0.05

0.8

53

0.1

49

0.15

45

0.2

41 Exceedance
Probability of

Weighted Frequency

0.3

NStrain/yr

EQ WGTD AV

0.2
0
1

10

100

nstrain/yr

Figure 2.4: Strain rate (nanostrains/a) histograms for multiple model realisations for one of the ‘example
locations’ in the Tohoku Case Study area, calculated (clockwise, from top left) from seismic data, GPS
residuals and tilt data. The larger diagram shows the results compiled on a cumulative probability plot,
showing the probability of exceeding any given value of strain. The red line is the equally weighted
average.

3

The Kostrov equation relates the seismic strain rate to the sum of the seismic moment tensors of all
the earthquakes occurring in a given volume of the crust during a given time-interval.

12

Step 3b.3: Calculating Strain Rates: For the GPS logic tree example shown in Figure 2.3,
there were 148 different strain models (branches) for the Tohoku Case Study. The strain rates
for each of these models are calculated separately and as a weighted average. A histogram
can be produced of the frequency of calculated strains of a given magnitude at a given
location, using all the model results for a particular indicator, which can then combined withthe same results from the other indicators (see Figure 2.4). The Step 3b sub-steps in setting
up and using the logic trees are outlined in Figure 2.5.

Define distinct tectonic blocks
or sub-regions with region ofinterest and their boundaries

Overallassessment of timestability of blocks
and driving
mechanisms

Identify mechanisms drivingrock deformation in eachblock and at boundaries

Uncertainty analysis on driving
mechanism models

ExpertElicitation

Alternative Conceptual
ModelsExpertElicitation

Combine ACMs
into Logic Tree(see text for procedure)

Weight each node (alternative
conceptual model) in Logic TreeUse each branch to define strain
calculation for each alternative istrains for each model toprobability mapping in Step 4b

Figure 2.5: Flowchart showing sub-steps used in Step 3b.

STEP 4a: Magma Intrusion Probability Maps
Step 4a.1: Assessing Correlations: The first step in producing probability maps is to see
how far the spatial distribution of volcanoes can be correlated with topographical andgeophysical indicators of crustal processes. This provides evidence that distribution is not
simply random. The check can be made both for all volcanoes in the region of interest and forthe groupings derived from Step 3a. The probabilities and related uncertainties can then besuitably weighted to reflect these correlations. This sub-step may need to utilise expert
elicitation.
As an example, in the Tohoku Case Study, the broad distribution of volcanoes is correlated(although by no means perfectly) to the isostatic gravity anomaly map of the region. The
isostatic gravity anomaly map is produced by combining the Bouguer gravity anomaly map
with the topographic map, making various assumptions. In the Tohoku study it was calculated
assuming a thin elastic crustal plate 10 km thick. The isostatic anomalies reflect magma

13generation potential, with the rate of magma accumulation at the intra-crustal Conrad
discontinuity and, possibly, the rate of magma flux at the surface (hence the likelihood of
future volcanism) being indicated by isostatic anomalies. This can be tested by plotting thehistoric magma production rate against the isostatic anomaly per unit area, but this has not so
far been tested in the ITM methodology development.
In Kyushu, the distribution of volcanism is closely tied to tectonic strain, manifest in the
distribution of active faults, GPS-derived strain and the distribution of historical seismicity. On
local scales, some volcanoes are closely associated with fault zones (Figure 2.6). Regionally,
volcanism in Kyushu closely follows regional tectonic structure. For example volcanoes of theNorthern Extensional arc are closely associated with the Shimabara-Beppu Graben. ITM
methodology calls for development and implementation of probabilistic models that reflect
these geological patterns.

Figure 2.6: Regional gravity about Aso caldera and the SW Simabara-Beppu graben. Two small-volume
Quaternary volcanoes (Akai and Omine) are found SW of Aso along graben-bounding fault. Active fault
traces (dashed lines) differ in some areas from distribution of crustal-scale gravity anomalies. Volcanismin this area provides a clear example of volcano-tectonic interaction that is common in Kyushu.

Step 4a.2: Calculating Probabilities of Magma Intrusion: For the probabilistic mapping,
three types of probability can be estimated:

P1 – the probability of a volcano edifice forming in the region of interest during theperiod of interest (e.g. a probability of 2 x 10-4 for a period of one year)P2 – the probability that a volcano will form in a specific area within the region ofinterest, such as a 5 x 5 km block, or a region extending to 15 km beyond theboundaries of a PIA (e.g. a probability of 1 x 10-4 for an area of 25 km2)

P3 – given that a volcanic event occurs in this specific area, the probability that it will
impact the repository site itself.

14

Then, the probabilistic volcanic hazard is given by P1 x P2 x P3. A variety of well-developed
statistical methods is available for estimating such probabilities, and the estimations can bedone either for all magma intrusion modes or for the various alternative groupings defined in
Step 3a. For subsequent assessment of impact scenarios in Step 6 it is important to assess
the probability of different types of event occurring, so looking at each alternative group is a
primary strategy of the ITM methodology.
The ITM methodology produces (principally) estimates of P2, in the form of regional
probability maps, as these are of most use for assessing specific locations or sites. The same5 x 5 km squares are used as for the rock deformation evaluation. Estimation of P3 will be asite-specific issue for Step 6, which will need to look at both the structural and geologicalproperties of the location, the type of intrusion being considered and the repository conceptthat would match the site.
Geostatistical and Bayesian methodologies provide a way to assess conceptual, spatial andparametric uncertainty. The methods used in the ITM methodology are:

Kernel Method: Generation of a non-homogeneous distribution map, using a
Gaussian or Epanechnikov kernel method, with an applied smoothing function (withthe effect of a range of bandwidths tested). The method is subject to uncertainties incalculated probabilities at any given point on the regional map, principally caused by
local variability of data density (or overall sparsity in terms of the number of volcanic
events over the last 2 Ma). These uncertainties vary from point to point. Figure 2.7
shows an example probability map for a small sub-region of the Tohoku Case Study
area (Chapman et al., 2009), with the two green locations selected to show how
uncertainty can vary depending on proximity to an existing cluster. Figure 2.8 shows
example results for different alternative groupings of volcanoes. It can be seen thatthe optimum bandwidth for the kernel function is different for the two groups.
As discussed under Step 3a, the introduction of additional ‘synthetic’ volcanoes canhelp quantify this uncertainty and the ITM methodology is also testing a Monte Carlosampling approach for estimating uncertainty in probability at a given site for a given
data set and a given smoothing bandwidth. An alternative method under test is to use
adaptive kernel functions, where the value of the kernel depends on the local data
density. The Monte Carlo approach is shown in the flow chart in Figure 2.9.

Figure 2.7: Spatial density of likelihood of new volcano formation in a sub-region of the area used in the
Tohoku Case Study (Chapman et al., 2009). This realisation uses a 6 km Gaussian kernel and theprobability refers to a period equivalent to that over which the mapped volcanic features were formed –about 2 Ma.

15

Figure 2.8: Probability contours (per year) for new volcanoes in part of the Tohoku Case Study area,
showing the different kernel bandwidths that are required to provide an acceptable model for two
different groups of volcano: left, explosive volcanoes, with a 33 km bandwidth; right, extrusive volcanoes,
with a 12 km bandwidth.

Input volcano distribution

Calculate a probability map (potential) for a given
volcano distribution and fixed bandwidth

Sample the potential for a new set of volcanoes (a possible
volcano distribution based on the estimated pdf)
repeat 500
times
With this new sample, recalculate the potentialof new volcano formation from new distribution
repeat foreach bandwidth
Use the range of estimates to calculate error bar(e.g., 90 percent confidence interval)
for the probability of new volcano formation at the site of interest

Output Probability with Uncertainty

Figure 2.9: Flowchart showing the Monte Carlo sampling approach that is being developed to assess
uncertainties in non-homogeneous probability mapping.

16

Cox Process Method: This multivariate approach is able to use a range of different
set of geoscientific data – for example, 3-D seismic velocity tomography of the crustand upper mantle, using a conceptual model where the tomographic features are
related to magma generation potential. This is supported by the correlation of thesestructures with specific volcanoes or with Quaternary volcano clusters.
The basis of the approach is to consider the potential for volcanism in an area to berandomly structured: even though our geological knowledge suggests there to becorrelations with geological and geophysical data, randomness is brought on by ourinherent uncertainty. Unlike Poisson distributions, which assume a constant potential
with time for new volcanoes, and the non-homogeneous approach (see above: theother method used by the ITM methodology) which assumes a deterministic potentialconditioned by selection of a kernel value, the Cox process approach assumes thepotential to be entirely stochastic. The detailed methodology used is described by
Jaquet et al. (2008a; 2008b; 2009).
The Cox process approach allows the estimation of a volcanic potential map, which isthen statistically correlated to the seismic tomography map, in order to produce aprobability map by Monte Carlo simulations. The overall approach is shown in Figure
2.10, while Figure 2.11 shows one of the results from the Tohoku Case Study.

Figure 2.10: Flowchart showing the Cox process approach used to derive volcanic probability maps.

17

Figure 2.11: (a1) and (a2): left, top and bottom: Two Cox simulations with a multivariate potential of
volcanism. The simulated events are likely to be located in zones with past activity as well as in zones
with seismic anomalies. (b): right: Probability of formation of a new volcano in the next 100,000 years
using the Cox process approach, correlated with the seismic tomography data for Tohoku region.

STEP 4b: Probabilistic Strain Maps
Step 4b.1: Constructing Different Strain Maps: For each indicator, the calculated strain
rates from Step 3b are converted to strain maps for each conceptual model branch of the
Logic Tree. The maps show calculated strains within the 5 km x 5 km areas. As noted above,
this is a reasonable resolution for the datasets being used and is also a useful size withrespect to expected repository footprint (~10 km2), but site-scale rather than regional scale
application of the methodology would require use of a finer grid. The strain rates are alsopresented together on a single map, combining them using the weightings assigned to eachbranch of the logic tree. The weighted map is thus a probabilistic representation of strain,
representing the most likely strain averaged over the time period for which the particular
indicator has been ‘recording’. For application in later Steps, only the maps of the most highly
weighted conceptual model alternatives (branches) and the combined, fully weighted
probabilistic version are likely to be useful.
Step 4b.2: Comparison and Differencing: The weighted, probabilistic maps for eachseparate indicator (i.e. GPS, seismic and tilt) are then compared. Because the different strain
indicators have variable coverage of a region, their use is complementary. The probabilistic
weighted maps for each indicator can, for instance, be differenced to assess the overallcorrelation between strain indicators. This picks out areas where the datasets are inconsistentin their strain estimates. Combined with the variability shown in the strain rate histograms for18

any selected area, such inconsistencies will identify locations where there is significant
uncertainty regarding deformation process, which may also be reflected in a wider range ofstrain rate potential (as can be seen in Figure 2.3, for example). If a potential repository site
lies within such a region, this would require special attention in Step 8, to ensure that
adequate data were gathered during the PIA investigation programme to try to reduce theuncertainty. An example of one of the strain maps generated in the Tohoku Case Study isshown in Figure 2.12, where the weighted average GPS strain data are shown together withthe location of volcanoes.

Figure 2.12: Weighted average strain map (contoured in nanostrains/a) for the Tohoku region, based on
all 148 strain maps from the GPS-based logic tree (Figure 2.3). Here, the location of volcanoes is also
shown (white triangles) as the integration of Steps 4a and 4b is a key exercise carried out in Step 5. A
weak positive correlation can be seen between the location of the volcanic front and the location ofelevated contractional strain, which is being investigated in the methodology development programme.

STEP 5: Integrated Evaluation of Each Potential Repository Site
The information from Steps 4a and 4b can be used directly in Step 6 – for example, to carryout a detailed assessment of a single site or a few alternatives. However, it is possible tocombine the data from Steps 4a and 4b to carry out an evaluation of a larger group of sites sothat they can be compared at a relatively simple level in terms of their overall susceptibility totectonic hazard. This was the objective of the two Case Studies. The text below assumes thata large set of sites (say 5 – 10) is being compared.
Depending on the interpretations arising from the separate strain maps for each indicator, it
may be considered useful to produce a combined, higher level logic tree that weights belief in
relevance of the three strain measures and factors volcanic strain into the logic tree and theweighting process. Conversely, the likelihood of magma intrusion, as indicated on theprobability maps, needs to be interpreted in the light of deformation history and the mappedstrain rate variations around the site.
Figure 2.13 shows an example of the estimated uncertainties (1σ) in strain values for thedifferent strain indicators at four different example locations from the Tohoku Case Study.

19

Step 5 combines the data from Steps 4a and 4b to carry out an evaluation of a large group ofsites so that they can be compared at a relatively simple level in terms of their overallsusceptibility to tectonic hazard. This was the objective of the two Case Studies. The text
below assumes that a large set of sites (say 5 – 10) is being compared.
For multiple site comparisons, Step 5 produces individual site assessments that provide thefollowing information in an identical format:


description of the geological and tectonic setting of the site;

evaluation of the likelihood of each different type of magma intrusion considered
possible, in both the region around the site and at the site itself, over a period of up to
100,000 years4;

evaluation of the uncertainties in the likelihoods;evaluation of the best estimate rock deformation potential (expressed as strainprobability histograms) and mechanisms over the same period of time – and therelated uncertainties.

Figure 2.13: estimateduncertainties (1σ) in strain
values for the different
indicators (GPS, tilt andseismic, plus weighted
average) at four different
example locations from the
Tohoku Case Study.

Depending on the interpretations arising from the separate strain maps for each indicator, it
may be useful to produce a combined, higher level logic tree that weights the three strain
measures, and factors volcanic strain into the logic tree and the weighting process.
Conversely, the likelihood of magma intrusion, as indicated on the probability maps, needs to
be interpreted in the light of deformation history and the mapped strain rate variations aroundthe site.
Figure 2.14 shows the equally weighted average strains using all indicators for all the 14
example locations in the Tohoku Case Study (Chapman et al., 2009). These might beregarded as ‘best estimate’ values for the purposes of this methodology demonstration but, ina real site inter-comparison exercise, expert judgement would be required to assign weights
to the different indicators, which would depend on confidence in both the data and theregional models. Clearly, these will vary from one area and one site to another.
4

The methodology could be developed further to provide estimates for 1 Ma, but this would require the use of muchlarger (longer duration, ~10 Ma) datasets and would involve greater uncertainties (especially concerning the timestability of the underlying tectonic processes). Knowledge of future volcanic hazard is, in any case, of diminishinginterest for safety assessment at times even after only 10,000 years, as the hazard of the waste has decreased tolevels equivalent to natural uranium ores by this stage.

20

Figure 2.14: Graph showing the probability or likelihood of exceeding strains of given magnitude, basedon the equally weighted average strains (using all three strain indicators) for all the 14 example locationsin the Tohoku Case Study (Chapman et al., 2009).

STEP 6: Interfacing with the NUMO Performance Assessment work
The methodology up to this stage is designed to deliver a set of probability maps that havetaken account of uncertainty in both conceptual models and data and which containintegrated interpretations of the sites being investigated by NUMO.
Knowledge of the likelihood of various tectonic hazards affecting a site is of limited value to
NUMO unless it can evaluate whether the impacts would be acceptable or not (in terms ofregulatory standards for radiological exposures to the public). This is a task for the NUMO
safety assessment team, who will be carrying out detailed performance assessment studiesof the long-term behaviour of the repository and its engineered barriers. However, information
on both likelihood and impacts is essential for safety assessment and both likelihood andconsequences are together directly linked through a specific ‘event definition’.
The role of the ITM methodology at this stage is thus to provide the PA team with informationon the nature of the tectonic hazards, so that it can construct scenarios upon which to basethese analyses, and to provide quantitative probability estimates of the likelihood of
occurrence of these scenarios. As discussed in Section 1 of this report, two approaches havebeen adopted internationally to utilise this information in safety assessment:5

To calculate the health risk5 to people in the future by combining the probability of a
disruptive event occurring with its radiological consequences in terms of releases

Health risk is normally defined as the risk of death or serious genetic effects.

21

from a repository: simply, risk = probability x consequence. With this approach,regulatory standards or targets can be defined in terms of risk to an individual.


To consider the impacts of a disruptive event and calculate the radiological doses6 topeople in the future and then, separately, to discuss the likelihood that this might
happen (the so-called ‘disaggregated’ approach). With this approach, separate
regulatory targets for radiation doses might be set for events (or scenarios) withdifferent degrees of likelihood (often expressed qualitatively; e.g. ‘likely’, ‘less likely’,‘highly unlikely’).

In either approach, an appreciation of probability is essential: in the first ‘risk approach’ asound quantitative estimate will provide more confident estimation of risk; in the second,some form of quantification of ‘likelihood’ is needed to decide which category to place anevent or scenario into.
The information required for either of these approaches is generated in Step 6 and comprises
the following:
1. A description of the nature of each magma intrusion event and rock deformation
process that could feasibly affect the repository (the basis for the scenario).
2. The likelihood of each magma intrusion event impacting both the repository directly
and the surrounding rock mass.
3. The variation of this probability with time over the next 100,000 years.
4. The best estimate of the magnitude and duration of rock deformation that could affectthe repository.
5. A description of how the events and processes would initiate, develop andprogressively impact the repository and the barriers.
Using this information, the PA team will be able to develop scenarios for tectonic impacts andassign probabilities to them.
The ITM methodology involves the production of a matrix that will compile the information initems (1) and (5) above. This matrix can be used to inform the scenario development work,
which could be carried out jointly by the PA team and the ITM group. Figure 2.15 shows ageneric example of part of a matrix for indirect (hydrothermal) impacts on the repository
structures (excluding the engineered barrier system – which is in a separate matrix) and thesurrounding rock. This type of matrix could be developed to be considerably more detailed,depending on the requirements of the scenario analysis.
The matrix will need to take account of the types of repository design and engineered barriers
that would be appropriate to the sites being studied, so input from NUMO repository design
work will be required. Simple factors, such as repository depth and horizontal and verticaldimensions could have a significant bearing on the level of impact of each type of rockdeformation or magma intrusion.
For the methodology development work, only one selected magmatic intrusion scenario and
one rock deformation scenario have been evaluated and presented as illustrations. For
eventual area and site-specific use of the ITM methodology, this exercise will have to becomprehensive.

6

Of course, a radiological dose can also be expressed in terms of health risk, by applying accepted dose-to-risk
conversion factors.

22

Barrier
SafetyFunction
Plugs: Shortcircuit EDZ
in tunnels
Seals: Prevent
fast transportin shafts

Temperature
Impacts

Hydrological
Impacts

MechanicalImpacts

Thermalalteration:
Cracking anddisaggregation
Thermalalteration:
Cracking anddisaggregation

Chemical
Impacts
Inflowof
magmatic brine:
Change in pH?
Inflowof
magmatic brine:
Change in pH?

Depth: Antiintrusion andradiation
protection
Hydrology: Long,
slow advective
transport

Increased
temperature:
Bouyancy
effects

Modifiedhydraulic head:
Changed flowrate and direction
Inflowof
magmatic brine:
Changes incomplexation?

Rock: Retard
radionuclides(sorption,
matrix diffusion)

No Impacts

Limited Impacts

Moderate Impacts

Extreme Impacts

Figure 2.15: Impact Matrix of the repository and surrounding rock for an illustrative indirect-volcanism
impact scenario assuming imposition of a hydrothermal convection system 10 km from a polygenetic
volcanic centre. Impacts can be categorised mainly as ‘global, slow, ‘permanent’, and ‘barrier
degradation’.

STEP 7: Utilising the Probabilistic Results
Step 7 is where the probabilistic results and any safety assessment results based upon them
are used by NUMO to assist in making siting decisions. Various decision points can beconsidered:
1. At the point where a single volunteer site comes forward in a non-excluded area. NUMO
may wish to consider the overall susceptibility of the site to tectonic impacts before it
accepts the site as a PIA, so that it can gain an initial impression of the extent to which
tectonic issues might be critical to a future safety case. This will enable them to gauge‘programme risk’ – whether they would be taking on a site with a low or high likelihood ofproving unsuitable from the tectonic viewpoint. This would use the data from the LS up to
the point of Step 4, possibly without carrying out any PA work in Step 6. The output would
inform NUMO decision makers so that can be fully aware of any tectonic contribution to
overall project risk and be able to answer questions about tectonic hazard with confidence.
2. Where there are several possible volunteer sites in the same region, so that the relative
susceptibility of the sites can be assessed – essentially at Step 5, as illustrated in the two
Case Studies and described further below. This comparative assessment could help
NUMO to focus or prioritise the site investigations and decide the order in which sites areevaluated.
3. When one or two sites are being considered for the final DIA. At this point, PIA
information would be available and the ITM methodology would have been iterated with
progressively improving data. In addition, feedback would be available from quantitative
23

safety assessments in Step 6, using Step 4 information. By this stage, there should be afull understanding of the actual tectonic hazard for any sites being considered for the DIA
and the ITM information should be at a sufficient level to feed directly into license
application level documentation.
However, each of these possible decision points is a potentially far-reaching stage of NUMO’swork and siting decisions will always involve many other factors (both technical and nontechnical). At present, it is not clear how NUMO might wish to manage this Step for any ofthese three stages of their programme. Further development is consequently anticipated here,
as the PIA siting programme begins to find potential sites, as the NUMO safety assessment
work evolves and as the national regulatory standards become more developed and the useof likelihood and risk become clearer.
No impact analyses have been carried out so far as part of NUMO’s parallel PA development
work, so it is not possible at the moment to assess radiological impacts associated with any
particular scenario. For the purposes of illustrating the methodology, Figure 2.16 shows anexample of how probability values for volcanism, derived from Step 4a might contribute tounderstanding siting confidence, as might be required at the second decision point describedabove (comparing multiple sites). It shows, for the 14 Tohoku Case Study example locations,
one example of how ‘low, medium and high’ levels of confidence might be allocated to eachsite with respect to susceptibility to a scenario of direct magmatic intrusion within the next
10,000 years. The quantitative boundaries between the levels of confidence are arbitrarily
selected here, simply to illustrate the approach.

1.0E-02
1.0E-03
1.0E-04
1.0E-05
Cox
Cox
LSCV
LSCV
SAMSE
SAMSE

1.0E-06
1.0E-07
1

2

3

4

5

6

7

8

9 10 11 12 13 14

Figure 2.16: An example of ‘low’ (red), ‘medium’ (yellow) and ‘high’ (green) confidence levels allocatedto each of the 14 Tohoku Case Study example locations, based on the probability of a direct magmatic
intrusion scenario occurring within the next 10,000 years (for different statistical methods in Step 4a.2).
The quantitative boundaries chosen for the levels are arbitrary examples, simply to illustrate theapproach.

STEP 8: Defining PIA and DIA field tectonic data requirements
The aim of Step 8 is to identify which additional data will be required from PIA site
investigations to reduce the uncertainties in the evaluations in each previous Step. In
particular, after Steps 4, 6 and 7 during the LS stage, the ITM methodology will need to bereiterated to produce more refined evaluations of probabilities and better definition of thenature of impacts for input to the decision to move from PIA to DIA (and to the progressive
development of SAs, as work on the PIAs proceeds). The following classes of information
feed into the ITM Methodology:

24

1. Regional (data coverage): filling gaps in the large regional databases that wereused in Step 3. It is clear that some classes of information will be of patchy quality
and coverage (e.g. age data for volcanic features; active fault parameters; seismicdata), which contributes to the overall uncertainty in the Step 4 probability mapping.
Whilst it cannot hope to fill all such gaps, NUMO should be prepared to gather thistype of information, so the development of an efficient programme that identifies themost important and ‘fillable’ gaps that would have maximum impact on reducing
uncertainty is important. Definition of these critical data gaps is region-specific, but
the two Case Studies have identified obvious improvements.
2. Regional (ground truth): ensuring that the key data used in Step 3 are fully
understood and being used correctly for classification and for setting up strain models.
A certain amount of this work will already have been undertaken in Step 2, but it may
be found that a more thorough evaluation is needed by the time Step 7 has beenreached. As was found for the Tohoku volcanics work (Step 3a), the results are highly
dependent on what a catalogue entry is assumed to mean and field investigation has
shown that all the data need to be checked. This is not necessarily a large operation,
but it would involve field reconnaissance visits to features (volcanic, faults, etc) by
experts across the whole of the region of interest, preferably during the LS stage, but
repeated if needed during the PIA investigations.
3. Local and Site-Scale: many topics of relevance to the ITM methodology will be
studied in any case during PIA investigations. For example, even without the need todevelop the tectonic probability models and evaluations of ITM, NUMO will already beseeking evidence of hidden active faults, characterising local active faults in detail,measuring uplift rates and looking at geothermal heat flux as part of its ‘normal’ site
investigation programme. However, the ITM methodology will provide better results if
it has access to additional information. The scope of this information will be sitespecific, but the following data needs will need to be considered (for the reasons
given):

2.3



age and eruptive style/volumes of local volcanics to constrain the intrusion
probability estimates and impact definition;

more detailed, local-scale gravity and magnetic surveys to identify hidden
volcanic structures or deformation zones that would affect the probabilistic
statistical evaluation;strain mechanism and history of faults and other large deformation features in
the neighbourhood of the site to constrain site context with respect to ‘rockblocks’ used in regional strain budget modelling;

establishment of local fixed and temporary GPS stations to localise GPS
strain within the regional picture, seeking small differences that could reflect
an overlay of site-scale deformation.

References for Section 2

Chapman, N., M. Apted, J. Beavan, K. Berryman, M. Cloos, C. Connor, L. Connor, O. Jaquet, N.
Litchfield, S. Mahony, W. Smith, S. Sparks, M. Stirling, P. Villamor and L. Wallace (2009).
Development of Methodologies for the Identification of Volcanic and Tectonic Hazards to Potential
HLW Repository Sites in Japan: The Tohoku Case Study. Nuclear Waste Management Organisationof Japan, Tokyo. Technical Report: NUMO-TR-08-03. 135 pps.
Hone, D.W.E, Mahony, S.H., Sparks, R.S.J. & Martin, K.T. (2007). Cladistics analysis applied to theclassification of volcanoes. Bulletin of Volcanology, 70, 203−220.
Jaquet O., Lantuéjoul C. and Goto, J. (2009 in press). Cox process models for the estimation of longterm volcanic hazard. In Volcanism, Tectonism, and the Siting of Nuclear Facilities, edited by C.B.
Connor, N.A. Chapman and L.J. Connor.Connor L. (2008a) Probabilistic methodology for long term
assessment of volcanic hazards, Nuclear Technology, 163, pp. 180-189.

25

Jaquet O., Lantuéjoul C. and Goto J. (2008b) Estimation of long-term volcanic hazard using a Cox
process with a multivariate potential, VIII International Geostatistics Congress, GEOSTATS 2008,
Santiago Chile, pp.167-176.
Jaquet O., Lantuéjoul C. and Goto, J. (2009 in press). Cox process models for the estimation of longterm volcanic hazard. In Volcanism, Tectonism, and the Siting of Nuclear Facilities, edited by C.B.
Connor, N.A. Chapman and L.J. Connor.

26

3 The Geology and Tectonics of Kyushu. Part 1: Tectonic
Setting and Evolution from 150 Ma to 15 Ma
Japan is part of the "Ring of Fire," the belt of earthquakes and volcanic activity that
distinguishes the active margins of the Pacific ocean from the passive margins of the Atlantic
ocean (Figure 3.1). In the 1920s, the great seismologist K. Wadati, discovered thatearthquakes beneath northern Japan form an inclined zone extending from locations very
near the Japan Trench to depths of about 500 km beneath the Japan Sea. This trench is anorth-south trending bathymetric depression about 150 km east of the mainland that is as
deep as 9000 m. The towering volcanoes of northern Japan are centred about 75 km fromone another and form a curving line that is about 100 km above the inclined seismic zone. In
the 1930s, Japanese seismologists discovered that many of the earthquakes near the Japan
trench are the result of large thrust fault movements indicating the floor of the Pacific basin ismoving beneath Honshu. A similar pattern of volcanism and earthquake activity is found along
the Ryukyu trench that is as deep as 5200 m and extends 2200 km from Kyushu to Taiwan.
The cause of the active tectonic movements affecting the Earth was not well explained until
the late 1960s when the realisation was made that the outer part of the Earth is divided into
pieces, known as plates, about 100 km thick (Takeuchi et al., 1970). The plates consist ofboth the crust and cool uppermost mantle that has sufficient long-term strength thatearthquake-generating ruptures can occur. Plate boundaries come in three forms that arerecognised by the common type of fault movement: divergent with normal faulting, convergent
with thrust faulting, and transform with strike-slip faulting. Plates separate from one anothercreating new ocean crust in the process of seafloor spreading at ocean ridges and cometogether consuming ocean crust in the process of subduction at locations demarked by ocean
trenches, inclined seismic zones, and lines of explosive volcanoes. Plates slide past one
another at transform boundaries.
Most of the geology of Japan is the result of subduction-related processes since the Mesozoic(Sugimura and Uyeda, 1973). The patterns of active faulting and seismicity along with direct
GPS measurements delineate where tectonic motions are underway (Sagiya et al., 2000). To
a first order, the current tectonics of the Japanese islands can be explained by the interactionof four plates: Pacific, Philippine Sea, Eurasian and North American (Figure 3.1). The easternpart of the Eurasian plate is broken with a large fragment, the Amur subplate moving at aslightly different speed and direction than the parent plate (Wei and Seno, 1998; Heki et al.,1999). With the development of GPS geodesy in the 1990s, small differential subplatemovements can be directly measured. The North American plate continues across the Bering
Sea into eastern Asia and down past the Kamchatka-Kurile trench segments to Japan. An
elongate southern prong of the North American plate extends southwards to Japan. This
prong has broken off and is moving as the Okhotsk subplate.
The active tectonics of Northeast Japan (northern Honshu and Hokkaido) are the
manifestation of the interactions between the Amur and Okhotsk subplates with the Pacificplate. Subduction along the Japan Trench at a speed of about 9 cm/a (90 km/Ma) is
concurrent with convergence near the eastern edge of the Sea of Japan at a speed of 1 to 1.5
cm/a (10 to 15 km/Ma) (Okamura et al., 1995). The active tectonics of Kyushu at the northernend of the Ryukyu arc-trench system are the focus of this Case Study. The active tectonics ofSouthwest Japan (Kyushu, Shikoku, and southwest Honshu) are the manifestation of theinteractions of the Philippine Sea plate and Amur subplate (Seno, 1977; Taira, 2001). Plateconvergence beneath Kyushu is at a speed of about 7 cm/a (70 km/Ma) (Seno et al., 1993;
Zang et al., 2002). East-central Honshu, just south of Tokyo, near 34°N, is the convergent
junction of three plates: a triple junction between the Philippine Sea, Pacific, and North
American (Okhotsk) plates.
Prior to middle Cenozoic time, the entire length of the Japanese islands was underthrust by
the Pacific plate (Taira, 2001). At about 45 Ma, tectonic movements changed in the western
Pacific region and the Philippine Sea plate was created to the south. This plate is uniqueamongst the large plates in that it is nearly entirely surrounded by subduction zones. Because27

of the lack of a spreading ridge connection to a surrounding plate, the movement history of
the Philippine Sea plate is not well determined (Hall et al., 1995; Lee and Lawver, 1995).

Figure 3.1: Tectonic setting of Kyushu within the Japanese island arc. The locations of active faults andvolcanoes that have been active in the last 10,000 years are also shown.

The eastern edge of the Philippine Sea plate is underthrust by the Pacific plate forming the
Izu-Bonin-Mariana arc-trench system. The western edge of the Philippine plate subducts
beneath Asia creating the Ryukyu arc-trench system. The Izu arc and triple junction
interSection has migrated northwards along the Ryukyu trench passing southern Japan to its
present position. In so doing, subduction beneath Shikoku, and southwest Honshu changed
from the underthrusting of the Pacific plate to the underthrusting of the Philippine plate along
the Nankai Trough.

28

3.1

Kyushu Basement Terranes

Kyushu is the third largest Japanese island with an area of nearly 36,000 square kilometers.
Most of the basement of Kyushu is composed of four geologic terranes, from north to south:
Sangun, Ryoke, Chichibu, and Shimanto belts. The brief descriptions of these terranes that
follows are mostly summaries from Kimura et al. (1991) and Taira (2001).
The basement of the northern part of the island is primarily composed of the Sangun
Metamorphic Belt, a terrane largely composed of Permian to Triassic sedimentary rocks thatwere variably metamorphosed under high-pressure/low-temperature conditions (Nishimura,1998). This terrane was intruded and widely metamorphosed by voluminous Jurassic to
Cretaceous arc plutons. The southern boundary of the Sangun belt is the Oita-Kumamoto
Tectonic Line.
The Ryoke Belt includes sedimentary rocks of late Paleozoic to Middle Mesozoic that wasmetamorphosed by voluminous Cretaceous (~100 to 80 Ma) arc plutons. The Higo subterrane,
located only in west-central Kyushu, consists of greenschist to granulite facies rocks that weremetamorphosed in the Triassic (Hamamoto et al., 1999). The extent of high temperature/lowpressure metamorphism varies greatly in intensity depending upon proximity to theCretaceous plutons. The Ryoke belt is separated from the southern basement terranes by the
Usuki-Yatsushiro Tectonic Line, an extension of the Median Tectonic Line that continues as awell-defined right-lateral strike-slip fault zone into Shikoku and the Kii peninsula. No Mesozoic
magmatic rocks occur south of the Usuki-Yatsushiro/Median Tectonic Line.
In east-central Kyushu and south of the Usuki-Yatsushiro/Median Tectonic Line is a 10 km
wide peninsula underlain by a wedge-shaped exposure of the Sanbagawa Belt. This outcrop
area is the southwestern limit of the 700 km long belt that is well exposed in northern Shikoku
and central Kii (Banno and Nakajima, 1992). The Sanbagawa Belt is composed of highpressure/low-temperature blueschist facies metabasalts with lesser sediments recrystallisedin the middle Cretaceous, between 90 to 70 Ma (Wallis, 1998). The Ryoke and Sanbagawa
belts were recognised as the classic example of paired metamorphic belts (Miyashiro, 1961)
that were later juxtaposed along a major strike-slip fault zone.
The Sanbagawa Belt is in fault contact with the Chichibu Belt, a 10 to 20 km wide terrane thattrends northeast across the middle of Kyushu. This terrane is composed of weakly
metamorphosed Carboniferous to Jurassic strata with local chaotic melange zones containing
blocks of greenstone, chert, and limestones. The southern boundary of the Chichibu Belt is
the Butsuzo Tectonic Line.
The southern third of the island is composed of the Shimanto Belt, the late Cretaceous to
Paleogene accretionary prism that is the western end of the 1800 km long terrane that forms
the southern half of Shikoku and the Kii peninsula of Honshu (Taira et al., 1982; Mackenzie etal., 1987). The Shimanto Belt is composed of two main units: relatively coherent sequences ofinterbedded sandstone and shale and chaotically mixed units of shale-matrix melange
containing slabs and blocks of basaltic lavas that are commonly pillowed, limestones andradiolarian cherts.

3.2

Tectonic History of Kyushu Region

Most of the bedrock of the Japanese islands was assembled as a result of westward
subduction along the eastern margin of Asia. Large accretionary prisms were constructed in
the late Paleozoic to early Jurassic that are mostly composed of deep ocean sediments(shales, cherts, and pelagic limestones) and trench deposits (sand-rich) that were deformedby folding, faulting, and flowage during offscraping and underplating and then locallyblanketed by slope deposits. In the Mid-Jurassic, between about 175 to 135 Ma, a major
period of strike-slip faulting occurred along the margin of Asia that created the OitaKumamoto Tectonic Line and other high-angle fault contacts between displaced pieces of thelate Paleozoic to early Jurassic accretionary prism.

29

In the very latest Jurassic or earliest Cretaceous, a major phase of fast subduction began that
continues to this day off northeast Japan. This generated the enormous volume of arc
magmas that invaded the Ryoke and Higo terranes and caused widespread hightemperature/low-pressure metamorphism. At the same time, the Sanbagawa and Chichubu
terranes were accreted. The Sanbagawa terrane is largely composed of ocean crust andoverlying sediments that was imbricated and underplated beneath the leading edge of the
new subduction zone and thoroughly metamorphosed and intensely deformed under highpressure/low-temperature conditions. The Chichubu Belt and the older part of the Shimanto
Belt are mostly trench sediments that were offscraped or slope deposits that accumulated at
the same time. All of this accretion and metamorphism was caused by the subduction of the
Pacific plate beneath the eastern edge of Asia.
Between 50 to 40 Ma, a profound tectonic event occurred. The Pacific plate changed direction
from northwest to a more westerly trend. This change is recorded as the major kink (dated at
43 Ma) in the Hawaii-Emperor seamount chain as well as several other seamount chains in
the Pacific plate farther to the south. The change in plate motion was coeval with the initiationof subduction along a major northwest-trending transform zone extending from New Zealand
to a location north of the Equator. This formed the Izu-Bonin-Mariana and Tonga-Kermadec
arc-trench systems (Hall et al., 1995; Lee and Lawver, 1995). Whether the initiation of
subduction caused the change in Pacific plate motion or the change in Pacific plate motion
caused the initiation of subduction is debated. Either way, the northern part of the Philippine
Sea plate was bounded by a pair of westward-dipping subduction zones. The Easternboundary of the Philippine plate north is the Izu-Bonin-Mariana arc-trench system. The
western boundary north of Taiwan is a west-dipping subduction zone that generated the
Ryukyu arc-trench system. South of Taiwan, the western boundary is east-dipping subduction
zone that generated the Manila trench and Luzon arc as the edge of Eurasia descends
beneath the Philippine plate. The trench-trench-trench (TTT) triple junction now located off theIzu peninsula that was established somewhere between Taiwan and Kyushu has migrated
northwards ever since (Figure 1).
The TTT triple junction appears to have reached the southern tip of Kyushu at about 25 Ma(Kimura et al., 2005). From about 32 to 23 Ma, Japan began to rift from Asia (Jolivet et al.,1994). Seafloor spreading in the Sea of Japan created new ocean crust in the backarc region
between 23 to 12 Ma as Southwest Japan (southern Honshu and Shikoku) rotated clockwise
about 45 degrees and Northeast Japan shifted eastward with a small counterclockwise
rotation (Ishikawa, 1997). As this occurred, the TTT triple junction migrated more eastward
than northward resulting in highly oblique and very slow convergence beneath southwest
Japan. This movement caused significant left-lateral strike-slip offset along the Median
Tectonic Line. As the TTT triple junction migrated northwards, the plate interactions along
southern Japan abruptly switched from the subduction of the Pacific plate to the Philippine
Sea plate.
The tectonic changes along southern Japan were concurrent with a major event in theeastern edge of the Philippine Sea plate that may somehow be related. The Izu-BoninMariana arc split with backarc spreading creating the Shikoku Basin between 25 to 15 Ma(Karig, 1974; Sdrolias et al., 2004). The Kyushu-Palau Ridge is the remnant arc, nearly 400
km behind the Izu-Bonin-Mariana arc. The opening of the Shikoku Basin created new ocean
crust that was still hot as it was underthrust beneath Kyushu and Shikoku islands and the Kii
peninsula. The combination of spreading and underthrusting accounts for the minor but very
anomalous forearc magmatism from 17 to 12 Ma (Hibbard and Karig, 1990; Kimura et al.,2005) and the widespread, low-temperature metamorphism of the Shimanto Belt on Kyushu
and Shikoku. The remnant of the split arc is the Kyushu-Palau submarine ridge, a segment ofocean crust greatly thickened by subduction-generated magmatism.

3.3

Summary

The overall geologic history that created the basement terranes of Kyushu is well understoodas a result of the long-term westward subduction of the Pacific ocean seafloor. Subduction in
the late Paleozoic to mid-Mesozoic created the Sangun accretionary prism, which makes up
the northern part of the island. This accretionary event was followed by a period of major
30

strike-slip transform faulting between about 175 to 135 Ma that shuffled the accretionary
prism and made some of the high angle fault zones that are mapped as tectonic lines. The
reinitiation of westward subduction in the very latest Jurassic or earliest Cretaceous
generated voluminous magmatism that invaded the northern half of the island and the large
accretionary prism that makes up most of the southern half of Kyushu. The major plate motion
change at about 43 Ma that isolated the Philippine Sea plate led to the northwards migrationof the trench-trench-trench triple junction that is now located at the Isu peninsula. As the triple
junction passed south Japan, backarc spreading occurred to form the Sea of Japan. Slow
convergence with left-lateral strike-slip movement along the Median Tectonic line caused the
Ryoke belt with abundant Mesozoic plutons to become juxtaposed directly against the
Sanbagawa-Chichibu-Shimanto accretionary terranes. Westward subduction of the Philippine
plate generated the Ryukyu trench and arc.

3.4

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Kamata, H., 1989, Volcanic and structural history of the Hohi volcanic zone, central Kyushu,
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31

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32

Utu, K., Hoang, N., and Matsui, K., 2004, Cenozoic lithospheric extension induced
magmatism in Southwest Japan: Tectonophysics, v. 393, p. 281-299.
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in the fault zone of middle Kyushu: 29th International Geological Congress (IGC) Field Trip
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Yamaji, A., 2003, Slab rollback suggested by latest Miocene to Pliocene forearc stress andmigration of volcanic front in southern Kyushu, northern Ryukyu Arc: Tectonophysics, v.,
634, p. 9-24.
Zang, S. X., Chen, Q. Y., Ning, J. Y., Shen, Z. K., and Liu, Y. G., 2002, Motion of thePhilippine Sea plate consistent with the NUVEL-1A model: Geophysical JournalInternational, v. 150, p. 809-819.
Zhao, D., Ochi, F., Hasegawa, A., Yamanto, A., 2000, Evidence for the location and cause oflarge crustal earthquakes in Japan: Journal of Geophysical Research, v. 105, p. 13,57913,594.

33

4 The Geology and Tectonics of Kyushu. Part 2: Tectonic
Evolution of southwest Japan since 15 Ma
The tectonics of southwest Japan has undergone complex and marked changes over the last
15 Ma, but throughout this period it has been dominated by the interaction between thePhilippine Sea Plate (PSP) and the Amur, or Amurian Plate (which is considered a “sub-plate”
of the Eurasian plate) (Figure 3.1). The major driver of tectonics in southwest Japan over thelast 15 Ma is the ever-evolving subduction history of PSP. This contrasts with northern Japan
which has been experiencing relatively straightforward, long-lived subduction of the PacificPlate in a northwest direction beneath northern Japan. The subducting PSP can be spatiallysplit into two parts of significantly different ages separated by the Kyushu Palau Ridge (KPR),
with Eocene-Cretaceous PSP crust of the West Philippine Basin subducting at the Ryukyu
trench and beneath southern Kyushu, and younger (27-15 Ma) Shikoku basin crust subducting
at the Nankai Trough. The age difference of the subducted segments of the PSP has a crucial
influence over the angle of subduction achieved by each slab segment, which in turn has animportant spatial control on the complexity of the tectonic and volcanic evolution of this region.
This will be discussed fully in this Section, and later Sections.
For convenience of discussion, the tectonic development in southwest Japan since 15 Ma canbe broken down into periods from 15-10 Ma, from 10-6 Ma, from 6-2 Ma and from 2 Ma present.

4.1

15 Ma to 10 Ma

A summary of the tectonic configuration during the 15-10 Ma period is shown in Figure 4.1. By
15 Ma, back-arc rifting in the Sea of Japan and spreading in the Shikoku Basin had ceased.

Figure 4.1. Major tectonic events occurring in the time period 15-10 Ma. At 15 Ma the Sea of Japan and
Shikoku Basin ceased spreading. Between 15-12 Ma the Kyushu Palau Ridge collides with the Nankai
trough (green star shows subduction point). At 13 Ma the hot spreading centre of the Shikoku basin
begins to subduct (thick red line shows subduction point). The Median Tectonic Line (blue line) shows
normal faulting during the mid Miocene. KPR = Kyushu-Palau ridge; IBM = Izu Bonin Marianas arc; PSP= Philippine Sea Plate

34

The northern margin of the Shikoku basin reached southwest Japan at roughly 15 Ma, andPSP/Eurasian relative plate motions were such that the PSP began to subduct northwards
beneath the Eurasian Plate (Watanabe, 2005). The basement of this central zone is intruded
by a line of mid Miocene (15-10 Ma) I-type felsic granite plutons, providing good evidence forsubduction related volcanism occurring at approximately 15-10 Ma in this area (Kimura et al.,2005). The continued northward subduction of the PSP also led to the collision of the Kyushu
Palau ridge with the Nankai Trough, at around 15-12 Ma (Kimura et al., 1996). The Shikoku
basin crust was still very young when it began subducting beneath Eurasia, with volcanic
activity in the Shikoku basin continuing until around 12 Ma, as recorded on the Kinan
seamounts (Kobayashi et al., 1995). As such, at around 13 Ma the most recently active
spreading centre of the Shikoku basin began subducting beneath southwest Japan. The
response to this was distinctive adakitic fore-arc volcanism (Takahashi et al., 1999).

4.2

10 Ma to 6 Ma

Some workers propose that northward subduction of the PSP beneath the Eurasian Plateceased from 10-6 Ma (Uto, 1989; Kamata, 1992) (Figure 4.2). Conflicting views are held byother authors who either suggest there was no late Miocene halt in subduction of the PSP (e.g.
Kimura et al, 2003), or suggest an alternative time period for a ‘stagnant’ phase in subduction(e.g. Taira, 2001 suggests 14-8 Ma).

Figure 4.2: Major tectonic changes occurring in the time period 10-6 Ma.

The proposed halt in subduction is summarised by Kamata and Kodama (1994) and mainly
supported by volcanic evidence (i.e. lack of a subducted plate signature in erupted lavas during
this time). Whether or not there was an actual halt in PSP subduction from 10-6 Ma isinherently difficult to resolve, as it is very challenging to find direct evidence for an actual halt in
subduction of the PSP slab, while it is much more straightforward to recognize initiation of
subduction-related processes (e.g. arc volcanism, back arc spreading etc).

35

4.3

6 Ma to 2 Ma

In Kyushu, characteristics of the modern-day subduction regime began around 6 Ma, as shownin Figure 4.3. Re-initiation of subduction was accompanied by a change in subduction direction,with the PSP subducting in a NNW direction (Seno, 1989). The NNW direction of PSP
subduction meant that the intersection point of the Kyushu Palau ridge with the trench migrated
north east along the Nankai Trough. Subduction of the PSP beneath the Ryukyu arc at thistime was coincident with the commencement of back arc rifting in the Okinawa Trough (e.g.,
Sibuet et al., 1987). NNW subduction of the PSP at the Nankai trough during this time period
was nearly perpendicular to the plate boundary (e.g., Kamata and Kodama 1994) (comparedwith the present day, where there is currently dextral obliquity).

Figure 4.3: Major tectonic changes occurring in the time period 6-2 Ma. The Philippine Sea Plate (PSP)began subducting once again, in a NNW direction. The Kyushu Palau ridge (KPR) subduction point
began migrating NE up along the Nankai Trough due to relative plate motions. The Okinawa Troughbegan opening. The MTL began dextral motion, and there was clockwise rotation of the forearc sliver insouth Kyushu. The Hohi volcanic zone (HVZ) formed on the edge of the Median Tectonic Line (MTL).
IBM = Izu Bonin Mariana arc.

Palaeomagnetic data led Kodama and Nakayama (1993) to conclude that southern Kyushu
underwent about 30 degrees of anticlockwise rotation after the latest Miocene. Anticlockwise
rotation of southern Kyushu also facilitated rifting in the Beppu-Shimabara Graben (BSG)
region. Evidence for extensional deformation in southern Kyushu for the past 5 Ma (Tada,
1985; Yamaji, 2003), and slab rollback of the PSP after 5 Ma (Yamaji, 2003), is also consistent
with the anticlockwise rotation of southern Kyushu since ~6 Ma. Wallace et al. (2009) suggestthat anti-clockwise rotation of southern Kyushu occurs in response to the along-strike change
in buoyancy properties of the subducting PSP (e.g., along-strike change from young Shikoku
Basin subduction to subduction of the Eocene-Cretaceous crust of the West Philippine Basin).

36

North of the MTL in northeastern Kyushu, a rectangular volcano-tectonic depression 70 kmlong and 40 km wide (the Hohi volcanic zone and associated Beppu Graben), began forming at~6 Ma (Kamata & Kodama, 1994). Temporal evolution of volcanism in the Hohi Volcanic Zone
suggests that volcanism subsequently migrated inwards towards the central part of the current
Beppu graben (Kamata, 1989; Kamata & Kodama, 1994; Figure 4.4).

Figure 4.4: Evolution of volcanism in the Hohi Volcanic Zone after Kamata and Kodama (1994).
Radiometric ages of volcanic rocks are shown. This map demonstrates that older volcanics occur on theouter boundaries of the graben, while younger deposits concentrate in the centre of the Graben.

Extensional faulting also migrated in the same fashion, from a 45-km-wide zone in 5-6 Ma to a
11 km-wide zone around 1 Ma (Katama, 1989). Presence of lineations (old faults?; see Figure6.1) parallel to and outside (north and south) the current graben boundaries from analysis ofgravity and magnetism maps support the inward migration of faulting. This inward migration of
faulting is common in rifts worldwide, as the crust gets thinner along the rift axis, the boundary
faults become inactive and activity shifts to the axial faults (Ethiopian Rift: Hayward andEbinger, 1996; Taupo Rift, New Zealand: Villamor & Berryman, 2006). This also agrees withthe presence of thinner seismogenic crust in the Beppu graben area than that outside thegraben (Figure 4.5).

4.4

2 Ma to Present

Since the beginning of the Quaternary, Kyushu has entered its latest tectonic phase (seeFigure 4.6). At this time the PSP shifted its subduction direction from NNW to NW (Matsuda,
1980; Nakamura et al., 1984; Okamura, 1988). This resulted in an increased dextral
component of subduction, leading to intensified dextral motion along the Median Tectonic Line
(MTL) (Kamata and Kodama, 1994; Kamata, 1998; Itoh et al., 1998a). Okada (1980), Ichikawa(1980) and Sangawa (1986) suggest that the MTL was a thrust/reverse fault in the early
Quaternary and then became a dextral strike-slip fault during the second half of the Quaternary.
The change in PSP subduction direction also heralded a change in migration direction of KPR
along the PSP-Eurasian margin. Since 2 Ma the position of the KPR subduction has been
migrating southwestward, opposite to the previous direction (Figure 4.7). There may have also
been a decreasing rate of extensional deformation in the Hohi area (Beppu Graben) with time
(Kamata and Kodama, 1999), which could be related to encroachment of the KPR and
subduction of the buoyant Shikoku Basin beneath northeast Kyushu (thus lessening theextensional stress in the region with time). Between 1-0.7 Ma, a north-south extensional stress37

field was present in the Hohi volcanic zone (Kamata et al., 1988). By 0.7 Ma, a compressional
stress regime was active within the Hohi volcanic zone, in particular in the area of the
Shishimuta caldera (Kamata et al., 1988).

Figure 4.5: Map of the depth of the seismogenic crust from earthquake depth distribution data

38

Figure 4.6: Major tectonic changes occurring in the time period 2-0 Ma. The Philippine Sea Plate (PSP)changed subduction direction to NW, which caused an onset of dextral motion on the Median Tectonic
Line (MTL). The KPR began migrating SW along the Nankai Trough due to the change in plate motion
direction. This may have caused a change form extensional to compressional regime in the HVZ leading
to a slow down in magma production.

Figure 4.7: Reconstruction of the position of the Kyushu-Palau ridge from 250 ka to 2 Ma, shown at 250
ka intervals. Ridge position reconstructed based on present-day relative motin between the Philippine
Sea Plate and the Amurian Plate. This shows that the ridge was subducting adjacent to the NE end of
Kyushu at 2 Ma, and has since migrated southwest to its current position.

39

4.5

Present Day Tectonic Situation

Current tectonics in Kyushu is dominated by ~72-79 mm/a convergence of the PSP and theAmurian Plate at a slightly oblique (right-lateral sense) angle. Strike-slip and extensional
faulting is ongoing in the BSG in central Kyushu (e.g., Kamata, 1992; Kamata and Kodama,1999) (Fig. 4.8). Southwest, along-strike of the BSG, active back-arc rifting continues to occur
in the Okinawa Trough (e.g., Lee et al., 1980; Sibuet, et al., 1987). Active extension also
occurs in the Kagoshima graben in southern Kyushu (e.g., Aramaki, 1984; Kodama et al.,1995) at rates (from GPS) of <7-8 mm/a (Wallace et al., 2009) and there is some geologicalevidence for active normal faulting (e.g., Japan’s Active Fault Research Centre’s Active Faults
Database: http://www.aist.go.jp/RIODB/activefault/cgi-bin/index.cgi). North-south extension in
the Shimabara Graben, right lateral shear along the Oita-Kumamoto line, and strong left-lateralshear across southern Kyushu is evident from modern-day GPS velocities (see discussion in
Section on Strain Rates from GPS). GPS velocities also suggest that anti-clockwise rotation ofsouthern Kyushu may be ongoing today. Wallace et al. (2009) suggest that the left-lateralshear in southern Kyushu revealed by GPS measurements is a result of Kyushu-Palau Ridge
collision (see Section on Strain Rates from GPS).

Figure 4.8: Active faults in southwest Japan from the Active Fault Research Centre’s active
fault database (http://www.aist.go.jp/RIODB/activefault/cgi-bin/index.cgi). The faults are colorcoded by sense of movement (green = dextral; blue = normal, red = reverse, yellow = sinistral).
In general, historical seismicity in Kyushu consists of numerous offshore thrust earthquakes
related to subduction at the Nankai/Ryukyu Troughs, and normal and strike-slip events withincentral Kyushu related to deformation in the BSG (Figure 4.9). Most normal faulting
earthquakes in central Kyushu indicate approximately north-south directed extension,40

consistent with geological studies. There is also a zone of east-west trending left-lateral strikeslip earthquakes northwest and northeast of the Kagoshima Graben, which corresponds withthe zone of high left-lateral shear strain from GPS. Similarly, in northern Kyushu, there areseveral significant left-lateral strike-slip earthquakes with focal planes that strike transverse to
the margin that correspond to some known, active left-lateral faults (Figure 4.9, and see Figure
4.8 for location of left-lateral faults near these earthquakes in northwest Kyushu). The March
2005 Mw 7.0 earthquake offshore Fukuoka City is related to this zone of faulting in northern
Kyushu. Previous workers have suggested that these left-lateral events occur due to“bookshelf” style faulting within a region of overall right-lateral transpression (Kanaori and
Kawakami, 1996). A large variety of earthquakes representing virtually every sense of faulting
occur in Kyushu, reflecting the complex and dynamic active tectonic setting there.

Figure 4.9: Focal mechanism plots for earthquakes in southwest Japan from 1997-2006. Based on
CMT solutions from the JMA catalogue (data from http://www.fnet.bosai.go.jp).

41

4.6

References for Section 4

Aramaki, S., 1984, Formation of the Aira Caldera, southern Kyushu, 22,000 years ago, J.
Geophys. Res., 89(B10), 8485-8501.
Hayward, N., and Ebinger, C., 1996, Variations in the along-axis segmentation of the Afar rift
system: Tectonics, v. 15, p. 244–257.
Ichikawa, K. 1980. Geohistory of the Median Tectonic Line of southwest Japan. Mem. Geol.
Soc. Jpn. 18, 187–212.
Itoh, Y., K. Takemura, H. Kamata. 1998. History of basin formation and tectonic evolution at
the termination of a large transcurrent fault system: deformation mode of central Kyushu,
Japan. Tectonophysics. 284, pp135–150.
Kamata, H., Uto, K., Uchiumi, S., 1988. Geochronology and evolution of the post-Shishimuta
caldera activity around the Waitasan area in the Hohi volcanic zone, Kyushu, Japan. Bull.
Volcanol. Soc. Jpn. 33, 305-320.
Kamata, H., 1989, Volcanic and structural history of the Hohi volcanic zone, central Kyushu,
Japan: Bulletin of Volcanology, v. 51, p. 315–332.
Kamata, H. 1992. Right-lateral movement of the Oita-Kumamoto Tectonic Line as a westernextension of the Median Tectonic Line, originated from rightward, oblique subduction of thePhilippine Sea plate. Mem. Geol. Sot. Jpn., 40, pp53-63.
Kamata, H. 1998. Quaternary volcanic front at the junction of the South-west Japan Arc andthe Ryukyu Arc. Journal of Asian Earth Sciences. 16, pp67-75.
Kamata, H. & K. Kodama. 1994. Tectonics of an arc-arc junction: an example from Kyushu
Island at the junction of the Southwest Japan Arc and the Ryukyu Arc. Tectonophysics.
233, 69-81.
Kamata, H. & K. Kodama. 1999. Volcanic history and tectonics of the Southwest Japan Arc.
The Island Arc. 8, 393-403.
Kanaori Y, Kawakami S. 1996. Microplate model and large inland earthquakes of southwest
Japan: implications for generation of the 1995 M7.2 Hyogo-ken-nanbu earthquake. Zishin
49:125–39 (In Japanese)
Kimura, G., 1996, Collision orogeny at arc-arc junctions in the Japanese islands: The Island
Arc. 5, pp 262–275.
Kimura, J. I., T. Kunikiyo, I. Osaka, T. Nagao, S. Yamauchi, S. Kakubuchi, S. Okada, N.
Fujibayashi, R. Okada, H. Murakami, T. Kusano, K. Umeda, S. Hayashi, T. Ishimaru, A.
Kimura, J-I., R. J. Stern, T. Yoshida. 2005. Reinitiation of subduction and magmatic
responses in SW Japan during Neogene time. GSA Bulletin. 117, pp. 969–986, doi:
10.1130/B25565.1
Kobayashi, K., S. Kasuga, K. Okino. 1995. Shikoku Basin and Its Margins. In “Backarc
Basins: Tectonics and Magmatism”. By Brian Taylor. Published by Springer, 1995. ISBN
0306449374, 9780306449376. p398
Kodama, K., H. Tashiro, T. Takeuchi. 1995. Quaternary counterclockwise rotation of south
Kyushu, southwest Japan. Geology. 23, pp 823–826.
Kodama, K., K. Nakayama. 1993. Paleomagnetic evidence for post-late Miocene intra-arc
rotation of south Kyushu, Japan. Tectonics. 12, pp35–47.
Lee, C.S., G.G. Shor, D.D. Bibee, R.S. Lu and T.W.C Hilde, 1980, Okinawa Trough: Origin ofa back-arc basin, Mar. Geol., 35, 219-241.
Matsuda, T., 1980, Traces of Izu Peninsula—its direction of movement during the recent
several million years, Earth Monthly, 2, 164-8.
Nakamura, K., K. Shimazaki, N. Yonekura. 1984. Subduction, bending and eduction. Present
and Quaternary tectonics of the northern border of the Philippine Sea plate. Bull. Soc.
Geol. Fr. 26, pp221–243.

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Ninomiya, A. Tanase. 2003. Late Cenozoic volcanic activity in the Chugoku area, southwest
Japan arc during back-arc basin opening and reinitiation of subduction. The Island Arc. 12,
pp22–45
Okada, A., 1980. Quaternary faulting along the Median Tectonic Line of southwest Japan.
Mem. Geol. Soc. Jpn. 18, pp79–108.
Okamura, Y., 1988, subduction of seamount and paleo subduction direction of the Philippine
Sea Plate, Earth Monthly, 10, 603-7.
Sangawa, A. 1986. The history of fault movement since Late Pliocene in the central part ofsouthwest Japan. Bull. R. Soc. N.Z. 24, pp75–85.
Seno, T., 1989. Philippine Sea plate kinematics. Modern Geol. 14, 87–97.
Sibuet, J.-C. and ten co-authors, 1987, Back arc extension in the Okinawa Trough, J.
Geophys. Res., 92, 14041-14063.
Tada, T., 1985. Spreading of the Okinawa Trough and its relation to the crustal deformation in
Kyushu (2) (in Japanese with English abstr.) Zisin, J. Seismol. Soc. Jpn. 38, pp1–12.
Taira, A. 2001. Tectonic Evolution of the Japanese Island Arc System. Annu. Rev. Earth.
Planet. Sci. 29. 109-134
Takahashi, M. 1999. Large felsic magmatism of the Miocene outerzone of southwest Japan.
Earth Monthly, Special Volume 23, 160—168. (in Japanese)
Uto, K. 1989. Neogene volcanism of Southwest Japan: Its time and space based on K-Ar
dating. Ph.D. Thesis, Univ. Tokyo, 184 pp.
Villamor, P., and Berryman, K.R., 2006, Evolution of the southern termination of the Taupo
volcanic zone, New Zealand: New Zealand Journal of Geology and Geophysics, v. 49, p.
23–37.
Wallace, L.M., S. Ellis, K. Miyao, S. Miura, J. Beavan, J. Goto. 2009. Enigmatic, highly active
left-lateral shear zone in southwest Japan explained by aseismic ridge collision. Geology.
(In Press)
Watanabe, Y. 2005. Late Cenozoic evolution of epithermal gold metallogenic provinces in
Kyushu, Japan. Mineralium Deposita. 40. 307-323. doi: 10.1007/s00126-005-0025-7
Yamaji, A. 2003. Slab rollback suggested by latest Miocene to Pliocene forearc stress andmigration of volcanic front in southern Kyushu, northern Ryukyu Arc. Tectonophysics. 364,
pp9–24.

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5 Volcanic History of Kyushu
Section 4 gave an insight into the complexity of the tectonics (and, thus, the volcanism)encountered in Kyushu. This Section details the volcanic episodes that have occurred in
Kyushu throughout the last 15 Ma. The volcanism is discussed in similar time intervals to thetectonics in the Section 4, to allow the reader to draw comparisons with the two histories.
Unlike tectonic events, which are relatively standardised and well defined (e.g. strike slip vs.
dip slip faulting), volcanoes have much less well-established standards. This volcanic history
Section begins by introducing the broad classification of the volcanoes described in thisaccount, and the areas in which they occur.

5.1

Dating and Classifying Volcanoes

Tough challenges are faced in trying to establish when a volcano actually formed. It is fareasier to date the youngest activity (usually an eruption) of a volcano, than it is to pinpoint thetime when the first activity of that volcano occurred. The oldest rocks are often buried by later
eruptions or removed by erosion. In creating a detailed volcanic history of Kyushu, this dating
issue becomes an important factor in the confidence we can have in our reconstructions, as welater attempt to link the origins of volcanic episodes with other physical processes occurring atthat time. This lack of knowledge of exactly when volcanoes formed adds some ambiguity to
our assessments, specifically when working towards forecasting where future volcanoes will
form. Many volcanoes have age data regarding a range of eruptions or eruptive episodes. Thiscan help suggest how long-lived that particular volcanic centre has been and this knowledge
can then be used to suggest future volcano occurrences. In this study, the oldest available
eruption date of the volcano is taken to be the time at which it formed.
Another difficulty faced is classifying different ‘types’ of volcano. A thorough approach would beto treat each volcano like an entry into a field notebook. If information on the volcano isrecorded as a detailed description of all the features of the volcano, then volcanoes would not
get pigeon-holed as a certain ‘type’ of volcano, e.g. caldera, stratovolcano etc. The danger withclassifying a volcano as one type is that volcanoes do not tend to be ‘type A’ or ‘type B’. They
tend to be a mixture of types, forming a wide spectrum. Distinguishing clear definitions of
volcanoes is also difficult, due to their evolutionary nature. However volcanoes must be
assigned certain working classifications (e.g. caldera, lava dome), otherwise volcano studies
would be impossible. In the case of the Japanese volcanoes there are many workers collecting
excellent data; however there are classification issues. In the first edition of the Japanese‘Database of Quaternary Volcanoes, 1999’ (Committee, 1999) there were cataloguing issues of‘what is a volcano’ and ‘what is an edifice’, amongst others. In the Tohoku Case Study, one
method of classification was attempted on a set of these data, using cladistic methods toclassify the volcanoes broadly into groups (see Chapman et al., 2009).

5.2

The Kyushu Volcano Dataset

In this study, a dataset of the Kyushu volcanoes has been created by collating data from manydifferent sources, such as the excellent AIST online Quaternary volcano database (AIST, 2008),
as well as information gathered from various field trips, individual papers and Japanesewebsites. In this dataset, volcano ages mostly ranged up to 6 Ma; however information
regarding mid to late Miocene volcanism has also been included in this account. The
information regarding older volcanism generally has less accurate details regarding volcano
locations, due to poor preservation potential in older volcanic systems. This dataset was
created with the aim of characterising volcanism in Kyushu at different times and in different
areas. An overall aim was to elucidate the volcanological history of Kyushu and to relate it to
the complex tectonic history occurring concurrently.
The location of volcanism in Kyushu appears to be strongly linked to the local and regional
tectonics. There have been studies of the tectonic influence on volcanism (e.g. Nakajima &Hasegawa, 2007; Kamata and Kodama, 1999), using the composition and timing of volcanism
in Kyushu to explain the tectonic history and to establish the current tectonic setting44

underpinning Kyushu. The interpretation of the volcano-tectonic interaction in Kyushu
developed in the ITM project is dealt with in the following Section.

5.3

Introduction to the Three Volcanic Regions

Kyushu can be divided into four main tectonic ‘domains’ as described in the following Section(Section 6). However it was considered prudent to use slightly different divisions and names
for the areas of interest when describing the volcanic history. Partly, this reasoning is that thetectonic domains are based on tectonic events, so using these domains would assume thatthe tectonic and volcanic events are related. Also, the names of the tectonic domains relate to
the Quaternary tectonic situation, so could be confusing when looking at the Miocene topresent volcanic history. For the purpose of describing the volcanic history, Kyushu is divided
into 3 Sections: the north western, central and southern volcanic regions, demonstrated by
Figure 5.1.

Figure 5.1: The differences between the tectonic domains and the volcanic regions are subtle, but clear.
The thick black line marks how Kyushu is divided into the three volcanic regions for the purposes of thisstudy. The tectonic domains (see Section 6) evolve with time (not shown here) whereas the volcanic
regions are kept the same, to allow easy understanding of the accompanying descriptive account of the
volcanism. The north western/central volcanic region boundary is slightly different to that of the tectonic
domains. In the east the volcanic boundary is located further north, due to volcanism further east along
the BSG (e.g. Futago volcano). In the west the north western/central boundary is dashed, as this area isslightly ambiguous. Evolved rhyolite volcanoes (Arita rhyolites) occur alongside flood basalts(Higashimatsuura basalts), thus the boundary between different types of volcanism is unclear.

45

The northwest region has seen a range of volcanic activity over the past 15 Ma, fromwidespread flood basalts to mature rhyolitic volcanoes. Typical volcanism of this area is
episodic back-arc volcanism, with a proliferation of monogenetic volcanoes. The central
region is a long-lived area of volcanism, with activity changing in style through time. Styles ofactivity range from voluminous lava plateaus and monogenetic volcanoes to lava domes,
cinder cones, ignimbrite plateaus and huge calderas. The southeastern region has had
periods of major widespread lava eruptions as well as recent massive caldera formation along
the arc front since 0.3 Ma.

5.3.1

Types of volcanism and occurrence throughout Kyushu

Volcanism in Kyushu is driven by the subduction of the Philippines Sea Plate (PSP) beneaththe Eurasian Plate at the Ryukyu trench and the Nankai trough (see Figure 3.1). Kyushu
displays a clear volcanic arc, as well as sporadic back-arc volcanism. However there areanomalies, such as Unzen volcano, which appears to be influenced by arc and back-arc
processes. Back arc volcanism in Kyushu is largely found in the northwestern region;
however, influences of non-arc type magmas are found in other areas at different times, due
to the migration of the volcanic front. Current back arc volcanism is typified by monogenetic
volcanism. However, back arc volcanism in Kyushu has previous been characterised bywidespread flood basalts, and even some more evolved polygenetic systems (e.g. Arita
rhyolites). This mixture of types of behaviour in the back arc exemplifies the changing natureof volcanism in Kyushu.
The back arc region of Kyushu has been suggested by some workers (e.g. Shinjo et al.,2000) to also be influenced by a heat source off the coast of NW Kyushu, related to the Sea
of Japan/Shikoku basin. The Okinawa trough is actively spreading into the adjacent central
region of Kyushu, adding an extra dimension to the magmas generated in this region. Forearc
volcanism is currently non-existent in Kyushu. However, in the mid Miocene, SW Honshu
experienced fore-arc volcanism, so it should not be automatically assumed that volcanism
cannot occur in the fore arc region. The forearc region of Kyushu is the southeastern cornerof the island, coinciding with the forward domain (Figure 5.1).
Typical arc volcanism has been prevalent in Kyushu for the last 2 Ma, but the activity has
been notably more explosive since 0.3 Ma. Since 2 Ma, the volcanic arc has formed on aclearly defined NE-SW trend. The volcanic arc is defined by a variety of volcanic
manifestations, from huge calderas (e.g. Aso, 18 x 25 km diameter), small calderas (e.g.
Ikeda-ko), lava dome complexes (e.g. Kuju) and polygenetic stratocones (e.g. Sakurajima).
The volcanic front continues down through the Ryukyu arc towards Taiwan, tracing thesurface location of the 100 km depth contour of the subducting PSP. The large calderas in the
Kyushu volcanic arc often appear to have precursory voluminous andesitic lava flows.

5.3.2

Geochemistry of the volcanic rocks in Kyushu

This short Section summarises data on the geochemistry of the central volcanic region.
Geochemistry provides essential indicators to help infer the source conditions of magma
genesis beneath volcanic regions. There are key signatures that help to trace the stages ofmagma evolution and fingerprint the tectonic setting through well-established global
correlations between tectonic processes and magma chemistry. Figure 5.2 shows the six
geochemical discrimination diagrams used in this study with examples of data from Kyushu.
Back-arc basaltic alkaline volcanism typically shows elevated alkalis and trace and minor
element characteristics of Ocean Island Basalts (OIB) on a Ti-P-Mn plot and Intraplate
volcanics on a Nb-Zr-Y diagram. Such volcanic geochemistry characterises the back arc
region. Subduction zone volcanism is usually characterised by a subduction fluid component
and for example low Nb and Ti, defining distinctive geochemical fields. Arc related rocks canalso be divided in tholeiitic and calcalkaline varieties and both varieties occur in the young
volcanic arc (< 2 Ma). A special class of arc volcanic rocks are known as adakites and arecharacterised by high Sr/Y ratios. Adakites are commonly associated with subduction ofyoung hot lithosphere. This high ratio is seen in the arc volcanoes from Aso trending northeastwards along the arc, including Aso, Kuju, Yufu and Oninomi (Figure 5.2). Adakites are46thought to be caused by the initiation of volcanism, the subduction of a still hot plate, or direct
melting of the slab. Subducting beneath these volcanoes is the young, hot Shikoku basin, thelikely cause of these adakites.

Figure 5.2: This concise summary of the geochemistry of volcanoes in Kyushu supports tectonic andvolcanic evidence for the evolution of Kyushu. The upper left plot demonstrates elevated B/Nb and B/Zr
ratios at Aso and Kirishima volcanoes. This can be interpreted as the source magma of Kirishima andAso volcanoes being influenced by the water-rich Kyushu Palau ridge as it subducts. The high Sr/Y
ratios seen in Oninomi, Yufu and Kuju volcanoes represent adakitic lavas, a product of subducting thestill hot Shikoku Basin. The upper right plot demonstrates the changing composition of lavas produced
at Aso volcano with time. There is an apparent increase in the fluid-mobile elements of B/Nb and K20,
showing a trend from initial high magnesium andesites towards more typical island arc basalts. The
maps provide a geochemical interpretation of the tectonic and volcanic regimes affecting Kyushu
through the last 15 Ma.

47

In Kyushu rocks with exceedingly high ratios of fluid mobile elements such as potassium andboron (K2O and B/Nb or B/Zr) are observed as a distinctive class (Miyoshi et al., 2008). Suchcompositions suggest that the magma genesis region was very water rich. Aso and Kirishima
volcanoes show elevated B/Nb and B/Zr ratios compared to the normal arc values (Figure5.2). This suggests that material subducted at the Nankai trough was particularly rich in water
bearing minerals or water rich sediments. K2O, B/Nb and other fluid-mobile elements of Aso
basaltic rocks are also seen to have increased with time. The chemical composition of the
mantle beneath Aso volcano seems to have changed between 3.8 and 2.2 Ma to have amuch higher K2O and B/Nb content (Figure 5.2). This could be inferred to be a result of fluid
addition from the subducted PSP to the mantle beneath the Aso area.
Figure 5.2 shows a possible evolution of the tectonics in Kyushu based on the geochemical
tracers identified. High magnesium andesites (HMA) identify the volcanism above the leadingedge of the subducting plate, adakites (ADK) likely represent very hot areas of melt
generation, ocean island basalt (OIB) represent volcanism unrelated to subduction and islandarc basalts (IAB) are arc volcanoes. This reconstruction suggests how, with time, the leadingedge of the plate has retreated south eastwards, and rotated anticlockwise, or perhaps even
bent.

5.4

Volcanic history of Kyushu since 15 Ma

The main time divisions discussed here are similar to the divisions used in the tectonic history
in the previous Section 4. However, here the account begins at around 15 Ma, dealing withvolcanism from 15-10 Ma, 10-6 Ma, 6-2 Ma, then providing a more detailed account of the
Quaternary volcanism from 2-1 Ma, 1-0.3 Ma, 0.3-0.01 Ma and 0.01-0 Ma (Holocene). Eachof the major time divisions shows major changes in the volcanism that divide it from adjacent
Sections. Changes in volcanism are due to new volcanism, shut down of volcanism, erupted
volume, style, activity, etc. It should be noted that unless otherwise stated, all age information
comes from the online AIST database (AIST, 2008).

5.4.1

15-10 MaJapan is the product of repeated episodes of accretion via subduction processes, plus
associated arc volcanism. Beneath Kyushu, the most recent ‘episode’ of subduction (albeit
intermittent), involved the subduction of the PSP beneath the Eurasian Plate (EP), fromaround 15 Ma. Figure 5.3 presents a summary of volcanism in this period.
Evidence of volcanism associated with this mid Miocene subduction is only recognisable
today in the form of a band of I-type felsic plutonic rocks. These are assumed to represent theigneous activity associated with the arc volcanism occurring further northwest of this band at
the time. The NE-SW trend of this band of plutons across the central volcanic region implies anorthwestward subduction direction, similar to what is seen today. These plutons form part ofmuch more widespread igneous activity which took place within the SW Japan forearc
between 17-13 Ma (Kano et al., 1991). Tatsumi and Maruyama (1989) demonstrated that thissubduction of the young, hot PSP between 17 to 10 Ma (Takahashi, 1981; Tatsumi, 1983),
led to the formation of high magnesium andesites (Setouchi HMAs) in SW Japan.
Meanwhile, offshore, at 15 Ma the final stages of Sea of Japan opening led to rifting of thewestern margin of NE Honshu (Yamaji, 1990). This rifting was accompanied by eruption of alarge volume of submarine volcanic rocks, commonly called “Green Tuff” (Taira, 2001), as
well as some dykes in NW Kyushu.
Initial volcanic activity occurred at 15 Ma at the most northerly volcanic centres associatedwith Kyushu: Iki, Hirado and Oki Islands. The volcanism at Iki is very long lived, with the initialstage reported to occur from 15-4.3 Ma, and most recent stages occurring in the late
Quaternary. In the northern Goto Islands, volcanic activity occurred on Hirado Island at 15 Ma,then again from 9-6 Ma (Uto et al., 2004). It has been suggested that this is associated with
the Sea of Japan magmatism. However from around 11 Ma an asthenospheric heat source
off NW Kyushu (Shinjo et al., 2000) appeared to be feeding volcanism in that area, so
perhaps there are mixed magma sources. Back arc monogenetic volcanism in the48

northwestern region also began in this period, with monogenetic volcano clusters up to 15-40
km in diameter.

Figure 5.3: From 15-10 Ma the position of the volcanic front is unknown, however can be inferred fromthe location of a band of I-type felsic plutons trending NE-SW across central Kyushu. There is someminor volcanism in the northwestern volcanic region.
Notes to this and Figures 5.4 to 5.9: The location of the HVZ is taken from Kamata & Kodama 1994, the location of
the Beppu Shimabara Graben is after Hoshizumi, 2006, the location of the Median Tectonic Line, Hisatsu,
Hokusatstu, Nanasatsu volcanic rocks are taken from Shinjo et al., 2000, the location of the I-type felsic plutons areafter Kimura et al., 2005. Figures 4-10 are a series of maps illustrating the changes in volcanism in the past 15 Ma.

5.4.2

10-6 MaAt the same time as the proposed halt in PSP subduction beneath the EP, volcanism largely
ceased, but did continue in certain areas of Kyushu (see Figure 5.4). The nature of the
volcanism from 10-6 Ma was different to that which occurred pre-10 Ma: there were no largescale igneous systems as represented by the plutons which formed in the previous time
period); all volcanism was now predominantly effusive in nature.

49

Figure 5.4: 10 – 6 Ma. During the halt in Philippine Sea Plate subduction, voluminous lava outpourings
occur sporadically through time; however, they are only located along the very western edge of Kyushu.

The volcanism that occurred in Kyushu at this time was mostly localised in the northwesternregion, with a smaller amount in the southern region (see Figure 5.4). Iki island was still
volcanically active, and new volcanic episodes occurred in the Kitamatsuura region. These
formed eight, highly voluminous and episodic alkali basalt lava flows, all sloping gently to thenorthwest and each approximately 100 m thick. The geochemistry of the magmas erupted inthis time period showed a low plate component; for example, the Shimoshima area erupted
OIB type basalts at 7 Ma (Nagao et al., 1992). Calc-alkaline volcanism was absent during thistime. Howeve,r Kamata and Kodama (1994) describe how mantle xenolith-bearing basanites
erupted between 9.5-6.5 Ma, supporting the idea that the oceanic plate did not subduct during
this period (Uto, 1989).
In general, the late Miocene and Pliocene volcanic activity in the southwestern volcanic region
forms three zones from north to south, the Hisatsu, Hokusatsu and Nansatsu zones. The
most southerly of these zones, the Nansatsu zone, was the site of calc-alkaline volcanism at
the end of this period (Hedenquist et al., 1994), indicating the inception of subduction ataround 6.4 Ma.

5.4.3

6-2 MaSix million years ago significant changes took place in the subduction regime of the PSP,
when the PSP re-initiated subduction beneath the EP. The nature of much of the volcanism
throughout Kyushu also changed at this time (see Figure 5.5).

50

Figure 5.5: 6 – 2 Ma. Re-initiation of subduction at approximately 6 Ma is closely followed by theformation of the Hohi Volcanic Zone (HVZ). Volcanism in the HVZ initially formed at the edges of thezone and focused into the centre with time. Widespread andesite lava plateaus also formed from the
HVZ at this time. An increase in B/Nb and K20 are noted at Aso, likely due to the re-initiation of
subduction and so increase in the slab fluid content of magmas generated in this region. Widespreadlavas continue in the north western and southern regions. A series of rhyolite stratovolcanoes form atand near Arita suggesting a more long lived volcanic centre rather than the episodic behaviour of thelava plateau forming eruptions.

However the northwest volcanic region showed little change and still exhibited voluminous
effusive volcanism. The Kitamatsuura basalts continued, as well as strong alkali volcanism at
Oki Island from 7-4 Ma and Iki Island from 4.3-3.5 Ma. These were joined by the developmentof another flood basalt area the Higashimatsuura basalts, located to the east of theKitamatsuura area. The Higashimatsuura basalts erupted at about 3 Ma, forming five main,
50 m thick lava flow units, each episode occurring over one hundred thousand years or less.
Both the Kitamatsuura and Higashimatsuura basalt areas demonstrate short-lived
outpourings, showing how this northwestern region is typified by ‘episodic’ volcanism.
However, anomalous to the regional stereotype of episodic basalt outpourings, the eruption ofmuch more evolved rhyolite magmas occurred at about 2.7-2.2 Ma, at Arita and surrounding
areas. The Arita rhyolites lie to the east of the Kitamatsuura basalts and are accompanied byrelated andesites and basalts. The Arita rhyolites are very unusual for this region as they
imply a longer-lived volcanic centre, rather than short episodes of activity. Iki Island showed
an increase in the intensity of activity since the initial phase, with ‘Stage 1’ activity from 4.3-

51

3.5 Ma, swiftly followed by ‘Stage 2’ at 3.5-2.8 Ma. These stages were much more intensive
than the initial (Stage 0) basaltic activity, with Stage 1 producing andesites, basalts andtrachyandesites, and Stage 2 producing rhyolites and basalts (Sano 1995). The Stage 1
volcanism on Iki Island is rather unusual for Kyushu, as between 4-3 Ma there is very little
volcanism anywhere else in Kyushu.
The central volcanic zone also saw a discernable increase in volcanism at this time. The
formation of the Hohi Volcanic Zone (HVZ) in the central region marked the start of highly
explosive volcanism, last seen in Kyushu at about 12-10 Ma. Early HVZ volcanism occurredat the edge of the tectonic boundaries of the HVZ seen today. With time, the volcanism
focused towards the centre of the HVZ (Kamata & Kodama, 1994). Activity within the HVZ
produced voluminous eruptions of andesite as lava flow plateaus, which were intruded byeast-west trending feeder dykes.
The central volcanic regions of Kyushu have been studied in relation to their eruptive volumes
through time, with the HVZ recording its maximum eruption rate at 5 Ma, then decreasing
throughout the Pliocene to the present (Kamata and Kodama., 1994). At the same time,
eruptive volumes have increased in the Shimabara peninsular area (Yokose et al., 1999).
However the total volume of erupted material was much higher in the HVZ than in the
Shimabara area. These facts indicate the differing volcanic and tectonic histories within theeastern and western parts of the central volcanic region.
Further west than the HVZ, the K2O, B/Nb and other fluid-mobile elements in the Aso basaltic
rocks (still in the central volcanic zone) increased from 3.8 to 2.2 Ma. This suggests anincreasing fluid content in the mantle beneath the Aso area.
During this time period, the southern volcanic region underwent some repeated (but notcontinuous) activity in the Hokusatsu zone. This activity occurred from 4.8 to 1.6 Ma andcomprised widespread andesitic/dacitic lava flows and pyroclastic cones (Uto and Uchiumi,
1997). The more southerly, Nansatsu zone also produced plateau lavas from 6.4-5.9 Ma.
However, the exact vent locations of these are unknown.

5.4.4

2-1 MaThe start of the Quaternary is fairly well established as the start of recent arc volcanism inKyushu (e.g. Watanabe, 2005). Also occurring at this time were some fairly major tectonic
events, such as the collision of the Kashima-Daiichi and Japan Group sea mount chains at
the Kanto triple junction (near Tokyo), the opening of the Okinawa Trough near Kyushu, andthe change in subduction direction of the PSP from NNW to NW, beneath the EP. This slightchange in subduction direction had significant effects on the related plate tectonic andvolcanic regimes. The MTL exhibited strongly intensified dextral motion, which still continues
today. Significant changes in the mode of eruption and whole-rock chemistry also occurred
throughout Kyushu (Kamata and Kodama, 1994) (see Figure 5.6 for a summary).
The northwestern volcanic region was the site of a considerable amount of volcanism from 2 1 Ma. Continuing on from the previous time period (6-2 Ma), there was more volcanism in the
Iki monogenetic volcano group and the final stage of the Arita rhyolites at Ureshino volcano.
The Iki volcanics changed compositionally to form mainly basalt scoria cones during Stages
3-5 (2.5-2.2 Ma, 1.7-1.4 Ma, 1-0.6 Ma) (Sano, 1995). Further west, off the mainland of
Kyushu, the Goto Islands had considerable activity; at Kishuku from 1.07-0.66 Ma, Ojikajima
monogenetic volcano group from 1.1-0.3 Ma, and at Ukujima polygenetic stratovolcano from
1.3-1.1 Ma. In terms of eruptive volumes, Ojikajima peaked at 1 Ma and 0.6 Ma with intense
volcanism, lasting for 0.1 Ma in the centre of the volcanic field. Ukujima is unusual amongst
the volcanoes in the Goto Islands in that it was not basaltic or monogenetic. Ukujima
produced rhyolites, basalts and andesites similar to the Taradake basalts (Sudo et al., 1998).
Taradake is located on the mainland, in the Nagasaki prefecture, to the north of Unzen
volcano. It has been active in two main stages, the older stage being from 1.3-1.0 Ma, which
produced stratovolcanoes and lava domes of andesite, basalt and dacite. Further to the east52

(but still in the NW zone), Kurose volcano, a small islet north of Genkai-jima, had basaltic
activity at 1.1 Ma.

Figure 5.6. At 2 Ma the subduction direction altered to north-west, which in turn intensified the dextral
motion on the Median Tectonic Line (MTL), and opening in the Okinawa Trough reached western
Kyushu. Volcanism in the western part of the Beppu Shimabara Graben was dominated bystratovolcanoes. There were many lava shield volcanoes/lava plateaus formed at around 2 Ma,
commonly associated with locations of future more explosive volcanism. N.B. The size and shape of thelava plateaus of Pre-Kirishima, Pre-Aso, Pre-Kuju and Pre-Tsurumi are estimates.

Volcanism in the central volcanic region during 2-1 Ma largely took the form of voluminous
andesite shield lavas, laying the foundations for later stages of volcanism in the extensional
regime of the Beppu-Shimabara graben region. In the eastern part of the central zone, Futago
volcano produced andesitic and dacitic lava domes and lava flows, which formed from about
1.5/1.46-1.1 Ma. This activity was followed nearby at 1.1-0.6 Ma by Kanagoe andesitic
stratovolcano.

53

At this time, volcanism in the HVZ had passed its peak activity phase (6-5 Ma), but was still
evolving in nature, with the formation of andesite/dacite lava domes at 2 Ma and a marked
increase in K20 component at 1.6 Ma. Yufu/Tsurumi, Kuju and Aso are three of the largest
volcanic centres in the central volcanic zone today. However, the precursory activity of these
volcanic centres began around 2-1 Ma. From east to west, ‘Pre-Tsurumi’ formed andesite
lava plateaus at approximately 2 Ma. ‘Pre-Kuju’ was initially andesitic, with the Yoshinomoto
andesite from 2.8-2.4 Ma, the Matsubara andesite at 2.4-2.1 Ma, the Kusu group at 1.7-1.3
Ma, followed later by the Yamacogawa rhyolite at 1.3-1.0 Ma (Mashiro, 2006). Early activity at
Aso began at 2.2 Ma with basaltic eruptions (Takahashi & Kobayashi, 1999). However, thiswas just the beginning at Aso, as there was more precursory activity before the caldera
forming eruptions.
Further to the west but still in the central volcanic region, Kimpo volcano (near present day
Kumamoto City) was active from 1.4 Ma until around 0.18 Ma, as an andesitic and dacitic
stratovolcano with lava domes. ‘Pre-Unzen’ volcanics on the Shimabara peninsula initiatedaround 2.5 Ma, with episodic monogenetic volcanism of olivine basalt and pyroxene andesites,
until 0.5 Ma, when present-day Unzen began to form.
Volcanic activity in the southern volcanic region during 2-1 Ma follows a similar pattern to thecentral volcanic zone, with ‘pre-caldera’ andesitic lavas occurring where major volcanic
activity occurs today. ‘Pre-Aira caldera’ activity described by Masafumi et al., (2000), isapparent from 3-1 Ma with eruptions of andesitic lava flows (Hokusatsu volcanic rocks).
Younger Hokusatsu lava flows and pyroclastic cones are also obvious at 2 and 1.6 Ma (Utoand Uchiumi, 1997). ‘Pre-Kirishima’ formed shield volcanoes from andesite lava flows at 1.6
Ma forming the Kurino Group and at 1.4 Ma with the Siratori Group. On a smaller scale thanthe Aira and Kirishima activity, Sendai volcano (west of the present day volcanic front),
formed a pyroclastic cone and erupted basaltic and andesitic lava flows from 2.5-1 Ma.

5.4.5

1-0.3 Ma

During the period 1-0.3 Ma there were no dramatic changes in the tectonic regime of Kyushu.
The MTL still experienced an intensified dextral motion, and localised areas of the HVZ
experienced changes between compressive and extensional stress regimes. Figure 5.7
presents a summary of the volcanism at this time.
In the northwestern volcanic region, volcanism continued in the Fukue monogenetic volcanogroup with development of Kyonotake, a small shield volcano at 0.34-0.26 Ma. At the northernend of the Goto Islands, the Ojikajima basaltic monogenetic volcano group had four intense
volcanic episodes from 1.08-0.3 Ma. By the third intense episode at 0.7-0.56 Ma, the eruptive
locations spread out to the east and west.
The final episode of volcanism from 0.56-0.3 Ma displayed reduced intensity, and the ventlocations focused into the centre of the volcanic field. The final activity at Ojikajima stopped at
0.3 Ma, with higher MgO levels than the previous stages of volcanism.
The central volcanic region showed continued activity in all the major areas, with large
increases in the number of volcanic centres identified through this 1-0.3 Ma period. Working
from west to east, activity at the younger stage of Taradake from 0.8-0.4 Ma formed andesite,basalt and dacite lava domes as part of the stratovolcano. Further south from Taradake on
the Shimabara peninsula, Unzen volcano grew rapidly from 0.5-0.3 Ma, producing multiple
pyroclastic flows. In this first two hundred thousand years, the rate of growth of Unzen was
faster than the rate of subsidence in the Shimabara graben.
Further east, activity preceding the Aso caldera forming events continued intermittently, with
‘Pre-Aso’ andesite pyroclastic flow plateaus and pyroclastic cones at 0.84 Ma, lava flows from
Neko-dake (part of ‘Pre-Aso’) from 0.7-0.4 Ma, then biotite rhyolites at 0.45 Ma (Takahashi
and Kobayashi, 1999). Further east again, many new volcanic centres formed between 1-0.3
Ma within the HVZ. There are two loosely clustered groups of volcanoes, one group nearpresent day Kuju, and one group further east near present day Yufu/Tsurumi. Between 1-0.3
Ma the first activity in the group near Kuju occurred in the Shishimuta caldera. The
54

Shishimuta caldera is a breccia-filled, funnel-shaped depression, 8 km wide and greater than
3 km deep (Kamata, 1988), which violently erupted the 110-km3 Yabeki pyroclastic flow
between 1-0.9 Ma. This older, more productive, Shishimuta phase lasted from 1-0.7 Ma. A
modification in the local stress regime of the HVZ led to a change from a north-south
extensional regime to a dominantly compressional regime at 0.7 Ma. This change in local
stress conditions was coincident with a change in the style of volcanic activity at Shishimuta
caldera. The younger Shishimuta caldera activity began at 0.7 Ma, so that from 0.7-0.3 Ma,
small volume lavas and lava domes were produced.

Figure 5.7: 1 to 0.3 Ma. There are no major tectonic changes at 1 Ma, however the volcanism doeschange in nature. The major change is the start of caldera formation in Kyushu, at Shishimuta andKobayashi. The HVZ gains an influx of stratovolcanoes slightly north west of Shishimuta caldera.

Post Shishimuta caldera activity has been dominated by monogenetic volcanism (Kamata,1988). Near the Shishimuta caldera, three other centres in the Kuju area became active at 0.6

55

Ma. Haneyama volcano produced rhyolitic and dacitic lava flows for one hundred thousand
years, Waita yama produced lava domes and stratovolcanoes until 0.4 Ma (Kamata, 1988),
and Noinedake-Hanamureyama formed andesite and dacite lava flows and domes until 0.3
Ma.
In the far east of the central region, activity also began around 0.6 Ma, with OjikayamaAmagoidake and Fukumanyama-Tateishiyama erupting andesite and dacite lava domes andflows for one hundred to two hundred thousand years. From 0.4 Ma, TakahirayamaMizuguchiyama had a short one hundred thousand year burst of andesite and dacite lava
dome forming activity. In the same time period, Hiji volcano, located slightly further east, also
produced andesite and dacite lava flows. Offshore and slightly east of Futago volcano,Himeshima volcano showed recent (0.35-0.02 Ma) dacite and rhyolite lava dome andpyroclastic flow forming activity.
Southern Kyushu volcanism in the time period 1-0.3 Ma started at 0.9 Ma with ‘Pre-Aira
caldera’ lava plateaus formed by eruptions of andesitic lava flows in northern Aira
(Yuwandake andesite), and basaltic to rhyolitic lava and pyroclastic flows in western Aira.
Slightly to the northwest of the ‘Pre-Aira’ volcanism, a small volcanic depression called the
Imuta caldera formed in the centre of a ring of lava domes between 0.5-0.35 Ma. Nearby, thetwo Satsuma Maru-yama lava domes formed, also at 0.5 Ma. Further north from Aira, slightly
to the northwest of where present day Kirishima volcano resides, two calderas formed after
0.5 Ma. The first caldera to form was the Kobayashi caldera from 0.5-0.4 Ma. Kakuto caldera
formed one hundred thousand years later at 0.3 Ma, slightly east of Kobayashi caldera. Bothcalderas produced voluminous dacitic and rhyolitic pyroclastic flow plateaus.
Between 1-0.3 Ma the Ryukyu arc, south of Kyushu, started to become established. The large(ca. 20 x 17 km diameter) Kikai caldera is one of the most northerly volcanoes of the Ryukyu
arc and has been active with pre-caldera volcanism since 0.7 Ma. The pre-caldera volcanoes(Yahazu-dake and Takeshima) are located on the caldera rim. However, Kikai calderaforming eruptions occurred later and are described in the next Section. Moving south throughthe Ryukyu arc, Kuroshima andesite stratovolcano was active prior to Kikai, from 1-0.9 Ma.
Further south and at the same time as the initial volcanism at Kikai (0.7 Ma), Taria jima and
Suwanosejima formed andesitic stratovolcanoes with Strombolian and Vulcanian activity.

5.4.6

0.3-0.01 Ma

There were no large-scale tectonic changes that occurred at 0.3 Ma; the only notable tectonic
change was the change from extensional to compressional stress regime in the HVZ.
However, the nature of volcanism did change quite drastically at this time (see Figure 5.8).
The southern volcanic zone became a highly productive area: volcanism focused in thecentral zone, and activity in the northwestern zone declined steadily. This could be related torecent onset of extension and progressive rollback of the slab. The gigantic calderas of
Kyushu formed during this time period, with caldera forming eruptions of Aso, Aira, Kakuto,
Ata and Kikai.
The northwestern volcanic region at this time was concentrated into the Fukue monogenetic
volcano group, with activity from 0.25-0.13 Ma at Akadaki and cinder cones and lava flows at
0.018 Ma at Oni-dake.
The major events of the central volcanic zone were the four caldera forming eruptions of Aso
volcano, at 0.3 Ma (Aso 1), 0.14 Ma (Aso 2), 0.12 Ma (Aso 3), and 0.09 Ma (Aso 4). All ofthese massive eruptions formed widespread pyroclastic flow plateaus. Post-caldera
volcanoes of Aso such as Naka-dake and Take-dake began to form from 0.022 Ma(Miyabuchi et al., 2004). Aso has two flank volcanoes that have formed along a fault runningwest from Aso towards Kumamoto City. The first of these flank volcanoes to form was Akai
volcano at 0.3 Ma, which had continued sporadic activity until 0.18 Ma. Akai volcano erupted
immediately prior to the Aso 1 eruption, and produced dacitic lava flows and a pyroclastic
cone. Today, Akai stands only 33 m high and is located approximately 30 km west of Aso.
The other flank volcano of Aso is Omine monogenetic volcano, which formed between the56major eruptions of Aso 3 and Aso 4 (i.e. between 120-90 Ka), and is located approximately 15
km west of the centre of Aso caldera.

Figure 5.8: 0.3 to 0.01 Ma. From 0.3 Ma volcanism in Kyushu is dominated by gigantic calderas,
namely Aso, Kakuto, Aira, Ata and Kikai. These calderas all form within grabens, the Beppu-Shimabara
graben in the north and the Kagoshima graben in the south.

Other volcanism occurring in the central volcanic region between 0.3-0.01 Ma includes theHimeshima volcano group, off the eastern coastline of Kyushu (0.35-0.02 Ma), activity in the
HVZ at Tsurumi, Yufu and Kuju volcanoes, and at Unzen volcano in the west. Tsurumi and
Yufu volcanoes both formed andesitic and dacitic lava dome stratovolcanoes from about 0.04
Ma. The activity at Yufu ceased just over one thousand years ago, whereas Tsurumi was lastactive in 1974. The first activity of present day Kuju volcano began at 0.15 Ma, with thevolcano group now covering an area measuring 20 km E-W and 15 km N-S, lying on the wide
Hane-yama lava plateau of Plio-Pleistocene age (International Volcanological Association,57

1962). The early stages of Kuju formed multiple craters measuring up to 1 km x 0.5 km and
200 m deep, followed in later stages by massive lava domes which protruded through theflank of the main cone.
In the western part of the central volcanic region at this time, Unzen volcano transitioned
between the Middle and Younger stages. The Middle stage occurred at 0.3-0.15 Ma
producing lava flows, the Younger stage began at 0.15 Ma continuing to the present day
producing both lava flows, domes and pyroclastic flows.
The southern volcanic region shows evidence of massive changes throughout the time period
0.3-0.01 Ma, with the formation of several enormous calderas. The most northerly volcanic
centre in the southern volcanic region is that of Kirishima volcano. Kirishima has twooverlapping calderas associated with it, Kakuto and Kobayashi. Kakuto caldera is 15 x 10 km
in diameter and formed at 0.3 Ma, located slightly north west of present day Kirishima volcano.
The older stage of Kirishima volcano occurred from 0.3-0.15 Ma with andesitic shield
volcanism. The younger stage of Kirishima volcano has formed stratovolcanoes since 0.1 Ma,
with many eruptions in historical time. Further south is the massive Aira caldera(approximately 20 x 18 km), which is now the home of Sakurajima volcano. Following on fromthe previously mentioned ‘Pre-Aira caldera’ activity at 0.9 Ma, there were two more episodesof pre-caldera volcanism. The first of these was from 0.5-0.1 Ma with eruptions of basaltic andrhyolitic lava flows in the southern caldera area (Masafumi et al., 2000). The final pre-caldera
activity was from 0.1-0.025 Ma with eruptions of andesitic lava and pyroclastic flows in thenorthern part of the caldera and rhyolitic lava flows in both the northern and southern areas of
the caldera (Masafumi et al., 2000). An interesting feature noted by Masafumi et al., (2000) is
the presence of Osumilite, 15 km apart on opposite sides of the caldera rim during 0.04-0.03
Ma, perhaps suggesting that a large magma chamber has been present under Aira caldera
since about 0.04 Ma.
The Aira caldera forming eruption occurred at 0.025 Ma and was the source of the massively
voluminous Ito pyroclastic flow and widespread co-ignimbrite ash “AT”. The Ito pyroclastic
flow led to the formation of a huge ignimbrite plateau, which deposited thickly all oversouthern Kyushu and can be traced far beyond Kyushu. Sakurajima is a post caldera volcano,which formed at 0.022 Ma and is located on the southern rim of Aira caldera. In the northeastern corner of the Aira caldera is the submarine Wakamiko caldera. The Wakamikocaldera is younger (0.016 Ma) and smaller (approximately 6 x 3 km) than the Aira caldera. On
a smaller scale, Aojiki scoria cone (part of the Kamo monogenetic volcano group) is a small
basaltic maar, which erupted at 0.1 Ma and is located approximately 20 km NW of Sakurajima
volcano. Further south in the Kagoshima graben, lying at the mouth of Kagoshima bay, Ata
caldera formed at 0.11 Ma. Ata caldera is a 25 km (E-W) x 12 km submarine caldera, which
has two possible locations. The northerly location was the source of the Ata pyroclastic flow,which formed a rhyolitic and dacitic pyroclastic flow plateau. The more southerly possible
location of Ata caldera has post caldera cones located on the rim of the caldera, such as
Kaimondake volcano.
The formation of huge calderas continued down into the Ryukyu arc, with the enormous 20 x
17 km diameter Kikai caldera. Kikai is located approximately 40 km south west of thesouthern tip of Kyushu. After the pre-caldera activity (described in the previous Section) there
were two episodes of caldera formation, one at 95 ka and then again at 6.3 ka. The 6.3 kacaldera forming eruption produced a widespread co-ignimbrite ash fall tephra known as the KAh (Kikai-Akahoya). This eruption has been classed as VEI 7 (Newhall & Self, 1982) and thelargest eruption in the Holocene in Japan (Torrence and Grattan, 2002). There are several
post-caldera cones located within the submarine caldera. Further south in the Ryukyu arc isNakanoshima, an andesitic and dacitic stratovolcano that is still active today.

5.4.7

Active Volcanism (Holocene)

In the last ten thousand years there have been no major tectonic changes throughout Kyushu.
Using relative sea level change as an indicator of tectonic change (possibly associated withvolcanic unrest), the start of the Holocene did see a rapid sea level rise of 25 m, recorded in
Hiroshima Bay (Yasuhara & Seto, 2006), followed later in the Holocene by a sea level fall of 558

m to present levels. The sea-level curve for Hiroshima Bay is similar to curves for tectonicallystable areas of Japan (e.g. Osaka Bay), thus we can extrapolate that Kyushu experienced
similar sea level changes (Yasuhara & Seto, 2006). Large-scale caldera volcanism in Kyushu
during the Holocene is represented by the 6.3 ka eruption of Kikai caldera, mentioned above.
However, Kikai caldera is located in the Ryukyu arc, south of Kyushu, and there have been
no large caldera eruptions on mainland Kyushu in this period (see Figure 5.9). It is worth
noting that the Japanese Meteorological Agency (JMA) has defined active volcanoes in Japan
as “Volcanoes which have erupted within the past 10,000 years or volcanoes with vigorous
fumarolic activity.”

Figure 5.9: Holocene volcanism has seen the volcanic front firmly extend down in the Ryukyu arc, withthe largest Holocene eruption occurring at Kikai volcano. The long-lived centres of Fukue, Unzen, Aso,
Kuju, Yufu/Tsurumi, Kirishima, Sakurajima continue to have active stratovolcanoes. The Holocene has
had no widespread lava plateau eruptions.

Much of the volcanism in the previously discussed period (0.3-0.01 Ma) has continued to thepresent. In the northwestern volcanic region, a large proportion of the volcanism ismonogenetic, so Holocene activity is understandably located at new centres. Hino-dake ispart of the Fukue monogenetic volcano group in the southern Goto Islands and the youngest
active centre, forming basaltic lava flows at 3-2 ka.
59

The central volcanic region is still physically dominated by the enormous Aso caldera. Active
volcanism in Aso caldera today continues in the post-caldera phase of the volcano. There are
two centres within Aso caldera that are classed as active. The younger of these is Kometsuka
basaltic scoria cone, which formed at 10 ka and still shows perfect scoria cone morphology.
Kishima dake is the youngest new centre, which formed a basaltic scoria cone at 3.7 ka.
However, since Kishima dake formed, several of the ten or so stratocones and cinder cones
clustered in the middle of the caldera have erupted again, as recently as 2005. Whether thisactivity marks the end of caldera forming events at Aso caldera, or whether there are more tocome, is unclear. Previously, a progression in magma compositions from rhyolite to basalt
has been noted to occur in each caldera forming stage, suggesting an enormous caldera
must have been located beneath the volcano. Seismic tomography data suggests thatbeneath Aso today is an enormous ‘hot zone’ of primed mantle; whether this is part of a vast
magma chamber is unknown.
Further to the northeast, Holocene activity at Kuju volcano took the form of the growth of lava
domes, which produced six major andesitic to dacitic tephra deposits (Siebert & Simkin, 2002).
Eruptive activity at Kuju volcano has migrated eastward during the last 5000 years, with themost recent activity at Kuro-dake lava dome at 1.6 ka (Siebert & Simkin, 2002). Further to theeast, lava dome forming stratovolcanoes Tsurumi and Yufu are both classed as active.
Tsurumi produced lava flows within the last one thousand years and possibly magmaticrelated ash clouds in the mid 1970s. Yufu is classed as active but has had no activity since
1.1 ka. In the west, on the Shimabara peninsula, the currently active Unzen volcano resides in
the Shimbara graben. In the last ten thousand years, Unzen has shown repeated lava dome
formation, leading to devastating pyroclastic flows. The 1991 pyroclastic flows from the thennewly formed Heisei-shinzan lava dome were particularly destructive, due to the inhabited
nature of the flanks of the volcano. There were nearly ten thousand pyroclastic flows counted
in a five-year period after the formation of the Heisei-shinzan dome.
The southern volcanic region underwent continued volcanism in two of the major volcanic
centres, as well as several new volcanic centres. The most northerly of these is Kirishima
volcano, which lies on the southern edge of Kakuto caldera. Kirishima volcano comprises a
series (International Volcanological Association, 1962) of older, gently sloping volcanoes andabout 20 younger steep cones (International Volcanological Association, 1962). These
centres have formed an elliptical volcano zone (approximately 30 km x 20 km) trending NWSE, with volcanism younging towards the southeast. Further south, Sakurajima volcano
began erupting 22,000 years ago at the southern rim of the Aira caldera and is still one of themost active volcanoes in the world today. Sakurajima is the compound cone of threestratovolcanoes, namely Kita-dake, Naka-dake and Minami-dake, with the southern cone,
Minami-dake being active today. There have been several large eruptions; most notable inrecent times is the 1914 eruption, prior to which there was a large earthquake, followed a few
days later by a highly explosive phase of eruption. Another large earthquake occurred, then,shortly afterwards, the eruption became effusive and lava flows continued for several months.
After this eruption, the centre of the Aira caldera (north of Sakurajima) was observed tosubside, implying Sakurajima volano did not source its magma from directly beneath, butinstead drew the magma from the same magma source as the Aira caldera.
New volcanic centres in the southern volcanic region included two maars (Yonemaru andSumiyoshi-ike maar) that erupted at 7 Ka, northeast of Sakurajima, and three centres at thesouthern tip of Kyushu. The very southerly volcanism of Ikeda-ko and Kaimondake volcanoes
lies directly west of Ata caldera. Ikeda-ko is a small caldera lake, 4 km in diameter, which
formed at 5.7 ka with eruptive compositions of rhyolite, dacite and andesite lavas.
Nabeshima-dake lava dome formed within Ikeda-ko caldera at 4.3 ka. Ikeda-ko is part of the
Ibuski volcanic group, which comprises many maars, lava domes and strato volcanoes, somepre-dating Ata caldera and many post-dating Ata caldera. Slightly younger than Ikeda-ko at 4
Ka, and located 5 km southwest, is Kaimondake volcano. Kaimondake is a perfectly formed
cone, comprising a stratovolcano with a lava dome on top, on the western rim of Ata caldera.
Eruptions have occurred as recently as the year 885, with sub-plinian eruptions of mainly
basaltic and andesitic lava, as well as producing copious scoria.

60

The Ryukyu arc has shown a vastly increased rate of volcanism in the Holocene. The 6.3 ka
K-Ah eruption of Kikai caldera proved to be the largest in the Holocene, but other areas in the
Ryukyus also showed intensified volcanism with the formation of around 16 new volcanic
centres in the last 10 ka. The new volcanic centres mainly consist of andesitic lava domes,
stratovolcanoes and lava domes, although there is some rhyolitic volcanism and several small(e.g. 7 x 10 km) calderas. Several post-caldera cones formed on the Kikai caldera rim, suchas Satsuma Io-jima, which has been active since 5.2 ka with andesitic, rhyolitic and basaltic
stratovolcanoes, lava domes and pyroclastic cones.

5.5

Future volcanism in Kyushu

The highly dynamic and changeable nature of volcanism in Kyushu in the past 15 Ma
suggests that it is highly likely to continue to change in style and location into the future.
Insights into possible future locations of volcanism require use of more than just the volcanic
history. Tectonic and geochemical insights allow development of a complete evolutionary
story of Kyushu. Establishing a complete story is essential to understand the processes that
underpin these changes in location and styles of volcanism, and the timescales on whichthese changes operate. Section 7 integrates the volcanic, tectonic and geochemical histories
to formulate several alternative hypotheses of the evolution of Kyushu and the issue of thelikelihood of future volcanism is discussed in depth in Sections 12 and 13.

5.6

References for Section 5

AIST (2008). Active fault database of Japan. National Institute of Advanced Industrial Scienceand Technology, http://riodb02.ibase.aist.go.jp/activefault/index\_e.html.
Chapman, N., M. Apted, J. Beavan, K. Berryman, M. Cloos, C. Connor, L. Connor, O. Jaquet,
N. Litchfield, S. Mahony, W. Smith, S. Sparks, M. Stirling, P. Villamor and L. Wallace(2009). Development of Methodologies for the Identification of Volcanic and Tectonic
Hazards to Potential HLW Repository Sites in Japan: The Tohoku Case Study. Nuclear
Waste Management Organisation of Japan, Tokyo. Technical Report: NUMO-TR-08-03.
135 pps.
Committee for Catalog of Quaternary Volcanoes in Japan Ver.1.0 (1999): Catalog of
Quaternary Volcanoes in Japan, published by The Volcanology Society of Japan.
Hedenquist, J. W., Matsuhisa, Y., Izawa, E., White, N. C., Giggenbach, W. F., Aoki, M. 1994.
Geology, geochemistry, and origin of high sulfidation Cu-Au mineralisation in the Nansatsu
District, Japan. Economic Geology. 89. 1-30.
Hoshizumi, H. 2006. What type of volcano is in Kyushu? 13th Extension Lecture, Volcanol.
Soc. Japan. 9-10.
International Volcanological Association (1962). Catalogue of the active volcanoes of theworld including solfatara fields. Pts 10-14, 1960-62. Part XI: Japan, Taiwan and Marianas,
by Hisashi Kuno.
Kamata, H. 1989. Shishimuta caldera, the buried source of the Yabakei pyroclastic flow in the
Hohi
volcaniczone,
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Kamata, H. & K. Kodama. 1994. Tectonics of an arc-arc junction: an example from Kyushu
Island at the junction of the Southwest Japan Arc and the Ryukyu Arc. Tectonophysics.
233, pp69-81.
Kamata, H. & K. Kodama. 1999. Volcanic history and tectonics of the Southwest Japan Arc.
The Island Arc. 8, pp393-403.
Kano, K., Kato, H., Yanagisawa, Y., Yoshida, F., 1991. Stratigraphy and geologic history of
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Kimura, J-I., R. J. Stern, Y. Takeyoshi. 2005. Reinitiation of subduction and magmatic
responses in SW Japan in Neogene time. GSA Bulletin. 117, pp969-986.
Masafumi, S., Ishihara, K., Tatsumi, Y. 2000. Pre-caldera Volcanic History in Aira Calderaarea. K-Ar Dating of Lavas from Kajiki and Kokubu Areas in Northern Part and Ushine
Area in Southern Part of Caldera. Bulletin of the Volcanological Society of Japan. 45. 1-12.
Masahiro, D. and I. Michinori. 2006. Volcanic Stratigraphy in Western Oita, Kyushu, JapanPart 1: The Tsuetate Area. J Geosci Osaka City Univ. 49, pp119-135.
Miyabuchi, Y., H. Hoshizumi, K. Watanabe. 2004. Late Pleistocene tephrostratigraphy of Aso
volcano, southwestern Japan, after deposition of AT ash. Bull. Volcanol. Soc. Japan. 49,
pp51-64.
Miyoshi, M., Fukushima, T., Sano, T. and Hasenaku, T. (2008). Subduction influence ofPhilippine Sea plate on the mantle beneath northern Kyushu, S.W. Japan: an examinationof boron contents in basaltic rocks. J. Volc. Geotherm. Res., 171, 73-87.
Nagao, T., S. Kakubuchi, Y. Matsumoto. 1992. Trace element compositions of the Cenozoic
basalts in northern Kyushu, SW Japan. Exploration of volcanoes and rocks in Japan,
China and Antarctica, Commemorative papers for ProfessorYukio Matsumoto. Yamaguchi,
Semura Froppy Printing, pp265-271.
Nakajima, J. and A. Hasegawa. 2007. Subduction of the Philippine Sea plate beneathsouthwestern Japan: Slab geometry and its relationship to arc magmatism. Journal ofGeophysical Research. 112, pp B08306- B08306, doi:10.1029/2006JB004770.
Newhall, C. G. and S.Self. 1982. The volcanic explosivity index /VEI/ - An estimate of
explosive magnitude for historical volcanism. Journal of Geophysical Research, 87, p.
1231-1238. Doi: 10.1029/JC087iC02p01231
Sano, T. 1995. Geology of Iki Volcano Group: Lava flow stratigraphy mainly based on K-Ar
dating. Bulletin of Volcanological Society of Japan. 40b, pp329-347.
Shinjo, R., J. D. Woodhead, J. M. Hergt. 2000. Geochemical variation within the northern
Ryukyu Arc: magma source compositions and geodynamic implications. Contrib Mineral
Petrol. 140, pp263-282.
Siebert L, Simkin T (2002-). Volcanoes of the World: an Illustrated Catalog of Holocene
Volcanoes and their Eruptions. Smithsonian Institution, Global Volcanism Program Digital
Information Series, GVP-3, (http://www.volcano.si.edu/world/). Accessed 12th January
2009.
Sudo, M., K. Uto, Y. Tatsumi and K. Matsui. 1998. K–Ar geochronology of a Quaternary
monogenetic volcano group in Ojika Jima District, Southwest Japan. Bulletin ofVolcanology. 60, pp171-186.
Taira, A. 2001. Tectonic Evolution of the Japanese Island Arc System. Annu. Rev. Earth.
Planet. Sci. 29. 109-134
Takahashi, M. 1981. Abnormal magmatism in island arcs. Chikyu. 30, pp382-388.
Takahashi, M. and T. Kobayashi. 1999. Volcanoes in Kyushu, Field Guide, Volcanoes inJapan, 5, Tsukiji Shokan Co. Ltd.
Tatsumi, Y. 1983. High magnesian andesites in the Setouchi volcanic belt, southwest Japanand their possible relation to the evolutionary history of the Shikoku inter-arc basin, in
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region: American Geophysical Union Geodynamics Series, 11, p. 331–341.
Tatsumi, Y., and S. Maruyama. 1989. Boninites and high-Mg andesites: Tectonics andpetrogenesis, in Crawford, A.J., ed., Boninites and related rocks: London, Unwin-Hyman, p.
50–71.
Torrence, R., and J. Grattan. 2002. Natural Disasters and Cultural Change. Published by
Routledge. ISBN 0415216966, 9780415216968, pp313.
Uto, K. 1989. Neogene volcanism of Southwest Japan: Its time and space based on K-Ar
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Uto, K., N. Hoang, K. Matsui. 2004. Cenozoic lithospheric extension induced magmatism inSouthwest Japan. Tectonophysics. 393, pp281-299.
Uto, K. and S. Uchiumi. 1997. K–Ar ages for Maruyama lava domes in Hiwaki Town,
Kagoshima Prefecture, Southwest Japan: a Quaternary volcano constituting a second
volcanic row of Ryukyu arc. Bulletin of the Volcanological Society of Japan 42 pp. 299–302.
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Kyushu, Japan. Mineralium Deposita. 40. 307-323. doi: 10.1007/s00126-005-0025-7
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Yokose, H., T. Yanashima, W. Kikuchi, N. Sugiyama, A. Shinohara, T. Takeuchi, K. Nagao, K.
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6 Discrimination of Tectonic Domains and Selection of
Example Locations
One of the major outcomes of the Kyushu Case Study is recognition of the fact that eachregion of Kyushu has quite distinctive volcanic and tectonic processes occurring, whencompared to other parts of Japan. Moreover, due to the rapidly evolving tectonic setting, we
expect these processes to evolve both temporally and spatially. Thus, we anticipate that it will
become necessary to use approaches to volcanic and rock deformation hazard estimates thatare tailored specially for each tectonic region of Kyushu. This will particularly be the case if
the method progresses forward to a time-varying hazard model (e.g. allowing the futuretectonic hazard estimates to vary with time). To facilitate this, we have divided Kyushu into
domains that have distinct tectonic and volcanic characteristics.
Additionally, for the purposes of the present study, a group of eleven locations (‘example
locations’) was selected across the Kyushu region so that the results of the probabilistic rockdeformation and volcanic evaluations could be discussed in terms of different tectonic settings
within the region.

6.1

Definition of tectonic domains

We have divided the Kyushu region into 4 tectonic domains: the Back-arc, Extensional Arc,
Southern Arc and Forearc (Figure 6.1). Within each of these domains, there are common
tectonic and volcanic characteristics, but the intensity of these processes can vary within adomain (e.g., depending of the proximity of a location to an active fault and/or volcano).

Figure 6.1: Tectonic domains in the Kyushu region and active faults classified by the sense of
movement (see Section 8 for more information on active faults). We also include some “old” (inactive—
black lines) faults on this map, for reference.

64

The configuration of the tectonic domains in Kyushu is strongly influenced by the current
subduction processes. For example, the location of a domain with respect to the subducting
slab often determines whether or not the domain will be influenced by volcanic processes.
The buoyancy characteristics of the subducting plate will also influence the type and intensityof stresses that the upper crust will experience in response to the convergent plate motions.
The crust in Kyushu has an inherited geometry and rheology as a consequence of previous
deformation phases (see Section 3). The interaction between current subduction processes
and inherited crustal properties will also influence the characteristics of the domain.

6.1.1

The Back-arc domain

The Back-arc domain comprises the NW part of Kyushu and extends to the southwest of
Honshu (Figure 6.1). In most cases, this domain is located beyond the edge of the current
subducting slab (see Figure 3.1 for location of subducting slab). Current tectonic activity isdefined by the presence of NW trending, active left-lateral strike slip faults (Figure 6.1), strikeslip earthquake focal mechanisms and E-W maximum compressive stress tensor axis (Figure6.2). The tectonic activity is low in this area with sparse shallow (<30 km) seismicity (Figure6.3), and low fault slip rates <0.5 mm/a (see Section on strain rates from surface deformation).
Quaternary volcanism (mainly monogenetic) in this area has been present for a long time,and occurs in short bursts of volcanic activity. Chemical and petrological evidence show that
volcanism in this area is consistent with a back-arc environment (see Section 5).

Figure 6.2: Earthquake focal mechanisms (based on CMT solutions from the JMA catalogue, fromhttp://www.fnet.bosai.go.jp) and maximum horizontal compressional stress axes for the Kyushu region.
Stress data from World Stress maps Programme.

65Figure 6.3: Seismicity in the upper plate (earthquakes of all magnitudes, above 20 km of depth andfrom 1983 to 1995).

The tectonic history of the back-arc domain has not changed much in the last 6 Ma, so wemay reasonably not expect it to change much in the next 1 Ma. The area has been always
relatively distant from the subducting slab (a back-arc environment) and it is becoming moredistant from the subduction margin at its southern end due to the rollback of the subducting
slab and subsequent anticlockwise rotation of southern Kyushu (see Section 4). This implies
that the tectonic rates and style here might not be expected to change significantly over the
next 1-2 Ma. However, given the E-W oriented maximum compressive stress regime in thisdomain, it is possible that any pre-existing NW-trending faults that are currently inactive, could
reactivate in the future in response to the overriding tectonic stress field. Monogenetic
volcanism may also occur in a somewhat random fashion that will be difficult to predict.

6.1.2

The Extensional Arc domain

The Extensional Arc domain comprises the central part of Kyushu and the northern part of the
Okinawa Trough. The northwestern boundary of the Extensional Arc domain roughly delimits
the northwestern extent of normal faulting and arc-type volcanism. The southeasternboundary of the Extensional Arc domain roughly coincides with the Oita-Kumamoto Line
(which could be considered the southeast boundary of the Beppu-Shimabara Graben) (Figure1). The current tectonic regime, as expressed by focal mechanism and stress tensor analysis(Figure 6.2) is mainly dominated by N-S extension (with the maximum horizontal stress axis in
an E-W direction) within the onshore part (eastern half) of the domain. In the offshore(western) part of the domain, the maximum horizontal stress axis takes on a more NE trend
and most of the recent seismicity is largely strike-slip. Active faults onshore (Figure 6.1), aremainly E-W striking normal faults that form several E-W grabens known as the Beppu–
Shimabara graben system. Northeast-striking strike-slip faults occur along the southeast
boundary of the Beppu-Shimabara Graben, and may be a continuation of the Median
Tectonic Line in Shikoku. GPS data indicate high shear strain rates along the Oita-Kumamoto66

line (see Section 9) suggesting up to several mm/a of right lateral strike slip here.
Documented rates of active faulting are relatively high in this area compared to the Back-arc
and Forearc domains. Active fault slip rates range from 0.1 to 2.6 mm/a for the E-W trending
normal faults and from 0.5 to 1.5 mm/a for the strike-slip faults (see Section 8). Seismicity ishigh in the area with a larger percentage of earthquakes with Magnitudes < 2, typical of
volcanic areas (Figure 6.3; see also Section 10). Volcanism is extensive in this domain, withnumerous caldera-type eruptive centres (see Section 5.4). Most of the active volcanism in thisdomain is located above or near the 100 km slab contour line (with the marked exception ofUnzen; see Section on Evolution of volcanism from 15 Ma), consistent with models for arc
magmatism being generated where the slab is at ~100 km depth (see Figure 3.1 for locationof 100 km depth slab contour). In general, current volcanism in this domain has an intra-arc
and a back-arc character, sometimes within the same volcano.
One important aspect of the temporal evolution of the Extensional Arc domain (on timescalesof 1-2 Ma) is the expected change in subducted plate geometry with time. For example, as
the shallowly subducting Shikoku Basin migrates south along the margin (due to obliquerelative plate motions), extension and volcanism in the NE part of this domain may decrease
with time (see discussion in Section 7).

6.1.3

The Southern Arc domain

The Southern Arc domain comprises the southernmost part of Kyushu (Figure 6.1). The
domain is located above the current subducting slab and ongoing arc volcanism is present
above the 100 km depth contour of the subducting slab (Figure 3.1). Based on sparse focal
mechanism and stress tensor data (Figure 6.2), there is a dominance of strike-slip
earthquakes, and a NE trending maximum horizontal stress axis is present in the NW part ofthe domain (close to the main strike-slip faults on the Southern Arc domain’s boundary with
the Extensional Arc domain). Active faults (Figure 6.1 and Section 8) are mainly normal,
except for a few strike-slip faults in the northwestern corner of the domain. Active faulting in
the Kagoshima Graben, is associated with the N-S trending volcanic arc but mapped faults
seem to have a NE trend. Deformation rates from active faults in this domain are low (0.1 to
1.5 mm/a on ~ 5 faults; see Section “Strain rates from surface deformation”). However
seismicity rates are high, especially in the Kagoshima area (with a large number ofearthquakes with M<2; Figure 6.3 and Section 10) and on the northern boundary of thedomain (possibly associated with a 1997 earthquake sequence (Fujiwara et al., 1998).
GPS data suggest large extensional strain occurs throughout the southern arc domain(possibly up to 8 mm/a across the whole domain) and may be particularly focused on the
Kagoshima graben area. Given that this rapid extension is not reflected by the active faults
mapped there, it is possible that there are a number of undiscovered or uncharacterised
active normal faults; alternatively, much of this extensional strain budget may be
accommodated via volcanic processes such as dike intrusion. GPS data also reveal large
shear strains (left-lateral sense) cutting across southern Kyushu, in the northern portion of thesouthern arc domain (see Section on Strain rates from GPS). Wallace et al. (2009) suggestthat this left lateral shear occurs due to the collision of the Kyushu Palau Ridge (KPR) with thesubduction margin (see Section 9 for further discussion of this). We propose that the locationof this shear will migrate to the south with time, within the southern arc domain, as thecollision point of the KPR migrates south along the margin. This is an important aspect of thetemporal evolution of the margin that must be considered when estimating rock deformation
hazards out to time periods >500 ka.
Current volcanism in the Southern Arc domain is largely consistent with arc-type volcanism(see Section on evolution of volcanism from 15 Ma). Over the last few million years, the active
volcanic front in this area has migrated from west to east (to its current position today),probably as a consequence of rollback of the subducting Philippine Sea plate. This is a crucial
aspect of the plate boundary evolution that must be accounted for in any future volcanic
hazard models that are extrapolated out to 1-2 Ma from present.

67

6.1.4

The Forearc domain

The Forearc domain (Figure 6.1) corresponds to an area located mainly above the shallow
part of the subducting plate slab (<100 km depth; Figure 3.1). Only the southwestern-mostpart of the domain is located above the 100 km contour but volcanism is absent along thisSection of the subduction zone (see Section 5). The reason for the absence of volcanism inthis area has not yet been explained. The current tectonic regime is not well defined in thearea. Focal mechanism and stress tensor data are almost absent in the onshore part of thedomain (Figure 6.2). Offshore focal mechanism and stress tensor data are associated withprocesses occurring at the subduction plate boundary. The few known active faults in thearea have a NE strikes and normal senses of slip in the southern area but show strike slip
near the boundary with the Extensional Arc domain (Figure 6.1). Tectonic rates seem to be
low in the area. Active faults have slip rates of <0.5 mm/a (Section 8). The shallow seismicity
rate is low (Figure 6.3 and Section 10) compared to the Extensional Arc and Southern Arc
domains. Deformation inferred from GPS is patchy, and can be high in places. This may berelated to uncertainties and problems with the subduction interface coupling model used toremove the elastic component of GPS strain, rather than being due to actual ongoing tectonic
strain that will be accommodated on structures in the upper crust.
Given the proximity of this domain to the subduction margin, it is possible that the tectonic
and volcanic regime could evolve from its present state within the Forearc domain on 1-2 Matimescales. However, we are currently unable to predict how this might occur, and it is equally
likely that the general tectonic and volcanic characteristics of this domain may be static overthe next few millions of years.

6.2

Selection of example locations in Kyushu for more detailed
evaluation

The locations selected for this study are spread across the tectonic domains defined above tosample an array of tectonic and volcanic situations (Figure 6.4). The locations and the criteria
for selection are (Figure 6.4):


Within the Back-arc domain:

Location 1 is a representative location in the back-arc region, in an area of low
seismic, geologic, and GPS-derived strain rates. It is however, situated on a low
slip-rate (0.1 mm/a) active fault.



Location 2 is a representative location in the back-arc region between two leftlateral cross faults (with low slip rates, <0.1-0.3 mm/a).



Location 11 is a representative location in the back-arc region located in thesouthern part of the Abu volcanic field. This location is useful for exploring theinfluence of monogenetic volcanism on the volcanic hazard estimates.

Within the Extensional Arc domain:

Location 3 is located near Aso Volcano. This will probably be one of the higher
hazard locations.



Location 10 is located SW of Aso. This was chosen in order to assess theinfluence of our assumption that the Kyushu Palau Ridge subduction pointintersection with the BSG might influence the probability of Aso-type volcanism(e.g., highly active and voluminous increasing with time towards the southwesttime). It is also situated on an active fault with a moderate slip rate (0.9 mm/a).



Location 4 is near the northern-most volcanoes on the arc trend. This location
was chosen to test the influence on the hazard maps of our assumption that the68arc volcanism will eventually shut-down in northern Kyushu, as the flat slab
beneath Shikoku and southwest Honshu migrates southwest with time.

Figure 6.4: Map of example locations with faults and volcanoes overlain. Note: black lineations areinactive faults and are only shown for informative purposes.

Within the Southern Arc domain: Location 8 is located in the forearc just east of the Kagoshima Graben. Thislocation was chosen to look at the influence on the hazard probability estimatesof including the possibility of trenchward arc migration (as a function of possible
slab rollback). It is also situated on a low slip-rate (0.1 mm/a) active fault.
 Location 9 is just west of the Kagoshima Graben. This location occupies theformer volcanic arc (before it migrated eastward to the Kagoshima Graben area)(according to Yamaji et al., 2003). This location was also chosen to look at the
influence of the model that includes eastward arc migration. Presumably thehazard at location 8 might be higher than at location 9?

Within the Forearc domain: Location 5: while in the stable area of the forearc, this location is just east of thesouthwestern boundary of the BSG and east of Aso volcano. This location was

69chosen to assess the probability of volcanism just outside the boundary of the
BSG, close to Aso, and in the forearc terrane that appears unaffected by recent
volcanism.
 Location 6 is located in a stable area at a moderate distance to an active fault
and far away form volcanoes, but with possibly high uplift rates.
 Location 7 is located in an area at a moderate distance to an active fault
although slightly closer to the BSG, and above the 100 km slab contour.

6.3

References for Section 6

Fujiwara, S., H. Yarai, S. Ozawa, M. Tobita, M. Murakami, H. Nakagawa, and K. Nitta, 1998,
Surface displacement of the March 26, 1997, Kagoshima-ken-hokuseibu earthquake in Japan
from synthetic aperture radar interferometry, Geophys. Res. Lett., 25(24), 4541-4544.
Yamaji, A. 2003. Slab rollback suggested by latest Miocene to Pliocene forearc stress andmigration of volcanic front in southern Kyushu, northern Ryukyu Arc. Tectonophysics. 364,
pp9–24.

70

7 Volcano-tectonic Interactions in Kyushu and Implications
for Future Tectonic and Volcanic Evolution

In this Section we discuss the nature of volcano-tectonic interactions within the Kyushu region.
We use this information to anticipate how patterns of volcanism and faulting will evolve overthe next 2 Ma. We anticipate that some major plate boundary features such as the location of
the subducting slab, and changes in physical characteristics of the subducting plate, among
others will greatly influence future patterns of faulting and volcanism. This is supported by ourunderstanding of past spatial relations between volcanism and plate boundary configuration.

7.1

Interplay Between Volcanism and Faulting

There is a clear coincidence between extensional (normal) faulting in Kyushu and the locationof active volcanism (Figure 7.1).

Figure 7.1: Maximum seismogenic depth of crustal earthquakes overlain by active faults (coloured lines– solid lines from Active Fault Research Centre database,
http://www.aist.go.jp/RIODB/activefault/index_eng.html, dashed lines added in this study, see Strain
Rates from Surface Deformation Section)and active volcanoes (coloured symbols). Note the marked
correlation between active faulting and the location of active volcanism, particularly in the BeppuShimabara Graben (BSG) and the Kagoshima Graben (KG) region.

It is likely that volcanism and extensional faulting tend to be co-located in Kyushu. There are
numerous reasons that this could be the case. For example, tectonic thinning/extension of thecrust can create space for magma to migrate towards, and can influence the geometricdevelopment of a volcanic front. Crustal thinning can also induce decompression melting in71

the mantle. Conversely, magmatism can thermally weaken the crust creating a favourable
situation for the development of faulting and the localisation of tectonic strain. Such processes
will also create positive feedback loops, enhancing the co-existence of volcanism andextensional strain. Moreover, processes related to volcanism (e.g., dike-intrusion) can
accommodate some of the extensional strain budget (reducing the amount of tectonic
extension that must occur via faulting). The co-existence of active volcanic systems andextensional tectonics is observed in numerous other locations on Earth (for example, North
Island, New Zealand; eastern California; east Africa). Given the rapidly changing nature of theoverall tectonic boundary conditions in Kyushu (see this Section and Section on Tectonic
evolution in the last 15 Ma), and the likely interdependence between volcanism and rockdeformation in the Kyushu region, one might imagine that the temporal and spatial evolutionof volcanic and rock deformation activity in Kyushu will be highly variable.
One example of a likely interplay between the location of volcanism and rock deformation is in
the Kagoshima Graben. GPS data suggest that significant extension (<7-8 mm/a; Wallace etal., 2009) now occurs in the Kagoshima Graben area, and there is some evidence for active
normal faulting there (e.g., Japan’s Active Fault Research Centre’s Active Faults Database:
http://www.aist.go.jp/RIODB/activefault/cgi-bin/index.cgi). Given the lack of well-developed
fault zones in that region, it is likely that extension has only recently begun in the Kagoshima
Graben area, and it may be related to the recent trenchward migration of the active volcanicarc (see discussion in Section on evolution of volcanism from 15 Ma). For example, thermal
weakening of the crust in the Kagoshima area due to recent arc magmatism has provided aweak zone favourable for the initiation of crustal extension there.
Although we have not yet explored this in detail, the relationship between volcanism and thelocation of bedrock faulting (i.e., inactive, or “old faults”) should also be assessed. For
example, in the back-arc region of Kyushu the orientations of bedrock structures may
influence the spatial occurrence of monogenetic volcanism in that region.

7.2

Large-scale Plate Tectonic Influences

One of the first-order controls on the tectonic and volcanic evolution of Kyushu is theconfiguration of the subducting slab. Numerous studies have suggested that the location of
the modern–day volcanic arc is controlled by the position of the 100 km depth contour of the
subduction interface (e.g., Tatsumi, 1986; Yamaji, 2003, among others; Figure 7.2).

Figure 7.2: Contours of subduction interface (in 10 km intervals) beneath southwest Japan asdetermined from seismological studies by Nakajima and Hasegawa (2007). Red triangles show
locations of active volcanoes.

72One of the notable features of the slab configuration beneath southwest Japan is the variationin dip; beneath southwest Honshu the slab is very shallow-dipping, whereas beneath northern
Kyushu, the slab is much steeper (Figure 7.2). It is likely that this change in slab geometry is
influenced by the major along-strike change in buoyancy of the lithosphere (Shikoku Basinlithosphere is warmer and more buoyant; southwest of the Kyushu-Palau ridge the oceanic
lithosphere is colder and denser). Given that this boundary (marked by the Kyushu-Palau
Ridge) between Shikoku Basin crust and older oceanic lithosphere is continually migrating
southwest, we must take into account the impact on the future configuration of the subductedslab and how this might influence the spatial and temporal occurrence of volcanism and future
development of fault systems.

Figure 7.3: Inferred position of the subducted Kyushu Palau Ridge (outlined in green) relative to theposition of Aso volcano (red triangle) and the 100 km contour (red dashed line) of the subduction
interface.

The subducted Kyushu-Palau Ridge projects to the 100 km subduction interface contour at apoint roughly coincident with Aso Volcano (Figure 7.3). The Kyushu-Palau Ridge is a remnant
volcanic arc (>26 Ma; see Deschamps and Lallemand, 2002, and references therein), and islikely a rich source of fluids, which could be responsible for the voluminous magmatism at Aso,in the region of Kyushu-Palau Ridge subduction. The extensional nature of this region also
lends itself well to caldera-type volcanism, and the thinned nature of the crust there makes it afavourable location for magma migration to the surface. Thus, Aso volcano is (1) locatedabove the 100 km depth contour of the subducting slab, (2) the Kyushu Palau Ridge issubducting beneath Aso, and (3) Aso is located on the edge of the Beppu-Shimabaraextensional area. All of these factors together could explain the location and highly
voluminous nature of Aso volcano. As the Kyushu-Palau ridge/trench intersection point
migrates southwest with time, it is possible that voluminous Aso-type volcanism could
similarly occur further southwest of its present position in the future.
73

The position of the leading edge of the subducted slab may also play some role inmagmagenesis and flow patterns in the mantle wedge that lead to basaltic volcanism in the
back-arc region. To a first order, the region of back-arc volcanism coincides with the leadingedge of the subducted Philippine Sea slab.

7.2.1

Key tectonic elements that influence faulting

In Section 9, we discuss in detail how subduction of the Kyushu-Palau Ridge may drive rapid
left-lateral shear of southern Kyushu, as observed by GPS velocities and recent seismicity. In
our conceptual model, we suggest that where buoyant lithosphere is being subducted at the
Nankai trough (Figure 7.4), the forearc is pushed landward (e.g., northwest) due to thecollisional resistance forces transmitted across the plate boundary; conversely, theCretaceous oceanic slab subducting beneath southwest Kyushu at the Ryukyu Trench may
be rolling back (e.g., Yamaji, 2003), pulling the southern part of the Kyushu forearc seaward
relative to the northern half of the forearc. We suggest that these competing effects drive
rapid left-lateral shear across southern Kyushu (Wallace et al., 2009). Due to the southwestward migration of the Kyushu-Palau Ridge subduction point (at a rate of 40 km/Ma), this zoneof left-lateral shear is expected to migrate southwest with time, and this migration should betaken into account in the future evolution of rock deformation in Kyushu.

Figure 7.4: Plate tectonic setting of the Kyushu region. Red arrows represent motion of the Philippine
Sea Plate (PSP) relative to the Amurian (AMUR) plate. BSG = Beppu-Shimabara Graben. Straightarrows show approximate backarc rifting directions in BSG and Okinawa Trough. Figure also illustrates
projected migration of the Kyushu-Palau Ridge 2 million years into the future. Red triangles show
approximate locations of some of Kyushu’s active volcanoes.

74Collision of buoyant features (aseismic ridges, oceanic plateaus, seamount chains) with
subduction margins is known to cause rapid tectonic block rotations in many locations
throughout the western Pacific (e.g., New Zealand, Papua New Guinea, Tonga, Vanuatu, andthe Marianas; Wallace et al., 2005). A similar process may explain the rapid anti-clockwise
rotation of southeast Kyushu observed by paleomagnetic techniques (Kodama et al., 1995)
since ~2-6 Ma. The transition from collision of the Kyushu-Palau Ridge with the Nankai
Trough to subduction of normal, negatively buoyant oceanic crust southwest of the ridge
collision point may lead to rapid rotation of southeast Kyushu. Where a buoyant indentor
enters a subduction zone, convergence is inhibited due to high collisional resistance forces,
and the convergent plate boundary microblock is “pushed” into the upper plate as shortening
is transferred from the trench into the backarc region. Where subduction of more negatively
buoyant oceanic lithosphere occurs, the subducting slab may either roll-back (e.g, Molnar and
Atwater, 1978), or the position of the slab will remain stationary with respect to thesurrounding mantle (e.g., Uyeda and Kanamori, 1979). The slab suction effect (caused byflow patterns induced in the mantle by the subduction process) causes the large, upper plate
to move toward the subduction zone, and requires the forearc to stay in contact with thesubducting plate (e.g., Conrad and Lithgow-Bertelloni, 2004). Wallace et al. (2005) suggestthat these two competing effects (i.e., a landward push on the forearc at the collision point,
and a trenchward pull where normal subduction occurs) exert a torque on the forearc
microplate, causing it to rotate relative to the lower plate about a pole near where the buoyant
indentor enters the subduction zone (Figure 7.5). Therefore, in addition to influencing the leftlateral strike-slip faulting in southern Kyushu (see preceding paragraph), we also suspect that
the Kyushu-Palau Ridge greatly influences the kinematics of tectonic rotation of southeast
Kyushu and extension in the Kagoshima and Beppu-Shimabara Grabens. If our proposedmodel is correct, it can be used to construct models for projecting deformation patterns into
the future arising from possible collision/subduction induced rotation of southeast Kyushu.
Due to the rapid along-strike migration of the ridge collision point (Figure 7.4) the kinematics
of rotation and deformation of southeast Kyushu may be expected to change rapidly if ourconceptual model is correct.

Figure 7.5: Schematic of collision/subduction-induced tectonic block rotation model (after Wallace et al.,2005).

Extensional forces arising from ongoing rollback of the subducting Philippine Sea slab greatly
influence, and probably drive the extensional deformation in the upper plate in Kyushu. On
the other hand, as subduction of the more buoyant, shallowly-subducting Shikoku basin crust
migrates southwest towards Kyushu, extensional deformation may be decreased in someplaces in the future due to a lack of rollback of the Shikoku basin portion of the Philippine Sea

75

slab. For example, one might expect rifting in northeast Kyushu to shut-down at some point in
the next few million years as the shallowly subducting Shikoku basin moves into that region.

Figure 7.6: Future projections of the 100 km depth contour to the top of the slab using (a) the upper endof possible trench retreat (rollback) rates and (b) the lower end of likely trench retreat (rollback) rates. It
is hypothesised that most of the arc volcanism in Kyushu will occur roughly above the 100 km contour.

76

7.3

Evolution of Volcanism and Tectonism over the Next 1 Ma

The configuration of the subducting slab, as well as the laterally varying buoyancy propertiesof the subducting plate exert a major influence on the nature of volcanism and tectonic
deformation in Kyushu. Given that these properties will tend to evolve spatially with time, weshow a series of models predicting the position of the 100 km depth to the subducting plateinterface, and the Kyushu-Palau Ridge at intervals up to 1 Ma into the future, and discuss theimplications of this for evolution of faulting and volcanism in Kyushu over the next 1 Ma.
To calculate the future configuration of the subducted slab, we take the current trench retreat
rates and relative plate convergence directions and project the current subduction interface
configuration (Nakajima and Hasegawa, 2007) up to 1 Ma into the future, relative to a fixed
upper (Amurian) plate. We assume relative plate motions and trench retreat rates to beconstant throughout this period. We use trench retreat rates that reflect the rate of extension
in the back-arc during this time, which we assume is a proxy for the rate of trench retreat.
Given that the actual trench retreat rates are somewhat uncertain, we use high and low
estimates of trench retreat to show the range of possible models (Figure 7.6). The angle ofsubduction is also kept constant. The “forward” projections of the slab configuration is taken
at a variety of times (50 ka, 100 ka, 250 ka, 500 ka and 1 Ma into the future). We also project
the position of the Kyushu-Palau ridge at various time intervals 1 Ma into the future. The
Kyushu-Palau Ridge migration is constrained using current Philippine Sea-Amurian Plate
relative motions. Figure 7.6 shows the position of the 100 km contour for depth to the top of
the slab up to 1 Ma into the future. The future positions of the Kyushu Palau Ridge is shownin Figure 7.7. In the following paragraphs, we will discuss the influence of the migration of the
100 km subduction interface contour and the Kyushu Palau Ridge on the future volcanic andtectonic evolution of the margin.

Figure 7.7: Future position of the subducting Kyushu-Palau Ridge. Migration of ridge
determined by current ridge position and current Amurian-Philippine Sea Plate relative
motions.

77

Many previous workers have noted that most of the arc volcanoes in Kyushu (with the
exception of Unzen) lie in a region generally above the 100 km contour of the subducting slab.
Due to a southward increase in back-arc extension rate and southward steepening of the
subducting slab, there is a southward increase in slab rollback rate. This causes the 100 km
depth contour of the subducted slab to migrate eastward more rapidly in southern Kyushu(e.g., Kagoshima region) compared with regions of Kyushu further north (Figure 7.6). We
suggest that the projected future positions of this 100 km contour can be used as a proxy forpossible migration of the volcanic arc into the future. Such a model is consistent with ageneral trenchward migration of the volcanic arc with time (see Section 5). In practice, thiswould probably result in a “stretching” of elevated volcano hazard probabilities in a
trenchward direction. Zones of extensional faulting are likely to follow the arc migration, aszones of deformation continue to exploit areas of the crust weakened by magmatism.
Moreover, continued rollback of the Philippine Sea slab and possible tectonic rotation of the
Kyushu forearc will change the orientation of the trench, and the orientation of maximum
extensional stresses in the upper plate, which may impact the geometry and evolution ofrifting in Kyushu.
Conversely, due to the oblique relative plate motions in southwest Japan, the shallowlydipping portion of the subducting slab beneath southwest Honshu and Shikoku will eventually
migrate southwest along the plate boundary, and at some point in the future flat-slab
subduction will occur beneath Kyushu. We suggest that the migration of flab slab subduction
will eventually terminate arc volcanism in northern Kyushu. Such a model is justified by thelack of arc volcanism above the flat slab subducting beneath southwest Honshu.
The Kyushu-Palau Ridge subduction point is migrating southwest along the Ryukyu
Trench/Nankai Trough, due to relative plate motions and the orientation of the ridge (Figure7.4). Since 2 Ma, the subduction point of the ridge has migrated from the northeastern end of
Kyushu southwest to a point roughly coincident with Aso Volcano (Figure 7.3 and also see
Section on tectonic evolution in the last 15 Ma). It is assumed that the Kyushu-Palau Ridge
subduction point will continue to migrate southwest along the margin. Prior to 2 Ma, the ridge
was migrating northwards along the margin, due to a more northerly plate convergence
direction prior to 2 Ma (e.g., Yamaji, 2003). We suggest that region above the interSection of
the 100 km depth contour of the slab with the subducted Kyushu-Palau Ridge will have an
elevated likelihood of voluminous Aso-style volcanism (see previous discussion). Given that
the Kyushu-Palau Ridge subduction point will migrate southwest along the margin with time(at ~40 km/Ma), it is possible that regions in the Beppu Shimabara Graben <40 km southwestof Aso may have an elevated likelihood of Aso-style volcanism <1 Ma into the future. Likewise,
we suggest that left-lateral shear currently occurring in southern Kyushu will track above the
Kyushu-Palau Ridge subduction point; thus, we expect that the rock deformation hazard
south of the currently active left lateral shear zone in southern Kyushu will increase with time.

7.4

Implications for Tectonic/Volcanic Hazard Evaluation

In addition to the dramatic temporal evolution of the plate margin that may be caused by theimpingement and along-strike migration of the Kyushu-Palau Ridge, it is important toremember that the tectonic setting in Kyushu has undergone significant changes in recent
times. There was a change in plate motion direction at ~2 Ma (e.g., Matsuda, 1980; Okamura,1988), and reinitiation of subduction at the Nankai Trough at ~ 6 Ma (Kamata and Kodama,1994). Kamata and Kodama (1999) discuss how both of these events have led to majorchanges in the deformation and volcanism patterns in Kyushu in the last ~2 Ma. We cautionthat such recent changes in the tectonic setting and crustal deformation in the Kyushu region
may make it difficult to assume that the modern-day deformation pattern will be representativeof crustal deformation patterns in Kyushu in the near future (i.e., next hundred thousand to
million year time frame). In comparison to the Northern Honshu/Tohoku region, the tectonic
setting in Kyushu is much less temporally stable and will be far more difficult to assess
confidently in terms of future rock deformation hazard. Moreover, consideration of all viable
tectonic models (i.e., not just those discussed in this report) for the evolution of the Kyushu

78

region (using a logic tree approach) is imperative when conducting a probabilistic tectonic andvolcanic hazard assessment.

7.5

References for Section 7

Conrad, C.P. and Lithgow-Bertelloni, C., 2004, The temporal evolution of plate driving forces:
Importance of “slab suction” versus “slab pull” during the Cenozoic: J. of Geophy. Res., v.
109, doi:10.1029/2004JB002991.
Deschamps, A., and Lallemand, S., 2002, The West Philippine Basin: An Eocene to early
Oligocene back arc basin opened between two opposed subduction zones, Journal ofGeophysical Research, v. 107, p. 2322-2346.
Kamata, H., and K. Kodama, 1994, Tectonics of an arc-arc junction: an example from Kyushu
Island at the junction of the Southwest Japan Arc and the Ryukyu Arc.
Kodama, K., H. Tashiro, and T. Takeuchi (1995), Quaternary counterclockwise rotation ofsouth Kyushu, southwest Japan, Geology, 23, 823-826, 1995.
Molnar, P., and Atwater, T., 1978, Interarc spreading and Cordilleran tectonics as alternatives
related to the age of the subducted oceanic lithosphere: Earth and Planetary Sci. Letters, v.
41, p. 330-340.
Nakajima, J., and A. Hasegawa (2007), Subduction of the Philippine Sea plate beneathsouthwestern Japan: Slab geometry and its relationship to arc magmatism, J. Geophys.
Res., 112, B08306, doi:10.1029/2006JB004770.
Okino, K., Ohara, Y., Kasuga, S., and Kato, Y., 1999, The Philippine Sea: New survey results
reveal the structure and the history of the marginal basins, Geophysical Research Letters,v. 26, p. 2287–2290.
Sdrolias, M., Roest, W.R., and Mueller, R.D. (2004a), An expression of the Philippine Sea
plate rotation: the Parece Vela and Shikoku Basins, Tectonophysics.. 394, 69-86.
Tatsumi, Y., 1986, formation of the volcanic front in subduction zones, Geophys. Res. Lett,. ,13, 717-720.
Uyeda, S., and Kanamori, H. ,1979, Back-arc opening and mode of subduction: J. of Geophys.
Res., v. 84, p. 1049-1061.
Wallace, L.M., McCaffrey, R., Beavan, J., and Ellis, S., 2005, Rapid microplate rotations andbackarc rifting at the transition between collision and subduction, Geology, 33: 857-860.
Wallace, L.M., S. Ellis, K. Miyao, S. Miura, J. Beavan, and J. Goto, 2009, Enigmatic, highly
active left-lateral shear zone in southwest Japan explained by aseismic ridge collision,
Geology, 37(2), 143-146.
Yamaji, A., 2003, slab rollback suggested by latest Miocene to Pliocene forearc stress andmigration of volcanic front in southern Kyushu, northern Ryukyu arc, Tectonophysics, 365,
9-24.

79

8 Strain Rates from Surface Deformation
Surface fault traces, uplifted and subsided former shorelines, and deformed, formerly flat or
inclined surfaces such as river terraces, are all manifestations of past rock deformation, often
associated with major earthquakes resulting from fault rupture in the brittle shallow crust.
Fault slip rates are readily converted to strain rates provided the width over which thedeformation occurs (the fault’s process zone) can be determined. Uplift or subsidence ofmarine terraces often, but not always, results from movement on active faults in the coastal
area. By assuming fault dip then uplift can be converted to fault slip rate and thus to strain
rate. Deformation of marine and fluvial terraces and other areal measures of deformationsuch as elevations of former erosion surfaces are readily assessed as surface strain rates
provided the age of the deformed feature can be ascertained.
In this Case Study, we planned to develop an integrated surface deformation strain model by
combining active fault data with folding, uplift and subsidence, as defined by tilted fluvial andmarine terraces. However, the terrace data proved insufficient (see Section 8.2), so we have
only been able to use active fault data to determine surface deformation strain rates.

8.1

Active fault datasets

The active faults used to calculate strain rates in this study (Figures 8.1 and 8.2) wereobtained from two datasets. The faults denoted by solid lines in Figures 8.1 and 8.2 are fromthe active fault database provided on the website of the Active Fault Research Centre (AFRC)of the National Institute of Advanced Industrial Science and Technology (AIST)(http://www.aist.go.jp/RIODB/activefault/index_eng.html) (hereafter referred to as the AFRC
database). This database was used to obtain the general location and parameters for thefaults (discussed further below), which were then located more accurately by digitising thecentreline of active fault traces (and fault trace zones) from the digital database of Nakata and
Imaizumi (2002).

Figure 8.1: Active fault source map for the Kyushu region, colour coded by sense of movement. Solidlines are faults from the ARFC active fault database, dashed lines are faults added in this study.

80

Figure 8.2: Active Fault source map for the Kyushu region, colour coded by slip rate. Solid lines are
faults from the ARFC active fault database, dashed lines are faults added in this study.

The AFRC database currently only contains ~300 active faults in Japan, and as such likely
under-represent the total number of active faults in Kyushu. This is particularly likely to be thecase for slow slip faults (e.g., Class C Faults, which have slip rates of 0.01-0.1 mm/a). We
also suspect some fault traces are buried by the thick volcanic ash deposits near the
volcanoes. Therefore, active faults were added (dashed lines in Figures 8.1 and 8.2) basedon assessment of other available digital datasets. These datasets included topographic maps
(from Nakata and Imaizumi, 2002), geological maps (Geological Survey of Japan, AIST,
2002a), a gravity field map (Geological Survey of Japan, AIST, 2002b; Figure 8.3), and amagnetic field map (Geological Survey of Japan, AIST, 2005; Figure 8.4). Using thesedatasets a number of lineaments were identified, and those that have a sharp geomorphic
expression on the topographic maps and correspond to bedrock faults on geologic maps wereadded to the active fault dataset. Several of the newly added active faults (dashed lines)
correspond to active fault traces in other active fault databases such as the Research Groupfor Active Faults of Japan (1991), and many are extensions of the faults from the AFRC active
fault database (Figures 8.1 to 8.4). The remainder are shown as thin solid lines on Figures 8.3
and 8.4 and may constitute major “old faults”, i.e., those that are no longer active. These areuseful for consideration of both past and future tectonic evolution of the domains (seeSections 6 and 7).
For most of the faults from the AFRC database, parameters such as sense of movement(Figure 8.1) and mean net slip rate (Figure 8.2) were obtained directly from the database. Sliprate uncertainties had to be assigned however, which was done using our expertise withassigning uncertainties to other active fault datasets (e.g., the National Seismic Hazard Modelof New Zealand). This could certainly be improved by using local (Japanese) expert elicitation.
For the added faults that are along-strike of faults with parameters in the AFRC database, thesame parameters were assigned. For the remainder, parameters were assigned based onthat for the nearby faults, tectonic domain, and/or orientation. For example, all additional
faults in the extensional domain were assigned to be normal faults. In the Southern Arc
Domain, NE-striking faults were assigned to be normal faults, whereas NW-striking faults

81were assigned to be sinistral strike-slip faults (Figure 8.1). Many of the newly added faults or
faults with no slip rates in the AFRC database correspond to faults classified as “class B” (sliprate 0.1-1 mm/a) by the Research Group for Active Faults of Japan (1991). Therefore, forthese faults, we assigned a mean net slip rate of 0.1 mm/a in areas of generally low slip rate
(e.g., the forearc or backarc domains), whereas faults in areas of generally higher slip rate
(e.g. extensional domain and southern arc domain), were assigned a mean net slip rate of 1
mm/a (Figure 8.2). Slip rate uncertainties were assigned as described above.

Figure 8.3: Bouguer gravity map of the Kyushu region (Geological Survey of Japan, 2002b). Bold solidlines are active faults from the ARFC active fault database, bold dashed lines are active faults added inthis study, thin soild lines are lineaments identified which may constitute “old faults”. White lines are thedomain boundaries.

82

Figure 8.4: Map of the magnetic field of Kyushu area (Geological Survey of Japan, 2005). Bold
solid lines are active faults from the ARFC active fault database, bold dashed lines are active faults
added in this study, thin soild lines are lineaments that may constitute “old faults”. White lines are thedomain boundaries.

8.2

Vertical deformation datasets

Vertical deformation datasets, as compiled by CRIEPI and supplied by NUMO, are limited for
Kyushu (Figure 8.5). The most extensive dataset are marine terrace altitudes, particularly thealtitude of the peak last interglacial, 125 ka, shoreline (red dots on Figure 8.5). These record
relatively low rates of uplift around the coastline (calculated from terrace altitude divided byage), with the highest rates in the Miyazaki Plain area (MP, 0.7-0.9 mm/a).
Buried marine deposits of equivalent age to the marine terraces (125 ka) have been identified
in a few boreholes in the north and west, in a few places in sufficient detail to constructcontours of burial depth (blue dots and lines on Figure 8.5). These record relatively low rates
of subsidence around the coastline, with the highest rate being in the eastern side of the
Beppu-Shimabara Graben (BG, -0.7 mm/a).

83

The most limited dataset is uplift or subsidence data from inland. The available data arealtitude differences between river terraces formed during the penultimate glacial period (~140ka) and the Last Glacial Maximum (~20 ka; orange dots on Figure 8.5). These river terraces
can be correlated to the marine terraces and demonstrated to record uplift between terrace
forming events (Yoshiyama and Yanagida, 1995), thus uplift rate is the altitude difference
divided by the age difference. Data are available for only two locations, the Miyazaki Plain,
where the uplift rates match the marine terrace uplift rates, and an inland basin (Figure 8.5).
Because of the lack of inland data, it is not possible to construct a vertical deformation
contour map and, therefore, to calculate reliable uplift or tilt strain rates across Kyushu.
Therefore, these data are not used in the surface deformation analysis.

Figure 8.5: Available vertical deformation datasets. The numbers are representative uplift andsubsidence rates (mm/a). BG is Beppu-Shimabara Graben, MP is Miyazaki Plain. White lines are thedomain boundaries.

84

8.3

Active Fault Strain Rate Calculations and Logic Tree

Active fault strain rate is calculated from the formula:
Strain rate = horizontal slip rate (mm/a) / width (km) * 10-6
For dip-slip faults, horizontal slip rate has to be converted from net slip rate by:
Horizontal slip rate = net slip rate / cos(dip)Therefore, the components of the active fault strain rate calculation for which uncertainty
needs to be captured in a logic tree are slip rate, dip, and fault width.
The Kyushu active fault logic tree is shown in Figure 8.6. The first branch delineates fault type(or sense of movement, as described in the previous Section) to account for the extra
calculation for dip-slip faults, to convert net slip rate to horizontal slip rate. For strike-slip faults,
the net slip rate was assumed to be pure strike-slip (i.e., pure dextral or sinistral, with no
oblique component). The width options shown in the second branch were assigned taking into
account factors such as maximum fault zone width in the Nakata and Imaizumi (2002) active
fault database, fault spacing, and considerations of the styles of faulting in Kyushu. Fault dipsfor the dip-slip faults are based on the values listed in the AFRC database. The final branch is
net slip rate, which was described in the previous Section. All the weightings have been
assigned by the report authors (i.e., have not undergone local Japanese expert elicitation).

Figure 8.6: Summary logic tree for calculating strain rate from active faults. Note only one set ofbranches is shown. The logic tree branches and weightings (wt) were assigned by the report authors.

85

8.4

Active Fault Strain Map and Example Location Calculations

In Figure 8.7 we show an example active fault strain map for the highest weighted realisationof the active fault logic tree shown in Figure 8.6 (fault process zone width 2 km, fault dip of
60º, and average net slip rate). Active fault strains are calculated for only those grid squaresthat contain active faults, which results in very high strain rates in those squares, no strain
outside of them, and a blocky appearance to the strain map. Not surprisingly, the highest
strain rates correspond directly to the active faults with the highest slip rates (Figure 8.2),
namely those in the Beppu-Shimabara Graben, and along the Oita-Kumamoto Line.
Alternative strain maps derived from differing parameters would vary considerably, particularly
with estimation of fault width (encompassing fault complexity and the width of the processzone around an active fault). We also draw attention to the probable incomplete mapping ofClass C faults in the study area, and therefore the likely underestimation of active fault strains.

Figure 8.7: Strain map calculated for the active faults shown in Figures 8.1 and 8.2. The map is for thehighest weighted realisation of the active fault branch of the logic tree shown in Figure 8.4

86In Figure 8.8 we show the relationship between the locations of known active faults and the
11 example locations where we will compare rock deformation strain and hazard. Three
example locations (1, 8, 10) fall within the process zones up to 10 km distant from thedelineated fault traces.

Figure 8.8: Locations of the 11 example locations with respect to active faults. The centreline of eachactive fault is shown as a black line, and the shaded zones indicates the 5 km and 10 km wide process
zones around each fault.

The strain values for all of the branches of the active fault logic tree for the 3 example
locations which are situated on active faults are shown as histograms in Figure 8.9. The strain
rates for example locations 1 (Back-arc domain) and 8 (Southern Arc Domain) have a wide
distribution (note the maximum value is 355 nanostrains/a), but the majority is <30
nanostrains/a. This reflects the low slip rate of the faults on which they are situated (average
0.1 mm/a). The strain rates for example location 8 (Extensional domain) are more tightly
distributed (maximum 100 nanostrains), but the peak is 80 nanostrains/a, reflecting the higher
slip rate (average 0.9 mm/a) of the fault on which the example location is situated.

87

16
5

18
0

19
5

21
0

16
5

18
0

19
5

21
0

16
5

18
0

19
5

21
0

15
0
15
0
15
0

13
5

12
0

10
5

90

75

60

45

30

15

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

Weighted Frequency

Location 1

Nanostrains/yr

13
5

12
0

10
5

90

75

60

45

30

15

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0

0

Weighted Frequency

Location 8

Nanostrains/yr

13
5

12
0

10
5

90

75

60

45

30

15

1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0

Weighted Frequency

Location 10

Nanostrains/yr

Figure 8.9: Histograms of active fault strain at three example locations located on or close to active
faults, as shown in Figure 8.6. The discrete probability histograms show the probability of a strain rate
being within a 5 nanostrains/a bin width. The histograms are constructed by sampling all possiblecombinations of parameter values in the active fault logic tree (Figure 8.6) and calculating a strain rate
for each combination. The histogram is produced by sorting the calculated strain rates according to 5
nanostrain bins. The horizontal extent of the curve on the x-axis and shape of the histogram therefore
fully quantifies the underlying distribution of strain rate uncertainty. Note that the histogram for locations
1 and 8 are truncated, and the total range is 355 nanostrains/a.

88

8.5

References for Section 8

Geological Survey of Japan, AIST (2002a). Geological Maps of Japan 1:200,000 (Images)
Version 2. Digital Geoscience Map G-3.
Geological Survey of Japan, AIST (2002b). Geoscientific maps of Southern part of Korea,
western part of Japan and their adjoining areas. Digital Geoscience Map P-4.
Geological Survey of Japan, AIST (2005). Aeromagnetic Database of Japan. Digital
Geoscience Map P-6.
Nakata, T., Imaizumi, T. (2002). Digital active fault map of Japan.
Research Group for Active Faults of Japan (1991). Active Faults in Japan. Revised Edition,
University of Tokyo Press, 437 pp.
Yoshiyama, A., Yanagida, M. (1995). Uplift rates estimated from relative heights of fluvial
terrace surfaces and valley bottoms. Chigaku Zasshi = Journal of Geography, 104, 809-826.

89

9 Strain Rates from GPS
This Section presents our use of GPS data to quantify the rates of tectonic deformation in
Kyushu. We overview the GPS data used, review the elastic block model approach tointerpret the data and remove elastic strain effects from subduction coupling. We havedeveloped a logic tree for estimation of rock deformation strain in Kyushu from GPS, involving
120 different GPS strain models. For each of these strain models, we have estimated andremoved the influence of elastic strain due to subduction zone coupling from the GPS velocity
field using a variety of elastic block model configurations, which we also outline here. We
show a weighted average of the logic tree results, in terms of areal strain, shear strain, andthe 2nd invariant of the strain tensor. We also show the distribution of strain values at several
example locations in the region, and discuss these results within the context of the tectonic
setting of the region. We also present a hypothesis for the existence of a high shear strainzone in southern Kyushu detected by GPS.

9.1 GPS Data Used
Velocities of GPS sites throughout Japan are derived from combination of SINEX (Solution
INdependent EXchange format) files provided to us by the Geographical Survey Institute(GSI; http://mekira.gsi.go.jp/) from their daily processing of the Geonet Continuous GPS(CGPS) network in Japan (~1200 CGPS sites in total). The daily network processing of theGeonet network is conducted by GSI, with Bernese GPS processing software (Rothacher andMervart, 1996; Beutler et al., 2001) using standard processing methods. The SINEX files we
use are from one day every three months for the period 1996-2004. In order to combine thedaily SINEX files to estimate velocities for the CGPS sites in Japan relative to a known
terrestrial reference frame, we use GLOBK software (e.g., Herring, 2001).
To help place the Japanese dataset in a global context, we also use daily solutions from
Scripps Institute of Oceanography processing of the global IGS network of GPS sites(http://sopac.ucsd.edu), as well as SINEX files from processing of a subset of ~10 Japanesesites and several global sites that have been submitted by GSI to the Crustal Dynamics Data
Information System (CDDIS; http://cddis.gsfc.nasa.gov/). Using GLOBK we estimate arotation and translation of each dataset into the ITRF2000 reference frame (Altamimi et al.,2002), for each day. To accomplish this, we tightly constrain the coordinates of a subset ofthe most reliable IGS GPS stations to their known ITRF2000 values. We do this for each setof daily solutions, obtaining a time series of site positions in the ITRF2000 reference frame.
The ITRF2000 velocities at each GPS station are calculated by a linear fit to the daily
ITRF2000 coordinates. The uncertainties in the linear fits are derived using a white-noisemodel, so the uncertainties are seriously underestimated (e.g., Zhang et al., 1997; Williams etal., 2004). We multiply the formal uncertainties by 5 to give “reasonable” values of about 1
mm/a uncertainty in horizontal velocities for long-running stations within Japan (T. Nishimura,pers. comm., 2005). Ideally, the GPS velocity errors should be assessed more rigorously.
This will require maximum-likelihood analysis of (probably daily, perhaps weekly) time seriesof GPS positions, to define the appropriate noise model for the data and to calculate arealistic velocity uncertainty (e.g., Williams et al., 2004; Langbein, 2004).
It is important to avoid the effects on our velocity estimates from large earthquakes and slow
slip events. The major events influencing the GPS time series in Kyushu from 1996-2003 are
two large thrust earthquakes (Ms 6.7) near Hyuga-nada in 1996 (e.g., Yagi et al., 2001), the1996-1997 Bungo Channel slow slip event (e.g., Hirose et al., 1999), and the 1997
Kagoshima-ken-hokuseibu earthquake (Fujiwara et al., 1998). To remove the influence ofcoseismic displacements from the time series, we removed the time series data prior to 1998
in the regions affected by these events. GPS sites in the Kagoshima region were affected byan inflation event at Aira caldera throughout the GPS measurement period (Kriswati andIguchi, 2003); we used Mogi source parameters for the inflation event estimated by Nishimura
et al. (2004) to remove this effect from the dataset. We also conducted a visual inspection of
the daily position time series for all the sites in the Kyushu and southwest Honshu region to
be sure that there was no non-linear behaviour recorded by the GPS sites that is not
representative of steady movement during the interseismic period.

90

To interpret the GPS site velocities in a tectonically meaningful way, it is important to place
the velocities into a “plate-fixed” reference frame (in this case, we choose Eurasia-fixed). We
do this at the inversion/tectonic modelling stage of this work (see Section 4) by estimating arotation of the entire dataset that minimizes the velocities at sites known to be on the stable
Eurasian Plate. The GPS velocity field in southwest Japan is shown in a Eurasia-fixedreference frame in Figure 9.1.

Figure 9.1: The GPS velocity field for southwest Japan (relative to the Eurasian Plate). Thin black lines
show active faults.

91

In addition to the GPS velocities described earlier, we use published GPS velocities in the
inversion from Heki et al. (1999), Calais et al. (2003), Sella et al. (2002), Beavan et al. (2002),
and Prawirodirdjo et al. (2004). These datasets help us to further place the southwest JapanGPS dataset into a regional plate kinematic context. This is critical as we must be sure toaccount for all of the possible relative motion between the various tectonic plates in thesystem that could be influencing the GPS measurements in Japan.
In addition to the GPS velocities we also include earthquake slip vectors from events on the
NankaiTroughand
RyukyuTrench(fromHarvard
CMT,
http://www.globalcmt.org/CMTsearch.html) as these data give us information about therelative motion between the Kyushu forearc relative to the underthrusting Philippine Sea Plate.
We also include slip vector data for the Median Tectonic Line (MTL) in Shikoku that is
consistent with the geological observations of nearly pure right-lateral slip on that fault.

9.2 Approach Used to Model GPS Data in Kyushu
Large, complex strain patterns are apparent in the GPS strain map in southwest Japan(Figure 9.2). However, much of this strain is related to interseismic coupling on the offshore
subduction zone boundary. To isolate the strains that may reflect permanent deformation of
the upper plate, we must remove this effect.

Figure 9.2(a): Areal strain rates (in nanostrains/a) from the raw GPS velocity field (Figure 9.1; no elasticstrains removed) estimated using the Haines and Holt method.

92

Figure 9.2(b): Shear strain rates (in nanostrains/a) from the raw GPS velocity field (Figure 9.1; noelastic strains removed) estimated using the Haines and Holt method.

Recently, many studies have shown that GPS velocities measured in zones of active faulting
during the interseismic period are explained by interseismic elastic strains, as well as longterm rotation of crustal blocks in the deforming zone (e.g., McCaffrey et al., 2000; McCaffrey,
2002, 2005; McClusky et al., 2001; Meade and Hager, 2005; Wallace et al., 2004, 2007).
Methods have been devised by McCaffrey (1995, 2002) and Meade and Hager (2005) toinvert GPS velocities for long-term rotations of tectonic blocks, and elastic strain due tocoupling on block-bounding faults. Recently, Nishimura and Hashimoto (2006) have used asimilar method to the elastic block approach for interpreting GPS velocities in Kyushu. For the
purposes of rock deformation/tectonic hazards assessment, if the elastic deformation
estimated from the inversion for the fault coupling parameters is subtracted from the originalGPS velocities, the resulting velocity field will be approximately free of the elastic effects ofinterseismic coupling on known, major active faults in the region. These residual velocity andstrain-rate fields may be interpreted in terms of deformation due to other minor faults orperhaps zones of distributed deformation.
McCaffrey’s (1995, 2002) method performs a non-linear inversion to simultaneously estimate
the angular velocities of elastic blocks and coupling coefficients on block-bounding faults, togive the best fit to the GPS velocities, and optionally, earthquake slip vectors, and geologicalfault slip rates and azimuths. The data misfit, defined by the reduced chi-squared statistic (χn2),
is minimised. The method also allows us to optimally rotate multiple GPS velocity solutions
into a common reference frame. McCaffrey’s approach also has the benefit of including all of
the bounding plates so that we can establish the plate motion budget that needs to beaccounted for in the plate boundary zone. Once the elastic deformation effects due tointerseismic coupling on the Ryukyu Trench and Nankai Trough (estimated using the elastic
block method) have been removed from the GPS velocity field, the residual strain (largely due
93to possible upper-plate deformation) can be mapped using a variety of methods (e.g., Hainesand Holt, 1993; Beavan and Haines, 2001; Sagiya et al., 2000; Miura et al., 2004). Given theuncertainties inherent in GPS measurements, GPS techniques are unable to reliably detectstrain rates below a certain threshold (this threshold will be dependent on the quality of the
GPS network and data and distribution of GPS sites); thus, GPS should not be used on its
own to determine if a site is tectonically stable (i.e., additional geological and seismologicalinvestigations will be needed to confirm tectonic stability.)

9.3 Elastic Block Model Set-up for Kyushu
We have investigated six different elastic block model configurations for the southwest Japanregion. In all of the models, we define a Eurasian Plate, Amurian Plate, Philippine Sea Plate,Southwest Japan Block, and Okinawa Block (Figure 9.3).

Figure 9.3: Block model set-up for southwest Japan, where the southwest Japan forearc is divided into
four separate blocks. Note, we have conducted six different models where various combinations ofthese blocks are connected together, forming composite blocks (see discussion in text). Dark grey lines
are block boundaries, blue planes are a plan-view of faults used in the model (black dots represent
nodes defining the fault planes).

We define most of the boundaries of the large plates (Amurian, Philippine Sea and Eurasian
Plates) based on a digital compilation of tectonic plate boundaries by Bird (2003). The
94

inclusion of these larger plates also helps us in establishing the overall plate motion budgetthat must occur across the plate boundary zone in southwest Japan. The Southwest
Japan/Amurian Block boundary is defined by a zone of distributed faulting near the west coastof Japan (Gutscher and Lallemand, 1999), which is also associated with strike-slip events(probably right-lateral, striking parallel to the margin; Figure 9.4).

Figure 9.4: Focal mechanism plots for earthquakes in southwest Japan from 1997-2006. Based on
CMT solutions from the JMA catalogue (data from http://www.fnet.bosai.go.jp).

The distinction between the models we have conducted here is based upon the division of theforearc and backarc blocks in southwest Japan. For the most complex model, we have95

broken the southwest Japan forearc and backarc region into four different blocks (Figure 9.3;
Table 9.1), including the Shikoku block, eastern Kyushu block, Ohsumi, and Satsuma blocks,
and have divided the backarc into two blocks (the Beppu-Shimabara Block and the Southwest
Japan block).
The Shikoku block is bounded to the northwest by the MTL. The Ohsumi block is bounded on
the west by the Kagoshima Graben, and on the north by a possibly active left-lateral shear
zone that has been highlighted by earthquakes and GPS measurements (e.g., Kodama et al.,
1995; Nishimura and Hashimoto, 2006).
We use the Kagoshima graben as a boundary in some of our models based on evidence fromGPS (Figures 9.1 and 9.2) and geology (Aramaki, 1984) for active extension in the
Kagoshima Graben, and paleomagnetic evidence that southeast Kyushu has rotatedindependently of the rest of Kyushu for the last 2-6 Ma (Kodama et al., 1995). The Satsuma
block is bounded on the east by the Kagoshima graben and on the north by the hypothesised
active left-lateral shear zone. The eastern Kyushu block encompasses more than half of the
Kyushu forearc, whose western boundary is the southeastern boundary of the BeppuShimabara graben.
We have conducted ten different block model scenarios, testing various block combinations(Table 9.1). For example, Model 1 considers the Satsuma, Ohsumi, eastern Kyushu andShikoku blocks as one composite block. Table 9.1 summarizes the fits to the GPS andearthquake slip vector data in terms of the reduced chi-squared statistic (χn2). In Section 9.4,
we discuss the results of the best-fitting block model in further detail.
To define the subduction interface fault (Nankai and Ryukyu Troughs), we approximate theconfigurations for the Nankai subduction interface in Sagiya and Thatcher (1999) and Shiomi
et al. (2004) (Figure 9.3). On the subduction interface, we specify individual nodes defining
the interface spaced on average 50 km apart along strike, and at 10 km depth intervals
between 0 and 50 km depth (Figure 9.3). We define the MTL as a northward dipping fault,
based on geophysical evidence (Ito et al., 1996; Onishi et al., 1999; Tabei et al., 2002).
For the purposes of this initial study, we approximate the boundaries of the Beppu-Shimabara
Graben (BSG) block as a single fault on each boundary in the model, although more complex
deformation on several faults across a zone is likely to be a more realistic scenario there. We
set the southern BSG fault to dip northwest, in part, to deal with possible distributed
deformation due to faulting within the BSG block itself.
McCaffrey’s (2002) method is used to solve for coupling coefficients at nodes on the Nankai
and Ryukyu troughs, the faults representing the BSG boundaries, the extensional zone in the
Kagoshima Graben, the MTL, and a possible zone of left-lateral strike-slip cross-cutting
southeast Kyushu (Figure 9.3). To represent the change in coupling coefficient () values
between adjacent nodes,  values on 5 km x 5 km rectangular fault patches between thenodes are estimated by bilinear interpolation.
Additional free parameters in the inversion are the rotation parameters (three for each block)for various combinations of the Amurian Plate, Southwest Japan Block, Shikoku block, PacificPlate, eastern Kyushu, Beppu-Shimabara Graben, Ohsumi and Satsuma blocks relative toEurasia, and rotation parameters that rotate each GPS velocity dataset into a Eurasia-fixedreference frame.

96

Table 9.1: Description and fits to the data of the six different block configurations tested. SATS =Satsuma block; OHSU = Ohsumi block; SHIK = Shikoku block; KYUS = Eastern Kyushu block; SWJP =Southwest Japan block; BSG = Beppu-Shimabara Graben block. The block configurations are explainedin terms of whether adjacent blocks are connected as a single, composite block, or whether they
represent an independent block. For example, if the line in the table says “SATS+OHSU”, it means that
the Satsuma and Ohsumi blocks are combined into a single block, while if the line says “SATS” theSatsuma block is independent of the other blocks. χn2 is the reduced-chi squared for each best-fitting
model using the prescribed block configuration.
Model
Number

1

Block configuration
SATS+OHSU+KYUS+SHIKBSG+SWJP

χn2

Number of
independent blocks
in SW Japan

5.49

2

2.07

3

3.13

4

2.17

4

2.31

5

2.4

3

1.9

4

2.5

5

1.7

5

1.95

6

SATS+OHSU

2

KYUS+SHIKBSG+SWJP
SATS+OHSU

3

KYUS+SHIK
BSG
SWJP
KYUS+SHIK

4

SATS
OHSU
BSG+SWJP
KYUS+SHIK
SATS

5

OHSU
BSG
SWJP
SATS+OHSU+KYUS

6

BSG+SWJP
SHIK
SATS+OHSU

7

BSG+SWJP
KYUS
SHIK
SATS+OHSU
BSG

8

SWJP
KYUS
SHIK
KYUS
SHIK

9

SATS
OHSU
BSG+SWJP
KYUS
SHIK

10

SATS
OHSU
BSG
SWJP

97

9.4 Block Modelling Results for the Best-Fitting Tectonic Model
In general, the models with increasing complexity (i.e., more blocks) appear to produce an
improved fit to the data (Table 9.1). This is not unexpected given that there is an increase in
the number of free parameters with increasing block model complexity. The best-fitting model(Model 9) is where all four of the forearc blocks (Shikoku, eastern Kyushu, Ohsumi and
Satsuma) are independent of one another. The worst-fitting model occurs where all of theforearc blocks constitute a single tectonic block (including Shikoku; Model 1). Models 2 and 7
are similar to the block configuration considered by Nishimura and Hashimoto (2006).
When we break the Kyushu forearc into four separate blocks (Model 9), the fit to the GPS
velocities is much better (χn2 = 1.7) than any of the other block model configurations (Table
9.1). In this (the preferred model) 92 free parameters are estimated from 1251 data. Theinterseismic coupling distribution that we estimate for the Nankai Trough subduction interface(Figure 9.5) is similar to that obtained from previous studies (Nishimura and Hashimoto, 2006).

Figure 9.5: Interseismic coupling distribution (in terms of slip rate deficit, mm/a) for the subduction zone
from the best-fitting block model (model 9, Table 9.1). Also shown are observed GPS velocities (black),
velocities from best-fitting model (red), and relative motion at block boundaries (green, labelled arrows inmm/a). Depth labels for one set of nodes on the subduction zone are shown. Note that the scale for theGPS (observed and modelled) is not the same shown as for the relative block motion vectors. SS-RL =Strike slip-right lateral; KG = Kagoshima Graben.

98

The interseismic coupling on the subduction interface exerts a great influence on the GPS
velocity field (Figure 9.6). The convergence rates we estimate for the Nankai and Ryukyu
Troughs are ~70-89 mm/a (Figure 9.5). The rate we estimate for the MTL is ~6 mm/a of pureright-lateral strike-slip, which is in excellent agreement with geological estimates of 5-10 mm/a
(Okada, 1970; 1973). This model requires ~7-8 mm/a of right-lateral strike-slip in the eastern
BSG, which gradually transitions to more northerly directed extension at up to 8 mm/a. Northsouth directed extension in the BSG is consistent with focal mechanisms of normal faulting
events there (Figure 9.4).

Figure 9.6: Influence on GPS velocities from interseismic coupling distribution in Figure 9.8 shown forthe best-fitting model (Model 9, Table 9.1).

The Kagoshima Graben accommodates ~7 mm/a of slightly oblique extension in the bestfitting block model. On the northern boundary of the Ohsumi and Satsuma blocks, ~7-14mm/a of left-lateral strike-slip is required. The superior fit to the GPS data in Models 2, 4, 5, 7,
9 and 10 compared with Models 1 and 6 (Table 9.1) suggests that left-lateral shear on thenorthern boundary of the Ohsumi and Satsuma blocks is required by the GPS data (i.e., thatthe southern Kyushu forearc is a tectonic block independent from the northern Kyushu
forearc). The data are well-fitted by ~2 mm/a of right-lateral strike-slip on the offshore Amurian
Plate/Southwest Japan block boundary. Due to the small dimensions of the Satsuma andOhsumi blocks, it is difficult to determine uniquely (with GPS) if the anti-clockwise rotation ofsouthern Kyushu documented in paleomagnetic studies is ongoing today. For example, we
are able to fit the GPS data in the Ohsumi block equally well with rapid (~5°/Ma) anticlockwise vertical axis rotation, and with minimal vertical axis rotation (<0.2°/Ma). However,our best-fitting models do require ~2° per Ma of anticlockwise rotation of the Eastern Kyushu

99block about a nearby pole relative to the large, bounding plates (e.g., at 133.86 E, 31.85 S
relative to Eurasia).

9.5 Upper Plate Strains in Kyushu
To estimate the strain field due to possible tectonic deformation in Kyushu, we remove thecomponent of the velocity field due to interseismic coupling on the offshore subduction
boundary (Figs. 9.5 and 9.6) using Model 9 as an example (Figure 9.7).

Figure 9.7: GPS velocity field after the component due to interseismic coupling on the subduction zone
has been removed (Figure 9.6). This is the velocity field that goes into the Haines/Holt strain modelling(Figures 9.8 and 9.9). Shown for model 9 only.

There are a variety of methods that can be used to convert GPS site velocities to a map ofregional strain. Perhaps the most widely used method is one developed by John Haines andBill Holt (e.g., Haines and Holt, 1993; Beavan and Haines, 2001). To employ their method, agrid (in latitude-longitude) is developed over the area of interest; for the purposes of the
Kyushu Case Study, we use a ~5 km x ~5 km grid. Velocities are modelled as bi-cubic splines
within each cell, and the inversion attempts both to match the input velocity data and minimize
the strain rates within each cell. A strain-rate variance parameter (1/ in the terminology ofBeavan and Haines, 2001) is defined, with lower values of 1/ giving smoother solutions. The
strain-rate variance parameter is chosen such that the sum of squared residuals between themodel and input GPS velocities plus the sum of squares in matching the strain-rate
constraints is approximately equal to the number of degrees of freedom in the GPS data set

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(i.e., twice the number of velocities). In other words the reduced chi-squared statistic, 2N =(sum of squared residuals)/(degrees of freedom), is approximately equal to 1.
Figure 9.2 shows the shear and areal strain rates for the GPS velocity field without removing
the elastic strain due to offshore fault coupling. Clearly, these strain rates are quite high (>200 nstrain/a), but as discussed previously in this report, much of the strain is related toelastic strains from interseismic coupling on the major offshore subduction faults (Nankai and
Ryukyu Troughs) and are unlikely to lead to permanent deformation in the same location
where the strain is currently accumulating.
Figures 9.8 and 9.9 show the strain field (areal and maximum shear strain components) after
the elastic part of the GPS velocity field due to interseismic coupling (using Model 9) has
been removed from the raw velocity field.

Figure 9.8: Areal strain calculated from the velocity field in Figure 9.7 (with subduction-related elasticstrains removed).

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Figure 9.9: Maximum shear strain calculated from the velocity field in Figure 9.7 (with subductionrelated elastic strains removed).

The shear strain rate is greatly reduced (Figure 9.9), although the high shear strain zone
cutting across southern Kyushu (near the northern boundary of the Ohsumi and Satsuma
blocks) is still present, as is a band of elevated shear strain in the region of the MTL and the
BSG, consistent with observed strike-slip faulting there. The areal strain maps show high (upto ~50 nanostrains) extensional strain in the Kagoshima Graben region, and a broad zone ofresidual extensional strain in the western half of the Beppu-Shimabara Graben.

9.6 Tectonic implications
One of the notable results of this study is the discrepancy between the strain rates and fault
slip rates estimated from the GPS velocities (Figures 9.5, 9.8 and 9.9) compared with thegeological slip rates from the Active Fault Research Centre (AFRC) database (Figure 9.10).
Geological studies indicate ~1-3 mm/a of extension and strike-slip in the Beppu-Shimabara
Graben region (Figure 9.10), whereas the best-fitting GPS block modelling results indicate up102

to 8 mm/a of combined strike-slip and extension (Figure 9.5). Slip rates on normal faults in the
Kagoshima Graben are no greater than 1 mm/a in the AFRC database (Figure 9.10), whereas
the GPS block modelling results suggests that this graben is the site of up to 7 mm/a ofextension (Figure 9.5). Although the zone of large left-lateral shear strain cross-cutting
southeast Kyushu is associated with numerous left-lateral strike-slip earthquakes (Figure 9.4),
there are no mapped active faults (Figure 9.10) which could be accommodating this strike-slip(~7-14 mm/yr; Figure 9.5). These discrepancies suggest that either there may be some gaps
in the geological data (i.e., that some faults accommodating active deformation may be undiscovered or hidden, or that some known faults are not sufficiently characterised), or that
extensional strain in the Kagoshima Graben and BSG is partly accommodated by dike
intrusion and other volcanic/magmatic processes. In some cases, these discrepancies may
also be reconciled by testing alternate block model configurations for interpreting the GPSdata (although we suspect this will not be able to reconcile most of the discrepancies weobserve).

Figure 9.10: Active faults in southwest Japan from the Active Fault Research Centre’s active fault
database (http://www.aist.go.jp/RIODB/activefault/cgi-bin/index.cgi). The faults are colour-coded bysense of movement (green = dextral, blue = normal, black = reverse, yellow = sinistral. The numbers
beside the faults indicate slip rate (mm/a).

9.6.1

Is left-lateral shear in southern Kyushu caused by collision with the
Kyushu-Palau Ridge?

Subduction of buoyant features (such as aseismic ridges, oceanic plateaus, and continental
fragments) that exist on otherwise normal oceanic plates exert a profound influence on thetectonic and morphological development of subduction margins worldwide (Vogt et al., 1976;
McCabe, 1984; Cloos, 1993; Wallace et al., 2005). In some cases, subduction/collision of
buoyant features cause curvature (Vogt et al., 1976) and rotation of the subduction margin(McCabe, 1984; Wallace et al., 2005), and deformation of the upper plate (e.g., Marshall et al.,
2000; Taylor et al., 1995); in extreme cases, buoyant indentor collision can shut-down
subduction, causing it to re-initiate elsewhere (e.g., Cloos, 1993). Margin transverse leftlateral shear in southern Kyushu is localised above the point where there is a change from103

subduction of the relatively buoyant Kyushu-Palau Ridge/Shikoku Basin lithosphere (27-15
Ma; Okino et al., 19999) at the Nankai Trough to subduction of more negatively buoyant
Cretaceous oceanic lithosphere (Deschamps and Lallemand, 2002) at the Ryukyu Trough(Figure 9.11). We suggest that where buoyant lithosphere is being subducted, the forearc ispushed landward (e.g., northwest) due to the collisional resistance forces transmitted acrossthe plate boundary; conversely, the Cretaceous oceanic slab subducting beneath southwest
Kyushu may be rolling back (e.g., Yamaji, 2003), pulling the southern part of the Kyushu
forearc seaward relative to the northern half of the forearc. We suggest that these competing
effects drive rapid left-lateral shear across southern Kyushu as revealed by GPS andseismicity (Wallace et al., 2009). Similar conceptual models have been proposed to explain
deformation of the Costa Rican and New Hebrides forearcs (Marshall et al., 2000; Taylor etal., 1995).

Figure 9.11: Tectonic setting of southwest Japan and schematic illustrating migration of Kyushu/Palau
Ridge (KPR) collision point along the Nankai Trough and Ryukyu Trench.

Although GPS and seismological data together argue in favour of active left-lateral strike-slip
deformation of southern Kyushu, this interpretation is at odds with the lack of surface faulting
evidence for such a shear zone. However, when we consider the Amurian/Philippine Sea
Plate relative motion (~70 mm/a pure convergence, with a slight right-lateral sense of ~5mm/a) and the orientation of the Kyushu-Palau ridge relative to the subduction margin(entering the margin at a 64° angle), we estimate that the Kyushu-Palau ridge subduction

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point migrates southwest along the Nankai/Ryukyu trough at ~40 mm/a (or 40 km/Ma) (Figure
9.11). This implies that if the shear zone cross-cutting Kyushu indeed arises from the change
in lower plate buoyancy (as we propose here), that the location of this shear zone will follow
the Kyushu-Palau ridge subduction point southwest along the margin. Such a continual alongstrike migration of the left-lateral shear zone would preclude the accumulation of sufficientsurface displacements required to make this a recognizable, through-going fault system at the
ground surface.
To test this idea, we have constructed a three-dimensional numerical model of a deforming
upper plate (Figure 9.12). The model setup is kept deliberately simple, in order to minimize
the number of controlling parameters. Gravity is neglected, the strength of the upper plate isrepresented by a simple elasto-plastic rheology, and the geometry and strength of a weakvisco-elastic region representing the back-arc is imposed (Figure 9.12a).

Figure 9.12. (a) Simple three-dimensional model illustrating the effect of a moving ridge on back-arc
extension. The model uses the Abaqus/standard finite element code. Model domain is 30 km thick, 800
km long and 600 km wide. The side at y = 0 represents the trench, and an extensional force is applied
there with magnitude, F, that is adjusted with time to maintain a steady back-arc extension VBAof 2 cm/aat the left-hand corner (x = 0, y = 0). The base of the model has zero vertical velocity (out of the page),while horizontal velocity boundary conditions are applied at the other 3 edges of the model as indicated,
to prevent material leaving the domain. Rheology is elasto-plastic (light shaded region) and visco-elastic(heavy shaded region, representing a weak back-arc region), where elastic properties of the viscoelastic region are the same as for the rest of the model. A frictionless vertical fault is specified at y = 200
km. Zero horizontal velocities are imposed over a small, basal Section 25 km wide and 60 km long, tosimulate the retarding effects of a buoyant ridge on back-arc extension. Tectonic features in southwest
Japan corresponding to the numerical model components are labeled in italics. MTL = Median Tectonic
Line; KPR = Kyushu-Palau Ridge. (b) Contours of strain rate and velocity vectors early in model
evolution (0.2 Ma). Only the left-hand side of the model extends, owing to the presence of a weakviscoelastic region there. (c) Contours of plastic strain after 40 km of extension (2 Ma) for model
including ridge migration, illustrating diffuse plastic deformation between the material overlying the ridge,
and the corner of the viscoelastic region. (d) Contours of plastic strain after 40 km extension (2 Ma),similar to (c), except that the ridge does not migrate along-strike, producing more focused shear
deformation of the forearc.

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The subduction interface is simplified to a vertical block boundary at which a spatially uniform
extensional force is applied to mimic the effects of rollback, triggering extension in the backarc region. The magnitude of the force is adjusted automatically during the model run, toreproduce a maximum extensional velocity of 2 cm/a at the left-hand edge, similar to thepresent rate of extension across the southern back-arc region in Kyushu. The model does not
attempt to reproduce compressional effects such as those seen across the Shikoku block inJapan. Model dynamics are therefore simplified and may only be used qualitatively tocompare to the tectonics of southwest Japan.
In the model, the effect of a buoyant ridge subduction is simplified to a basal boundary
condition, by imposing a region where horizontal velocities are zero (Figure 9.12a). The
combined effect of the ridge and the termination along-strike in the visco-elastic region, focus
the extensional deformation to the left-hand side, and create a localised, left-lateral shearzone joining the upper plate immediately above the ridge to the right-hand tip of the viscoelastic back-arc region (Figure 9.12b).
Our ideas regarding the origin of the left-lateral shear zone cutting across southern Kyushu
are compatible with results from the simple numerical model, where the ridge is prescribed tomove along-strike a rate of 4 cm/a, similar to the along-strike rate of migration of the
Kyushu/Palau Ridge. In models where the ridge migrates, plastic strain in the upper platenear to the position of the ridge (Figure 9.12c) remains low and diffuse after 2 Ma ofdeformation. Conversely, when the ridge is stationary (Figure 9.12d), a zone of localised
plastic shear develops above it and cross-cuts the forearc region, with a similar geometry to
the contemporary strain-rate pattern (Figure 9.12b). Such a tectonic model will be important toconsider in terms of the future rock deformation hazard in Kyushu, because the zone of highshear strain (currently located just north of the Kagoshima Graben) may actually migrate
further south (following the Kyushu-Palau Ridge) during the time period of interest.
However, although our explanation of this shear zone as a response to migrating ridge
subduction is one viable model for the future tectonic evolution of Kyushu, we must consider
other alternative models for the presence of this left-lateral shear zone. For example, it ispossible that deformation across this shear zone is currently occurring at a more rapid rate
than normal, and that the current rate of strain does not represent the longer-term strain rate
across the shear zone. Using the probabilistic methodology we have developed, we canincorporate such alternative models into a tectonic hazard assessment via a logic tree
approach. Consultation with Japanese experts on Kyushu tectonics will also facilitate thedevelopment of alternate conceptual tectonic models for Kyushu.

9.7 Probabilistic GPS Strain Estimates
9.7.1

Weighted average of GPS strain models from logic tree

To estimate the rock deformation hazard probabilistically using GPS data, we must consider avariety of alternative tectonic models (i.e., using different block and/or fault configurations) toestimate the subduction-related elastic strains, as well as applying a range of smoothing
parameters to the strain-rate mapping done with the Haines and Holt method, or use a
different strain-rate mapping methodology altogether. An approach that incorporates all viable
tectonic models and expert opinions similar to that used in PSHA methodologies is the best
way to incorporate all available knowledge about the potential tectonic hazard into a
probabilistic rock deformation hazard assessment using GPS. To assess a range of possible
strain models from the available GPS data, we construct a logic tree (Figure 9.13) thatincorporates the various block model configurations explored in the preceding Sections, aswell as implementing different approaches for the strain mapping exercise itself. In terms of
the strain mapping approaches, we use the Haines and Holt approach (using a variety ofstrain variance parameters, see previous discussion), as well as a very simple approach
outlined in Miura et al. (2004), where we fit a surface to the GPS velocities, and take spatial
derivatives of the surface to estimate strain rates. Our logic tree consists of 120 different
branches; we have calculated GPS strain models for each of these branches. We have also
weighted each branch of the logic tree (Figure 9.13) based on our own opinion about whichmodels are more or less viable. Ideally, the logic tree would be constructed and weighted via
106

consultation of an expert panel, similar to what was done for the Tohoku Case Study(Chapman et al., 2009). However, given the limited time frame for the Kyushu Case Study,
the expert elicitation exercise was not possible.

Figure 9.13: Logic tree developed for GPS strain map. Numbers on logic tree branches represent
weighting for that logic tree branch.

Figure 9.14 shows a weighted average of all the strain models developed from the logic tree.
Given that there is a mix of large shear (Figure 9.14b) and areal strains (Figure 9.14a) within
the Kyushu region, we have also chosen to show the strain in terms of the second invariant of
the strain tensor (Figure 9.15), which gives us a single measure that incorporates both shear
and areal strain. This is largely important to use where we want to use a single strain rate
value (rather than both the areal and shear strain values) to determine whether or not there ishigh or low strain at a given site. The formula for the second invariant of the strain rate tensor(in two dimensions) is: sqrt (Exx^2 + Eyy^2 + 2*Exy^2), where Exx, Eyy, and Exy are thecomponents of the horizontal strain tensor. For reference, the formula for areal strain rate is:Exx +Eyy, and maximum shear strain rate is: sqrt (0.25*(Exx-Eyy)^2 + Exy^2 ).
Most of the strain patterns obvious in Figures 9.8 and 9.9 are still apparent in the weighted
average strain maps (Figure 9.14). For example, the high shear strain cutting across southern
Kyushu and along the SE boundary of the Beppu-Shimabara graben are still prominent in theweighted average strain map, suggesting that these features are indeed robust. Similarly highextensional strain persists in the Kagoshima region and southern portion of Kyushu,
suggesting that this extensional strain is also real, and not just a model dependent feature.

9.7.2

Histograms of GPS strain values at selected locations in Kyushu

To get an idea of the range of strain rates produced by the different GPS strain models in the
logic tree, and to assess the epistemic uncertainties in the GPS strain rate estimates, we canlook at histograms (Figure 9.17) of strain rates estimated at several sample locations within
Kyushu (Figure 9.16). Similar histograms have also been created for the surface deformationand seismologically-derived strain (see relevant Sections of this report). The strain rates
shown are the second invariant of the horizontal strain tensor (to encompass areal and shear
strain rates, see discussion in preceding Section).

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Figure 9.14: Weighted average areal (9.14a) and shear (9.14b) strain (in nanostrains/a) for all 120
strain models from the logic tree (see Figure 9.13).

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Figure 9.15: Weighted average of the second invariant of the horizontal strain tensor (in nanostrains/a)
for all 120 strain models from the logic tree (see Figure 9.13).

109

Figure 9.16: Map showing location of sites where we show histograms of GPS-derived strain rates in
Figure 9.17. The locations are super-imposed on a map of tectonic domains, discussed in Section 6.

110

Location 5

Location 1
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Figure 9.17 (continued on next page): Histogram of GPS strain rates (in terms of weighted frequency)
at example locations (see Figure 9.15), after full sampling of the GPS logic tree in Figure 9.13.

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Location 9
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Figure 9.17 (continued from previous page): Histogram of GPS strain rates (in terms of weighted
frequency) at example locations (see Figure 9.15), after full sampling of the GPS logic tree in Figure
9.13.

Backarc domain. The histogram for Location 1 shows a relatively narrow distribution of low
strain rates from the 120 different strain models, generally less than 20 nanostrains/year.
Such low strain rates are consistent with this site being located in the back-arc region, where
active fault slip rates are low. Similarly, Location 2 (also in the back-arc region) has strain
rates that are generally low (< 40 nanostrains/year), but with a slightly wider distribution ofstrain rates than Location 1 (indicating slightly higher uncertainties in the strain rates at
Location 2 compared to Location 1). Location 11 has higher strain rates than the other backarc sites, which we think are likely due to effects on the GPS site velocities from the nearby
west Tottori earthquake in 2000 (magnitude 7.3), and may not reflect actual backgroundtectonic strain rates.
Extensional Arc domain. Location 3, has a relatively narrow distribution of high strain rates(averaging ~100 nanostrains/year), consistent with its position within a zone of active tectonic
extension. The lowest strain rate values in this histogram come from the strain estimates112

using the Miura et al. (2004) approach to calculating strain. We suspect that this approach
excessively smooths the strain values, hence the very low strain rates we obtain here. In the
future, we would recommend strongly down-weighting the strain results using this approach,as these results are probably overly smoothed. The lowest strain rates in the other histograms
shown here also contain this problem. Although Location 4 is also in the Extensional Arc
domain, and also has reasonably high strain rates, probably due to the close proximity toactive faults of the BSG. Location 10 is also within the BSG, and as expected shows aconcentration of high strain rates from the GPS strain models, generally around 70-90
nanostrains/year.
Forearc domain. Example locations in this domain exhibit a broad range of strain rates.
Location 5, which is close to the southern edge of the BSG shows a broad range of strain
rates, but most of them are quite low, < 40 nanostrains/year. These low strain estimates are
somewhat surprising given the close proximity of this site to active faulting in the BSG.
Location 6 has a surprisingly high strain rate for the forearc domain, ~70-110 nanostrains/year,
although it is located close to the continuation of an active fault trend (see Section discussing
discrimination of tectonic domains and selection of example locations) and on the edge of anarea of moderately high uplift rate (see the strain rates from surface deformation Section).
The high strain rates at Location 6 may also be related to imperfect subduction coupling
models; perhaps we have not completely removed the elastic strain effect related tointerseismic subduction interface coupling, and that has influenced the strain rates there.
Location 7 has some of the highest strain rates of any of the locations, generally 120-140
nanostrains/year. This is surprising, given the long distance of this example location from the
known, active faults. However, this location is probably also in a region of high shear strain
related to the left-lateral shear zone that we have identified cutting across southern Kyushu,
as well as any extension along the southeastern margin of the BSG.
Southern Arc domain. Locations 8 and 9 show a large spread of strain rate estimates,
indicating a higher uncertainty in the strain rates in those locations. However, most of thestrain rates are >50 nanostrains/year, and are up to 140-180 nanostrains/year, so this region
does appear to be a generally high strain area. Location number 8 is located on a low slip rate
active fault (see Section 8). Strain at both of these example locations is probably influenced
by extension in the Kagoshima Graben region, as well as the strong left-lateral shear strainzone cutting across southern Kyushu.

9.8

Notes and Caveats

One of the advantages of the probabilistic approach is that it allows us to assess some of theuncertainties inherent in the strain rate estimates (e.g., epistemic uncertainties due to choiceof tectonic models and/or modelling techniques). However, we must also ensure that we haveaccurately estimated the uncertainties in the GPS velocity data. The large sample interval (3months) of the GPS timeseries data available to us for this study means that reliable noisemodels for the GPS data could not be developed. This affects the estimation of uncertainty inthe point GPS velocities, which translates into additional uncertainty in the strain-rate
estimates. A more complete temporal sampling of the CGPS dataset, and rigorous estimationof changes in CGPS site position due to earthquakes, equipment changes, etc. would
improve the velocity estimates substantially, and lower the resulting uncertainties. Additionally,
the GPS station spacing is still quite large compared to the 5-km grid over which strain rates
are required. In some parts of Japan there are more GPS stations (campaign and continuous,
operated by various Japanese universities and other organisations) in addition to the GSI-run
Geonet network, and data from these could contribute greatly to an improved strain-rate
estimation. Moreover, as longer GPS datasets are collected, the GPS velocity uncertainties
will slowly decrease. Also, the velocity uncertainties will become more reliable when proper
noise models are adopted. Both these improvements (as well as GPS velocity data fromadditional sites) would allow us to refine the results of the GPS strain-rate study described
here.

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9.9

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10 Strain Rates from Seismicity
In this Section we describe the historical seismicity of Kyushu, document the methods used to
calculate seismic strain rates for the region, and display and describe the results of theanalysis. We also discuss these results in the context of the overall Kyushu Case study.

10.1

Seismicity of Kyushu

The Kyushu region is characterised by generally high rates of crustal seismicity, notably in theform of a southwest trending band roughly bisecting the island (Figures 6.3 and 10.1). The
depth to crustal seismicity is around 20 km, the typical depth to the base of crustal seismicity
for Japan in general, and there is considerable spatial variability across the island (Figure 6.4).
Four seismotectonic zones are defined for Kyushu, and these are shown in Figure 10.1. Zone
1 encompasses the back-arc region of Kyushu, in which sinistral focal mechanisms striking
transverse to the coastline are commonly observed in northwestern Kyushu (Figures 4.9 and
6.2). Zone 2 is defined to encompass the Beppu-Shimabara Graben region (generally
coincident with the Extensional Arc domain) with associated high rates of seismicity (thesouthwest trending band of seismicity in the Kyushu region; Figure 10.1), and is bounded tothe southeast by the Oita-Kumamoto Tectonic Line (the southwest continuation of the Median
Tectonic Line). Zone 3 roughly defines the forearc region, and Zone 4 defines the Kagoshima
Graben, which also overlaps with the Southern Arc domain. The north end of Zone 4 isdefined by a prominent east-west trending belt of seismicity, which also coincides with a zoneof high left-lateral shear strain in the GPS dataset (see Section 9). These zones differ
somewhat from the tectonic domains shown the other Sections of the report. This is due tothe seismotectonic zones being defined largely on the basis of seismicity at the early stagesof the Kyushu Case Study, well before the domains in Section 6 were defined.

Figure 10.1: Crustal seismicity of the Kyushu region, All earthquakes M>4 for the period 1926-2006 anddown to 20 km depth are shown. The crustal seismicity zones are shown, as well as the seismicity
parameters and maximum magnitudes for the Gutenberg-Richter relationship a-value, b-value andMmax). The a and b-values are calculated from the seismicity catalogue, while the Mmax is based on
geologically-derived values from The Research Group for Active Faults of Japan (1992).

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Despite the high rates of seismicity in Figure 10.1, there have been relatively few large
historical earthquakes in the c. 430 year period of historical records. Only two earthquakes ofabout magnitude (M) 7 have been recorded in that time, one in the Kagoshima Graben (Zone4) and the other in Zone 2.

10.2

Methodology

Strain rates are estimated from the spatial and temporal distribution of the c. 430 year recordof historical seismicity for the region, using the same methodology as for the Tohoku Case
Study (Chapman et al., 2009). The Japanese Meteorological Agency (JMA) seismicity
catalogue of M>4 earthquakes for the period 1926-2006 is combined with the historicalcatalogue of M>7 earthquakes for the period 1580-1925 (Wesnousky et al., 1984) to give thec. 430 year record. These earthquakes are then used to define a spatial distribution of
Gutenberg-Richter rate parameters (Gutenberg and Richter, 1944), utilising the maximumlikelihood method of Weichert (1980) to derive the rate parameters (a- and b-values) of the
Gutenberg-Richter relationship:logN/yr = a – bM,
in which N/yr is the cumulative number of events greater than or equal to magnitude M, and a
and b are empirical parameters. Seismicity parameters are shown for each zone in Figure
10.1, along with the parameters for all zones combined. The seismicity parameters are thenused to define seismic strain rates via the well-established method of Kostrov (1972).
We calculate the seismic strain rates according to a logic tree developed to quantify the
epistemic uncertainty in seismicity parameters (Figure 10.2).

Figure 10.2: Logic tree for the Kyushu seismic strain rate model. Weights for each of the logic tree
options are shown as numbers beneath each limb.

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The logic tree is similar to the tree established for the Tohoku Case Study, except for the factthat we only use a raw seismicity catalogue as input. Here we found that the available
declustering (removal of aftershocks) programmes to be unsuitable for Japan in the Tohoku
Case Study (i.e. more than just aftershocks were removed), and now maintain the opinion
that aftershocks also contribute to seismic strain rates, we do not include a declusteredcatalogue as an option in the Kyushu logic tree. As with the Tohoku Case Study, we takeaccount of epistemic uncertainty in: (1) the seismicity modelling method (uniformly distributed
seismicity modelling within an area source versus continuously-varying seismicity modelling(e.g. Frankel, 1995); (2) parameters of the Gutenberg-Richter relationship, and; (3) the
maximum magnitude cutoff for the various zones. As with the Tohoku Case Study, thecontinuously-varying seismicity model is a lesser-weighted option than that of using uniformlydistributed parameters for each zone, due to lack of confidence in the c. 430 year record ofseismicity being representative of longer time periods. This was an opinion given by theexpert elicitation panel of Japanese seismologists at the time of the Tohoku Case Study, andwe apply the same opinion to the Kyushu Case Study.

10.3

Results

Seismicity parameters derived for Kyushu are in the realm of parameters expected for areas
characterised by extensional tectonic and volcanic processes. B-values of around -0.9 to -1.1
are observed in the various zones, with a b-value of -1 calculated for the entire region. Theseb-values are generally lower (“steeper”) than the b-values obtained for Tohoku (all less than 1), which indicates a higher ratio of smaller to larger earthquakes for Kyushu. Kyushu bvalues are also consistent with the global average of -1. The only b-value greater than -1 iscalculated for Zone 3, that is, the zone that roughly coincides with the Forearc Domain (about-0.9).

Figure 10.3: Seismic strain rates (nanostrains/a) developed using a smoothing kernel bandwidthof 15 km.

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Figure 10.4: Seismic strain rates (nanostrains/a) developed using a smoothing kernel bandwidthof 30 km.

We show some examples of output from our logic tree in map form in Figures 10.3 and 10.4.
The two strain rate maps are derived from the continuously varying seismicity parameters(see above) in Figures 10.3 and 10.4. The maps differ in the width of the Gaussian smoothingkernel used to develop the smoothed seismicity rates. The 15 km correlation distance orkernel bandwidth logically gives a much finer spatial distribution of seismicity rates than the
30 km bandwidth, and shows the highest strain rates in areas of highest seismicity (compare
Figures 10.1, 10.3 and 10.4). These are primarily within the Beppu-Shimabara Graben (Zone
2 in Figure 10.1), and the east-west-trending zone of seismicity at the northern end of the
Kagoshima Graben (north end of Zone 4 in Figure 10.1). Localised strain rates of between 10
and 50 nanostrains/ya are observed in these areas. In contrast, the definition of thesefeatures is largely lost when the 30 km bandwidth is applied (Figure 10.4).
Histograms of seismic strain rates for the 11 locations (Figure 10.5) generally show arelatively tight distribution of strain rates between 1 and 10 nanostrains/a. The histograms
show the distribution of strain rates that result from sampling of our logic trees. They aredeveloped by taking numerous samples of the logic tree by Monte Carlo methods to develop
multiple realisations of strain rates. The simple shape of the majority of histograms shows that
there is strong central tendency in the resulting strain rates, in that none of the parameters
produces a significant number of outlier strain rates and lead to (for example) a bimodal
distribution.

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Nanostrains/yr

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Figure 10.5 (continued on next page). Seismic strain rate histograms for the 11 locations.

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21
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Figure 10.5 (continued). Seismic strain rate histograms for the 11 locations.

10.4

Discussion

The <10 nanostrains/year strain rates calculated for specific example locations within Kyushu(see Section on Discrimination of Tectonic domains and selection of example locations) andshown in Figure 10.5, are surprisingly low relative to strain rate calculations made from the
GPS and surface deformation datasets for each of those example locations. The low strain
rates are certainly unusual, given the overall high seismicity rates of the island (Figure 10.1).
In contrast, our previous Tohoku Case Study analyses revealed much higher strain rates (ofthe order 10-100 nanostrains/a), despite relatively low observed seismicity rates in Tohoku.
The paradox is due to the difference in b-values for the two regions. The generally low bvalues of -1 to -1.1 for Kyushu translate to low rates of large earthquakes (the earthquakesthat contribute most to the strain rate calculations), whereas the high b-values in Tohoku(greater than -1) produce relatively high rates of moderately sized earthquakes. For example,
the seismicity parameters calculated across all four Kyushu zones (labelled Zone 5 in Figure
1; a=5.23, b=-1.02) translate to a recurrence interval of about 80 years for a M7 or greater

122earthquake. This compares to about 30 years for the equivalent calculation for Tohoku where
the b-value is relatively high (about -0.8). At larger magnitudes the differences between thetwo regions are even greater.
Given that the low seismic strain rates for Kyushu are conditioned by the low b-values
calculated in Figure 10.1, it is important to review the above results critically. The b-values of 1 to -1.1 are typical of regions characterised by a combination of extensional tectonism andvolcanism, and therefore appear defensible on that basis. The hydraulic fracturing processes
associated with volcanism are consistent with a high ratio of small to large earthquake events,
and therefore consistent the low b-values shown in Figure 10.1.

Figure 10.6: Magnitude – frequency distribution showing the cumulative seismicity rates predicted fromthe parameters calculated for the whole Kyushu region (Zone 5 on Figure 10.1) and the equivalent rates
for Kyushu active faults. The latter were derived from the active fault database provided on the websiteof the Active Fault Research Centre (AIST) (http://riodb02.ibase.aist.go.jp/activefault/index_e.html). The
fault magnitudes are estimated from fault length with three equations developed for intraplate Japanese
earthquakes given in Stirling et al (1996), and the recurrence intervals are taken directly from thedatabase. The three seismicity curves are calculated with the mean, minimum and maximum b-values
for Zone 5.

Our analysis shows that the predicted rate of occurrence of large earthquakes in Kyushu ishigher than the historically observed rate. Specifically, the predicted 80 year recurrence
interval for a M7 or greater earthquake would produce five events in the c. 430 year historicalperiod, more than double the number of similar-sized events in Kyushu for that same time
period (only 2 events of M>7 observed in c. 430 yrs). Comparison of historically observed andpredicted number of M7 or greater events does not, therefore, support the possibility that theseismicity parameters are underestimated. However, it is also possible that the historical
record could be a poor sample of the long-term ‘parent’ distribution of seismicity, in that overperiods of thousands of years the actual rates of large earthquakes could be higher than our
predicted rates.
In probabilistic seismic hazard analysis the standard approach to countering the possibility ofa poorly representative historical record is to combine geologically derived (active fault) andhistorically derived earthquake datasets to obtain a more complete long-term record ofseismicity. Since these datasets have instead been kept independent in the Kyushu Case
Study, it is possible that the seismicity dataset may be incomplete with respect to large events,
despite the predicted earthquake rates being extrapolated out to the large maximum
magnitudes (Mmax) shown in Figure 10.1. However, previous studies have shown that themagnitude-frequency statistics of fault-derived M>7 earthquakes over Japan are very similar
to the historically observed rate of these events, and that b-values of -1.1 or less are123reasonable across the country (Wesnousky et al., 1984). Given that these comparisons are ata regional scale, a useful exercise is to compare the geologically-derived earthquake rates toour predicted rates for Kyushu to see whether or not a similar match is observed at theregional scale.
In Figure 10.6 we compare the rates of moderate-to-large earthquakes from our Zone 5 (i.e.,
entire Kyushu area in Figure 10.1) seismicity parameters to the rates derived from the major
active faults of Kyushu derived from the active fault database provided on the website of theAFRC, AIST (see Figure 10.6 caption for website source). Clearly, the predicted rates of large
earthquakes in Kyushu are higher than the active fault-based rates, therefore indicating thatthe seismicity record does not under-predict the long-term rates derived from the active faults.
We do, however, acknowledge that the active fault data are probably incomplete in Kyushu,
raising the possibility that a complete fault database might result in a somewhat different
comparison to that observed in Figure 10.1.

10.5

Conclusions

A seismic strain rate model has been developed for Kyushu from the c. 430 year record ofseismicity. The methodology closely follows that used to develop a model for the Tohoku
Case Study (Chapman et al., 2009), including the treatment of epistemic uncertainty by wayof logic tree definition and Monte Carlo sampling. The only major difference is that the seismicstrain calculations are exclusively from a raw catalogue, whereas the Tohoku study also useda declustered catalogue. The seismicity parameters calculated are comfortably within therange of parameters observed in other regions of the world where seismicity is due to a mix oftectonic and volcanic processes. Surprisingly, strain rates calculated for the 11 locations
focussed on in this study are generally low (generally in the range of 1-10 nanostrains/a), an
order of magnitude lower than strain rates calculated for the lower seismicity Tohoku Case
Study. The paradox is explainable by the marked differences in b-values for the two regions,in that the lower b-values of Kyushu translate to fewer large strain rate-generating
earthquakes than in Tohoku. The greater number of small earthquakes in Kyushu is relatively
unimportant in terms of calculated strain rates. Further confidence in our results is gained
through noting that the low b-value is consistent with the combination of volcanic andextensional tectonic processes in Kyushu. Furthermore, comparison of the predicted rates oflarge earthquakes from the seismicity model to: (1) the historically observed rates of
equivalent events, and; (2) the predicted rates of equivalent events from active fault data do
not show an underestimation of these two criteria.

10.6

References for Section 10

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Hazards to Potential HLW Repository Sites in Japan: The Tohoku Case Study. Nuclear
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Frankel, A. (1995). Mapping seismic hazard in the central and eastern United States.
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Gutenberg, B., and Richter, C.F., 1944. Frequency of earthquakes in California. Bulletin of the
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Kostrov, B.V. 1974. Seismic moment and energy of earthquakes and seismic flow of rock. Izv,
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Stirling, M.W., Wesnousky, S.G., and Shimazaki, K. 1996. Fault trace complexity, cumulative
slip, and the shape of the magnitude-frequency distribution for strike-slip faults: a global
survey. Geophysical Journal International 124, 833-868.
The Research Group for Active Faults of Japan (1992). Map of active faults in Japan with an
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Weichert, D.H., 1980. Estimation of the earthquake recurrence parameters for unequalobservation periods for different magnitudes. Bulletin of the Seismological Society ofAmerica 70, 1337-1346.
Wesnousky, S.G., Scholz, C.H., Shimazaki, K., and Matsuda, T. Integration of geological andseismological data for the analysis of seismic hazard: a Case Study of Japan. Bulletin of
the Seismological Society of America 74, 687-708.

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11 Nature and Spatial Probability of Future Rock Deformationin Each Domain using Example Locations
Two approaches to considering the probability of future rock deformation in Kyushu havebeen developed. Neither of these approaches is mutually exclusive and, ideally, both should
be integrated into a single methodology. The first approach is based on the precept that thepast is the key to the future and that we may therefore use current rates of activity to assess
the probability of exceedance of various strain rates as a proxy for future rock deformation.
Simple calculations relating earthquake energy to scaling of fault slip to earthquake
magnitude, and converting that to strain rate, suggests that an annual strain rate of ≥4nanostrains/a within a unit area of 5 x 5 km is consistent with rock deformation that may occur
during the equivalent of 1 m of surface displacement (equivalent to a single magnitude 7
earthquake) in a period of 100,000 years. This calculation of course assumes that all strain
calculated for a particular 5 x 5 km area from the various rock deformation datasets will be
converted to a discrete faulting event across a discrete fault every 100,000 years, which isprobably far too simplistic an approach. However, given various assumptions, threshold“hazardous” strain rates can be defined for specific rock deformation scenarios of concern,once those scenarios are decided upon. Plots showing the probability of exceedance ofvarious strain rates for example locations in Kyushu (Figures 11.1 and 11.2) enable
comparisons between locations, and give us some idea of how likely a given example location
is to exceed a certain level of strain. Whether the various strain rates constitute a risk to arepository depends on the repository design and the manifestation of the rock deformation at
those locations. The deformation may range from concentrated displacement on a single fault
trace to widely distributed, diffuse, fracturing within a 5 x 5km area enclosing the location.
The second approach is to take note of the rapid geological evolution of the Kyushu region
and consider the possibility that future rock deformation may evolve in both space and time,
and that while past and current rates of deformation capture this evolution to some extent, we
can also qualitatively assess what sort of evolving activity may affect the example locations.
Key features or processes we observe to be driving the rapid evolution, and their impacts in
Kyushu are:(i)

Roll-back at the Nankai Trough, which may induce migration of arc volcanism
southeastward(ii)

Southwestward migration of the Kyushu/Palau Ridge (and its subducted part) beneath
Kyushu because of the oblique angle of subduction and the orientation of the volcanic
chain comprising the Kyushu/Palau Ridge. We suggest that an active left-lateral shear
zone in southern Kyushu (revealed by GPS and seismicity) occurs due to theimpingement of the Kyushu-Palau Ridge on the subduction margin (see discussion in
Section 9). It has also been suggested by previous studies that uplift in the forearc
above the ridge subduction point is related to Kyushu-Palau Ridge subduction. It is also
possible and localisation of heat flow and Aso-type volcanism in the arc is related to theposition of the Ridge (see Section 7). Thus, we could expect left-lateral shear ofsouthern Kyushu, uplift in the forearc, and localisation of certain types of volcanism tomigrate also.

(iii)

The northeast limit on extension associated with the Okinawa Trough is adjacent to thewestern end of the Beppu Shimabara Graben (BSG) in the vicinity of Nagasaki andKumamoto. It is unclear whether this zone of extension continues to develop, but it
could be increasing the likelihood of normal faulting and volcanism in that area.

(iv)

Extension in the Kagoshima Graben and volcanism such as at Sakurajima are
geologically recent phenomena in southern Kyushu. Future evolution and developmentof this zone may be expected. Future steepening of subduction at the Ryukyu Trench
as a consequence of slab roll-back will encourage migration of the volcanism in the
Kagoshima Graben toward the east and southeast.

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Figure 11.1 (continued on next page): Cumulative strain rate plots for the 11 example locations. EQ
WGTD AV is equal weighted average. See Figure 6.4 for location of the 11 example locations.

127

Figure 11.1 (continued): Cumulative strain rate plots for the 11 example locations. EQ WGTD AV isequal weighted average. See Figure 6.4 for location of the 11 example locations.

(v)

At present the volcanism and faulting associated with the Kagoshima Graben does notextend further northeastward than Kirishima volcano, with a marked gap in volcanism incentral Kyushu between Kirishima and Aso volcanic centres. In normal situations wewould expect that the volcanic arc would continue to the north approximately locatedabove the 100 km depth contour on the subducted Philippine Sea Plate. We are notaware of any current data to explain this gap in volcanism and thus should consider the
possibility of new volcanoes developing in that area.

(vi)

In the Back-arc domain of northern Kyushu and offshore Japan Sea, large earthquakes
are occurring on reactivated northwest- and northeast-striking faults. There may also becorrelation of these structures to some of the monogenetic volcanism. For example,
stress perturbations that arise at fault interSections may be preferred locations for
future small volume monogenetic events. The backarc province hosts a wide variety of
volcanism that is dominated by processes in the mantle and the mantle wedge rather
than subduction. Little is known of the processes leading to irregular volcanic “flare-ups”
in the Back-arc domain and there is large uncertainty is forecasting future activity.

128Based on the features and processes noted above we discuss current strain rates estimatedfor these example locations, and offer some suggestions of possible future rock deformation
evolution at or adjacent to each of the example locations in the various domains defined in the
Kyushu Case Study. To aid with the comparison between locations, Figure 11.2 shows all the
probability of exceedance curves on one plot.

Figure 11.2: Combined equal weighted average cumulative strain rate plot for the 11 example locations,colour-coded by tectonic domain. See Figure 6.4 for location of the 11 example locations.

11.1

Back-arc domain

Example Location 1. Located on an active fault with a low slip rate of 0.1 mm/a. There is a
general good agreement on strain rates between all 3 datasets: current GPS strain rates are
low-moderate, as are seismicity and active fault strain rates (Figure 11.1). Future deformation
can be expected in the form of sinistral strike-slip surface faulting associated withearthquakes up to about M 7.5 (see Section 10). Because the fault is reactivating an earlier
structure the fault zone complexity (the process zone) can be expected to be wider than on along-lived, well-developed, fault.
Example Location 2. Located about 20 km from a low slip rate (0.1 mm/a) active fault.
Current GPS strain rates are moderate to low, and seismicity strain rates are very low (Figure11.1). Future deformation is a possibility but it is unlikely the process zone of future faulting on
known active faults in the region (20 km away) will extend to this location. Secondary faulting
or fracturing of suitably oriented bedrock faults and fractures is possible in the event of a large
nearby earthquake.
Example Location 11. Located in southwestern Honshu in the southern part of themonogenetic Abu volcanic field. In the region there are both northeast- and northwest-striking
faults, although the closest known active fault is more than 20 km to the west. The west
Tottori earthquake of 2000 occurred approximately 200 km to the northeast in a similar
tectonic environment. Current GPS strain rates are moderate, and seismicity strain rates are129very low (Figure 11.1). Reactivation of bedrock structures in this region as strike-slip faults ispossible considering other active faults in the region. There is a possible structural control on
the location of volcanic edifices of the Abu volcanic field and, at this location, possible futureco-location of volcanism and faulting should be assessed.

11.2

Extensional domain

Example Location 3. Located west of Aso Volcano, within the BSG, and about 12km north ofa prominent normal fault extending westward from Aso Volcano. Thick, young pyroclastic
deposits cover the area, which may have obscured some faulting near to the location. Current
GPS strain rates are high, and seismicity strain rates are very low (Figure 11.1). Further
development of the normal faults can be expected in the region. Proximity to Aso Volcano
suggests that ground deformation associated with future volcanism is also probable.
Example Location 10. Located about 30 km SW of Aso Volcano near the southeast marginof the BSG, and within 5 km of a moderate slip rate normal fault (~0.9 mm/a). Thick, young
pyroclastic deposits cover the area, which may have obscured some faulting near to thelocation. Current GPS and active fault strain rates are high, and seismicity strain rates arevery low (Figure 1). Further development of the normal faults can be expected in the region.
Proximity to Aso Volcano and associated connected edifices suggests that grounddeformation associated with future volcanism is also probable. This location is possibly in theregion where future Aso-type volcanism and related tectonism may concentrate with the
migration of the Kyushu Palau Ridge subduction point interSection with the BSG. If thisprocess is significant in governing the location and migration of a nexus of volcanism, then
Aso-type volcanism could migrate to a location 10 position, 0.5-1.0 million years from now.
Current strain rates at Location 10 are among the highest observed in the study.
Example Location 4. Located in northeast Kyushu at the eastern margin of the BSG. The
location is on the flanks of a volcano but north of the current extent of normal faulting
associated with the BSG. Geodetic strain rates are high and very similar to other locations in
the BSG (Figure 11.1). Seismicity strain rates are very low (Figure 11.1). Future hazard may
diminish at this location as the shallowly subducting Shikoku Basin crust encroaches on thisregion and shuts down any remaining extensional deformation, over the next 1-2 Ma.

11.3

Forearc domain

Example Location 5. Located in the Forearc domain immediately east of the boundary with
the Extensional domain and about 15 km east of Aso Volcano. The location is between active
traces of low slip rate faults (~0.1 mm/a), and bedrock geology in the area has many steep
faults oriented parallel to the domain boundary between the Extensional and Forearc domains.
Current geodetic strain rates are high and seismicity strain rates are low (Figure 11.1). Future
deformation at this location might occur as a result of reactivation of bedrock structures thatevolve the current unlinked fault strands into a more continuous feature. If trench roll-back at
the Nankai Trough is significant process then it is likely the southeastern margin of the
Extensional domain will also migrate into the current Forearc domain. Deformation at thelocation could therefore be expected to switch to normal faulting and increase in rate. The
location is likely to be heavily inundated by ash fall in the event of a major eruption from Aso
Volcano.
Example Location 6. Located near the southeast coast of Kyushu approximately 15 km northof the nearest known fault at the margin of the Miyazaki Plain. Current GPS strain rates arehigh and seismicity strain rates are low (Figure 11.1). However, it is possible that the GPS
strain rates are artificially high here due to choice of subduction interface coupling models(see discussion in Section 9). Coastal uplift rates of about 0.2 mm/a have been noted nearby
and rates up to 0.7 mm/a further south along the Miyazaki coast. If these rates extend inland
to this location this could result in hundreds of metres of uplift over long time periods, andrepresent a significant rock deformation.

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Example Location 7. Located in an area about 30 km distant from the nearest active fault
and about 30 km from the BSG. The location is above the 100 km contour on the subducting
oceanic plate but in an area of no current or past volcanism. There is strong northeast striking
bedrock structure in the area. Current GPS strains are high and seismicity strain rates are low(Figure 11.1). This location is near to the junction of high left-lateral shear strain cross-cutting
Kyushu (as seen from the GPS velocities: see Section 9) and the southeast boundary of the
BSG. Given that this location is adjacent to the interSection of the left-lateral shear zone with
the BSG, we cannot rule out migration of future rock deformation into this region. Obtaining
improved mantle structure data may indicate reasons for the gap in arc volcanism. This would
be a key future requirement if a location such as this were to be considered as a potential
repository site.

11.4

Southern Arc domain

Example Location 8. Located about 15 km to the east of faults marking the easternboundary of the Kagoshima Graben. The location is on the southern end of one of thenortheast striking, low slip rate (~0.1 mm/a), faults of the region, and is just to the south of theleft-lateral shear zone cutting across southern Kyushu (from GPS). Current GPS strain rates
are high, active fault strain rates are moderate, and seismicity strain rates are low (Figure11.1). Future rock deformation may be characterised by rare surface rupture of the local
active fault, or speculatively by significantly increased rates of deformation if the Kagoshima
Graben is migrating with the roll-back of the subducted plate, and by southward migration of
the left-lateral shear zone cross-cutting southern Kyushu which may be driven by subductionof the Kyushu-Palau Ridge.
Example Location 9. Located about 15 km west of the western margin of the Kagoshima
Graben. The location is within the former volcanic arc (before it migrated eastward to the
Kagoshima Graben area) (Yamaji et al., 2003). The location is about 10 km south of northeast
striking normal cutting across the old arc volcanoes and is just to the south of the left-lateralshear zone cutting across southern Kyushu (from GPS). Current GPS strain rates are high,but slightly less than location 8, and seismicity strain rates are very low, also less thanlocation 8 (Figure 11.1). Future rock deformation at this location might be less likely than at
location 8 because it appears to be in the trailing position of the locus of volcanism in thecontemporary Kagoshima Graben. The characteristics and possibility of migration of thefaulting located about 10 km to north of the location would be important to understand in
further investigations at this locality. In particular, southward migration of the left-lateral shear
zone cross-cutting southern Kyushu which may be driven by subduction of the Kyushu-Palau
Ridge could be important in future rock deformation at this location.

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12 Probability of Future Volcanism
The Kyushu Case Study explored several aspects of volcanism in the greater Kyushu region(including SW Honshu) that were not specifically addressed in the Tohoku Case Study(Chapman et al., 2009). These aspects of volcanism are particularly clear in the Kyushu
region, but may be of general concern for proposed geological repository sites in several
areas of Japan. These include:

Development of a methodology for assessing monogenetic volcanism as part of along-term volcanic hazard assessment

Testing and refining of proposed models for monogenetic volcanism using data
gathered in the region (in this case the Abu volcanic field of SW Honshu)Developing general methods for assessing long-term probability of volcanic using a
regional Kyushu exampleAssess the influence of tectonic domains in Kyushu on probability models of
volcanism

Development of methodology for local scale volcanic hazard assessment with respect
to specific volcanic systems.



Illustrate methods of estimating the probability of volcanic disruption, including event
magnitude.

This Section summarizes the results of our work in all of these areas. Sections 12.1 and 12.2
describe a methodology specifically for evaluating monogenetic volcanism. The subsequent
Sections describe the overall analysis and are organised from regional to local scales,
reporting the influence of segregating volcanoes in Kyushu by tectonic domain, andsummarising results using kernel density methods and Cox Process methods for modelling
the potential of volcanism. The probability of volcanic disruption is illustrated using calderaforming events as an example.

12.1

A Methodology for Assessing Long-term Hazard Due to
Monogenetic Volcanism

Monogenetic volcanoes form when igneous dikes ascend through the crust and erupt at thesurface. These volcanoes are characterised by a single episode of eruptive activity that may
last from weeks to years and, in rare cases, for hundreds of years. During this activity avolcano is formed, such as a cinder cone, maar, tuff ring, dome, or fissure-fed lava flow. In
rare cases, shield volcanoes may also be monogenetic. Unlike polygenetic volcanoes, once
eruptive activity ceases at monogenetic volcanoes, these volcanoes never erupt again.
Rather, renewed magmatic activity involves the propagation of a new dike or dike swarm
through the crust and formation of a new monogenetic volcano. Thus, over time, groups ofmonogenetic volcanoes are created. These monogenetic volcano groups, also referred to as
volcanic fields, typically consist of tens or hundreds of volcanoes distributed over thousands
to tens of thousands of square kilometres.
In general, average eruptive rates in monogenetic volcano groups are very low compared tosingle polygenetic volcanoes. Individual eruptions commonly involve <0.1 km3 magma, anderuption rates are commonly 10-4 to 10-3 eruptive events per year. From a volcanic hazards
perspective, the problem is one of forecasting the potential for new monogenetic volcanoes toform in the site region during a long performance period, accounting for the dispersed natureof monogenetic volcanism and its generally low recurrence rate. This is different from issues
associated with polygenetic volcanism, which involves eruptions from existing volcanoes
affecting the site, and the extremely rare formation of new polygenetic volcanoes.
Monogenetic volcanism has received a great deal of attention in HLW siting investigations in
the US, because Yucca Mountain, the proposed site of a HLW repository, is located at themargins of an active volcanic field. A variety of methods for assessing monogenetic volcanic132

hazards at Yucca Mountain have been developed over decades of siting activities. In addition,
volcanologists worldwide have considered monogenetic volcanic hazards, particularly tourban areas, such as Mexico City and Auckland, which are built in monogenetic volcanic
fields. Some of the methodologies proposed here draw on this experience, but are modified
considering the distinctive tectonic settings of potential HLW sites in Japan, compared withthe western USA.

12.1.1 Background to an Overall Methodology
The basic problems are to estimate the potential spatial distribution of future monogenetic
volcanism, the temporal recurrence rate of volcanism, and the area, particularly in the shallow
subsurface, potentially impacted by volcanism. Worldwide, probabilistic methods are used toassess monogenetic volcanism. This is due to the complex distribution of monogenetic
volcanism, which requires the application of stochastic models for its characterisation and forevaluating associated uncertainty. Although we develop geologic models for the causes ofmonogenetic volcanism at a variety of scales, these models cannot be used to forecast
precisely the distribution and timing of future events. Nevertheless, probabilistic models may
account for our current understanding of the underlying causes of monogenetic volcanism,and even include empirical information derived from the investigation of geologicalcorrelations. For example, in the Yucca Mountain region it was eventually determined thatmonogenetic volcanism tends to concentrate in a tectonic zone known as the Amargosa
Trough, and this empirical information was included in probabilistic models (Connor et al.,2000).
In order to estimate volcanic hazards due to monogenetic volcanism, three probabilities mustbe estimated. These are:

the probability that monogenetic volcanism will occur within some area about the
HLW site; this is determined through estimation of the spatial density of volcanism;the probability that monogenetic volcanism will occur within the timeframe of interest,
say annually, or within the overall performance period of the site; this is determinedthrough estimation of the temporal recurrence rate of volcanism;the probability that, given a volcanic event, the HLW repository will be disrupted insome way; this is determined through study of the magnitude of potential
monogenetic events and their interaction with the repository system.

Estimation of these probabilities usually involves using a variety of different models and sitespecific information about the volcanic field of interest. For example, geophysical surveys
have proven to provide basic and key information about the distribution of monogenetic
volcanic events (often some monogenetic events do not crop out at the surface or have beenburied by subsequent geological processes), structural and / or tectonic controls on the
distribution of monogenetic volcanism, and direct evidence of the potential for futuremonogenetic volcanism, for example through seismic tomographic studies.
Estimation of these probabilities is also complex. There may be different or competing
geological models for volcanism in a region, or different mathematical approaches toestimating probability using these data. In such circumstances, expert assessment is often
used to compile and evaluate disparate analyses. This approach has recently been used toassess monogenetic volcanic hazards in the Yucca Mountain region. Here, we consider asimilar approach to the assessment of monogenetic volcanic hazards in Japan.

12.1.2 Methods of Estimating Spatial Density
The goal of estimating spatial density in the context of volcanic hazards assessments for a
HLW repository site is to determine the likely locations of future igneous events, or the
probability of an igneous event at a specific location, given that such events occur within thesite region. In this context, spatial intensity refers to the expected mean number of events perunit area defined at a point, s (Diggle, 1985; Diggle and Marron, 1988; Gatrell et al., 1996).

133

Suppose there is a set of events (e.g., monogenetic volcano locations) that occur within aregion, R. These events can be designated as X 1 , X 2 , X 3 ... X N  R , where N is the total
number of events and Xn is a matrix whose elements are the x, y, location (e.g., given in
Easting and Northing coordinates, or, latitude and longitude) of the nth event. These events
are realisations of a random variable, X, a function that describes the set of all possible
realisations. In this case, X is the distribution of potential igneous events, from which a set ofobserved realisations (those volcanoes mapped in the monogenetic volcanic field) are drawn.
The spatial intensity is formally written as (Gatrell et al., 1996):

 E ( X (ds)) 
ds  0ds (s)  lim (1)where E ( X ( ds)) is the expected number of events that fall within a small area ds about the
point s (hence if the location, s, is given in Easting and Nothing (metres), the units of  (s) arem-2). At first glance it appears that the statistical definition of intensity is equivalent to the term
density as commonly used in geosciences. This is not quite true. The stochastic process that
leads to the formation of volcanoes is not known, so the true value of the local spatialintensity,  (s) , is also unknown. That is, the observed distribution of events is only one
realisation of the underlying process that gives rise to these events. Our goal is to find an
estimate of the spatial intensity, ˆ (s) , that approximates the true but unknown value of spatialintensity,  (s) .
In hazard assessment, we have a further requirement that we can use this information toforecast the spatial distribution of potential future events. Here we are interested in spatialintensity in terms of the probable location of a future igneous event, given that one occurs
within the region of interest. This conditional probability can be estimated by:~ ( s ) d( s )f̂ ( s )  ~  ( s ) d( s )(2)R

Integrating fˆ (s) across the region of interest, R, gives unity, if R is sufficiently large.
Furthermore, all values of fˆ (s) within this region are greater than or equal to zero, so fˆ (s) is
a probability density function and may be used in probabilistic hazard models, and we canrefer to fˆ (s) as an estimate of the spatial density.
Assumptions behind Spatial Density Estimates
How do we develop a best estimate of spatial density? The problem is that we have only one
realisation of the underlying statistical process, the distribution of past volcanic events, andgeology is not conducive to repeating the experiment in a natural system. In a region ofmonogenetic volcanism in Japan, for instance, there is only one geologic map of volcano
distribution. Perhaps ideally we would have a complete geophysical model for events.
Presumably, the distribution of melt in the asthenosphere and lithosphere were known, and if
the state of the lithosphere through which the magma rises were known, we might have abetter sense of where volcanoes are most likely to form next. Currently, we lack such acomplete geophysical perspective. Some data sets, for example seismic tomographic modelsof “slowness” in the lithosphere and asthenosphere, give an idea of where partial melting of
the mantle might occur (e.g., Zhao, 2001, Humphries, personal communication). Other data,such as gravity and magnetic anomalies in the region (Connor et al, 2000; Parsons et al.,2006), may show some correlation with the existing distribution of volcanoes in somecircumstances, but the mechanisms relating gravity anomalies to the origin of magmas areincompletely understood and the correlation is likely imperfect. As a result, these types of134

data have been used to support estimates of spatial density of monogenetic volcanism (e.g.,
Connor et al., 2000; Martin et al., 2004), but no model has yet been proposed that does notrely principally on the spatial distribution of past events. Similarly, in seismology there havebeen attempts to create blended hazard maps based on a variety of geophysical criteria, butthese methods generally rely on the earthquake catalogue (e.g., Ward, 1994).
The reliance on the distribution of past events implies that these realisations are
representations of the underlying random variable, X, that will govern the distribution ofpotential events in the future. This assumption immediately raises a fundamental question.
Which are the past events that should be used to develop the spatial intensity estimate, ˆ (s) ,and density, fˆ (s) ? Event datasets used to estimate the spatial density of future events need
to be consistent with several features of geological processes that give rise to monogenetic
volcanism.
First, any spatial intensity function for a geologic process must change with time. On timescales of tens of millions of years, plate boundaries change, volcanic systems wax, wane, andmantle is depleted. In very long term probabilistic hazard assessments for high-level waste
repositories, which may have 106 year performance periods, these factors have to beconsidered in weighing the validity of using specific data in developing spatial intensity
models. For processes like monogenetic volcanism, where a geologic record of past events
usually persists of tens of millions of years, consideration needs to be given to which events
best represent the distribution of future volcanism. For example, the distribution of Miocene
monogenetic volcanoes might be much less relevant than the distribution of Pliocene andQuaternary volcanoes. Thus, in order to develop an estimate of the spatial intensity, a modelof the geologic evolution of the system is required. This geological model is used to justify the
inclusion of some geological features in the event dataset, and the exclusion of others.
Second, it is necessary to assess the completeness of the geologic record. The record ofigneous events in monogenetic volcanic fields might give an incomplete picture of theunknown distribution of potential igneous events,  (s) . Igneous events might be missed bygeological investigations, as volcanic vents might be buried in sediment or otherwise
obscured (e.g., Connor et al., 1997). Furthermore, it is well-known that igneous events that
did not produce volcanic features but which intruded to the stratigraphic level of the proposed
repository are unlikely to be detected, except through drilling and /or extremely detailed
geophysical investigations.
Therefore, a major assumption of the spatial density estimate is that the distribution of
intrusive igneous features is described by the distribution of known volcanic features. This is
an important assumption because, as described further in the following, the limited number of
volcanic events creates uncertainty in the spatial density estimate. Therefore, it generally is
assumed that the distributions of igneous events in these areas are captured by the
distributions of surface features.
Third, igneous events, even when they are all identified, may be so rare that they give an
incomplete picture of the underlying geophysical process that gives rise to monogenetic
igneous events. Consider an igneous event as a single event, Xn, a realisation of a randomvariable, X. If, for example, X has a uniform random distribution, then it is likely that theobserved set of realisations will have a completely spatially random distribution in the region
R. The underlying density often has additional statistical structure, causing independent
realisations to cluster. Volcano clustering in monogenetic volcanic fields is well-documented
in virtually every tectonic setting on Earth (e.g. Connor, 1990; Connor and Conway, 2000;
Weller et al., 2006, Jaquet and Carniel, 2006), presumably because such clusters form above
zones of partial melting in the mantle. For random variables with a great deal of statisticalstructure, such as many modes in spatial intensity, a great number of events might berequired to identify the statistical structure of the random variable.
Obviously monogenetic volcanism in many parts of Japan, such as NW Kyushu, iscomparatively rare, even in comparison with other active monogenetic volcanic fields in the135western US (Connor and Conway, 2000), or the site would not be considered for a potential
HLW repository at all. Because relatively few igneous events are known to have occurred,
statistical structure in the spatial intensity of volcanism may be missed. This uncertainty canbe assessed, as is done in the following, but cannot be eliminated from the analysis. In apractical sense, because few events are used to construct the spatial density estimate, thespatial density is a smoothed representation of reality. Some areas might have higher spatialintensity, and others lower spatial intensity, than estimated.
Fourth, it is critical to ascertain which geologic features are actually independent events. The
statistical structure of the random variable, X, might be obscured if some events included in
the event dataset are not independent. For example, great earthquakes are followed byaftershocks. An earthquake aftershock, however, is not a random sample of the randomvariable “spatial distribution of great earthquakes”, because these aftershocks are notrealisations of this particular random variable. Rather, they are independent realisations ofanother random variable, say “spatial distribution of aftershocks about a great earthquake”.
So, the distribution of aftershocks does not necessarily give the best sense of the spatialintensity of great earthquakes, although these two random variables are correlated.
Similarly, volcanoes are complex geologic structures. In monogenetic volcanic fieldsalignments of volcanic cones develop in response to single igneous events, episodes ofmagma rise through the shallow crust. This is because single igneous dikes ascending
through the crust might form segments and rotate within the shallow crust, each segment
feeding a separate vent and each building a volcanic cone. If the goal of analysis is toforecast the distribution of future igneous events, each of which might produce more than one
monogenetic volcano, geological data must be gathered and volcanoes formed by the same
magmatic event must be somehow grouped as single events.

12.2

Testing Models with Data from the Abu volcanic field, SW Honshu

The ITM Group investigated rates and patterns of activity in the Abu Monogenetic Volcano
Group, located in Southwest Honshu, as a Case Study to illustrate methodologies for volcanic
hazard assessment in monogenetic volcanic fields. The goal of this effort was to refine amethodology for volcanic hazard assessment for monogenetic volcanism, paying particularattention to how various models of volcanism (e.g., constant magma supply rates) and data
(e.g., seismic tomographic data), might influence such hazards assessments.
This monogenetic volcano group is ideal for such analysis because many K-Ar dates havealready been reported (Uto and Koyaguchi, 1987; Kakubuchi et al., 2000 and Kimura et al.,2003) and because the field has been mapped in detail. We analyzed the temporal-spatial
distribution of volcanism in the Abu area using 56 individual monogenetic volcanoes mappedin the field. Spatial density was assessed using a nonparametric kernel method. Overall
volcanoes in the Abu Monogenetic Volcanic Group are located in a broadly E-W elongatedarea. NW-SE extended parallel volcano bands within the field, perhaps reflecting structuralcontrol on volcanism, are also seen in some spatial density analysis. Furthermore,orientations of individual dikes are deduced from volcano alignments. These more local scalevolcano alignments lie in various directions. Different scales of geological mechanisms should
govern these different characteristics of the monogenetic volcano distribution. Based onvolume of each volcano and reported K-Ar dates, we infer that the size and the activity ofbasaltic magma source in upper mantle had been constant yet, the volcanic activity on thesurface increased due to partial melting of lower crust caused by the induced heating frombasaltic dike intrusions. This idea is supported by seismic tomographic results, which indicate
a broad one of low velocity anomalies beneath the volcano field. Furthermore, the volcano
eruption pattern is possibly affected by regional stress conditions.

12.2.1 The Abu Monogenetic Volcano GroupIn the Southwest Japan Arc, late Cenozoic monogenetic volcano groups are thought tooriginate from the upwelling of small mantle diapirs (Iwamori, 1991; Uto, 1995; Kimura et al.,2003). The Abu Monogenetic Volcano Group is one such monogenetic volcano groupconsisting of alkaline basalt and calc-alkaline andesite-dacite lavas and pyroclastics
136

distributed in an area of 400 km2 (Figure 12.1; Oji, 1961; Koyaguchi, 1986). Some volcanoesof this Group are located under the Sea of Japan (The Maritime Safety Agency of Japan,
1996a, b). Basement rocks of these volcanoes are Cretaceous and Palaeogene in age(Koyaguchi, 1986).
Koyaguchi (1986) concluded that geochemical trends observed in Abu lavas over time were
produced by the magma mixing of primitive alkali basalt magma and dacite magma, which
was provided by the partial melting of the lower crust, caused by induced heating fromrepeated intrusion of basalt. Reported K-Ar dates range for the entire field range from 10 ka to
3.3 Ma (Uto and Koyaguchi, 1987; Kakubuchi et al., 2000 and Kimura et al., 2003). There is aquiescent period from ca. 0.8 to 1.6Ma in the field, during which no volcanic activity appears
to have occurred. Kakubuchi et al. (2000) classified the volcanic activity into alkaline basalt
dominated early period (ca.1.6 - 2 Ma) and calc-alkaline andesite-dacite dominated lateperiod (ca. <0.8 Ma), concluding that these distinct episodes originated from different mantle
diapirs.

Figure 12.1. Location map for the Abu Monogenetic Volcano Group.

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Although seismic activity is not high in the area of the Abu Monogenetic Volcano Group, E-W
compressive stress is suggested by the focal mechanisms of earthquakes in this area (Figure
12.2a). Ito and Arato concluded this stress condition (σ1 = N70-90W) has continued since 4
Ma based on an analysis of active structures in the Sea of Japan. Kanaori (1997) divided thisregion into three sub-areas by two NE-SW oriented fault systems (Figure 12.2b); the Abu
Monogenetic Volcano Group is located in the area where both NW-SE and NE-SW oriented
lineaments and faults are found. The initiation of the lineaments, faults and related cataclasite
zones probably date back to late Cretaceous-early Palaeogene, and reactivation partly
occurred along the old cataclasite zones during the Quaternary (Kanaori, 1997). Various
planer structures within the cataclasite zones indicate lateral shear sense. Also, many minor
earthquakes occur along the zones, whose depth of the epicentre is less than 30 km (Figure
12.2a). These active fault systems may cut entirely through the earth’s brittle crust (Kanaori,1997).

Figure 12.2. Tectonic setting around the Abu Monogenetic Volcano Group. (a) Distribution and E-W plotof earthquakes and focal mechanisms (Depth <30 km). Dots show hypocentres of earthquakes (M >2)
occurred from 1988 to 1998 based on the Japan University Network Earthquake Catalogue (JUNEC).
The focal mechanisms are based on a broadband data recorded in the F-net network of the National
Research Institute for Earth Science and Disaster Prevention (NIED) in Japan between January in 1997
and October in 2007. Shuttle Radar Topography Mission (SRTM) used for the digital elevation model.
Swarms of earthquakes occur at more than 40km in depth delineating the subducting Philippine Sea
plate. Although an ENE-WSW-trending earthquake zone lies to the east, seismic activity is not high inthe area of the Abu Monogenetic Volcano Group. (b) Distribution of faults and lineaments around thisregion modified after Kanaori (1997).

13812.2.2 Determination of volcanic centres in the Abu Monogenetic Volcano
Group
To analyze the temporal-spatial distribution of the Abu Monogenetic Volcano Group, we
identified volcanic centres of the individual volcanic events based on topographic features andgeological studies. According to the criteria of Condit and Connor (1996), individual lava flows
were distinguished geologically and morphologically, and traced to their sources such as
cinder cones, lava domes and topographic highs on lava flows. If a lava flow can beassociated with a cinder cone or lava dome, the cinder cone or lava dome can be regarded as
its source. The tops of these cones, and one dome, were recognised as volcanic centres.
Some lava flows cannot be associated with cones or domes by geologic mapping. In thesecases, the topographically highest point of the lava flow was considered to be the volcanic
centre. Characteristic arc-shaped landforms, which were probably formed by collapse ofcinder cones or lava domes, are seen on higher areas of some lava flows. The volcanic
centres of these lava flows are determined at the centres of these landforms. We also
identified one volcanic centre based on the presence of a depression 630 m long, 350 m wideand more than 20 m deep, because it is located on the highest part of a lava flow. Based onthese criteria, 56 volcanic centres were identified in the Abu Monogenetic Volcano Group.
Eruption dates of volcanic centres were determined by relating these centres to lava flowsthat have previously been radiometrically dated by K-Ar methods. If several similar dates
were obtained from one unit, the mean of these radiometric age determinations was used.
When a variety of dates were obtained in one unit, a geologically acceptable value wasdeduced from stratigraphic relationships. Volumes of individual units were calculated from the
distribution and thickness of lava flows, cinder cones, and similar features. For individual
volcanoes and their lava flows, each considered to be an igneous event, volume ranges from
10-4 to 10-1 km3, and the total volume of the Abu Monogenetic Volcano Group is about 4 km3.

12.2.3 Spatial density and temporal recurrence rate
Once this dataset is compiled, the next step in the hazard assessment is to estimate spatial
density and temporal recurrence rate. A two-dimensional elliptical kernel bandwidth that
depends on direction is used to estimate spatial density, given by (Connor and Connor,
2008):

where b=H-1/2x and H is a 2×2 element bandwidth matrix that is positive and definite, |H| is thedeterminant of this bandwidth matrix, H-1/2 is read as the inverse of the square root of thematrix H, bT is the transform of the matrix b, and x is a distance matrix composed of distances
between the set of points {s}, usually distributed on a grid to make a map in the region R, andvolcanic centre locations, {X1,X2,X3…XN}∈R.
A difficulty with using elliptical kernel functions is that all elements of the bandwidth matrix
must be estimated. We use an optimal bandwidth selector algorithm minimising the sum of
the asymptotic mean square error (SAMSE) of spatial density (Wand and Jones, 1995).
We also show a result of the least square cross validation (LSCV) of spatial density (Wandand Jones., 1995) because this kernel estimation method may indicate a relationship between
crustal structure and volcanism for the Abu Monogenetic Volcano Group. For the dataset of
56 volcanic centres, the SAMSE and LSCV bandwidth selector algorithm yields:

and

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respectively. The square roots of the matrixes are calculated to show units of bandwidth inkilometres. The diagonal elements are unequal in magnitude, indicating that the kernels areelongate and the off-diagonal elements indicate that the kernels are rotated to NNE and NW.
Overall, they create NNE-trending and NW-trending bands of comparatively high estimatedspatial density of volcanic centres through the map region.
Some features of volcano distribution are revealed by this method (Figure 12.3). The result
based on SAMSE shows a E-W elongated broad high-density area (Figure 12.3a). Thismethod yields a relatively smooth estimate of spatial density. In this case, spatial density isbasically characterised by several modes in vent distribution. On the other hand, high-density
areas based on LSCV appear as NW-SE extended narrow bands (Figure 12.3b). Thesebands are relatively evenly spaced, and each band contains both basaltic and andesitedacitic volcanoes erupted at various times.
Next, we considered the temporal pattern of volcanism in the Abu Monogenetic Volcano
Group. Figure 12.4a shows the cumulative number of volcanic centres and their volume. Agesof all the volcanic units have not been determined and the volume estimates of the volcanoes
have some error. Nevertheless, it is possible to discuss the trend of volcanic activity assumingthat samples for K-Ar dating were collected without spatial bias, and the estimated volumes
reflect real changes in eruption volume.

Figure 12.3. Spatial density maps of the Abu Monogenetic Volcano Group. (a) The map based on the
SAMSE algorithm. The maximum density is 4.0×10-3event/km2. (b) The map based on LSCV. The
maximum density is 7.9×10-3 event/km2. The contour lines are 25, 50, 75, 95 and 99% percentile. The
elevation data of Shuttle Radar Topography Mission (SRTM) used to draw the map.

140

The early activity of the Abu Monogenetic Volcano Group occurred 1.9 - 1.6 Ma, and the lateactivity is < 0.46 Ma. During this second episode of activity, the rate of volcanism increased,
at approximately 0.2 Ma. This change corresponded to the onset of andesitic volcanism(Figure 12.4a and 12.4b). On the other hand, Figure 12.4b also shows the eruption rate ofbasalt decreased somewhat at this time. Although formation of both basaltic and andesitic
volcanoes increased since about 0.2 Ma, the volume of individual basaltic eruptions
decreased.
The early activity of the Abu Monogenetic Volcano Group was low volume and occurred inlimited areas. By contrast, the later volcanism impacted a much larger area larger, including
the early active parts of the volcanic field. Although the eruption rate increased about 0.2 Ma,
obvious change of area of volcanic activity did occur at the time. In addition, there is no
obvious difference between distributions of basaltic and andesitic volcanoes.

Figure 12.4. Timing of volcanism in the Abu Monogenetic Volcano Group (a) Cumulative number andcumulative volume of 31 dated volcanoes. (b) Relative and cumulative volume of basalt and andesite
volcanoes during the most recent stage of activity. (c) Cumulative volume of volcanoes and estimatedbasalt supply rate.

12.2.4 Seismic tomography
Correlation between spatial density data and other geologic information, such as seismic
tomography anomalies, can help reinforce geological modes of monogenetic volcanism, andreduce uncertainty in the application of spatial density and temporal recurrence rate models.
Figure 12.5 shows P-wave tomographic images under the Abu volcanic area from the surface
down to 40 km depth, which are determined by applying the tomographic methods of Zhao etal. (2002, 2007) to a large number of high-quality arrival time data of local earthquakes

141

recorded by the dense seismic networks operated by the Hi-net, Japanese national
universities and Japan Meteorological Agency.
The image shows the perturbations (in percent) from the average velocity at each depth
under Southwest Japan. The resolution scale of the tomography is 20-25 km in the horizontal
direction and 5-10 km in depth. It is clear that significant low-velocity zones exist in the lower
crust and uppermost mantle under the Abu volcanic area. These seismic velocity anomalies
may reflect high-temperature anomalies associated with zones of dike intrusion, sill formation,and partial melting under the volcanic field. The upper crust exhibits average to slightly higher
velocities with some crustal earthquakes, suggesting that shallow magma chambers do notexist under the Abu volcano field, at least in the brittle upper crust. This is entirely consistent
with geological models of volcanic fields, which are thought to form due to low rates ofmagmatism and low rates of heat transfer in the brittle crust.

Figure 12.5. Vertical cross Sections of P-wave tomography under the Abu volcanic area (red triangle)
along the profiles shown on the insert map. Red and blue colours denote low and high velocities,
respectively. The velocity perturbation (in %) scale is shown at the bottom. The two dashed lines denote
the Conrad and Moho discontinuities. White dots show the local crustal earthquakes (M > 1.5) occurred
within 10 km of each profile.

There is a tendency that earthquake hypocentres become shallower toward the volcanic field,
which also reflects the higher temperature in the lower crust right beneath the volcanic field.
Such a feature of seismicity was also revealed in other active polygenetic volcanoes such as
Unzen in Kyushu (Zhao et al., 2002), but has not been previously observed for monogenetic
volcanic fields.

14212.2.5 Discussion
The two different kernel bandwidth optimisation algorithms (SAMSE and LSCV) yield very
different spatial density maps. The result based on the SAMSE algorithm provides asmoothed distribution of volcanoes in the Abu Volcano Group. The geometry of the E-W
elongated high volcano density area (Figure 12.3a) suggests the shape of a magma source
region. The scale of this region is consistent with the seismic tomography results, whichindicate roughly the same scale of low velocity zones in the mantle and lower crust. Thissuggests that the SAMSE method provides a reasonable estimate of the spatial density of
volcanism for the Abu Volcanic Group.
On the other hand, the LSCV algorithm identifies NNW-trending bands in the volcano
distribution (Figure 12.3b). A legitimate question is whether these bands represent an
important feature of volcano distribution in the Abu Monogenetic Volcano Group, for example
reflecting regional structural control. If so, the likelihood of future volcanism is significantly
higher in some parts of the field than in others, compared to the spatial density estimated with
the SAMSE method. One geological explanation for the parallel high-density bands and theiralmost constant separation is that feeder dikes occur in the field in this orientation. This
direction is not concordant with the azimuth of horizontal maximum compressive stress in thisregion (E-W). Furthermore, the compositions and K-Ar dates of volcanoes in each band are
not same strongly suggesting they were fed by different dikes.
Alternatively, fractures in the crust may also affect volcano distribution. Perhaps the bands involcano distribution reflect NNW-trending structures in the crust. However, no NW-SE
lineaments and faults have been mapped in the region, and the bands do not appear to beparallel to closely spaced vent alignments, presumably related to very shallow igneous
intrusions. Furthermore, the separation of bands (<5 km) are much narrower than the crustal
thickness (30-40 km) of Southwest Japan, so it is difficult to relate these bands to pre-existing
crustal structure.
Of these possibilities, it may be that the best explanation for the LSCV spatial density is thatits complexity is an artefact of the sparse volcano density in the area, and does not reflect
deep-seated geologic processes. If so, this points out that spatial density estimates requireinterpretation and expert judgment, in this case facilitated by comparison with seismic,
seismic tomographic, and structural data.
The change in the geochemistry of magmas, and change in the eruption rates of magmas
with time are important features of the Abu Monogenetic Volcano Group. In most recent
volcanic activity, the andesitic magma eruption rate increased, and volumetric output of
andesite was greater than basalt since 0.2 Ma (Figure 12.4b). During this change in thegeochemistry of the system, the spatial distribution of volcanism did not obviously change.
This is in contrast to volcanic fields in the western US, where geochemical changes involcanic fields have correlated with changes in spatial distribution of volcanoes (e.g. Condit
and Connor, 1996).
These observations support the conclusion of Kakubuchi et al. (2000) that heating fromrepeated intrusion of basalt caused partial melting of the lower crust and higher magma
productivity in the most recent history of the volcanic field. Partial melting of the lower crust in
response to continued influx of basaltic magmas is supported by the seismic tomographic
Section (Figure 3.5), which clearly indicates the occurrence of low velocity anomalies in thelower crust (> 20 km depth) beneath the volcanic field.
Assuming that andesite magmas in this volcanic field result from mixing of silicic and basaltic
magmas, a model can de developed for volumetric output of magma (Kakubuchi et al., 2000).
Koyaguchi (1986) suggested that a silicic end member for this mixing model must have lessthan 70% SiO2. Following from Kakubuchi et al. (2000), therefore, we consider the change oforiginal basalt production by adding half the erupted volume of andesite to the erupted
volume of basalt (a 1:1 mixing ratio). A cumulative plot of the result (Figure 12.4c) shows thebasalt supply rate to the lower crust is almost constant since the initiation of volcanism 0.46
Ma, assuming this mixing ratio is correct.
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Thus, there is a geological argument that the timing of volcanism in the Abu Monogenetic
Volcanic Field is relatively steady-state. The estimated basalt productivity (Figure 12.4c)
behaves as a volume-predictable model rather than a time-predictable model, with a constant
supply of magma to the lower crust. On the other hand, the volume decrease of individual
basaltic eruptions since about 0.2 Ma may reflect a change of regional stress field. Thechange of crustal conditions from cold to hot might ease the crustal differential stress. In thissituation, coalesce of dikes is more difficult (Takada, 1994) and volume of individual eruptions
would be smaller.
Unlike volcanic fields in the western US, for example near Yucca Mountain (Valentine andPerry, 2006), the Abu Monogenetic Volcanic Field is in a compressive tectonic setting. In sucha setting, one might imagine that eruptions stop when magma pressure in feeder dike isreduced to some threshold value. In this case, little volume of magma remains in each feeder
dike after eruptions under compressive stress conditions, assuming that dike walls relax
sufficiently following eruptions.
In contrast, in the Coso volcanic field, California, basaltic eruptions show time-predictable
relations under extensional stress condition (Bacon, 1982). It is expected that dike intrusions
relieve extensional strain in roof rocks accumulated at a constant rate. In these circumstances,
there would be variations in the volume of magma left in feeder dikes after eruptions
depending on the extensional stress conditions. Thus, under constant magma supply,
volcanic fields in compressional setting may be volume-predictable, and in extensionalsettings time-predictable, but not volume-predictable.
As in the case of spatial density estimation, models and data for the temporal recurrence rateof volcanism in the Abu Monogenetic Volcanic Field help constrain the expected rates of
future volcanic activity. In the case of the Abu Volcanic field, a volume-predictable model
appears to work best, and we have developed a model to suggest that in compressional
tectonic settings with constant magma supply, monogenetic volcanism should be generally
volume-predictable. In other words, the exact timing of future eruptions is uncertain, but in agiven time interval a given volume of magma should erupt. Once again, it is clear that thesevarious models of the recurrence rate of monogenetic volcanism are amenable to assessment
using expert elicitation.
Having looked specifically at monogenetic volcanism, Section 12 now goes on to look at theoverall, comprehensive assessment of volcanism in the Kyushu region.

12.3

Introduction to the Comprehensive Evaluation of Kyushu

We advocate the use of probability models to forecast potential future volcanic events for
HLW repository sites. The primary reason for using probability models is that these models
can account for the inherent uncertainties in geologic processes that give rise to complex
variability in the spatial distribution and timing of volcanic events. In addition, geologicalmodels of volcanism are evolving rapidly, as new data become available and newunderstanding of volcanic processes is achieved.
At some point in time, the volcanic hazard assessment will be finalised for a specific site using
the current state of knowledge. Application of these volcanic hazard models will be uncertainin part because they are based on the current state of knowledge. Probability models,
especially when coupled to expert assessment, can transparently account for data uncertainty
and model uncertainty.
As in our assessment of the Tohoku region, it is clear that there is uncertainty in the use ofdata to assess volcanic hazards in the Kyushu region. Figure 12.6 shows the distribution of
Quaternary volcanic vents in Kyushu. Our compilation of vents is based on the analysis of
map and literature data by Dr. Masaya Miyoshi of Kumamoto University and Koji Kiyosugi of
the University of South Florida. A total of 240 vents were identified from the literature in the
study region. These vents are further organised into 31 volcanic systems. Spatial density
maps were created using these two alternative datasets (volcanic vents and volcanic
systems). The organization into volcanic systems reflects the fact that individual volcanic
144

vents may not represent independent events. That is, some vents (e.g., cinder cones anddomes) are formed at a specific location due to their association with a larger volcanic system.
Thus, using all vents in the spatial analysis may bias the spatial density estimate. The 31
volcanic systems are thought to represent independent magmatic systems. That is, theirdistribution reflects regions of partial melting and ascent through the crust, rather thaninteraction at shallow crustal levels. Analysis of their distribution, therefore, indicated thatareas more prone to formation of similar volcanic systems in the future. Of course, thisindependence is difficult to verify and a range of data sets would be considered in a sitespecific hazard assessment.
Furthermore, it is noted that only Quaternary events are included in this analysis. This
constraint is simply made because the Quaternary record is far more comprehensive for
Kyushu than the record of pre-Quaternary volcanism. Nevertheless, it is clear that older
volcanism on Kyushu may offer insight into the distribution of future volcanism, and reduce
uncertainty associated with using the Quaternary record alone. In a site-specific hazard
assessment, it is recommended that a longer record of volcanism be used and that the effectof using data sets encompassing different time scales of volcanism be assessed.
We developed a set of methods for estimation of spatial density of volcanism as part of the
Tohoku Case Study and these were applied in the Kyushu Case study on different scales.
These methods are summarised in detail in Connor and Connor (2009), Kiyosugi et al. (2009),
Jaquet et al. (2008), Jaquet et al. (2009), and Mahony et al. (2009). Briefly, non-parametrickernel density functions with anisotropic bandwidths estimated by optimisation algorithms(Smoothed Asymptotic Maximum Squared Error, SAMSE) were applied in the Kyushu Case
Study. Application of these methods suggests that different sites, in different tectonic settings,
are best assessed using different bandwidth estimates. Consequently the analysis was
repeated by subdividing volcanism into three tectonic domains, the northern Extension arc,
the Southern arc, and the Back-arc, as described in the rock deformation Section of thisreport. Cox Process models were also applied in the Kyushu Case Study. Cox processmodels were refined for this study to include the use of multivariate potential, which integrates
geophysical data and rock deformation data directly into the analysis.
NUMO will eventually identify a specific site for a HLW repository. In addition to developmentof an understanding of volcanism on a regional scale, and hence regional variations involcanic hazard, it is important to assess how models of volcanic hazard operate on a site
scale. Consequently volcanic hazard was assessed in the region about Kirishima volcano,
located in the northern part of the Southern arc, to illustrate the application of these models
on a local scale. These spatial density maps do not directly account for event magnitude. For
example, caldera-forming volcanic events affect a much larger area of the subsurface than aone square kilometre area. The methodology of including event magnitude and its impact on
probability is explored in the final Section.

12.4

Assessing volcanism on a regional scale

Figure 12.7 shows the spatial density map of volcanism calculated for all of Kyushu andadjacent areas using a single optimal kernel bandwidth. The map is dominated by zones ofhigh spatial density in the Southern arc and Extensional arc. Volcanic events in southern
Honshu (e.g., in the Abu volcanic field) also contribute significantly to spatial density.
While the kernel bandwidth creates a smooth elongate region of high volcano spatial density
in the Extensional arc and in the Southern arc, the map pattern is more complicated in the
Back-arc. Volcanoes cluster on Goto Island, but isolated volcanoes also occur in the Back-arc.
For example, one small volume basaltic volcano on Iki island forms an isolated high involcano density in the back-arc. This suggests that the same kernel bandwidth might not beappropriate across the entire map region and some model refinement is useful.

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12.5

Assessing volcanism within tectonic domains

Several tectonic domains were identified during modeling of GPS-derived strain on Kyushu,
as described in previous Sections. Volcanism lies entirely within three of these domains, theSouthern arc domain in southern Kyushu, the Extensional arc domain in northeast Kyushu,
and the Back-arc domain including northwest Kyushu and the Goto Island region.
These tectonic domains subdivide Kyushu in ways relevant to volcanic processes. Subduction
processes dominate in the Southern arc and Extensional arc domains, and both domains are
dominated by formation of large volume volcanic systems, but the geometry of the subduction
zone is different in these two zones. Magmatism in the Back-arc domain is dominated bysmall-volume basaltic volcanism, monogenetic volcanism, and short-lived isolated centres.
It makes sense that the spatial density of volcanism would differ between these zones, given
the variation in tectonic setting and the processes giving rise to magmatism. Consequently werecomputed the optimal kernel bandwidths for volcanoes within each tectonic domain andformed a composite map of the vents (Figure 12.8) and the volcanic systems (Figure 12.9)
within each domain. It is clear from comparison of Figures 12.7 and 12.8 that the kernel
bandwidths on these maps are not vastly different. For example, in Figure 12.8 the Back-arc
domain is still dominated by isolated systems.

Figure 12.6: Locations of 240 volcanic vents in Kyushu and adjacent regions. Quaternary volcanic vents
are shown as red circles. Faults are shown as dashed lines. Data compiled by M. Miyoshi and K.
Kiyosugi.

146

In contrast, the map of the spatial distribution of volcanic systems by tectonic domain has asmoother structure with more pronounced differences among the kernel bandwidth estimates
between tectonic domains (Figure 12.9). In the Southern arc domain, the kernel bandwidth iselongate N-S, parallel to the arc and major fault systems in this region. Similarly, in the
Extensional arc domain (including Unzen volcano) the kernel bandwidth is elongate NE, also
parallel to the arc and to major fault systems in this region. The most obvious change on the
map involves the Back-arc domain, for which the kernel bandwidth is larger, and hence
volcanic systems less isolated, that shown on previous maps of volcanic vent spatial density(Figures 12.7 and 12.8). In addition, the kernel bandwidth is trending east-northeast at aclearly oblique angle to the trend in spatial density in the Extensional arc tectonic domain.
Overall, we prefer the spatial density estimate using the tectonic domains and volcanic
systems (Figure 12.9), rather than volcanic vents either on a regional scale or separated into
tectonic domains (Figures 12.7 and 12.8). This preference arises because (i) there aresubstantial differences between optimal kernel bandwidth estimates in the different tectonic
domains, and (ii) the map using volcano systems creates a smoother model in the Back-arc
tectonic domain, resulting in fewer isolated centres.

Figure 12.7: Spatial density of volcanism in Kyushu and adjacent regions calculated using SAMSE
bandwidth optimisation. A single kernel is fit to the entire dataset. Thus, this regional map is notsensitive to more local changes in the pattern of Quaternary volcanic activity. The spatial density of
volcanism (volcanoes per square kilometre) is contoured.

147

Figure 12.8. Spatial density map of volcanism in Kyushu created using different optimisation
bandwidths for the Extensional arc, Southern arc, and Back-arc tectonic domains.

148

Figure 12.9: Spatial density of volcanic systems in Kyushu

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12.6

Assessing volcanic hazard on the scale of individual volcanic
systems

Eventually, NUMO will apply these models of a site-specific scale in order to compare andcontrast a range of potential hazards in the context of performance assessment. As illustrated
by Section 12.8, the scale of the investigation does influence the hazard estimate. Spatial
density of volcanoes changes, depending on the event definition and the events included in
the estimate (Figures 12.7 – 12.9). In this Section we explore spatial density on the scale ofan individual volcanic system – the Kirishima volcano complex, located in the Southern arc
tectonic domain near it northern border.

Figure 12.10: The Kirishima volcano complex consists of a number of vents. These form a NW-trending
group and young to the SE (photograph: Photo Gallery of Kirishima-yama, AIST database “Quaternary
Volcanoes in Japan”, and perspective geological map: 11 Geologic Map of Kirishima Volcano, AIST
database “Database of Japanese Active Volcanoes” , AIST permission No. 63500-A-20090824-001).

150

Kirishima volcano is an active volcanic system with historical volcanic eruptions and lastreported eruptive activity in 2008. Kirishima comprises a group of at least 26 Quaternary
volcanoes located north of Kagoshima Bay (Figure 12.10). Late-Pleistocene to Holocene
volcanic activity has been dominantly andesitic and includes eruptions from composite
volcanoes, pyroclastic cones, maars, and underlying shield volcanoes located over an area ofapproximately 20 x 30 km. The larger composite volcanoes include the 1700-m-high
Karakuni-dake. Two large maars in the system are Onami-ike and Mi-ike, located SW of
Karakuni-dake and at its far eastern end, respectively. Holocene eruptions have been
concentrated along an E-W line of vents from Mi-ike to Ohachi, and at Shinmoe-dake to theNE (Figure 12.10). Kakuto caldera lies adjacent to this volcano complex. Overall there
appears to be a trend in the spatial distribution of vents with time, with younger vents tending
to be located to the southeast.

99

95

90

75

50

25

Vents per square kilometer
Figure 12.11: Spatial density of vents in the area of Kirishima volcano complex

Fitting an optimum kernel bandwidth to these 26 Quaternary vents emphasizes a NW-trend in
the vent distribution (Figure 12.11), in contrast to the volcano systems map (Figure 12.9),
which shows and overall N-S trend in spatial density of volcanic systems in the region. The
estimated spatial density of volcanic vents is also much higher on the local map of Kirishima
volcano (Figure 12.11) than the regional map of volcanic events (Figures 12.7 and 12.8)
because of the close spacing of volcanoes in the complex, and hence the smaller bandwidthof the optimal kernel.

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This scale dependence reemphasizes a basic point from the ITM Tohoku Case Study(Chapman et al., 2009), that different models are likely most appropriate for hazard evaluation
at different sites. In Tohoku, we discovered that strongly clustered models were appropriate
for modelling the spatial density of volcanism, and hence volcanic hazard, within the volcanicarc. These models emphasised the clustering of volcanoes along the axis of the arc. At
Kirishima volcano, the volcanic vents also have a different trend than the overall trend of thearc, possibly a reflection of slab roll-back or perhaps some crustal control on dyke orientationand magma ascent. In contrast, smoother models were more appropriate for investigating
volcanism in the back-arc of Tohoku, as in the Back-arc tectonic domain of Kyushu. Similarly,
the model of volcano systems of the Southern arc tectonic domain emphasizes the overall NS trend of the cluster (Figure 12.9), while the map based on vent distribution is more strongly
clustered and similar to the results based on Kirishima vent distribution alone.
What scale of investigation is most appropriate for a specific site? Obviously this depends on
the site and a sensible approach is to formulate a range of models using regional to local data
sets and explore the differences in the resulting hazard estimates. The Kirishima example
brings out an additional factor that warrants consideration, particularly on a local scale.
Volcano distribution is stationary on a regional scale. That is, the geophysical processes thatgive rise to volcanism within the arc do not result in changes in the rate of new volcano
system formation along the arc. On local scales this assumption may not be appropriate. At
Kirishima volcano, vent distribution appears to change with time, with the youngest volcano
vents in the southeast. This non-stationarity can be modelled, for example by weighting
events based on their age or by using Markov-process models. Again, it is most appropriate
at a specific site to use stationary and non-stationary models to explore the differences inhazard estimates made with these different models.
Finally, the events themselves vary widely at Kirishima volcano. The 26 Quaternary vents
consist of composite volcanoes, where eruptive activity persists over time scales of 10,000
years or more, and maars characterised by single explosive events. These different events
have different consequences for repository performance. For example, the formation of a newcomposite volcano near a repository would result in prolonged exposure to dyke injection andlong-lived hydrothermal systems. In contrast, maars are short-lived, although intense,
disruptive events. Incorporation of magnitude into probability estimates is discussed in thefollowing Section.
A fundamental result of this spatial analysis is that the spatial density of volcanism varies
throughout Kyushu, and this variation in spatial density appears to be related to tectonic
setting. In the context of volcanic hazard assessment, it is generally much more appropriate
to attempt to screen hazards based on spatial distribution of events rather than their temporal
recurrence rate, which is always less well-constrained. The regional analysis of Kyushu
volcanism confirms this. Whereas spatial models may be developed for the region andevaluated, temporal data, such as the time of initial formation of many of the volcanoes, isless well known. Screening of sites based on recurrence rate and temporal patterns of activity
requires site specific data that are simply not available on a regional scale at this time. Thatsaid, large events, such as the frequency of caldera formation (see following sections) anddetailed analyses of specific site areas, will yield sufficient temporal models, where it isnecessary to do so.

12.7

Event Magnitude and Probability Estimates

What is the probability of disruption of the repository directly by magma? Several factors must
be considered to address this question including:

Definition of the disruptive volcanic eventEstimation of the recurrence rate of these events

Estimation of the spatial density of these eventsDefinition of the repository area and performance period

152



Method of calculating probability

In the following Sections, the probability of a caldera-forming event is considered for arepository footprint of 25 km2 and performance period of 100,000 years.

12.7.1 Event definition
For caldera-forming eruptions, the event is the formation of a new caldera system. Nine
caldera systems have formed in Kyushu (including islands just south of Kyushu) during thelast approximately one million years. Although some of these calderas have experienced
multiple large eruptions (e.g., Aso caldera) we are primarily concerned with the formation of
new caldera systems that may disrupt the site of a geological repository in the future. The
effects of caldera eruption at the surface are widespread, and in fact the effects of such
eruptions can be global in scale. However, from the point of view of geological repositories,
we are concerned with the potential of disruption of the repository in the subsurface andconsequent transport of radionuclides into the biosphere. Thus, to a first order the effects ofthe caldera-forming eruption in the subsurface are confined to the lateral extent of the caldera
itself.
Caldera eruptions potentially disrupt large volumes of rock in the shallow subsurface – atrepository depths. At the surface, calderas in Kyushu range in diameter from about 4 km (e.g.,
Ikedo-ko caldera) to roughly elliptical structures of diameter 25 x 18 km (e.g., Aso caldera).
The average radius of Quaternary calderas in Kyushu is 7 km. For the purposes of thisexample analysis, the disruptive event is defined as a circular caldera that is 7 km in radius. It
is assumed that rock within a 7 km radius of the centre of a caldera is completely disrupted. A
repository in such a zone would experience disruption by magma injection, rock deformation,and the development of hydrothermal systems. Caldera-forming eruptions are of sufficientvolume and intensity that they may potentially transport HLW directly to the surface.
For assessment of a specific site, this event definition can become more complicated toreflect a range of possible processes. For example, a probability distribution of caldera shapeand radius might be developed. Hydrothermal systems can develop beyond the geographic
boundaries of calderas and such secondary effects might be considered. Similarly, othertypes of volcanic phenomena (e.g., dyke injection associated with monogenetic volcanism)may be considered using appropriate geometries for such events. In volcanic hazard
assessment studies for the proposed Yucca Mountain repository, a library of events wasconstructed and this library of events was used in the probabilistic assessment of disruption of
the planned repository by volcanic events.
Event: The formation of a new caldera system, 7 km in diameter. The analysis, therefore,
considers the probability of a given geographic location falling within 7 km of the centre of one
or more new calderas of this radius, formed during the performance period of the HLW
repository. It is assumed that the repository itself does not influence the probable future
distribution of such events.

12.7.2 Recurrence Rate of Caldera Events
Numerous models are extant in the volcanological literature to estimate the recurrence rate of
volcanic events. For rare geological events, such as the formation of new calderas, Ho (1991)
has shown that a maximum likelihood estimate of the recurrence rate of volcanic events is:

t 

N 1
to  t ywhere N is the number of events having occurred within the time interval defined by the oldest
event (to) and the youngest event (ty).

153

In Kyushu, nine calderas have formed since 1 Ma. The oldest of these calderas, Shishimuta
caldera, is thought to have formed 0.99 Ma and the youngest of these calderas, is thought tohave formed 0.01 Ma. Therefore a maximum likelihood estimate of the rate of caldera
formation in the Quaternary is 8 events per one million years.
Recurrence rate: The maximum likelihood estimate of caldera-forming events in Kyushu ist= 8 events per million years, based on the geologic record of known caldera formation in the
Quaternary.

12.7.3 Spatial Density of Caldera Events
As illustrated in this report, the spatial density of volcanic events may be estimated using avariety of techniques, such as the Cox Process and the nonparametric kernel density models.
In analysis of volcanic hazards at a specific site, it is recommended that a variety of models
be used, as the results give a sense of epistemic uncertainty in the probability estimate.
Here we use the nonparametric kernel density model to estimate spatial density of calderaforming events in Kyushu. We base the spatial density on the distribution of calderas andother volcanic systems formed in the Quaternary in the Extensional- and Southern arc
tectonic domains, including composite volcanoes such as Kuju in the spatial density estimate.
The reason for including composite volcanoes is the assumption that the distribution of these
volcanoes provides additional information about the possible location of calderas that may
form during the next 100,000 years. Volcanoes in the Back-arc tectonic domain are notincluded in this spatial density estimate because there is no evidence of caldera formation in
the back-arc during the Quaternary. The distribution of these monogenetic volcanoes is notthought to be an indication of the location of possible caldera formation.
Spatial density: The spatial density s is estimated from the distribution of volcanic systems
in the Extensional and Southern arc domains of Kyushu. These values are convolved with theevent magnitude (7 km radius caldera) to estimate the magnitude-weighted spatial
density,s,M. The magnitude-weighted spatial density is the probability that a given small
region (the repository area: 25 km2) will be disrupted by a caldera-forming eruption, given thatsuch an eruption occurs within the region.

12.7.4 Calculation of probability
The probability of one or more caldera-forming disruptive events can then be estimated using
Poisson’s equation. The probability that an event will occur during the performance period of
the repository, here taken to be 100,000 yr is:

P1 [ N  1]  1  exp t t 
where :

t  8  10 6 events  yr 1
t  100000 yr.
The probability that a repository site will be disrupted, given an event in the region, is:P2 [ N  1]  1  exp  s ,m 
where s,M is the magnitude-weighted spatial density. Then the probability of disruption of any
portion of the repository directly by caldera formation is:P[ N  1]  P1  P2

154

Figure 12.12 illustrates the probability estimates for such an event across the entire map
region. Probabilities range from >0.1% in some locations within the active volcanic arc <1 x
10-5% across wide regions of the back-arc and forearc.
These calculations and map (Figure 12.12) are provided simply to demonstrate amethodology for estimating probability of volcanic disruption of a potential repository site. It isemphasised that for a specific site, a range of models and model assumptions would be used
to estimate probability and to estimate uncertainty in these estimates. Such an investigation isfacilitated through the use of expert judgment.
It is useful to compare the probabilities of disruption of a 25km2 location by a caldera-forming
eruption. Eleven locations are shown in Figure 12.12. These example locations illustrate therange in probabilities across the region and are the same as those illustrated in the previous,rock deformation Sections of this report.
Probabilities at these sites (see Table 12.1) range from 6.9 x 10-12 at location 11 in SW
Honshu to 1 x 10-2 at location 3 in the northern extensional arc. In general, sites located inand near the southern arc and northern extensional arc have the highest probabilities. These
probabilities decrease quickly away from the arc, but particularly in the forearc (e.g., locations
8 and 6. Note that locations 1 and 11 in the backarc have low probabilities of being disrupted
by caldera-forming eruptions. Location 11, however, is near the Abu monogenetic volcanicgroup. Although the probability of caldera-forming eruptions is low at this location, other typesof volcanism, such as dyke intrusion during monogenetic volcanism would need to beanalyzed as part of a comprehensive volcanic hazard assessment.

Table 12.1: Probability of a caldera-forming eruption disrupting a 25 km2 area
during a 100,000 year performance period, calculated at each example site
location.

ExampleLocation

Probability of
Eruption1

2.6 x 10-42

5.3 x 10-63

1.0 x 10-2

4

5.7 x 10-3

5

3.6 x 10-3

6

1.3 x 10-87

1.3 x 10-38

1.4 x 10-3

95.3 x 10-3

109.2 x 10-3

11

6.9 x 10-12

155

low

Figure 12.12: Map showing the probability of a caldera-forming event in the next 100,000 years. The
red circles represent the volcanic events used to calculate volcano spatial density. The white circles
represent known calderas. The numbered white squares are example site locations where probability
values are known and can be compared (see Table 12.1). Volcano spatial density is based only on
those volcanic events located within the northern extensional arc and the southern arc. Zones ofincreasing probability are coloured from blue to orange. The colour coding is clipped at the sea-landborder although the contour lines extend into both regions.

156

12.8 Estimation of volcanic hazard using a Cox process with amultivariate potential
The Cox process constitutes the second stochastic model that was applied for the estimationof volcanic hazard in Kyushu. Besides describing the distribution of volcanic events, the Cox
process enables the uncertainty characterisation related to its intensity function, considered
as random. Therefore, this stochastic process becomes doubly stochastic and corresponds toa generalisation of the non-homogeneous Poisson process applied when using kernel density
methods.
For the Kyushu Case Study, developments were undertaken in order to extend theassimilation capabilities of the Cox process with respect to additional data types, such as rockdeformation and age data.
First, the conceptual elements are given that constitute the basis for the model development,
and then the stochastic model is presented followed by its application to the Kyushu Case
Study. The volcanic hazard estimation was performed at the scale of the whole island as wellas in relation to specific tectonic domains determined by rock deformation analysis.

12.8.1 Conceptualisation
The stochastic model is based on the following conceptual elements and hypotheses related
to the current geoscientific knowledge:The distribution of Quaternary volcanic events presents spatial patterns described using arandom potential of volcanism.

The spatial distribution of events is statistically correlated to the geophysical signature ofcrustal and mantel structures.

The spatial distribution of events is statistically correlated to the location of active faults.

The spatial distribution of events is statistically correlated to strain rates obtained fromrock deformation modelling.



Future events are likely located in zones of past activity and their location presents somedegree of statistical correlation with geophysics and rock deformation data.

12.8.2 Model development
The notion of potential of volcanism (Jaquet et al., 2009) is applied for the characterisation of
volcanic events; it is defined as the propensity of a given region to be affected in the future byvolcanic events. The potential of volcanism, being unknown, is considered as randomly
structured within the context of the stochastic model. Such randomly structured behavior canbe detected and parametrised using the variogram. While the structured part represents thecurrent geoscientific knowledge, the random part corresponds to the uncertainty associatedwith this information. Using the potential of volcanism allows for the description of volcanic
events in terms of statistical distribution and scale of spatial patterns as well as for the
integration of additional relevant information such as geophysics and rock deformation data.
The probability of new volcanic events occurring within a small domain of the region ofinterest is estimated using the Cox process (Lantuéjoul, 2002):

Zn P  N i  n  E  e Z i i 
n! 


157

The volcanic region of interest is partitioned into small domains, where the random potentialof domain Ai is denoted by Z i and E   is the mean. The potential corresponds to themean number of volcanic events in domain A i and is interpreted as a realization of a random
intensity function. In addition, the numbers of volcanic events within disjoint domains are nolonger independent, due to the structured behaviour of the random potential modelling theobserved patterns. In comparison, the potential of the non-homogeneous Poisson process (cf.
kernel density methods) is described using a smooth deterministic intensity function, and thenumbers of volcanic events remains independent within disjoint domains.
The multivariate character of the random potential is modelled by introducing the following
Z

S

T

dependence relation between the Gaussian potential Yi and additional data Yi and Yi ,expressed in Gaussian space:    ZT  ST  S   ZT   ZS  ST  T
 Yi +  Yi  f  ZS ,  ZT ,  ST  Yi R
Yi Z =  ZS
2
2



 1   ST
 1   ST

Where

 ZS

represents

thecorrelation

betweenpotential

andgeophysicaldata;

 ZT corresponds to the correlation between potential and tectonic data; and  ST is thecorrelation between geophysical and tectonic data. Under the assumption that thecorrelations for the potential prevail, it follows:2
2
Yi Z =  ZS Yi S +  ZT Yi T  1   ZS
  ZT
Yi R

In the developed model, the potential of volcanism assimilate multiple statistical correlations,since it presents dependencies with past volcanic activity as well as with geophysical andtectonic data.
The estimation of volcanic hazard is performed by simulating the distribution of volcanic
events likely to occur during a certain period of time in the future within the region of interest.
In addition, the simulation has to deliver volcanic events that are more likely to be located inzones of past activity. Therefore, the simulation requires to be conditioned to the number ofpast volcanic events known in each domain Ai . The idea is to simulate the potential of
volcanism conditioned on the number of volcanic events and on the geophysical and tectonic
data known for the analyzed region. Based on the iterative algorithm, given in Jaquet et al.
(2008), the following extended algorithm is proposed for the multivariate conditional
simulation of the potential:(i) generate Yi

R~ Gaussian  0, 1 for each A i ;(ii) select an index i at random;(iii) generate y0 ~ D Yi | Y j = y j , j  i ;(iv) compute y i =  S
S

1

R

R

R

 si  , yiT = T1  Ti  and





2
2
z 0 =  Z ρ ZS y iS + ρ ZT y iT  1  ρ ZS
 ρ ZTy0 ;(v) generate n0 ~ Ρoisson  z 0  ;R(vi) if n0 = ni ; then put y i = y0 and z i = z 0 ;158

(vii) go to (ii).
In its design, the algorithm runs forever. In practice, it is stopped when each potential z i has
been effectively updated more than several hundred times.
The simulated potential z i is representative only of the period of time t p at which all data
originate. For the simulation of volcanic events, occurring in a future period of time t f , theffuture potential z i is required. Its estimation requires the simulation of the distribution of
volcanic events likely to occur during a certain interval of time in the future. Such simulation is
performed using the Cox process with a random potential in time:Zn P  N k  n  E  e  Z k k n! where N k is the number of volcanic events in a time interval  k , partitioning the time series
of interest, and Z k its corresponding potential. The time series of interest describes theoccurrence of past volcanic events during a given period, e.g. the Quaternary. Therefore,
along the time axis, each volcanic event is therefore characterised by its age; generally taken
as the age of the onset of volcanic activity. The period under study is partitioned into small
time intervals; the analysed variable is then the number of events that have occurred in thesetime intervals. The characterisation of the past behaviour of volcanic events enables tosimulate the distribution of the number of events likely to occur in a future time interval with agiven duration, for example, of 100,000 years. In general, future volcanic activity is notindependent of the volcanic patterns that have occurred in the past. Therefore, this additional
complexity requires the application of Monte Carlo Cox simulations for the estimation of theffuture potential in time, z k . This leads to the following algorithm to simulate the futurefvolcanic events ni :(i) compute z i = z i  t f t p  z kff(ii) generate ni ~ Ρoissonfz Poisson for each A ;i z  for each A .
fii

The conditional simulation algorithm allows the estimation of volcanic hazard for each domainof the region of interest during the period of time considered. A Monte Carlo approach (cf.
Figure 12.10) is performed using several thousand simulations in order to derive stableprobability estimates for the future volcanic events:



K sim

 K1  1  n  1P N if  1 

f

i

k

sim

k =1





where K sim is the total number of simulations and 1k ni  1 equals 1 when the kfthsimulation assigns the domain Ai one or more volcanic events, and 0 otherwise.
The estimation of volcanic hazard was carried out at the scale of Kyushu and for two tectonic
domains: the Extensional arc domain and the Southern arc domain.

159

Figure 12.10: Cox stochastic modelling with assimilation of multivariate datasets for the estimation of
volcanic hazard.

12.8.3 Application at regional scale to Kyushu
The datasets used for the hazard assessment comprises (Figure 12.11):-

240 Quaternary volcanic events compiled by Masaya Miyoshi (Kumamoto University)and Koji Kiyosugi (South Florida University) from a literature search.

-76 active faults from the dataset of the Active Fault Research Centre (AFRC) of the
National Institute of Advanced Industrial Science and Technology (AIST)(http://www.aist.go.jp/RIODB/activefault/index_eng.html) and from the analysis of theadditional digital datasets described in Section 8.1.

-

Regional digital map of gravimetric anomalies (Geological Survey of Japan, AIST,
2002b).

160

Figure 12.11: Datasets for Kyushu, with Quaternary volcanic events (black triangle), active faults (reddashed line) and gravimetric anomalies (colours).

The spatial variability of fault data was characterised using the density of faulting expressed interms of cumulative fault length, Lc , in domain Ai (Figure 12.12):

Lc  Ai    l jj

Where l j is the length of fault j within the domain Ai .

Figure 12.12: Cumulative fault length for domains A1 (in green) and A2 (in blue).

161

The size of domain Ai is 5 km x 5 km, which corresponds to the representative area selectedby the ITM methodology to include a geological repository. The cumulative fault length wasestimated at the regional scale of Kyushu (Figure 12.13).

Figure 12.13: Cumulative fault length for Kyushu for 5 km x 5 km domain.

For the estimation of volcanic hazard for Kyushu, the potential of volcanism assimilates thefollowing spatial distributions: (a) Quaternary volcanic events, (b) gravimetric anomalies and(c) cumulative lengths of active faults. All of these datasets were sampled at a 5 km x 5 km forthe whole of Kyushu island. The parameters required for the simulation of the potential were
estimated using the methodology described in Jaquet et al. (2009); their values are given in
Table 12.2. Past and future potential in time were assumed proportional; i.e.

z if = z i   t f t p  . Using the Cox model, simulations of future volcanic events were

performed over a period of 0.1 Ma for Kyushu. 10,000 Monte Carlo simulations were carriedout to obtain stable probability estimates. These results were displayed in form of a volcanic
hazard map for Kyushu (Figure 12.14).
Table 12.2: Parameters for Kyushu island.

Case

 ZS *

 ZT **

Variogram correlation
scaleTime component

I

-0.18

0.40

50 km

proportional* S: gravimetric data; ** T: cumulative fault length.

162

Figure 12.14: Volcanic hazard map for Kyushu displaying the probability of one or more volcanic eventfor the next 100,000 years and 5 km x 5 km domain (case I).

12.8.4 Application to Kyushu Tectonic Domains
The datasets used for the hazard assessment of the two tectonic domains comprises thefollowing data types (Figure 12.15):-

Extensional arc domain: 87 Quaternary volcanic events, 31 active faults and strain
rates from GPS data.

-

Southern arc domain: 86 Quaternary volcanic events, 28 active faults and strain
rates from GPS data.

The GPS data is from the Geographical Survey Institute's CGPS network called Geonet(http://mekira.gsi.go.jp). The strain was calculated after removing a subduction coupling
component that was estimated; more details can be found in Section 9.2.

163

Figure 12.15: Datasets for the Extensional (left) and Southern arc (right) domains with Quaternary
volcanic events (black triangle), active faults (red dashed line) and strain rates (colours).

For the estimation of volcanic hazard for the arc domains of Kyushu, the following spatial
distributions are assimilated by the potential of volcanism: (a) Quaternary volcanic events, (b)strain rates and (c) cumulative lengths of active faults. All these datasets were sampled at a 5
km x 5 km for the different tectonic domains. As before, the simulation parameters for the
potential simulation were estimated as described in Jaquet et al. (2009); their values are
given in Table 12.3.
Table 12.3: Parameters for the Extensional and Southern arc domains of Kyushu.

Case

Domain ZS *

 ZT **

Variogramcorrelation scale

Time component

IIa

Extensional

-0.20

0.27

45 km

proportional

IIb

Southern

0.26

0.31

30 km

proportional

IIIa

Extensional

-0.20

0.27

45 km

simulated

IIIb

Southern

0.26

0.31

30 km

simulated* S: strain rate; ** T: cumulative fault length.

The correlation between potential and strain rate differs remarkably when comparing
Extensional and Southern arc domains. For the former domain, the correlation value isnegative and for the latter domain, it is positive. Such correlation means that for the
Extensional arc domain, volcanic events would tend to be more located in zones wherecontraction occurs. This result would deserve additional analysis as most of the strain rates
present quite low values. However, such difference in correlation values justifies theestimation of volcanic hazard for each tectonic domain. Regarding the correlation between
potential and cumulative lengths of active faults, the Extensional and Southern tectonic
domains present similar values.
164

Sensitivity cases were carried for the time component of the Extensional and Southern arc
domains. For cases IIa and IIb, past and future potential in time were assumed proportional,
and for cases IIIa and IIIb, the future potential was estimated by Cox simulations using aMonte Carlo approach (Figure 12.16). Compared to the proportional case, this leads toincrease of the future potential to about a factor 6 for the simulated case.

Figure 12.16: Histogram of simulated number of events for a future period of 100,000 years. The future
potential in time is estimated at 73.3 events.

Using the Cox model, simulations of future volcanic events were performed over a period of
0.1 Ma for each tectonic domain and for the two sensitivity cases. As before, 10,000 Monte
Carlo simulations were required to obtain stable probability estimates. The hazard estimates
are mapped for the regional scale of Kyushu in order to correspond to the Extensional andSouthern arc domains (Figures 12.17 and 12.18).
Table 12.4: Percentiles and surfaces with low probability values at 100,000 year scale.

Case

P5

P15

P33

P50

P67

P84

P95

Surface [km2] withprobability value
below 10-3

I

NA

NA

NA

10-4

3·10-410-3

2·10-233’432

IIab*

10-4

2·10-4

4·10-4

6·10-410-3

5·10-3

4·10-2

14’020

IIIab*

8·10-410-3

2·10-3

3·10-3

7·10-3

3·10-2

2·10-1

3’139* Extensional and Southern arc domains cover about 53 % of Kyushu island (≈ 39,800 km2).

165

Figure 12.17: Volcanic hazard map for Extensional and Southern arc domains displaying the probabilityof one or more volcanic event for the next 100,000 years and 5 km x 5 km domain (case IIab).

Figure 12.18: Volcanic hazard map for Extensional and Southern arc domains displaying the probabilityof one or more volcanic event for the next 100,000 years and 5 km x 5 km domain (case IIIab).

166

The results of sensitivity calculations can be measured in terms of surfaces presenting low
probability values (Table 12.4); i.e. these surfaces include only 5 km x 5 km domains
presenting probability values below 10-3. When comparing case I (whole Kyushu) and case II(with tectonic domains), the surface with low probability values decreases from 84 % (of thewhole Kyushu region) to 67 % (of Extensional + Southern arc domains). Regarding case III,the higher values taken for the future potential, compared to case II, leads to a major
reduction (down to 15 %) of the surface with low probability values. This result highlights theimportance of gathering good and sufficient radiometric data as input for hazard assessments.
Therefore, such domain delineation is of importance, as it allows fitting a model that betteraccount for local characteristics of tectonic domains and potentially avoids overestimating thesurface of Kyushu island presenting low probability values of volcanic hazard.

12.8.5 Conclusions and perspectives
By considering a Cox process with a multivariate potential of volcanism, the assimilation of
Quaternary geological information (events and ages), strain rates and geophysical data
becomes operational for hazard calculations. This modelling approach constitutes a step
forwards with respect to the integration of rock deformation and volcanism results forprobabilistic hazard assessments; i.e. it improves the characterisation of uncertainty when
forecasting volcanic activity on the long term. In particular, the use of multivariate datasets will
enhance hazard estimation for regions located between clusters of past volcanic events.
The comparison of hazard results relying on different concepts such as the nonhomogeneous Poisson process using kernel density methods (with a smooth deterministic
potential) and the Cox model (using a random potential) shows that the integration of theuncertainty for the potential of volcanism causes the resulting probability estimates to have an
increased spatial variability (Jaquet et al. 2009). Such results reveal the importance ofaccounting for the potential of volcanism uncertainty (aleatory) when assessing long-term
volcanic hazard. In addition, the application of several probabilistic models is of paramountimportance for hazard estimation, as they provide contribution to the perception of epistemic
uncertainty; i.e. evidence in terms of its variability and possible reduction.
Further developments are needed for the assessment of volcanic hazard at site scale. This
downscaling methodology (see Section 14.5) will integrate hazard estimates from regional
simulations and consider various volcanic scenarios (with uncertainty) likely to occur, as wellas assimilate data from site investigations (Figure 12.19).

Figure 12.19: Principles for a probabilistic downscaling methodology at site scale with various volcanic
scenarios likely to occur.

167

12.9

References for Section 12

Chapman, N., M. Apted, J. Beavan, K. Berryman, M. Cloos, C. Connor, L. Connor, O. Jaquet,
N. Litchfield, S. Mahony, W. Smith, S. Sparks, M. Stirling, P. Villamor and L. Wallace(2009). Development of Methodologies for the Identification of Volcanic and Tectonic
Hazards to Potential HLW Repository Sites in Japan: The Tohoku Case Study. Nuclear
Waste Management Organisation of Japan, Tokyo. Technical Report: NUMO-TR-08-03.
135 pps.
Geological Survey of Japan, AIST (2002b). Geoscientific maps of Southern part of Korea,
western part of Japan and their adjoining areas. Digital Geoscience Map P-4.
Jaquet O., Lantuéjoul C. and Goto J. (2008). Estimation of long-term volcanic hazard using a
Cox process with a multivariate potential. VIII International Geostatistics Congress,
GEOSTATS 2008, Santiago Chile, pp.167-176.
Jaquet O., Lantuéjoul C. and Goto J. (2009). Cox process models for the estimation of longterm volcanic hazard. In Volcanism, Tectonism, and the Siting of Nuclear Facilities, edited
by C.B. Connor, N.A. Chapman and L.J. Connor, Cambridge University Press, pp.369-384.
Lantuéjoul C. 2002. Geostatistical simulation: models and algorithms. Springer, 256 pps.

168

13 Comparison of Rock Deformation Strain and Volcanic
Hazard Results for Kyushu
As we have discussed throughout the Kyushu Case Study report, there appear to be spatial
and temporal relationships between volcanism and rock deformation processes (see, also,
Section 7). In that sense, it is useful to compare spatial variations in rock deformation andvolcanic hazard directly. Moreover, combining the rock deformation strain and volcanic
hazard estimates can give us some idea of areas within Kyushu that are “high hazard” or “lowhazard” from both rock deformation and volcanological perspectives.
The outputs of the rock deformation and volcanic hazard studies are quite different. Volcanic
hazard is assessed in terms of spatial density of volcanic systems and reflects the probability
that a volcano will form in a specific area, given the development of a new volcano in theregion. Rock deformation results are presented in terms of a common measure of strain rate.
In previous work, we have converted the rock deformation strain rates to a hazard value using
the ITM methodology (see Tohoku Case Study: Chapman et al., 2009) by defining somearbitrary “hazardous” strain threshold. However, converting the rock deformation strain rates
to a hazard value that has some meaning requires a clear definition of the event that is
considered “hazardous”, which requires input from performance assessment teams modelling
repository performance.
In order to facilitate comparison between the volcanic hazard estimates and rock deformation
strain rates, we have normalised7 the volcanic hazard estimates and the rock deformation
strain estimates (separately, for GPS, surface deformation and seismicity). We plot them inthe figures in this Section in terms of percentiles; for example, the highest 5% of strain values
in a given dataset is shown in dark orange. Although these are not probabilistic hazard
estimates, normalising these values allows us to look at the datasets in terms of a “relative”
hazard for the Kyushu region and to identify regions of relatively high and low overall tectonic
hazard with the aid of these normalised maps.

13.1

Normalised rock deformation strain rates

Some interesting patterns arise from normalisation of the surface deformation, seismological,
and GPS-derived strain rate estimates from the Kyushu Case Study. For the surface
deformation derived from fault strain (Figure 13.1) and seismologically-derived strain rateestimates (Figure 13.2 and Figure 13.3), we use the median strain value from the suite ofstrain models defined in the active fault deformation and seismicity logic trees (see Section 8
and Section10).
The GPS strain estimates used in the normalised GPS map (Figure 13.4) are a weighted
average of all logic tree branches (using the second invariant of the strain tensor), withweightings based on logic tree branch weightings (see Section 9). Given that the uniform
source area model is weighted very highly in the seismic strain rate model (due to therecommendation of the expert panel in the Tohoku Case Study; see discussion in Section 10),
we also show our preferred model, which is a continuously varying model that is more
representative of the spatial patterns of historical seismicity.
Uniform source area models assume that future earthquakes will be “gap-filling” events anddistributed uniformly over the region, rather than being concentrated along particular zones ofconcentrated deformation.

7

Normalisation means that the sum of the probabilities in the area of the normalised map integrates to 1.

169(Percentile)

Figure 13.1: Relative rock deformation hazard in Kyushu as described by strain rate derived fromknown surface deformation (active fault) features. On this and following figures, volcanic systems areplotted as red circles. Rather than contouring probability, the map contours represent relative hazard.
Each successive contour encloses an increasing percentage of the relative hazard. So for example,
given an event, there is a 95% chance of it falling within the 95th percentile contour, and a five percent
chance of it falling within the fifth percentile contour, based on this relative hazard model. On land thepercentages are color-coded based on the shown color scale. The contours extend off land but the color
codes are clipped at the sea-land border.

In the surface deformation (active fault) normalised strain map (Figure 13.1) the highest
strains are concentrated within the Beppu Graben of northeast Kyushu, and along the OitaKumamoto line. Elevated strains are also apparent in the Kagoshima Graben and along leftlateral faults in the back-arc region. The normalised median seismic strain map (Figure 13.2)
is quite different from the other normalised strain maps due to the dominance of the uniform
source area assumption in the seismicity strain logic tree (see Section 10). The strain rates in
the median seismic strain model largely reflect the defined boundaries chosen for the seismic
source areas. However, our preferred seismic strain model (assuming a continuously varying
model) highlights some interesting patterns. The highest strains in this model are in the regionof large, historical earthquakes (e.g., the 2005 Mw 7.0 offshore Fukuoka, and the 1997
Kagoshima-ken-hokuseibu earthquake). However, the normalised seismic strains are also170elevated in the Shimabara Graben region, and along the zone of left-lateral shear deformation
cutting across southern Kyushu identified by GPS (see Section 9). The highest GPS-related
strains occur along the zone of left-lateral shear cutting across southern Kyushu, in the Beppu
Graben (northeast Kyushu), and in the Kagoshima Graben. The lowest GPS strain rates arein the back-arc region.

(Percentile)

16

Figure 13.2: Relative rock deformation hazard in Kyushu as described by strain rate derived fromseismic normalised strain (median value—dominated by uniform source).

171

(Percentile)

16

Figure 13.3: Preferred model of rock deformation hazard derived from seismic strain (continuously
varying model).

172

(Percentile)

Figure 13.4: Relative rock deformation hazard in Kyushu as described by strain rate derived from GPS
weighted average strain (normalised).

13.2

Normalised volcanic hazard

As described in previous Sections, volcanic hazards for proposed geological repositories
largely involve the potential for magma disruption of a geological repository and eruption ofmagma and radioactive waste materials into the biosphere.
Volcanism in Kyushu may be broadly divided into zones that are consistent with the tectonic
domains developed for rock deformation hazard assessment in the Kyushu Case study.
These zones are the extensional arc, southern arc, and back-arc. Volcanoes cluster in thesethree tectonic domains on several scales. For example individual volcanic systems, such as
Kuju volcano, consist of many vents formed over hundreds of thousands of years. This
clustering means that given future volcanic activity volcanism is most likely to occur within ornear currently active volcanic systems.
On larger spatial scales and longer temporal scales, magmatism results in new volcanic
systems and currently active systems may become extinct. The probability of new volcanic

173systems forming is of particular interest for assessment of volcanic hazard for geologicaldisposal facilities.
Given the clustered nature of volcanism, nonparametric kernel density models are used tocharacterize the spatial density of volcanic events, with the understanding that future
volcanism, say during the next 100,000 years, is mostly likely to follow the pattern of volcanic
activity developed during the Quaternary (e.g., the last 2 million years) (Connor and Connor,
2009; Mahony et al., 2009). Other spatial density estimates, made for example using the Cox
Process model, may also be used to estimate spatial density (Jaquet et al., 2008; Jaquet etal., 2009).
The spatial density map of volcanic hazard shown in Figure 13.5 was constructed using the
SAMSE bandwidth optimisation algorithm to objectively estimate spatial density of volcanic
systems within each of the three tectonic domains. The map shows the spatial density of
volcanic systems. For example, Kuju volcano is shown as a single volcanic system, rather
than as a set of volcanic vents. From a hazard perspective, spatial density maps like Figure13.5 represent a conditional probability.
Given the formation of a new volcanic system in Kyushu, what is the likelihood that thissystem will form in a specific area? This probability is represented by the spatial density,
assuming that the formation of new volcanic systems is a stationary process within each of
the three tectonic domains. That is, spatial shifts in the formation of new volcanic systems
have not occurred in the Quaternary and are unlikely to occur during the performance periodof the geological repository. This assumption is reasonable, given the distribution and timingof volcanism in these volcanic systems and the nature of geophysical processes involved inmagma generation, ascent, and eruption in arc and back-arc settings.
As is the case for the rock deformation maps (Figure 13.1 – Figure 13.4), the volcanic hazard
map is normalised (integrates to one), and is contoured by percentile. Thus areas outlined by
the 5% contour represent the highest volcanic hazard (dark orange) as this contour outlines
the highest 5% of spatial density estimates. Conversely the 95% contour outlines all but theareas of relatively lowest volcanic hazard in Kyushu.

13.3

Comparison and combination of rock deformation strain andvolcanic hazards

Given that the rock deformation and volcanic hazard maps are normalised it is possible tomathematically blend these maps (e.g., Connor et al., 2000; Martin et al., 2003). For each grid
point, i, representing a small area a within the map region, RTi V i  Di Vi Di
R

where Vi is the relative volcanic hazard and Di. is the relative rock deformation hazard, as
defined previously and given the assumptions discussed previously. Then Ti is the relative“tectonic hazard”, here defined as the relative likelihood of a volcanic or rock deformation
events within the area a. This definition assumes that there is no causal relationship between
Vi and Di. That is, volcanic events are not caused by rock deformation events and vice versa.
Given the methods of constructing these maps (e.g., the use of regional fault and volcano
databases, GPS and seismicity) this assumption is reasonable, as discussed in the following.
It is emphasised that for a specific site for a proposed repository, independent analyses of
volcanic hazards and deformation hazards should be made. These tectonic hazard maps
(Figure 13.6 – Figure 13.9) are prepared only to facilitate comparison of the spatial
distribution of volcanic and rock deformation hazards in Kyushu.

174

(Percentile)

Figure 13.5: Relative volcanic hazard in Kyushu as described by the spatial density of volcanic systems.

175

(Percentile)

Figure 13.6: Relative tectonic hazard in Kyushu derived by equally weighting the surface deformation(active fault) map (Figure 13.1) and the volcano spatial density map (Figure 13.5).

13.4

Volcanic hazard and surface deformation

Some interesting patterns emerge from combination of the normalised volcanic hazard andsurface deformation (active fault) strain datasets. Due to the co-location of zones of active
normal faulting and active volcanism, the highest values (>15%) occur in the Kagoshima
Graben and Beppu-Shimabara Graben region (Figure 13.6). The combined tectonic hazard isgenerally the lowest in the Forearc, due to the lack of volcanism or significant active faulting inthis region. In contrast, the Extensional Arc and Southern Arc domains have the highest
combined tectonic hazard values. Parts (but not all — see the region of active monogenetic
volcanism) of the Back-arc domain also have low combined values.

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13.5

Volcanic hazard and GPS-derived strain

The highest relative tectonic hazard, based on the highest 5% of the combined GPS strain
and volcanic hazard values, occur in the Kagoshima Graben region (Figure 13.7). The BeppuShimabara Graben has similarly elevated values (15% and higher) due to the complementaryhigh strain and high volcanic hazard estimates there. Some of the GPS strain patterns appear
to be quite noisy (possibly due to problems with velocity estimates at specific GPS sites
and/or the variety of subduction coupling models considered in the logic tree; see further
discussion in the Strain from GPS Section). The affect of this “noise” is minimised when the
GPS normalised strain values are combined with the normalised volcanic hazard estimates(which are more smoothed, due to kernel density estimation methods used to calculate thevolcanic hazard). Combined values of hazard and GPS strain in the Forearc and Backarc
domains are generally the lowest (with the exception of the area of monogenetic volcanism inthe back-arc), while the Extensional Arc and Southern Arc domains have the highest
combined values.

(Percentile)

Figure 13.7: Relative hazard in Kyushu derived by equally weighting the GPS-derived normalised strain
map (Figure 13.4) and the volcano spatial density map (Figure 13.5).

177

13.6

Volcanic hazard and seismic strain

The seismic-derived strain map blended with the volcanic hazard map (Figure 13.8) yields a
relatively smooth surface. Comparison with the GPS-derived strain and volcanism map(Figure 13.7), relative values are lower within the Extensional arc and Southern arc domains
and higher in isolated areas of the Back-arc and forearc region due to the distribution of highestimated seismic strain. Overall, this map correlates less well with tectonic features andproduces isolated areas of relative high hazard away from the volcanic arcs.

(Percentile)

Figure 13.8: Relative hazard in Kyushu derived by equally weighting the seismic strain map(continuously varying source) (Figure 13.3) and the volcano spatial density map (Figure 13.5).

13.7

Combination of volcanic hazard and all rock deformation strain
estimates

As different aspects of strain are reflected in the active fault deformation-, GPS-, and seismicderived strain maps, it is possible to combine these three maps, weighting each map equally,
and combine the resulting comprehensive strain map with the volcanic hazard map (Figure13.9). The resulting, blended map is quite consistent with tectonic and volcanological models
178

of Kyushu, with highest relative values in the Extensional arc and Southern arc, and a broadzone of relatively high values in the Back-arc region.

(Percentile)

16

Figure 13.9: One method of assessing rock deformation hazards is to combine the results of the strain
maps derived from surface deformation, GPS, and seismicity. This map shows the results of combining
such an averaged strain map with the volcano spatial density map.

13.8

Discussion

It is emphasised that, while the combination of strain data and spatial density of volcanism
may reflect overall tectonic hazard, these maps are not hazard maps in the sense of providing
a probabilistic estimate of hazard. This is because the event definition varies among thesemaps. Nevertheless, the maps are useful for comparing areas where rock deformation andvolcanism are relatively high or low in the study region. The maps also provide a basis forconsidering the possible mechanisms that give rise to common distribution of strain estimatesand volcano distribution.

179

Comparison of the relative tectonic hazard maps (Figure 13.6 – Figure 13.9) suggests that
regions characterised by relatively high volcanic hazard also are regions of high rockdeformation hazard. Conversely, there are broad areas on Kyushu that are characterised byrelatively low volcanic and low rock deformation hazards. This correlation is strongest within
the Extensional and Southern Arc domains, the most tectonically active portions of Kyushu.
Volcanic hazard is lower in the Back-arc domain and less closely correlated with relative rockdeformation hazard (Figure 13.9).
The differences between relative tectonic hazard maps based on different strain models(surface deformation, GPS strain, and seismicity) are also interesting to assess. Volcano
distribution and faults may be more likely to be correlated in the sense that these are both
long-term data sets. The volcano spatial density is based on the distribution of Quaternary
volcanic systems. The fault dataset is Holocene, but certainly reflects the distribution of longactive structures. The spatial correlation in the combined maps (Figure 13.9) suggests that acommon geological process leads to the development of these features on a regional scale.
This is reinforced by the fact that the best-fit kernel in the extensional-arc domain is elongate
parallel to the Shimabara-Beppu graben, and some volcanoes in this domain (e.g., Akai andOmine) lie along faults and fault planes appear to have influenced dyke ascent at these
volcanoes. Similarly, the best-fit kernel in the Southern arc is N-S trending, parallel to faults inthis region. Such a correlation may arise from many possible geological processes. For
example, crustal extension associated with crustal thickening (underplating due tomagmatism), localisation of strain accommodation due to rheologic changes in the lower crustnear volcanic systems, and focusing of magmatism in regions of preferential strain
accommodation all might account for this correlation on a regional scale. Of course we cannot
identify the mechanism by identifying the correlation, but the correlation does reinforce thenotion that relative tectonic hazards are high (and concentrated) in parts of Kyushu. Suchstrong clustering also implies that other areas are comparatively lower in tectonic hazard
rates in the long term.
The rock deformation hazard based on GPS strain rate is a much shorter-term indicator ofstrain than faulting. Because the volcanic hazard map is based on a 2 million year record andthe GPS-derived strain map is based on a decadal record, it is not surprising that thecorrelation with volcanism is more complex than the correlation between volcanism andfaulting. The timescales represented by the maps are vastly different. Nonetheless, thepatterns seen on the "long term volcano-surface deformation" map (Figure 13.6) are
reinforced by the “volcano-GPS strain map” (Figure 13.7), which is sensitive to the current
tectonism. This suggests that there is no reason to discount previously active regions as lessactive now. Furthermore, Figure 13.7 seems to suggest that the volcano gap between the
Extensional arc and the Southern arc is more prone to tectonic hazard than it appears to be
based on the volcano hazard map alone. It does not necessarily follow that because the rockdeformation hazard is high in this volcano gap region, volcano hazards might be higher thanforecast by the volcano hazard map alone, but hazard models would need to explore thispossibility for a potential site in this gap region. Features of the tectonic hazard map based on
GPS strain (Figure 13.7) offer additional insights into the correlation between GPS strain andvolcanism. The correlation between volcano density and GPS strain is best where the GPS
strain is extensional. The worst correlation occurs in high GPS shear strain zones (e.g., themajor left-lateral shear zone cutting across southern Kyushu), suggesting that the main link
between volcanism and rock deformation is in the extensional environments on Kyushu.

13.9

References for Section 13

Chapman, N., M. Apted, J. Beavan, K. Berryman, M. Cloos, C. Connor, L. Connor, O. Jaquet,
N. Litchfield, S. Mahony, W. Smith, S. Sparks, M. Stirling, P. Villamor and L. Wallace(2009). Development of Methodologies for the Identification of Volcanic and Tectonic
Hazards to Potential HLW Repository Sites in Japan: The Tohoku Case Study. Nuclear
Waste Management Organisation of Japan, Tokyo. Technical Report: NUMO-TR-08-03.
135 pps.

180

Connor, C.B., and L. J. Connor, Estimating spatial density with kernel methods. In: C. B.
Connor, N. A. Chapman, and L. J. Connor (eds.), Volcanic and Tectonic Hazard
Assessment for Nuclear Facilities, Cambridge University Press, in press.
Connor, C.B., J. Stamatakos, D. Ferrill, B.E. Hill, G. Ofoegbu, and F.M. Conway, 2000,
Volcanic hazards at the proposed Yucca Mountain, Nevada, high-level radioactive waste
repository Journal of Geophysical Research, 105: 417-432.
Jaquet, O., C. Lantuéjoul and J. Goto. Cox process models for the estimation of long-term
volcanic hazard. In: C. B. Connor, N. A. Chapman, and L. J. Connor (eds.), Volcanic andTectonic Hazard Assessment for Nuclear Facilities, Cambridge University Press, in press.
Jaquet, O., C. Connor, L. Connor, 2008. Probabilistic modeling for long-term assessment of
volcanic hazards, Nuclear Technology, 163(1), 180-189.
Mahony, S. H. , R. S. J. Sparks, L. J. Connor and C. B. Connor, Exploring long-term hazards
using a Quaternary volcano database. In: C. B. Connor, N. A. Chapman, and L. J.
Connor (eds.), Volcanic and Tectonic Hazard Assessment for Nuclear Facilities,
Cambridge University Press, in press.
Martin, A.J., K. Umeda, C.B. Connor, J.N. Weller, D. Zhao, M. Takahashi, 2004, Modelinglong-term volcanic hazards through Bayesian inference: example from the Tohoku
volcanic arc, Japan, Journal of Geophysical Research, 109, B10208,
doi:10.1029/2004JB003201.

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14 Conclusions
The Kyushu Case Study has helped to extend and test the ITM Methodology further, from thebasis developed in the Tohoku Case Study. However, it is emphasised at the start of these
conclusions that both Case Studies were intended to be development tools, rather thandemonstrations of a completed, mature and systematic technical methodology that was
already available and widely tested. However, despite this caveat, we believe that the two
Case Studies together have produced all the main elements of such a methodology andillustrated comprehensively how it can be used to provide probabilistic assessments oftectonic hazard. The methodology is now ready, at a ‘beta-test’ level, for deployment in any
areas that may arise in NUMO’s repository siting programme.
The methodology will need to be refined, adjusted to the scale of the area underconsideration and adapted to the geological and tectonic complexity of the region. In
particular, those early Steps of the methodology that use expert elicitation to develop andweight appropriate models will need to be managed rigorously – and with the input ofconsiderably more resources and local expert knowledge than was possible in our
methodology development project.
In these conclusions, we look first at how the methodology performed when extended fromthe relatively simple Tohoku application and applied to the considerably more tectonicallyvaried terrain of Kyushu. We make observations about the limitations that are inevitably
evident at this ‘working prototype’ level of development. These observations lead on tosuggestions for how the methodology would need to be upgraded for actual application in
making siting decisions at ‘real sites’ and in feeding quantitative information to safetyassessment work. Finally we look at two aspects of extension of the methodology for suchuse: how it might be downscaled to a single site level and how it might address time periods
that are longer than those of principal concern in siting (i.e. beyond 100,000 years).

14.1

Performance of the ITM Methodology in Kyushu

The Kyushu and Tohoku Case Studies differ in a number of respects. Tohoku is a region thathas a relatively stable plate tectonic setting, while the Kyushu region is one of the mostdynamic and rapidly changing plate boundaries in the world. The situation in Kyushu isintrinsically more complex. The datasets are also less complete or confident in Kyushu
compared to Tohoku. GPS rates appear to be higher in Kyushu, while paradoxically the active
faults and seismicity are much lower in Kyushu. The apparent discrepancy in Kyushu
between GPS data and uptake of deformation on active faults may reflect some combinationof the historical seismicity sampling too short a time period, the presence of active faults thathave not yet been identified, deformation by aseismic creep, and that some of thedeformation is taken up by magmatic intrusions in regions of extensional tectonics. The
various modes of strain accommodation in the Kyushu are not yet fully understood, as
reflected in these multiple explanations. The style of volcanism and geochemistry of themagmas varies considerably across Kyushu, but is reasonably simple arc volcanism in
Tohoku. Predictability of future volcanism and faulting in Kyushu is less certain because
geological setting is evolving very rapidly, while the fundamental assumption in Tohoku thatthe plate motions driving intraplate deformation are stable over periods up to a hundred
thousand years are not appropriate in Kyushu.
Overall, the Methodology has performed very well in Kyushu, with good agreement between
the essentially seismotectonic domains and the volcano chemistry dataset, good generalagreement between probability surfaces and volcanism (although the volcanism is too young
and the tectonics too dynamic to establish steady state patterns), and good integration
between volcanism and tectonism, owing to the use of coherent geological models on which
the probabilistic procedures are based.
We note that the differences observed between the different rock deformation datasets aremark of the success of the methodology, in the sense that it highlights specific issues that182

would need to be resolved before one could be sure of the level of confidence in thegeological model of the region.

14.2

Limitations of the Current Version of the Methodology

The Kyushu Case Study has revealed a number of limitations to the methodology, whichshould be addressed when it is being deployed in an actual siting programme.
Depending on the area being evaluated and the stage of the site evaluation programme, there
may be a requirement to look at the likelihood of lower impact volcanic events (than ventformation at the repository site), such as dike or sill intrusion, the development of large-scalehydrothermal activity or, possibly, events that could affect surface facilities over the multidecade operating life of a repository (e.g. tephra deposition).
Some of the rock deformation datasets used in this study are inherently regional to subregional in their utility – GPS for example. Here, a denser network of observations and datapoints can refine uncertainties, but it cannot yield variations in strain rates at <10 km spatial
dimension, unless the deformation is very shallow in the crust.
Faults and seismicity are currently assessed as independent datasets but it may be more
appropriate in future to merge them, as is done in PSHA for nuclear facilities. This would
provide areal continuity, although it mixes temporal scales. The seismicity dataset requires
further assessment – it shows uniformly low strains and it is unclear whether this is a data
limitation or a methodological problem.
At a site scale, the methodologies cannot be a substitute for site-specific deterministic studiesof the nature, history and impacts of tectonic processes as evidenced by field investigation
data. However, these site data should be consistent with, and further inform, the range ofalternative geological models that underpin the ITM Methodology. At the PIA and DIA stagesof the project the ITM probabilistic methods will support the site investigations and the resultsof site investigation can be used to modify the weightings of various parameters in theprobabilistic model – perhaps providing more confidence (and weight) to one or othergeological model.

14.3

Limitations of the Kyushu Dataset

Significant data limitations have been identified in the study. These include active fault
parameters, inland uplift data, the very limited range of recorded historical seismicity (which is
generally of low magnitude events that nowhere approach a Mmax event) and uncertainty inhistorical earthquake catalogues. Inverting the seismicity rates suggests the return time for an
Mmax event somewhere in Kyushu is about 80 years. No M 7.5 events are known and only twoevents in the 400-year period of the record are near M7 (1912 in the Kagoshima Graben and2006, near Fukuoka).
There are many opportunities for further datasets, especially geophysical, to be used to
inform alternate viable geological models of both rock deformation and volcanism For
example, it is not clear at present whether the gap in the volcanic arc is related to thesubducting slab or whether there are upper plate or mantle processes that control the extent
of the active arc. An obvious question is whether the gap will continue to be an anomaly out to,say, 1 Ma. A related question is whether volcanism in the Beppu-Shimabara graben and in
the Kagoshima trough is localised owing to tectonic extension, or whether slab geometry or
mantle constraints are the fundamental driver. Also, we have not yet explored how much of
the geodetic strain observed in Kyushu may be related to volcanism in various parts of theregion.

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14.4

Opportunities for Further Methodology Development

Prior to, or in the course of, applying the Methodology in an actual site evaluation programme,
whether in Kyushu or elsewhere in Japan, it would be reasonable to carry out some further
developments. Itemised below is an initial checklist of topics that could be considered,although it is suggested that this is revisited at the time of deployment.
There are opportunities for considerably more analysis of how various logic tree options andbranches should be weighted and further analysis of what elements of the probabilistic
formulation constitute aleatory uncertainties (natural, earth system variability) and epistemic
uncertainties (knowledge uncertainties that can be reduced with further investigation). It is
probable that different weighting profiles could be used in different time periods, reflecting
confidence in the applicability of data over different periods of interest.
Further opportunity exists to assess whether strain has the same annual probability for all
return periods and the form that the strain will manifest itself in. At some stage of site
evaluation, it will be essential to explore the range of forms that strain release may take in a
volume of rock, which will depend on the geological context. Only by considering the
manifestation of the strain can the estimated strain rates and likelihoods be factored properly
into safety assessment models for a repository. One aspect that needs to be consideredwhen looking at strain manifestation is how much strain is taken up by volcanic intrusion
across a region. When repository design becomes more specific, then a useful strain
parameter may be displacement. This measure can be developed from the probabilistic
procedures already in place.
Further development of analysis methods pertaining to the seismicity catalogue may be
warranted. The uniformly low seismicity strain rates in Kyushu may be related to the use of
the maximum likelihood method of developing seismicity parameters “a” and “b”. This,
coupled with the strong effect in the model of smoothing by placing highest weights on a
uniform source characterisation, strongly implies that earthquakes of similar magnitude andrate can occur anywhere in Kyushu. This represents a strong preference for “gap-filling”. The
alternative is that earthquake occurrence is localised to crustal processes and structure andthat future earthquakes are more likely to occur in places where they have occurred in thepast.
There are many opportunities for further analysis of uncertainties and sensitivities in theprobabilistic methodologies. The figures produced to demonstrate the application of the ITM
probabilistic methodologies for future volcanism and rock deformation in Kyushu often
represent preferred or mean estimate models, but the sensitivity and uncertainties of thesemean estimates is only rather poorly assessed at present.
The potential for uplift of the repository into the oxidising zone could be a significant issue insome locations if the safety case extends to time periods of 1 Ma. Uplift rates as low as 0.2
mm/a may represent significant risk if the requirement for geological stability extends to 1 Ma.
Very detailed work is required to assess uplift and uncertainty confidently, at this level ofprecision.

14.5

Refining the Methodology for Downscaling to Sub-regional or Site
Scale

The ITM Methodology has been developed principally to consider hazard from future
volcanism and tectonic activity on a regional scale. In the context of Japan this means regionsof the order 105 to > 106 km2. The methodology has been developed for a variety of purposes,
including comparison of the relative hazard at competing sites, testing the adequacy of
exclusion criteria, investigating the uncertainty in the assessment of hazard at specific places,
and mapping out hazard across the region to identify places of low hazard and therefore withpromise as site location. The hazard is expressed in terms of strain rates, locations of major
tectonic structures, and for volcanism the probability of a new volcano forming in a specified
time period. Such hazard assessments will inform the PIA stage. Once a potential site or184volunteer community has been identified then the detail with which the hazards are assessedneeds to be commensurately improved. While the same essential questions are being asked,
in essence what is the probability of a site being disrupted or compromised by volcanic or
tectonic activity, the change in scale will require some refinement of the methodology.
Perhaps the most important aspect of down-scaling is that there needs to be an increase in
the volume and completeness of data. Existing data can be gathered during the PIA stage
and gathering of new data with associated interpretive analysis may be essential as the
programme moves towards specific site investigation. The more detailed data and analysis of
all the tectonic and volcanic datasets will be needed principally to reduce or constrain theuncertainties.
For volcanic hazards the questions to be addressed are likely to change with reduced scale.
At large-scale whole regions of order 105 to > 106 km2 have been considered using the ITM
methodology. The hazards have been expressed as probability of a volcanic event defined asa volcano at a specific site over a specified period of time. The ITM Methodology thus far has
considered specific sites to mean areas of 5 x 5 km and time-scales of 104 to 106 years. The
modification of the methodology at intermediate and site specific scales is now discussed.
At sub-regional scale (~104 to 103 km2) the probability of new volcanoes forming needs toinvestigated not just at the site itself but in areas that are close enough to the site that theymight pose direct or indirect effects. The “15 km” exclusion criterion provides a yardstick but
indirect effects (such as hydrothermal systems) might indicate larger distances should beconsidered. The method could, for example, be modified to look at the probability of a new
volcano forming in an area being affected by a volcano sited at a distance of 15 km from thesite. At this intermediate scale, the different kinds of hazards may need to be considered
individually. The most important hazard is intrusive disruption of the site. Thus, models mayneed to be developed which look in more detail at the probability of intrusive disruption acrossthe region, taking into account local tectonic, geophysical and geological data in more detail.
It is likely that much more detailed geochronological, paleovolcanological andpalaeosiemological studies will be needed within the sub-region to characterise recurrence
rates of volcanism, styles of volcanism and past history of faulting. Deterministic assessments
are likely to also be important. For example, probability of rock deformation or volcanism
might change discontinuously across a major fault between two different tectonic domains,
and using smoother probability algorithms may give misleading results. As another example,
probability of intrusion might be represented as some functional form of the distance from thecentre and the hazard of intrusion might be some combination of intrusive hazard related tomultiple volcanic events.
At a scale close to the repository footprint itself (~ 102 to 101 km2) the very local factors will
start to affect probabilities and a more deterministic approach may be essential. Siteevaluation should be based upon standard geological principles, but guided by “potential
issues” identified via coherent geological models at regional and sub-regional scale. An
example from the rock deformation perspective would be found when the fault process zone
width is highly dependent on factors such as the sense of fault movement and lithology of thesurrounding rock. For instance, the zone may be wider for a reverse fault in weak rock versus
a strike-slip fault in hard rock. Alternatively, the fault zone complexity may reflect fault maturity.
Therefore the distance from a known fault will have to be carefully defined (in the current
Kyushu Case Study we have taken a rough centreline for the fault position) and the likelihoodof future changes to the fault zone width should be assessed. Another example is that a site
located along strike of a region of young volcanic vents and related tectonic features might be
expected to have higher hazard than the other sites located in the same distance fromvolcanoes but in a direction normal to the volcanic lineament. Local geological or tectonic
fabrics are likely to have a major influence on the nature of future volcanism and its
interaction with the repository. At the scale of the repository the hazards analysis is likely to
be strongly linked to consequence analysis.
In regions like Kyushu, the arc volcanism and active fault systems are exceptionally young
and unsteady and therefore harder to predict. Down-scaling will require incorporation of185

understanding of how the large scale changes in plate configuration and tectonics relate to
the smaller scale relationships at the sub-region scale and specific sites.
A key aspect of sub-regional and site scale characterisation will be to understand stress
distributions and strain rates. Monitoring of strain rates down bore-holes is an example of thekind of technique that could be applied.

14.6

Refining the Methodology for the 100 k years to 1 million years
periodIf the requirement for forecasting geological stability were to be extended to 1 million years it
would present formidable challenges. Plate motions and configurations may not remain
constant in space and time. For example, the volcanic front can be expected to migrate andsubducted features that may affect the intensity, style, and location of volcanism and faulting,such as sea floor island chains or ridges, may change their position beneath Kyushu. The
forcing factors in back-arc volcanism are poorly understood with apparent volcanic “flare-ups”
such that the hazard is not constant in time. Other datasets would be needed to inform
coherent geological models to overcome the requirement to drop the constraint of plate
tectonic stability over the time period of interest. Potential unsteadiness in tectonics andvolcanism will lead to greater uncertainty in the probabilistic models. A wider range of viablegeologic models could accommodate increased uncertainty of the temporal and spatial
evolution of faulting and volcanism.
For volcanism, this time period presents formidable problems in that very large volcanic
systems, such as calderas, can develop on time scales that are well within this time period,
Aso being a very good example. Any location that is within the footprint of the volcanic arc orback arc region could be affected by future volcanism. Back arc regions are less prone to
major changes, but over a million years the possibility of any given location within the backarc region with no previous record of volcanism developing monogenetic volcanism cannot be
ruled out. Slab roll back provides issues for assessing fore-arc regions, especially close to thecontemporary volcanic front. It is clear that significant adjustments in plate configurations andregional tectonics are possible in a period of 1 million years, so assessment over such a long
period would necessarily have large uncertainties.

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