- compute the total estimated probability and compare up to five different scenarios.

This tool has been used for determining the eruption probability on El Teide (Tenerife, Spain) (Sobradelo and Martí 2010), El Hierro (Canary Islands) (Becerril et al. 2014), and Deception Island (Antarctica) (Bartolini et al. 2014a).

In both long-term and short-term evaluations HASSET can be useful for determining the occurrence probabilities of eruptive scenarios and can assist decision-makers assess the required mitigation actions associated with each scenario and estimate the corresponding potential risk.

## Simulation Models

Simulating eruptive scenarios caused directly (e.g. lava flows, fallout, surges) and indirectly (earthquakes, landslides) by an eruption requires a detailed analysis of the past activity of the volcano or volcanic area, and must take into account all the possible hazards associated with the eruptive activity. Volcanic hazard can be assessed via two different types of approaches: deterministic, which defines a maximum extension area affected by an eruptive episode based on deposits generated by past activity, or probabilistic, based on the probability that a certain area will be affected by an eruptive process. In order to generate hazard maps (Fig. 5d), it is important to understand past eruptive behaviour and to employ physical simulation models that will permit the behaviour of future volcanic activity to be foreseen. In this type of approach, accurate and detailed geographic and cartographic data are required for high-quality analysis with a GIS.

Here, we describe some of the e-tools freely available for download that allow volcanic hazards to be evaluated (Fig. 5c):

- VORIS 2.0.1 is a GIS-based tool, developed by Felpeto et al. (2007) that allows users to simulate lava flows, fallout, and pyroclastic density current scenarios. Lava-flow simulations are based on a probabilistic model that assumes that topography is the most

important factor determining the path of a lava flow. The determination of the probability of each point being invaded by lava is performed by computing several random paths with a Monte Carlo algorithm. Fallout simulation models are advection diffusion models that assume that away from the vent the transport of the particles from a Plinian column is controlled by the advective effect of the wind, by diffusion due to atmospheric turbulence, and by the settling velocity of the particles. The model for simulating pyroclastic density currents is the energy cone model proposed by Sheridan and Malin (1983). The input parameters are the topography, the collapse equivalent height (H), and the collapse equivalent angle (θ). The intersection of the energy cone, originating at the eruptive source, with the ground surface defines the distal limits of the flow.

- HAZMAP is a free program for simulating the sedimentation of volcanic particles at discrete point sources that predicts the corresponding ground deposits (deposit mode) (Macedonio et al. 2005). HAZMAP is also able to evaluate the probability of overcoming a given loading threshold in ground deposits by using a set of different wind profiles recorded on different days (probability mode). Using a statistical set of recorded wind profiles (and/or other input parameters), it can also be used to draw hazard maps for ashfall deposits. In HAZMAP, settling velocities can be calculated using several models as a direct function of particle diameters, densities, and shapes. The advantage of HAZMAP is that it is a simple tool able to predict ashfall during hypothetical or real eruptions of a given magnitude and wind profile.

- LAHARZ is a semi-empirical code for creating hazard-zonation maps that depict estimates of the location and extent of areas inundated by lahars (Schilling 1998). The input parameters for this model are the Digital Elevation Model and the lahar volume, which provide an automated method for mapping areas of potential lahar inundation. These hazard zones can be displayed in a GIS with
other types of volcano hazard information
such as the proximal hazard zone, infras-
tructure, hydrology, and population, as well
as contours and shaded relief, to produce
volcano hazard-zonation maps. Such maps
show the proximity and intersection of
potential hazard zones to people and
infrastructures.

- TITAN2D is a computer program model developed by the University at Buffalo (Patra et al. 2005) that simulates granular flows over digital elevation models, based on a “thin layer model”. The input for the computer code includes simulation time, minimum thickness of the final deposit, internal and bed friction angles, starting coordinates, and the initial speed and direction of the flow. Additionally, this program allows users to define the specific starting pile dimensions or a dynamic flow source. The outputs from the program (represented dynamically) are flow depth and momentum, which yield the deposit limit, run-out path, average flow velocity, inferred deposit thickness, and travel time.

Vulnerability E-tool

Once we have obtained hazard maps, the next
step consists of adding population, infrastruc-
tures, and land-use data to evaluate the vulnera-
bility associated with the impact of a determined
hazard. The data required for generating vulner-
ability maps are very complex and varied, and
depend on the observation scale. Vulnerability is
directly dependent on the type of phenomena in
question and on the socio-economic characteris-
tics of the environment. VOLCANDAM is a new
e-tool based on the methodology developed by
Scaini et al. (2014) that generates maps

estimating the expected damage caused by vol-
canic eruptions. VOLCANDAM (Fig. 6) con-
sists of three main parts: exposure analysis,
vulnerability assessment, and the estimation of
expected damages. The exposure analysis iden-
tifies the elements exposed to the potential haz-
ard and focuses on the relevant assets of the
study area (population distribution, social and
economic conditions, and productive activities
and their role in the regional economy). The
vulnerability analysis defines a physical vulner-
ability indicator for all exposed elements, as well
as a corresponding qualitative vulnerability
index. Systemic vulnerability considers the pos-
sible relevance of each element in the system and
their interdependencies by taking into account all
exposed and non-exposed elements (people,
buildings, transportation network, urban services,
and productive activities). Damage assessment is
performed by associating a qualitative damage
rating to each combination of hazard and vul-
nerability, bearing in mind their specific contexts
and roles in the system. The way one element can
be damaged—and thus lose its functionality—
depends in fact on the type of hazardous event
and the characteristics of the element. The result
is damage maps that can be displayed at different
levels of detail, depending on user preferences.
This tool aims to facilitate territorial planning
and risk management in active volcanic areas.

Decision-Making

The evaluation of the “direct costs” and “factors”
(indirect costs) that have an impact on the eco-
nomic growth of an area affected by a volcanic
event needs to take into account a number of
elements. A cost-benefit analysis may assist the
decision-making process by evaluating the eco-
nomic impact of the different scenarios. The
approach used by Sobradelo et al. (2015) (Fig. 7)

<figure><img src="image_21.png" /><figcaption>**Fig. 6** Steps in the vulnerability analysis in the VOLCANDAM approach (after Scaini et al. 2014). See text for details</figcaption></figure>
<figure><img src="image_22.png" /><figcaption>**Fig. 7** The volcanic crisis management cycle: stages and phases (after Sobradelo et al. 2015). See text for details</figcaption></figure>

is a Bayesian decision model that applies a general, flexible, probabilistic approach to the management of volcanic crises by combining the hazard and risk factors that decision-makers need for a holistic analysis of a volcanic crisis. These factors include eruption scenarios and their probabilities of occurrence, the vulnerability of populations and their activities, and the costs of false alarms and erroneous forecasts. This model can be implemented before an emergency to (i) pinpoint actions for reducing the vulnerability of a particular area, (ii) identify the optimum mitigating actions during an emergency and how these may change as new information is obtained, (iii) assess after an emergency the effectiveness of a mitigating response and, in light of results, (iv) how to improve strategies before another crisis occurs. BADEMO (BAyesian DEcision MOdel) is part of this integrated approach, and enables the previous analysis of the distribution of local susceptibility and vulnerability to eruptions to be combined with specific costs and potential losses. Indeed, BADEMO should be seen as a tool for improving communication between the monitoring scientists who provide volcanological information and those responsible for deciding which action plans and mitigating strategies should be put into practice.

## Discussion and Conclusions

Modern volcanology is a scientific discipline with social and practical applications that derive from its direct involvement in risk reduction. Today, a multidisciplinary approach is required to risk assessment since new methodologies are constantly being developed and explored, and, for example, geo-spatial technologies are becoming extremely helpful in the disaster-risk management. A defined and solid methodology for assessing volcanic hazard and managing risk is fundamental in both long-term evaluations and in unrest phases when monitoring information is available. Furthermore, e-tools can be progressively modified and implemented in light of the outcomes obtained in order to integrate as many models as possible into the assessment and management of volcanic risk.

The possibility of foreseeing an event such as a volcanic eruption is more effective—and guarantees greater economic savings—than acting after a disaster. The potential of simple e-tools such as those presented in this chapter lies in the fact that they are freely available and have been developed on accessible and dynamic graphical user interfaces. The main advantage of
using these e-tools is that new data or new model
results can be easily incorporated into the pro-
cedures for updating the hazard assessment. By
contrast, the use of expensive commercial e-tools
hampers the exchange of information and com-
plicates their testing in situ in volcanic fields.
Furthermore, non-free tools may hinder effective
risk assessment since they are often beyond the
means of advisory and management groups with
limited financial, technological, and manpower
resources (Leidig and Teeuw 2015). However,
this does not mean that anybody can use the
e-tools discussed here for, on the contrary, they
require expertise in volcanic hazards and related
issues and their use implies scientific knowledge
that will avoid incorrect outcomes.

In this chapter we have presented different
statistical methodologies and e-tools for inter-
preting volcanic data and assessing long- and
short-term volcanic hazards and vulnerability,
and for carrying out cost-benefit analyses. Sta-
tistical analysis enables us to extract information
about the future behaviour of a volcano by
looking at the geological and historical activity
of the volcanic system (VERDI database, Bar-
tolini et al. 2014c). The QVAST tool (Bartolini
et al. 2013) can be used to analyse past activity
and to calculate the possibility that new vents
will open (volcanic susceptibility), while the
Bayesian event tree statistical method HASSET
(Sobradelo et al. 2014) can be applied to calcu-
late eruption recurrence. Using these calcula-
tions, we can identify a number of significant
scenarios using GIS-based e-tools (i.e. VORIS
2.0.1, HAZMAP, ...) and evaluate the potential
extent of the main volcanic hazards expected to
occur in volcanic areas. The results obtained
allow us to generate volcanic hazard maps for
different levels of hazards, evaluate vulnerability
(VOLCANDAM e-tools, Scaini et al. 2014),
conduct cost-benefit analysis (BADEMO e-tools,
Sobradelo et al. 2015), and, finally, manage
volcanic risk.

During a volcanic crisis, emergency plans
must be put into practice and so different gov-
ernment departments need to be prepared in
advance. Therefore, it is important to have

conducted previous long-term hazard assessment
—among many other tasks—to properly manage
a volcanic crisis. They allow scientists and
managers to understand the characteristics of the
volcano or volcanic area and its past eruptive
history, and to infer the possible eruptive sce-
narios that may occur in the future. With this
previous information, short-term hazard assess-
ment can be conducted when volcanic unrest
starts and hazard maps can be drawn up and
alert levels be defined; nevertheless, it is
important to always bear in mind that the best
form of protection is the evacuation of the
population at risk. Volcanic monitoring is an
essential part of short-term assessment and so
should be performed by experts and based on a
good understanding of volcanic processes.
Despite the possibility of conducting cost-benefit
analysis, which can help maintain the economic
order, the security and health of the population
should always be the main concern.

One of the purposes in the near future is to
create a new software platform (VolcanBox, see
VETOOLS European Project—www.vetools.eu)
with a user-friendly interface. This platform will
contain different e-tools that, via a homogeneous
and systematic methodology, will help minimise
risk. However, the feasibility and applicability of
each tool will have to be analysed by different
groups of experts with experience in regions
possessing different volcanological and
socio-economic scenarios. This evaluation must
also bear in mind potential end users (i.e. Civil
Protection agencies), not only to test the ability
of existing tools but also to understand
decision-makers’ needs and requirements, and
train them in the use of these tools. If we are to
reduce volcanic risk we must ensure that scien-
tists, managers, and decision-makers are all fully
prepared to confront this phenomenon since the
best way to guarantee risk reduction is to possess
good knowledge of its causes.

**Acknowledgements** This work was supported by the European Commission (FP7 Theme: ENV.2011.1.3.3.1; Grant 282759: VUELCO). The English text was corrected by Michael Lockwood.
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<figure><img src="image_23.png" /></figure>
# The Need to Quantify Hazard Related to Non-magmatic Unrest: From BET_EF to BET_UNREST

Laura Sandri, Roberto Tonini, Dmitri Rouwet, Robert Constantinescu, Ana Teresa Mendoza-Rosas, Daniel Andrade and Benjamin Bernard

## Abstract

Most volcanic hazard studies focus on magmatic eruptions and their accompanying phenomena. However, hazardous volcanic events can also occur during non-magmatic unrest, defined as a state of volcanic unrest in which no migration of magma is recognised. Examples include tectonic unrest, and hydrothermal unrest that may lead to phreatic eruptions. Recent events (e.g. Ontake eruption, September 2014) have demonstrated that the successful forecasting of phreatic eruptions is still very difficult. It is therefore of paramount importance to identify indicators that define the state of non-magmatic unrest. Often, this type of unrest is driven by fluids-on-the-move, requiring alternative monitoring setups, beyond the classical seismic-geodetic-geochemical architectures. Here we present a new version of the probabilistic model BET (Bayesian Event Tree), called BET_UNREST, specifically developed to include the forecasting of non-magmatic unrest and related hazards. The structure of BET_UNREST differs from the previous BET_EF (BET for Eruption Forecasting) by adding a dedicated branch to detail non-magmatic unrest outcomes. Probabilities are calculated at each node by merging prior models and past

L. Sandri (✉) · D. Rouwet
Sezione di Bologna, Istituto Nazionale di Geofisica e
Vulcanologia, Via Donato Creti 12, Bologna, Italy
e-mail: laura.sandri@ingv.it

R. Tonini
Sezione di Roma 1, Istituto Nazionale di Geofisica e
Vulcanologia, Via di Vigna Murata 605, Rome, Italy

R. Constantinescu
Seismic Research Centre, University of West Indies,
Gordon street, Saint Augustine, Trinidad & Tobago

Present Address:
R. Constantinescu
School of Geosciences, University of South Florida,
4202 E. Fowler Avenue, NES 107, Tampa, FL
33620-5550, USA

A.T. Mendoza-Rosas
Instituto de Geofisica, Universidad Nacional
Autónoma de México. C. Universitaria, Coyoacán
04510, México, D.F., Mexico

Present Address:
A.T. Mendoza-Rosas
CONACYT-Centro de Ingeniería y, Desarrollo
Industrial, Av. Playa pie de la cuesta 702, Desarrollo
San Pablo, Querétaro, Qro. 76125, Mexico, C.P.,
Mexico

D. Andrade · B. Bernard
Escuela Politécnica Nacional, Instituto Geofisico,
Quito, Ecuador
data with new incoming monitoring data, and the results can be updated
any time new data has been collected. Monitoring data are weighted
through pre-defined thresholds of anomaly, as in BET_EF. The
BET_UNREST model is introduced here, together with its software
implementation PyBetUnrest, with the aim of creating a user-friendly,
open-access, and straightforward tool to support short-term volcanic
forecasting (already available on the VHub platform). The BET_UNREST
model and PyBetUnrest tool are tested through three case studies in the
frame of the EU VUELCO project.

Resumen extendido

La mayoría de los estudios sobre amenazas volcánicas están enfocados en las erupciones magmáticas y fenómenos relacionados. Sin embargo, fenómenos volcánicos peligrosos pueden también ocurrir durante una fase de “unrest” no-magmático, definido por el estado de unrest volcánico en el cual no se reconoce la migración de un magma. Ejemplos de esto son unrest tectónico (capaz de causar preocupación independientemente del resultado posterior) y unrest hidrotermal, que pueden resultar en erupciones freáticas. Eventos recientes (e.g. la erupción de Ontake en septiembre 2014) han demostrado que las erupciones freáticas siguen siendo dificilmente previsibles. Por estas razones, es de extrema importancia identificar señales que permitan definir un estado de unrest no-magmático. Muchas veces, este tipo de unrest es provocado por fluidos en movimiento, y requiere la instalación de un sistema de monitoreo alternativo, más allá de la clásica arquitectura sismo-geodético-química. En este capítulo, presentamos la nueva versión del modelo probabilístico BET (Arbol de Eventos Bayesiano, por sus siglas en inglés), llamado BET_UNREST, específicamente desarrollado para incluir la previsión de unrest no-magmático y sus peligros relacionados. La estructura de BET_UNREST difiere de la versión anterior BET_EF (BET para Previsión de Erupciones, por sus siglas en inglés), añadiendo una rama dedicada para detallar los resultados potenciales de unrest no-magmático. Las probabilidades están calculadas para cada nodo juntando modelos a priori y datos pasados con los datos nuevos, provenientes del monitoreo. Los datos de monitoreo están ponderados mediante umbrales predefinidos de anomalía, como es el caso en BET_EF. Este capítulo ilustra el modelo, y su herramienta, con tres casos de estudio, en el marco del proyecto EU VUELCO:

(i) un análisis retrospectivo para el volcán Popocatépetl, en donde no hay necesidad de la rama hidrotermal, debido al carácter magmático; Popocatépetl ha permanecido en estado de unrest desde diciembre 1994 hasta el presente. Para esta aplicación, BET_UNREST fue corrido usando la base de datos de la UNAM (1997-2012), con una aplicación retrospectiva para prever
erupciones mayores (columnas eruptivas >8 km) durante el período
abril-junio 2013.

(ii) una aplicación basada en un ejercicio de simulacro en el Cotopaxi; en este caso se probó con BET_UNREST de manera retrospectiva, pero, esta vez, usando datos creados específicamente para el simulacro, junto a datos de entrada basados en la historia real del volcán preparados antes del simulacro. Presentamos la previsión de erupciones magmáticas resultantes del simulacro mismo.

(iii) el simulacro en tiempo casi-real organizado en el marco de VUELCO en Dominica (mayo 2015). El sistema volcánico de Dominica es el prototipo para BET_UNREST debido a su carácter hidrotermal. Actividad freática/freatomagmática ocurrió realmente durante el simulacro, lo cual de hecho era bastante probable según BET_UNREST (la probabilidad media de unrest hidrotermal fue de 0.73, mientras la probabilidad media de una erupción hidrotermal fue de 0.32). También se produjó un mapa de probabilidades para la apertura de ventos eruptivos en caso de erupciones magmáticas y freáticas.

Con estos ejercicios, estamos convencidos de haber llevado BET un paso más
cerca hacia una implementación completa en situaciones de crisis. Al final,
BET_UNREST funcionó como se esperaba. Sin embargo, es importante ser
consiente de algunos puntos críticos que han resultado de estas aplicaciones,
incluso realizar más pruebas para mejorar su diseño y comprobar su utilidad
en casos reales en el futuro. BET_UNREST se introdujo junto a su imple-
mentación digital PyBetUnrest con el objetivo de crear un instrumento de fácil
uso, libre y de acceso directo (disponible en el sitio web Vhub) para ayudar en
la evaluación de la amenaza volcánica a corto plazo.

Keywords

Volcanic unrest • Forecasting • Hydrothermal • Magmatic • Bayesian
inference

Palabras clave

Unrest volcánico • Previsión • Hidrotermal • Magmático • Inferencia
Bayesiana

# 1 Introduction

Monitoring activities represent the main source
of information to understand the behaviour of
volcanic systems on short time-scales and, pos-
sibly, during emergency crises. In this frame-
work, one of the main challenges of volcano
monitoring is the identification and characteri-
sation of the phase defined as “unrest”, which
consists of a relevant physical or chemical

change in the volcanic system with respect to its
background behaviour, leading to cause for
concern. Unrest can be due to several factors and
depends on the local characteristics of each vol-
canic system, making it very difficult to find
general features or patterns (Phillipson et al.
2013). Unrest may be followed by volcanic
eruptions due to the movement of magma, but
can also be associated with other dangerous
phenomena: indeed, in addition to
magma-related hazards (e.g., tephra fallout, lava
flows, ballistics), hydrothermal and tectonic activities, without evidence for “magma-on-the-move”, can also lead to dangerous outcomes (i.e., flank collapses, gas emissions, phreatic explosions, lahars). Such hazardous events related to non-magmatic unrest are not easy to track and, in volcanic hazard evaluations, are sometimes underestimated (Rouwet et al. 2014). For instance, many volcanoes pass through a phase of hydrothermal unrest for years, decades or even centuries. Due to this long-term behavioural similarity, it is often difficult to recognise how hydrothermal unrest can lead to related hazards in the short-term. Where the driving agent and the main eruptive product is not magma, but water (liquid or vapour) and occasionally liquid sulphur, or gas, this type of unrest can lead to non-magmatic eruptions. On the other hand, non-eruptive hydrothermal unrest can also promote volcanic hazards after prolonged gas emissions, acidic fluid infiltration into aquifers, soils and the hydrologic network, or deformation induced by a rising fluid front (see Rouwet et al. 2014).

In this light, although most volcanic hazard assessments focus only on magmatic eruptions as potential hazard sources, hazardous events can also occur during non-magmatic unrest, which in this chapter is defined as a state of volcanic unrest in which no migration of magma is recognised. Examples of non-magmatic unrest include the tectonic (which causes concern independently on how it evolves and eventually ends), and hydrothermal unrest types; the latter may eventually lead to phreatic eruptions. Recent occurrences of phreatic eruptions (e.g. Ontake eruption, September 2014, Japan) have demonstrated that they are still very hard to anticipate from classical observations based on seismic-geodetic-geochemical monitoring architectures. For these reasons, it is of paramount importance to identify indicators that define the state of non-magmatic unrest. Often, this type of unrest is driven by “fluids-on-the-move”, requiring alternative and innovative monitoring setups, beyond the classical ones.

In the last decade it has become crucial to provide forecasts of the possible outcomes of volcanic unrest, to give quantitative support and scientific advice to decision makers (e.g., Woo 2008; Marzocchi and Woo 2007, 2009). Because of this, event tree schemes have been proposed (e.g., Newhall and Hoblitt 2002; Marzocchi et al. 2004), and a few probabilistic tools based on event trees and Bayesian inference have been developed (e.g., BET_EF, Marzocchi et al. 2008; HASSET, Sobradelo et al. 2013) with the ability to quantify the probability of different possible outcomes related to magmatic unrest. However, the need for recognising and tracking the evolution of *any* type of volcanic unrest, and to quantify the probability linked to non-magmatic unrest as well, have led us, within the VUELCO project, to the development of a new probabilistic model, able to forecast both magmatic and non-magmatic hazardous events related to volcanic unrest: BET_UNREST. The BET_UNREST model is based on an event tree, whose structure is extended with respect to the previous schemes such as BET_EF (see the generalisation from BET_EF to BET_UNREST in Fig. 1, highlighted in red) by adding a specific branch to detail the track and outcome of non-magmatic unrest. Nonetheless, BET_UNREST adopts from BET_EF the Bayesian inferential paradigm and the ability to account both for long-term data (typically from the geological record) and short-term information from monitoring networks.

In this chapter, we briefly present the BET_UNREST model and its implementation in the PyBetUnrest software tool (Tonini et al. 2016), made with the aim of providing a user-friendly, open-access, and straightforward tool to handle probabilistic forecasts and visualise results, and that has already been included on the Vhub platform (https://vhub.org/resources/betunrest). The new event tree and tool are applied here as illustrative examples to the VUELCO target volcanoes Popocatépetl (Mexico), Cotopaxi (Ecuador) and Dominica (West Indies).
<figure><img src="image_24.png" /><figcaption>**Fig. 1** The new event tree as defined for the BET_UNREST model (on top) and its visual implementation in the software PyBetUnrest (on bottom). The red branch corresponds to the previous BET_EF model</figcaption></figure>
## 2 BET_UNREST Model and PyBetUnrest Tool

As with all the previous BET models (e.g., BET_EF, for short- and long-term eruption forecasting, Marzocchi et al. 2008; BET_VH, for long-term volcanic hazard associated to any potential hazardous phenomenon accompanying an eruption, Marzocchi et al. 2010; Tonini et al. 2015; BET_VHst, a model that merges the previous two, Selva et al. 2014), BET_UNREST performs probabilistic assessments in the frame of volcanic hazard analysis, based on an event tree scheme. The main novelty in the BET_UNREST event tree is the introduction, with respect to the BET_EF tree, of a new branch (Fig. 1) for exploring and forecasting the outcomes of non-magmatic unrest (Rouwet et al. 2014). Due to the resemblance of BET_UNREST to other BET models from a methodological and computational point of view, here we will only give a brief overview. The papers by Marzocchi et al. (2004, 2008) provide a more detailed description.

BET_UNREST probabilities are evaluated by a Bayesian inferential procedure, in order to quantify both the aleatory and epistemic uncertainty characterising the impact of volcanic eruptions in terms of eruption forecasting and/or hazard assessment. Such a procedure allows merging all the available information, such as models, a priori beliefs, past data from volcanic records and, when available, real-time monitoring data in order to include, in principle, all the knowledge about the considered volcanic system.

In general, the Bayesian inference procedure at the basis of BET_UNREST assigns a probability to each node, providing a framework where:

- probabilities are expressed through a probability density function (pdf), and not as a single number, to account for a best-evaluation value (for example the mean of the probability density function, representing a degree of aleatory uncertainty) and for a measure of the epistemic uncertainty (the dispersion of the pdf);

- the posterior pdf, at each node, is achieved by statistically combining, through Bayes' theorem, a prior probability distribution (usually coming from theoretical models and/or expert judgement) and information from the available data relevant for that node.

As in BET_EF, the probability [$\theta_k$] at each node k is actually described by a statistical mixing of two pdfs, describing respectively the “so-called” long-term [$\theta_k^{\{\bar{M}\}}$] and short-term [$\theta_k^{\{M\}}$] regimes of the volcano as follows:

$$[\theta_k] = \gamma_k[\theta_k^{\{M\}}] + (1 - \gamma_k)[\theta_k^{\{\bar{M}\}}]$$

where $\gamma_k$ represents the weight in the interval [0,1] depending on the degree of unrest (Marzocchi et al. 2008). With such mixing, BET_UNREST switches between the two “regimes”. In practice:

- When anomalies with respect to the volcano's background activity are not observed at time $t = t_0$, BET_UNREST relies on the so-called long-term information to assign the probabilities (hereinafter also referred to as background probabilities) at the various branches. Such background probabilities (i.e., [$\theta_k^{\{\bar{M}\}}$]) are based on theoretical models and information from the geological and eruptive record of the volcano studied, or of similar volcanoes, and describe the long-term frequencies of magmatic or non-magmatic unrest, and subsequent outcomes at these volcanic systems.

- When a clear state of unrest of whatever nature is detected at $t = t_0$ by BET_UNREST, the probabilistic assignment at all the successive nodes is based mainly on the monitoring information. In practice, monitoring data are transformed into subjective pdfs (i.e., [$\theta_k^{\{M\}}$]) relative to the occurrence of magmatic or non-magmatic unrest and the following branches. Actually, at some nodes, monitoring data are not considered as relevant (for example, in forecasting the size of an
eruption, magmatic or not), and here BET_UNREST continues to rely on theoretical models and long-term frequencies.

* When, at time $t = t_0$, BET_UNREST observes a “degree of unrest” (of whatever nature) without it being completely clear, the statistical mixing provides a resulting pdf which accounts for both the regimes, giving the short-term regime a weight equal to the degree of unrest, and to the long-term regime its complement.

In this way, during a phase of unrest, the past data have less (null, in the case of complete unrest) importance. The short term hazard/ eruption forecasting depends exclusively on the translation of observed anomalies into pdfs describing all the branches of the event tree. This is done, separately at each node, by weighting monitoring data through pre-defined thresholds of anomaly (Marzocchi et al. 2008) and converting the resulting “degree of anomaly” into a best-evaluation probability, to which a degree of variance is associated (Fig. 2). This is a very simple and intuitive procedure, in which the basic assumptions are:

1. the first anomaly detected is the most informative

2. subsequent anomalies contribute less and less to the increase of the degree of anomaly

3. strong non-linear coupling among anomalies are neglected.

At each node, BET_UNREST evaluates the following probabilities (see also Fig. 1) by means of Bayesian inference (we give the acronyms used throughout the chapter to indicate the probability at each node in brackets):

* *Unrest*: probability ($P(U)$) of unrest in the time period $[t_0; t_0 + \cdot\cdot\cdot]$, given the monitoring observations at time $t = t_0$; the time window $|\cdot\cdot\cdot|$ is defined by the user;

* *Magmatic unrest*: probability ($P(MU)$) that the unrest is due to “magma-on-the-move”, given the unrest;

* *Magmatic eruption*: probability ($P(MEr)$) of a magmatic eruption, given magmatic unrest; the following sub-branches mirror the BET_EF structure, so we point the reader to Marzocchi et al. (Marzocchi et al. 2008) for them;

* *Non-magmatic unrest*: this is the complementary of the *Magmatic unrest* branch, so by definition is the probability of non-magmatic unrest, given an unrest;

* *Hydrothermal unrest*: probability ($P(HU)$) of hydrothermal unrest, given a non-magmatic unrest;

* *Tectonic unrest*: this is the complementary of the *Hydrothermal unrest* branch, so it describes the probability ($P(TU)$) of a tectonic unrest, given a non-magmatic unrest;

* *Hydrothermal eruption*: probability ($P(HER)$) of a hydrothermal eruption, given a hydrothermal unrest;

* *Vent of hydrothermal eruption*: here we explore the spatial probability of vent opening in a hydrothermal eruption, given a hydrothermal eruption occurring; this node is an extension with respect to the event tree proposed in Rouwet et al. (2014);

* *Size of hydrothermal eruption*: probability of an explosive hydrothermal eruption, given a hydrothermal eruption occurring from a specific vent; its complementary branch is the effusive hydrothermal eruption.

In order to keep the structure of BET_UNREST as simple as possible, an effort has been made to maintain, where possible, a dichotomic branching into complementary (i.e., exhaustive and mutually exclusive) events. This is why the *Unrest* node does not branch directly into magmatic, hydrothermal and tectonic, but first it branches into magmatic-or-not. This allows a simplification in the evaluation of short-term probabilities. In particular, with this type of ramification, the user defines which monitoring measurements (plus thresholds and weight) affect the pdf of one of the two branches; the pdf of the complementary branch then comes automatically.
<figure><img src="image_25.png" /><figcaption>**Fig. 2** This figure explains how monitoring measures are transformed into a best-evaluation probability at a given node of the event tree. First, a monitoring measure $x_i$ is translated in a degree of anomaly $z_i$ according to a selected anomaly function $\mu(\cdot)$ (a). In the above example, a measure below $x_1$ is considered background, above $x_2$ is anomalous, and in between it has a certain degree of anomaly. After collecting the degree of anomaly for all parameters considered at the node, we combine them using</figcaption></figure>

a weighted average ($\omega_i$ is the weight of the i-th parameter) in order to obtain the total degree of anomaly (b). Then the total degree of anomaly is transformed into an average probability using a predefined function, in BET_UNREST, we use the function in (c). The parameters, weights, and thresholds are selected by the user, possibly through expert opinions' elicitation. Figure modified from (Marzocchi and Bebbington 2012)

The new BET_UNREST model is applied here with its software implementation PyBetUnrest presented in Tonini et al. (2016), which aims to provide an open and usable tool to bridge between the scientific community and decision makers, with a graphical user interface which allows the exploration of the event tree and the visualisation of the results (see Fig. 1). This solution was also implemented in the VHub cyber-infrastructure (http://vhub.org/resources/betunrest). In the present PyBetUnrest tool only one file needs to be adapted when new monitoring information is gathered. This structure makes PyBetUnrest extremely fast and user-friendly during crisis situations. More on the technical background of the BET_UNREST model and PyBetUnrest tool can be found in the

VUELCO Deliverable 7.3 (at http://vhub.org) and in Tonini et al. (2016).

So far BET_UNREST and PyBetUnrest have not yet been blindly tested in real-time during an actual volcanic crisis, but only retrospectively (Tonini et al. 2016) at Kawah Ijen (Indonesia), for the time period 2010–2012 (after a learning period based on the observations from 2000 to 2010). The term “blindly” signifies that the rules of BET_UNREST (the long-term pdfs, and the monitoring parameters, thresholds and weight at the different nodes) are set *before* the beginning of the application, on different data (the *learning* dataset), and then the model is applied untouched to new data (the voting dataset), typically covering a different time period (as in the case of Tonini et al. 2016).
In the next section of this chapter, results and performances of the new model and tool will be discussed and validated by analysing the unrest crises for VUELCO target volcanoes Popocatépetl, Cotopaxi and Dominica through blind applications of BET_UNREST. The latter two applications show the results of the VUELCO crisis simulation exercises held in Quito (November 2014) and Dominica (May 2015).

# 3 BET_UNREST Applications

## 3.1 Popocatépetl, Mexico: A Retrospective Application Based on the Popo- DataBase

Here we apply the BET_UNREST model to Popocatépetl Volcano (Mexico), based on a catalog of monitored parameters of the 1994-ongoing eruptive period. Popocatépetl volcano awakened in December 1994, after almost 48 years of volcanic quiescence. Since 1994, Popocatépetl volcano has been one of the most active volcanoes in the world, and magmatic activity has been nearly constant. This fact raises the need to first redefine the concept of volcanic unrest for Popocatépetl, as BET_UNREST, at the *Unrest* node, requires indicative parameters to verify if the given volcano is in a state of unrest, or not. In *stricto sensu*, Popocatépetl has remained at least in a state of unrest, or even magmatic or eruptive unrest, since 1994, as its common manifestations are dome growth and vulcanian eruptive phases. The continuous state of unrest is reflected by the decision to never decrease the level of alert from orange to green (traffic light, De la Cruz-Reyna and Tilling 2008). Nevertheless, many of these eruptions are of no cause of concern (so, no unrest in *lato* sensu), neither for volcanologists nor for population. On the other hand, a practical scope of the BET_UNREST application at Popocatépetl is to forecast *major* eruptions, which can be considered a deviation from its current background activity. During the past 23,000 years, nine Plinian eruptions occurred at Popocatépetl (Mendoza-Rosas and De la Cruz-Reyna 2008), while, since 1994,

three eruptions with an eruption column >8 km have occurred. No Plinian eruptions have occurred during the 1994-ongoing eruption cycle, and thus none of the past Plinian eruptions have been monitored. For practical purposes, we thus define a *major* eruption for Popocatépetl as an eruption *with an eruption column >8 km*, as they are recorded during the current monitoring period. These eruptions have caused ash fall in the Puebla-Mexico City metropolitan area, thus having an impact on human activity. We aim at finding precursory signals for major eruptions (>8 km, VEI 3) for the period 1997-2012 (the learning period), and test the BET_UNREST retrospectively, using monitoring data of the volcanic activity observed during 2013 (the voting period). The time window, $|t|$, is defined as 1 month.

In Table 1 we report the activity carried out 24/7 with regards to monitoring at Popocatépetl, available as short-term information for unrest, origin of unrest and eruption. However, for the time period 1994–2012, the available data (as listed in Mendoza-Rosas, *VUELCO deliverable 5.1*), are restricted mainly to seismicity (VT, tremor, number of events) and visual observations (i.e. number of eruptions, column height). No real-time SO₂ flux is available for our purpose, and deformation data would need further processing. Regarding past data (long-term information for unrest, origin of unrest, and eruption), there have been 13 unrest episodes, and constant unrest since December 1994 (so, a priori probability to be in a state of unrest for the next month is about 85%). Out of the 13 unrest episodes, 6 were due to magma-on-the-move (magmatic unrest), of which 3 lead into a magmatic *major* eruption. The monitoring parameters listed in Table 2, along with respective thresholds and weight, have been identified in the UNAM (Universidad Nacional Autónoma de México) database for the period 1997–2012, and used to set BET_UNREST for Popocatépetl. The volcano is a stratocone with a higher probability of an eruption to occur from the central vent. For the period of observation (1997–2012) all eruptions were magmatic and occurred at the central crater. The a priori spatial distribution of vent opening is assigned as in Table 3. As a prior
<table><caption>**Table 1** Activity carried out 24/7 as regards monitoring at Popocatépetl</caption><tr><td>Observations</td><td>4 cameras for visual observations<br>5 three-component seismic stations<br>5 BB seismic stations<br>1 video camera + microwaves<br>1 doppler radar<br>3 biaxial inclinometers<br>Geochemical observations (3 sites)</td></tr><tr><td>Routine actions</td><td>Automatic alarm for anomalies in seismicity<br>Cell phone messages to personnel<br>Comité Técnico Científico Asesor UNAM/CENAPRED<br>Reports by SMS to population</td></tr></table>

model to define the size/style of magmatic eruptions we take the power law from Simkin and Siebert (1994). As past data we take the Mendoza-Rosas and De la Cruz-Reyna (2008) catalog for the past 23,000 years, and assume it to be complete for VEI >= 2 (Table 3).

We retrospectively applied BET_UNREST for the voting period April–June 2013, in which respectively 10, 11 and 2 eruptions of 2, 3 and 4 km-high columns were observed. No major eruption occurred. Observed anomalies include ash eruptions up to 130/day (all with columns <4 km), seismic tremor, incandescence in the crater/dome, and VT events (but no shallow event with depth <5 km). There was no anomalous deformation, no dome growth, and no SO₂

data available. Results of P(MEr) for the retrospective application period (weekly updated) are presented in Fig. 3. For the whole period, P(MEr) of a major eruption (>8 km eruption column) was <1% per month.

## 3.2 Cotopaxi, Ecuador: Retrospective Application Inspired by the VUELCO Simulation Exercise in Quito

A volcanic unrest simulation exercise for Cotopaxi volcano (5897 m.a.s.l.) was performed on November 13th, 2014 in Quito, Ecuador. The ice-capped stratovolcano, with an andesitic to

<table><caption>**Table 2** Monitoring parameters set for BET_UNREST at Popocatépetl</caption><thead><tr><th></th><th>Parameters</th></tr></thead><tbody><tr><td>Unrest</td><td><ul><li># exhalations with ash (&lt;4 km) &gt; 20/day</li><li>Tremor Y/N</li><li>Increase VT Y/N</li></ul></td></tr><tr><td>Magmatic unrest</td><td><ul><li>Incandescence dome Y/N, weight 2</li><li>Duration tremor &gt;6000 s, weight 1</li><li>SO₂ &gt;2000 t/d, weight 1</li></ul></td></tr><tr><td>Magmatic eruption</td><td><ul><li>Dome growth Y/N</li><li>SO₂ &gt;9000 t/d</li><li>Tectonic EQ &gt; M5.5 (along the coast/arc Michoacán-Chiapas) Y/N</li><li>Incandescent debris Y/N</li><li>Change in # tremor Y/N</li><li>VT depth &lt;5 km</li><li>Increase # VT &gt;M2 Y/N</li><li>Duration tremor &gt;30,000 s (inertia 2 months) Y/N</li><li>Increase # ash eruptions &gt;2000–4000 (inertia 2 months)</li><li>Deformation Y/N</li></ul></td></tr></tbody></table>
<table><caption>**Table 3** *Left Part:* Spatial probability of vent opening for magmatic eruptions assigned for BET_UNREST at Popocatépetl: best guess a priori values. No past data are used. *Right Part:* Parameters of the magmatic eruption size distribution assigned for BET_UNREST at Popocatépetl: best guess a priori values and past data</caption><thead><tr><th colspan="2">Spatial probability of vent opening in magmatic eruptions</th><th colspan="3">Size of magmatic eruption</th></tr><tr><th>Vent location</th><th>A priori probability (best guess values; equivalent number of data = 1)</th><th>Size</th><th>A priori (best guess values; equivalent number of data = 1)</th><th>Past data</th></tr></thead><tbody><tr><td>Central vent</td><td>0.99</td><td>VEI 1</td><td>0.83</td><td>975</td></tr><tr><td>North flank</td><td>0.0025</td><td>VEI 2</td><td>0.14</td><td>13</td></tr><tr><td>East flank</td><td>0.0025</td><td>VEI 3</td><td>0.023</td><td>3</td></tr><tr><td>South flank</td><td>0.0025</td><td>VEI 4</td><td>0.0038</td><td>7</td></tr><tr><td>West flank</td><td>0.0025</td><td>VEI ≥ 5</td><td>0.0008</td><td>2</td></tr></tbody></table>

<figure><img src="image_26.png" /><figcaption>**Fig. 3** Time history of probability (expressed in percentage) to have a magmatic eruption in the retrospective analysis at Popocatépetl</figcaption></figure>

rhyolitic composition, is one of the most active and hazardous volcanoes in Ecuador. Historic eruptions at Cotopaxi produced large lithic-rich pyroclastic flows, ash flows, lava flows as well as large lahars (Barberi et al. 1995; Hall and Mothes 2008; Biass and Bonadonna 2011). Some lahars reached the Pacific Ocean at >200 km distance (Aguilera et al. 2004; Pistolesi et al. 2013). Recent unrest periods at Cotopaxi occurred in 1975–1976 and 2001–2002 and were characterised by increased fumarolic activity, elevated seismicity and edifice deformation (Molina et al. 2008). Fumarolic activity is a concern due to the heat transfer that may affect the ice cover resulting in non-eruptive debris flows or lahars.

A still unstable version of PyBetUnrest was set up (along with parameters and thresholds at each node for Cotopaxi volcano derived from monitoring information) before the simulation exercise, based on the available data in the literature up to the beginning of the simulation (the learning period stopped with the beginning of the exercise), in order to preliminarily test its value in decision support by providing near-real time probabilities of (i) the occurrence of unrest, (ii) the origin and nature of unrest and (iii) eruptive activity. However, during the simulation, the reports from the “volcano team” did not reflect the real eruptive and unrest history of Cotopaxi, as the past activity for the simulation was
"invented". A different setting of BET_UNREST (and consequently of PyBetUnrest) on site was not possible due to the lack of time and the still premature customisability of the tool. This obliged us to set up and run the old BET_EF tool during the exercise (Constantinescu et al. 2015). Obviously, this prevented us from providing probabilistic assessment of non-magmatic events during the exercise at Cotopaxi: this would have been possible with BET_UNREST, enabling the calculation of probabilities for hydrothermal unrest and hydrothermal eruptions (*P*(HU) and *P*(*HER*)). Nevertheless, the unrest scenario proposed by the "volcano team" (Bulletins 1-5) did not emphasise a significant state of hydrothermal unrest, which, on the one hand, made our output less biased in not providing an evaluation for *P* (HU) and *P*(*HER*); but on the other hand this simulation was probably not the best case to test BET_UNREST.

Here, we will re-run BET_UNREST and PyBetUnrest at Cotopaxi retrospectively for the unrest phases described in the five bulletins provided by the “volcano team” during the simulation exercise and using the BET_UNREST setup prepared prior to the simulation based on the *real* past activity of the volcano (Table 4). The time window | was set to 1 month. In Table 5 we show the probabilities resulting from the run of the code, after each bulletin:

(1) *Phase 0: The background activity of Cotopaxi (NO anomalies)*: results are based on the past activity of Cotopaxi, with all observation within background limits.

(2) *Phase 1 (Bulletin 1)*: the observed anomalies in this phase were limited to an increase in seismic activity compared to background level. Such an increase is indicative, according to pre-set parameters, of magma-on-the-move (*P*(MU) = 0.68). The considerable uncertainty is summarised by the 10th to 90th percentiles confidence interval.

(3) *Phase 2 (Bulletin 2)*: the observed anomalies in this phase were: a drastic increase in seismicity, an increase in SO₂ emission (5 times background levels), and a crater thermal anomaly. As a consequence, the mean *P*

(MU) increases, along with a decrease in the associated uncertainty.

(4) *Phase 3 (Bulletin 3)*: the observed anomalies in this phase were: an increase in VT and LP events, occurrence of tremor, appearance of new fumaroles, an increase in SO₂ emission, and an increase in the crater thermal anomaly. As a consequence, the *P*(MU) is similar to Bulletin 2, but the *P*(HU) increases slightly, due to the new fumaroles.

(5) *Phases 4 and 5 (Bulletins 4 and 5)*: the observed anomalies in these phases were similar, and included: intense fumarolic activity, occurrence of hybrid seismic events, an increase in SO₂ emission, and an increase in the crater thermal anomaly. As a consequence, *P*(MEr) increases from 0.21 (phase 3) to 0.57, combined with a lower uncertainty.

## 3.3 Dominica, West Indies, Lesser Antilles: VUELCO Simulation Exercise, Dominica, May 2015

Dominica is characterised by hydrothermal activity manifested as thermal springs (up to boiling temperature), boiling-temperature fumarolic emissions (e.g. Valley of Desolation) and a crater lake, known as ‘Boiling Lake’, with a particular hydrodynamic behaviour (Fournier et al. 2009; Joseph et al. 2011; Rouwet et al. 2017). No high-temperature manifestations occur on the island, so no clear evidence of active magmatic degassing exists at the present time.

The simulation exercise, and consequently the BET_UNREST application, for the VUELCO target island of Dominica mainly focused on an unrest scenario for the southern part of the island. The purpose of the exercise was to test the tracking/assessment of an unrest period, and the decision making process undertaken by the scientific advisory group and local authorities.

Due to the hydrothermal character of Dominica, the application of BET_UNREST is highly suited. *Before* the simulation exercise, the
<table><caption>**Table 4** Monitoring parameters set for BET_UNREST at Cotopaxi</caption><thead><tr><th>Node-parameter#</th><th>Parameter and threshold(s) (Y/N indicates a Boolean observation)</th></tr></thead><tbody><tr><td>Unrest-parameter 1</td><td>LP/month (205–335) (Garcia-Aristazabal 2010)</td></tr><tr><td>Unrest-parameter 2</td><td>VT/month (24–32) (Garcia-Aristazabal 2010)</td></tr><tr><td>Unrest-parameter 3</td><td>M Tectonic EQ (3–4)</td></tr><tr><td>Unrest-parameter 4</td><td>SO<sub>2</sub> (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 1</td><td>EQ depth (&gt;4.5–5.5 km)</td></tr><tr><td>Magmatic unrest-parameter 2</td><td>Deep VLP (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 3</td><td>T fumarole (&gt;119 °C)</td></tr><tr><td>Magmatic unrest-parameter 4</td><td>Appearance of acidic gas (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 5</td><td>VT/month (&gt;32)</td></tr><tr><td>Magmatic unrest-parameter 6</td><td>Increased deformation (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 7</td><td>VLP + LP together (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 8</td><td>Harmonic LP tremor (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 9</td><td>SO<sub>2</sub> flux (t/d) (&gt;100–350)</td></tr><tr><td>Magmatic eruption-parameter 1</td><td>sudden stop (Y/N)</td></tr><tr><td>Magmatic eruption-parameter 2</td><td>SO<sub>2</sub> flux (t/d) (&gt;2000–2500)</td></tr><tr><td>Magmatic eruption-parameter 3</td><td>Tornillos (Y/N)</td></tr><tr><td>Hydrothermal unrest-parameter 1</td><td>New fumarole (Y/N)</td></tr><tr><td>Hydrothermal unrest-parameter 2</td><td>Anomalous glacier volume decrease (defrosting) (Y/N)</td></tr><tr><td>Hydrothermal unrest-parameter 3</td><td>LP/month (&gt;205–335) (Garcia-Aristazabal 2010)</td></tr><tr><td>Hydrothermal eruption-parameter 1</td><td>Increase in T of fumarole (&gt;120–200 °C)</td></tr><tr><td>Hydrothermal eruption-parameter 2</td><td>Increase in extension of fumarolic field (Y/N)</td></tr><tr><td>Hydrothermal eruption-parameter 3</td><td>Inflation of fumarolic field (Y/N)</td></tr><tr><td>Hydrothermal eruption-parameter 4</td><td>Landslides in hydrothermal areas (Y/N)</td></tr><tr><td>Hydrothermal eruption-parameter 5</td><td>New/extension of alteration areas (Y/N)</td></tr></tbody></table>

PyBetUnrest tool was set for Dominica, based on (1) existing literature of the past volcanic activity; (2) insights on the current hydrothermal activity; (3) discussion-based expert elicitation sessions (4 sessions at SRC and 1 at INGV-Bologna); and (4) exchanges with local experts in order to fine-tune the code with the monitoring parameters. We remark that all of this was done prior to the start of the simulation exercise (the learning period stopped at the beginning of the simulation exercise, as for Cotopaxi), and again no hindsight tuning was made. The long-term setup of PyBetUnrest is done by filling up a configuration file that includes the a priori and past data specifically for Dominica, whose main information is summarized in Table 6. The short-term information

is listed in Table 7 (parameters and thresholds identified prior to the exercise onset, see above). Further details on the Dominica simulation exercise and on the BET_UNREST application are given in Constantinescu et al. (2016).

During the simulation exercise (May 14–15, 2015) three phases of changes in volcanic activity, each with a duration of six months, were distributed by the “volcano team” to the operators of the unrest crisis. The reports included four types of observations: (1) seismic bulletin, (2) GPS, (3) geothermal monitoring data, and (4) other observations.

The translation of the reported bulletins into the values for the selected parameters in the BET_UNREST for Dominica setup were reported back to the team of experts in real-time
<table><caption>**Table 5 Resulting probabilities from retrospective application of BET_UNREST at Cotopaxi**</caption><thead><tr><th></th><th></th><th>P(U)</th><th>P(MU)</th><th>P(MEr)</th><th>P(HU)</th><th>P(HEr)</th></tr></thead><tbody><tr><td rowspan="4">Phase 0<br>(Background)</td><td>Mean</td><td>0.005</td><td>0.002</td><td>0.0005</td><td>0.001</td><td>0.0006</td></tr><tr><td>10th prctile</td><td>0.0013</td><td>0.0002</td><td>0</td><td>0</td><td>0</td></tr><tr><td>50th prctile</td><td>0.004</td><td>0.001</td><td>0.0002</td><td>0.0006</td><td>0.0002</td></tr><tr><td>90th prctile</td><td>0.009</td><td>0.004</td><td>0.001</td><td>0.003</td><td>0.001</td></tr><tr><td rowspan="4">Phase 1</td><td>Mean</td><td>1</td><td>0.68</td><td>0.18</td><td>0.08</td><td>0.02</td></tr><tr><td>10th prctile</td><td>1</td><td>0.07</td><td>0</td><td>0</td><td>0</td></tr><tr><td>50th prctile</td><td>1</td><td>0.84</td><td>0.02</td><td>0.001</td><td>0</td></tr><tr><td>90th prctile</td><td>1</td><td>1</td><td>0.69</td><td>0.30</td><td>0.04</td></tr><tr><td rowspan="4">Phase 2</td><td>Mean</td><td>1</td><td>0.83</td><td>0.22</td><td>0.05</td><td>0.013</td></tr><tr><td>10th prctile</td><td>1</td><td>0.27</td><td>0</td><td>0</td><td>0</td></tr><tr><td>50th prctile</td><td>1</td><td>1</td><td>0.04</td><td>0</td><td>0</td></tr><tr><td>90th prctile</td><td>1</td><td>1</td><td>0.75</td><td>0.13</td><td>0.008</td></tr><tr><td rowspan="4">Phase 3</td><td>Mean</td><td>1</td><td>0.80</td><td>0.21</td><td>0.13</td><td>0.07</td></tr><tr><td>10th prctile</td><td>1</td><td>0.14</td><td>0</td><td>0</td><td>0</td></tr><tr><td>50th prctile</td><td>1</td><td>1</td><td>0.04</td><td>0.002</td><td>0.0003</td></tr><tr><td>90th prctile</td><td>1</td><td>1</td><td>0.72</td><td>0.54</td><td>0.22</td></tr><tr><td rowspan="4">Phase 4 and 5</td><td>Mean</td><td>1</td><td>0.81</td><td>0.57</td><td>0.12</td><td>0.07</td></tr><tr><td>10th prctile</td><td>1</td><td>0.23</td><td>0.02</td><td>0</td><td>0</td></tr><tr><td>50th prctile</td><td>1</td><td>1</td><td>0.65</td><td>0.0004</td><td>0.0002</td></tr><tr><td>90th prctile</td><td>1</td><td>1</td><td>1</td><td>0.49</td><td>0.28</td></tr></tbody></table>

<table><caption>**Table 6 Set up of BET_UNREST at Dominica in terms of long-term information**</caption><thead><tr><th></th><th>A priori mean (equivalent n data in brackets)</th><th>Past data</th></tr></thead><tbody><tr><td>Unrest</td><td>0.5 (1)</td><td>Past data (successes) = 14<br>Past data (total) = 608</td></tr><tr><td>Magmatic</td><td>0.5 (1)</td><td>Past data (successes) = 13<br>past data (total) = 14</td></tr><tr><td>Magmatic eruption</td><td>0.58 from Phillipson et al. (2013) (1)</td><td>Past data (successes) = 0<br>Past data (total) = 13</td></tr><tr><td>Magmatic vent location</td><td>file</td><td>file</td></tr><tr><td>Hydrothermal vent location</td><td>file</td><td>file</td></tr><tr><td>Size distribution (Magmatic)</td><td>Dome extrusion: 0.83<br>Small explosive: 0.14<br>Large explosive: 0.03<br>(1)</td><td>Dome extrusion: 0<br>Small explosive: 5<br>Large explosive: 2</td></tr></tbody></table>

Some of the data are too many to be listed (this is indicated by the label “file” in the table). They can be provided in the form of files on request

during the simulation. In Table 8 we provide the probabilities resulting from the run of the code after each bulletin. In Fig. 4 we also provide the time evolution of some of the most relevant

probability distributions, across all the time periods spanned by the simulation exercise in Dominica. For each bulletin, among the output information from PyBetUnrest, there were two
<table><caption>**Table 7** Monitoring parameters set for BET_UNREST at Dominica</caption><thead><tr><th>Node-parameter#</th><th>Parameter and threshold(s) (Y/N indicates a boolean observation)</th></tr></thead><tbody><tr><td>Unrest-parameter 1</td><td>Increased CO<sub>2</sub> flux above background (Y/N)</td></tr><tr><td>Unrest-parameter 2</td><td>Increase in T of hot springs and/or fumaroles (Y/N)</td></tr><tr><td>Unrest-parameter 3</td><td>Changes in H<sub>2</sub>O/CO<sub>2</sub> (Y/N)</td></tr><tr><td>Unrest-parameter 4</td><td>Appearance of new fumaroles and/or hot springs (Y/N)</td></tr><tr><td>Unrest-parameter 5</td><td>Vegetation die back (Y/N)</td></tr><tr><td>Unrest-parameter 6</td><td>Appearance of LPs and hybrid EQs (Y/N)</td></tr><tr><td>Unrest-parameter 7</td><td>Large regional tectonic event (M > 7) (Y/N)</td></tr><tr><td>Unrest-parameter 8</td><td>Number of VTs [if >10/day for two weeks] (Y/N)</td></tr><tr><td>Unrest-parameter 9</td><td>Detectable ground deformation (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 1</td><td>Increase in C/S, or decrease after increase (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 2</td><td>Detectable SO<sub>2</sub>, HCl, HF (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 3</td><td>Extreme increase in T [>300 °C] (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 4</td><td>Any VLPs (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 5</td><td>No. of LPs after significant VT swarms (#/day) (>5–10) (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 6</td><td>Consistent increase in No. of VTs for 1 month (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 7</td><td>Deep VTs [>8 km] (#/week) (4–5) (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 8</td><td>Detectable radial deformation (localized-coherent signal) (Y/N)</td></tr><tr><td>Magmatic unrest-parameter 9</td><td>Surface deformation (island wide, >6 cm in over 6 months) (Y/N)</td></tr><tr><td>Magmatic eruption-parameter 1</td><td>Decreasing C/S after increase (Y/N)</td></tr><tr><td>Magmatic eruption-parameter 2</td><td>Increase in Cl, Br, F content in hot springs/pools (Y/N)</td></tr><tr><td>Magmatic eruption-parameter 3</td><td>Decrease in H<sub>2</sub>O/CO<sub>2</sub> and/or H<sub>2</sub>S/SO<sub>2</sub> and/or SO<sub>2</sub>/HCl (Y/N)</td></tr><tr><td>Magmatic eruption-parameter 4</td><td>Phreatic activity (Y/N)</td></tr><tr><td>Magmatic eruption-parameter 5</td><td>Large thermal anomaly [incandescense] (Y/N)</td></tr><tr><td>Magmatic eruption-parameter 6</td><td>Landslides in hydrothermal areas (Y/N)</td></tr><tr><td>Magmatic eruption-parameter 7</td><td>Acceleration of VTs, LPs, hybrids [weekly] (Y/N)</td></tr><tr><td>Magmatic eruption-parameter 8</td><td>Presence of harmonic tremor (Y/N)</td></tr><tr><td>Magmatic eruption-parameter 9</td><td>Shallowing of VTs hypocenters in the edifice or shallow depths [<3 km] (Y/N)</td></tr><tr><td>Magmatic eruption-parameter 10</td><td>Sudden reversal of activity (Y/N)</td></tr><tr><td>Hydrothermal unrest-parameter 1</td><td>Anomalous behavior of Boiling Lake [overflow, lower or higher T than usual, no return of lake, etc.] (Y/N)</td></tr><tr><td>Hydrothermal unrest-parameter 2</td><td>Changes in hydrothermal features (Y/N)</td></tr><tr><td>Hydrothermal unrest-parameter 3</td><td>Increase in B and/or NH<sub>4</sub> concentration in waters (Y/N)</td></tr><tr><td>Hydrothermal unrest-parameter 4</td><td>Increase in CH<sub>4</sub>/CO<sub>2</sub> (fumaroles) (Y/N)</td></tr><tr><td>Hydrothermal unrest-parameter 5</td><td>Increase in T of fumaroles (Y/N)</td></tr><tr><td>Hydrothermal eruption-parameter 1</td><td>Increase in T of fumaroles (fuzzy 120–200 °C) (Y/N)</td></tr><tr><td>Hydrothermal eruption-parameter 2</td><td>riSe of water level in pools/overflow of BL (Y/N)</td></tr><tr><td>Hydrothermal eruption-parameter 3</td><td>Increase in extension of fumarolic field (Y/N)</td></tr><tr><td>Hydrothermal eruption-parameter 4</td><td>Muddy pools (Y/N)</td></tr></tbody></table>

(continued)
<table><caption>**Table 7** (continued)</caption><thead><tr><th>Node-parameter#</th><th>Parameter and threshold(s) (Y/N indicates a boolean observation)</th></tr></thead><tbody><tr><td>Hydrothermal eruption-parameter 5</td><td>Boiling/bubbling of pools that previously didn't (Y/N)</td></tr><tr><td>Hydrothermal eruption-parameter 6</td><td>Inflation of fumarolic field (Y/N)</td></tr><tr><td>Hydrothermal eruption-parameter 7</td><td>Landslides in hydrothermal areas (Y/N)</td></tr><tr><td>Hydrothermal eruption-parameter 8</td><td>New/extension of alteration areas (Y/N)</td></tr></tbody></table>

<table><caption>**Table 8** Resulting probabilities from real-time application of BET_UNREST at Dominica during VUELCO simulation exercise</caption><thead><tr><th></th><th></th><th>P(U)</th><th>P(MU)</th><th>P(MEr)</th><th>P(HU)</th><th>P(HEr)</th><th>P(TU)</th></tr></thead><tbody><tr><td rowspan="4">Phase 1</td><td>mean</td><td>1</td><td>0.26</td><td>0.06</td><td>0.62</td><td>0.42</td><td>0.12</td></tr><tr><td>10th prctile</td><td>1</td><td>0</td><td>0</td><td>0.05</td><td>0.01</td><td>0</td></tr><tr><td>50th prctile</td><td>1</td><td>0.06</td><td>0</td><td>0.73</td><td>0.32</td><td>0</td></tr><tr><td>90th prctile</td><td>1</td><td>0.85</td><td>0.22</td><td>1</td><td>0.95</td><td>0.5</td></tr><tr><td rowspan="4">Phase 2</td><td>mean</td><td>1</td><td>0.82</td><td>0.53</td><td>0.13</td><td>0.03</td><td>0.05</td></tr><tr><td>10th prctile</td><td>1</td><td>0.29</td><td>0.01</td><td>0</td><td>0</td><td>0</td></tr><tr><td>50th prctile</td><td>1</td><td>1</td><td>0.56</td><td>0.001</td><td>0</td><td>0</td></tr><tr><td>90th prctile</td><td>1</td><td>1</td><td>1</td><td>0.54</td><td>0.06</td><td>0.06</td></tr><tr><td rowspan="4">Phase 3</td><td>mean</td><td>1</td><td>0.70 (0.24)</td><td>0.17 (0.07)</td><td>0.08</td><td>0.02</td><td>0.22</td></tr><tr><td>10th prctile</td><td>1</td><td>0.09</td><td>0</td><td>0</td><td>0</td><td>0</td></tr><tr><td>50th prctile</td><td>1</td><td>0.87</td><td>0.02</td><td>0</td><td>0</td><td>0.08</td></tr><tr><td>90th prctile</td><td>1</td><td>1</td><td>0.68</td><td>0.27</td><td>0.03</td><td>0.80</td></tr></tbody></table>

In bracket estimates of mean values without including HCl anomaly in Phase 3

maps of the spatial probability of vent opening: one for the case of magmatic eruption, and one for hydrothermal eruption (Fig. 4). We believe this could be particularly useful, for example in a volcanic system like Dominica, where there are numerous areas showing hydrothermal activity, thus increasing the uncertainty on the position of a possible phreatic event.

The parameter “detectable SO₂, HCl, HF” created confusion and opened up a scientific discussion. For the sake of transparency, we provide the mean values of *P(MU)* and *P(MEr)* including, or not, the HCl anomaly (Table 8). Beyond the scientific implications of this issue, this concern reflected the sensitivity of BET_UNREST to the interpretation of some parameters. When relatively few monitoring parameters are provided, the weight of a single anomaly can be high: this is somehow a measure of the epistemic uncertainty.

## 4 Discussion and Implications for Unrest Tracking

This chapter presents the need for an updated BET model and tool that is able to account for the non-magmatic nature of some volcanic unrest episodes, which can often go under-estimated, if not totally neglected. The new model (BET_UNREST) and tool (PyBetUnrest) allow the tracking of unrest phases at volcanic systems and enables short-term volcanic forecasts. It has been fully developed within the VUELCO project, during which time it has been applied to some of the project's target volcanoes. In general, when we are able to distinguish magma-on-the-move (Rouwet et al. 2014) from the monitoring observations the new model basically “collapses” to BET_EF (or, better, the assessment of the probabilities related to
<figure><img src="image_27.png" /><figcaption>**Fig. 4** Average values (top left) obtained by BET_UNREST during the three phases of Dominica exercise for *P(MU)*, *P(HU)*, *P(MEr)* and *P(HEr)*. Asterisk points are the alternative average values for *P(MU)* and *P(MEr)* without considering HCl as detectable. On the right column the same probabilities are shown together with their</figcaption></figure>

<figure><img src="image_28.png" /></figure>

confidence interval between 10th and 90th percentiles. On bottom left, a snapshot of PyBetUnrest tool shows the spatial probability of vent opening during Phase 1, localising the most probable position of the phreatic eruption

magmatic outcomes provided by the two models coincide). On the other hand, if we are not able to identify a magmatic “active role” in the unrest (from the available monitoring observations), BET_UNREST is still able to provide the probabilities of hazardous events that accompany non-magmatic volcanic unrest, rather than neglecting them. As discussed in Rouwet et al. (2014), a very difficult case is presented by phreatomagmatic eruptions that, sometimes, can occur without any precursors indicating magma movement. This is surely an important limit to overcome which requires further efforts to detect subtle changes in the very short-term (hours to minutes) by improving monitoring techniques.

The chapter illustrates the development and implementation of BET_UNREST model and PyBetUnrest tool through three different applications:

(i) the pure retrospective analysis at Popocatépetl volcano, where there is no compelling need for a hydrothermal branch due to the current magmatic nature of the unrest episodes. Popocatépetl has remained in unrest from December 1994 to present and, for this application, BET_UNREST and PyBetUnrest were run using the UNAM Data Base for the learning period 1997–2012, with a
retrospective application aiming to forecast major eruptions (column heights greater than 8 km) for the April–June 2013 volcanic activity.

(ii) the application based on a simulation exercise at Cotopaxi. Here we tested the BET_UNREST retrospectively, but, this time, using the invented data provided during the VUELCO simulation exercise, in addition to data based on the real past history of the volcano.

(iii) the almost real-time simulation exercise organised by the VUELCO project in Dominica (May 2015). The volcanic system of Dominica presents a “prototype” setting for BET_UNREST due to its hydrothermal character. Phreatic/ phreatomagmatic activity occurred during the simulation, coinciding with high associated probabilities from BET_UNREST (the average values P(HU) = 0.73 and P(HER) = 0.32). We also positively tested the feasibility of providing different maps of the spatial probability of vent opening in case of magmatic or phreatic eruption.

As mentioned in previous sections, we implemented the BET_UNREST model into PyBetUnrest software tool using a graphical user interface aiming to provide a fast, open and user-friendly tool, which extends the usage of BET_UNREST to volcanologists with different expertise. The PyBetUnrest tool reached a mature and usable version during the Dominica simulation and its first stable release has been uploaded to Vhub cyber-infrastructure.

With these exercises we strongly believe we have brought BET a step closer to a full and proper implementation during a crisis situation. The PyBetUnrest tool eventually worked as expected, but it is important to take advantage of the lessons learned during these applications and pursue more tests that will improve its design and prove its usefulness in real-case scenarios.

As a final comment, we would like to remark that, as with any other event tree model (e.g. BET models by Marzocchi et al. 2004, 2008, 2010; HASSET model by Sobradelo et al. 2013),

one can always apply and “ populate ” the BET_UNREST model in any “ volcanic ” cir- cumstance. The uncertainty on the results pro- vided by BET_UNREST, and consequently their practical use, will however be strongly dependent on the available information and data used to set up the models rules. If only a few pieces of evidence are available, the models results will be characterised by a large uncertainty, and thus might be not very helpful for decision-makers. As more and more knowledge is gathered, BET_UNREST output probabilities will become more attractive from a practical point of view, since their uncertainty will be increasingly small. This is an intrinsic feature of the Bayesian inferential procedure at the basis of the model.

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<figure><img src="image_29.png" /></figure>

# Groundwater flow and volcanic unrest

Alia Jasim, Brioch Hemmings, Klaus Mayer
and Bettina Scheu

**Abstract**

Hydrology around active volcanoes is strongly controlled by the interaction between groundwater, and the fluids, dissolved elements and heat associated with magmatic intrusion. The chemical and mechanical processes associated with magmatic unrest can result in observable changes in the hydrothermal system. Consequently, observations of chemical and physical hydrothermal variations may provide insights into the state of volcanic activity. Additionally, the interaction between hydrological and volcanic systems leads to the presence of high-temperature, pressurised, and often acidic fluids, which add to, and intensify, the volcanic hazard. In the following chapter we present the major components of, and controls on, magmatic hydrothermal systems focusing on the mutual perturbation between the groundwater flow system and the volcanic system. We

explore how these conditions can be modified by volcanic unrest and we identify feedbacks between dynamic hydrothermal behaviour and on-going unrest. The interaction between these systems, and therefore the associated monitoring signals, are the result of complex groundwater-volcano coupling within multi-phase flow system in evolving lithologies. Nonetheless, detailed monitoring of hydrothermal and hydrological behaviour can provide insights into unrest and the evolution of hazards at restless volcanoes.

**Keywords**

Hydrothermal system · Groundwater
Fluid flow · Permeability · Unrest-monitoring

## 1 Resumen

Brevemente resumimos nuestra comprensión de los sistemas magmáticos hidrotermales y discutimos las mayores incógnitas y sus implicaciones en el vigilancia volcánica. También proveemos directrices adicionales para la recolección de datos a usarse en la calibración de la variabilidad del sistema de aguas subterráneas, alrededor de volcanes activos, como un paso crucial para desacoplar las señales magmáticas de las puramente hidrotermales.

<footnote>A. Jasim (✉)
University of Bristol, Earth Science School,
Wills Memorial Building, Queen's Road,
Clifton BS8 1RJ, UK</footnote>

<footnote>K. Mayer · B. Scheu
Department of Earth and Environmental Sciences,
Ludwig-Maximilians-Universität München (LMU),
Theresienstrasse 41/III, 80333 Munich, Germany</footnote>

<footnote>B. Hemmings
GNS Science, 1 Fairway Drive, Lower Hutt,
New Zealand</footnote>
La interacción entre los sistemas hidrológico y volcánico es un elemento importante durante reactivación volcánica. Los cambios en el comportamiento hidrológico de un volcán activo, como la elevación del nivel del agua subterránea, la descarga de manantiales, los cambios de temperatura y de la química, pueden ser indicadores preliminares de evolución de la actividad volcánica. Las interacciones hidrológicas pueden también alterar y aumentar el peligro volcánico existente. Las interacciones físicas y químicas entre la roca encajante y los diferentes tipos de fluido pueden modificar los caminos de desgasificación, generando distribuciones de presión dinámicas dentro del edificio volcánico. Aún los procesos lentos, como el desarrollo creciente de zonas de alteración permanentes, pueden manifiestarse como un peligro dinámico asociado con una reactivación continua o futura, ya que las rocas altamente cristalinas son hidrotérmicamente alteradas produciendo arcillas débiles secundarias. Discutimos los principales parámetros que controlan las reacciones y sus efectos en la distribución de la alteración en ambientes volcánicos.

Debido a la introducción del calor de la fuente en el sistema del agua saturada, se presentan peligros adicionales. Esto frecuentemente conlleva a explosiones freáticas y freato-magmáticas. La presencia de paquetes de gases bajo la superficie además incrementa este peligro. El balance entre el ingreso de agua freca fría, la desgasificación y la disipación de calor, está críticamente relacionado con la abilidad del sistema para transmitir fluidos, el mismo que evoluciona en función tanto de los procesos químicos (ej., las reacciones de dilución/precipitación mineral) como físicos (ej., fracturamiento y compactación de la roca), produciendo así propiedades hidrológicas de la roca fuertemente dependientes de la escala (ej., porosidad, permeabilidad y conductividad térmica).

En resumen, las señales físicas y químicas, o la perturbación hidrológica asociada con la reactivación magmática, son complejas y dependientes del sitio. Las contribuciones de los diferentes componentes del fluido, y sus interacciones con caminos de flujo existentes, pueden determinar cómo un sistema evoluciona en

períodos de calma. Esta evolución controla la posible respuesta a la perturbación termodinámica y química asociada con la iniciación de la reactivación volcánica. Dadas las intrincadas retroalimentaciones entre el magma, la hidrología, y los cambios repentinos de los sistemas involucrados, únicamente mediciones de alta frecuencia (de horas a semanas) de la temperatura, pH, conductividad eléctrica del agua, profundidad del nivel del agua subterránea, del contenido de REE (Elementos de Tierras Raras), RFEs (Elementos de Formación de Rocas) y gas disuelto, conjuntamente con mediciones geofísicas, pueden aclarar la evolución del sistema magmático, la apertura/cierre de fracturas y la dinámica estacional del agua subterránea.

## 2 Introduction

Much of the research relating to the interaction between hydrological and volcanic systems has focused on the role of hydrothermal systems in the development of economic mineral deposits. hydrothermal systems have formed vast ore-deposits around the world, most of them clearly result from the interaction between magnetic and meteoric fluids (Hedenquist and Lowenstern 1994).

The interaction between hydrological and volcanic systems is an important element in volcanic unrest. Changes in hydrological behaviour, such as water table elevation, spring discharge, temperature and chemistry, at an active volcano can provide early indications of changes in volcanic activity. Hydrological interactions can also alter and augment the existing volcanic hazard. Chemical and physical interactions between host rocks and different fluid types can modify fluid degassing pathways, generating dynamic pressure distributions within a volcanic edifice. Additional hazards are also presented by the introduction of a heat source into a water saturated system, this frequently results in dangerous phreatic and phreatomagmatic explosions. Understanding the controls on hydrological and hydrothermal behaviour in volcanic settings is essential for understanding the array of hazards
presented by volcanic unrest. Continued devel-
opment of this understanding is also providing
new volcano monitoring opportunities. Despite
the clear relevance and importance of hydrolog-
ical and volcanic interactions in relation to vol-
canic unrest, the dynamics of this interaction
remain poorly constrained.

3 Hydrothermal System

Although insulated or distal, cool groundwater
aquifers can respond to volcanic perturbation, the
clearest manifestation of volcanic and hydrologi-
cal interactions is a hydrothermal system. Fumar-
oles, often visible on active volcanoes, represent
the surface expression of this hydrothermal
system.

A magmatic hydrothermal system is com-
posed of three main elements: a **host rock** (or
reservoir), which contains a circulating **fluid**, set
in motion by an igneous **heat source** (Fig. 1).
While the difference in relief between stratovol-
canoes and calderas can lead to contrasting
hydrological systems, the lower limit of any
hydrological system is commonly defined as the
brittle-ductile transition zone. Within this zone,
fluid pressures transition from hydrostatic to
lithostatic as rock permeability becomes severely
reduced (Fournier 1999). When this region is
subjected to high strain rates, fracturing may
occur, leading to episodic influxes of mass and
heat to the hydrothermal system (Bodnar et al.
2007). While agreement exists on the definition
of lower limit of a volcanic hydrological system,
the same is not true for the upper limit.

We consider the water table as the upper limit
of the hydrological system. However, the earth's
surface could equally be considered part of the
system. This adds the further complexity of flow
within the unsaturated (vadose) zone (Hemmings
et al. 2015a). Furthermore, the upper limit of the
hydrological system closely depends on the
precipitation regime. Precipitation is a function
of geography, including both latitude and eleva-
tion. Together with surface processes it deter-
mines the recharge dynamics of the aquifer and

the depth and fluctuation of the water table.
A number of studies suggest a correlation
between the fluctuation of the water table and
elevated seismicity. Both the reduction in effec-
tive stress due to a seasonal increase in hydro-
static pore pressure, and the snow unloading,
lead to a seasonal peak of seismicity (Saar and
Manga 2003; Christiansen et al. 2005). Further-
more, Mason et al. (2004) identify seasonal
peaks in the eruption rate of volcanoes, which
may be due to the load/unload seasonal stress
cycle imposed by the hydrological cycle.

The definition of water table implies a water
saturated medium below it. However, crater lakes
(Fournier et al. 2009) and caldera settings (Bruno
et al. 2007; Jasim et al. 2015) often have portion
of the water table sustained by a two phases
system (liquid and gas). Similarly, the conden-
sation of magmatic gases (primarily vapour)
often feeds the groundwater reservoir (Chiodini
et al. 2001). While the location of the water table
is important, the conventional definition does not
really apply in volcanic settings, especially in
high-relief stratovolcanoes, in which it is often
unclear to what extent the edifice is water satu-
rated. Specifically, the local water table may
differ from the regional water table, with highly
dynamic elevation changes controlled by both
meteoric and volcanic processes.

Many studies suggest the presence of high
elevation springs, however it is not clear whether
they are fed by the regional water table or by
perched saturated layers high up on the cone
(Cabrera and Custodio 2004; Custodio 2007;
Cruz and Oliveira Silva 2001; Hemmings et al.
2015a; Ingebritsen and Scholl 1993; Join et al.
2005; Peterson 1972). Access to wells on the
flank of volcanoes and geophysical imaging
methods can help resolve the hydrogeology
behind such springs (Finn et al. 1987, 2001;
Aizawa et al. 2008). However, in either scenario,
the development of saturated flow units at high
elevation has a particular relevance to forecasting
volcanic hazards such as lahar, landslides and
flank collapses, which can be sudden, unpre-
dicted and deadly. Such mass wasting events all
involve the displacement of material from the
<figure><img src="image_30.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_31.png" /><figcaption>**Fig. 1** Conceptual model of a hydrothermal system in, a high-relief volcano (modified after Goff and Janik 2000) and b caldera (modified after Kuhn 2004)</figcaption></figure>
upper part of the volcano to the surrounding valleys. They can be triggered by gravitational instability (often in response to heavy rainfall), increase in pore pressure, reduction of rock strength, volcano-tectonic earthquake or intrusion of magma. The depressurisation induced by mass movement on the volcanic edifice may also result in the sudden reactivation of the magmatic system.

The waters circulating within volcanic systems are often high-temperature, sometimes supercritical fluids. They interact with the host rock through chemical reactions that result in hydrothermal alteration. The evolution and dynamics of hydrological and volcanic interactions are strongly controlled by fluid flow. This, in-turn, is a function of the pressure, temperature, fluid composition, and, critically, the system's ability to transmit fluids.

## 3.1 Fluid flow

Laminar flow through saturated porous media is described by Darcy's Law (Eq. 1),

$$q = -\frac{k dp}{\mu dl} \qquad (1)$$

where q is specific discharge (m/s), k is permeability of the porous media (m²), μ is fluid viscosity (Pa s), and dP/dl is the hydraulic head, or pressure, gradient (Pa/m) along length l (m). The fluid viscosity is usually approximated as that of water (liquid or vapour). However, the combined effect of topography and the presence of a deep source of heat and fluids likely produce vast unsaturated (or two-phase) portions within the volcano. Dissolved air and gas near the surface, the phase transition from water (liquid) to vapour due to the temperature gradient, and the decompression of upwelling fluids are some of the processes that produce two-phase (liquid and gas) fluid flow regions within a volcanic system. To extend the Darcy's equation to two phase flow we define the liquid saturation (S_w) as the fraction of a representative bulk volume of the porous medium filled by water and, similarly, the

gas saturation (S_g) as the fraction of a representative bulk volume of the porous medium filled by the gas phases, such that (Eq. 2)

$$S_g + S_w = 1, \qquad (2)$$

The capillary pressure P_c (in Pa) is due to the pressure difference between the two phases (Eq. 3), hence

$$P_c = P_g - P_w \qquad (3)$$

and is a unique function of water saturation (S_w). Finally, due to the competing flow of the two phases, the relative permeability (kr) is less than or equal to the single phase (usually water) permeability, k (m²), of the medium. We define the relative permeability of the gas phase (kr_g) and the relative permeability of the liquid phase (kr_w) as (Eqs. 4 and 5)

$$kr_g = \frac{k_g}{k} \qquad (4)$$

$$kr_w = \frac{k_w}{k} \qquad (5)$$

where k_g and k_w are the effective permeabilities for each of the two fluids. Again the relative permeabilities are assumed to be a unique function of water saturation (S_w). Hence, we define the flow velocity vector (m/s) of the gas phase along a direction D (m) as (Eq. 6)

$$v_g = -k \frac{kr_g}{\mu_g} (\nabla P_g - \rho_g g \nabla D) \qquad (6)$$

and similarly the velocity (m/s) of the water (Eq. 7)

$$v_w = -k \frac{kr_w}{\mu_w} (\nabla P_w - \rho_w g \nabla D) \qquad (7)$$

where μ and ρ are respectively the viscosity (Pa s) and the density (kg/m³) of the gas (g) and liquid (w) and g is the acceleration (m/s²) due to gravity. Similarly, for non-laminar flow further terms (e.g., Forchheimer term) can be added to Darcy's equation of fluid flow to consider the inertial effects due to turbulence.
### 3.1.1 Permeability and Porosity

Permeability and porosity are the primary parameters controlling flow and storage of fluids in the subsurface (Manning and Ingebritsen 1999). The permeability also controls the heat regime of the hydrothermal system: high permeabilities ($\ge 10^{-14} \text{ m}^2$) favour advective transfer of heat away from the igneous source, resulting in low-temperature vapour-dominated systems. Contrastingly, low-permeabilities ($<10^{-16} \text{ m}^2$) favour slow heat conduction, also producing low-temperature systems. The hottest hydrothermal plumes reside in intermediate permeabilities, around $10^{-15} \text{ m}^2$ (Hayba and Ingebritsen 1997). Whilst permeability ($k$) is an important parameter, it is often one of the least well constrained. It can vary over 17 orders of magnitude, from $\sim 10^{-20} \text{ m}^2$ in intact crystalline rocks to $\sim 10^{-9} \text{ m}^2$ in porous and fractured basalt (Table 1).

Porosity ($\phi$), the ratio of voids over total volume (voids and solid) in a given material, defines the storage capacity of the rocks; it also affects permeability and effective thermal conductivity. In many porous and fractured media, there is a positive correlation between $\phi$ and $k$, as permeability is simply the interconnected pore network. Clays and volcanic tuff are unusual in that they can have high porosity values but low permeabilities, at least in part due to their very small particle sizes, propensity to bridge pores and form aggregates (Neuzil 1994). Permeability in such lithology is greatly enhanced by the presence of discontinuities such as fissures, joints, shears and faults that can connect otherwise isolated pores.

Porosity and permeability of volcanic units are primarily controlled by the type of volcanic product (e.g., lava, pyroclastic density current, ash fall) and depositional environment. Inherent heterogeneities between deposits can be enhanced by subsequent compaction, fracturing and chemical alteration. The resulting hydrogeology is complex; hydrological rock properties (porosity, permeability and thermal conductivity) are strongly scale dependent, particularly when fluid flow is focussed along high permeability

channels or fractures, as is common in volcanic settings. Such flow pathways themselves are an active component of a dynamic system. Their ability to transmit fluids and therefore the role they play in hydrothermal circulation can be modified by physical changes - the opening or closing of fractures in response to stress-field changes - and chemical alteration, which can both enhance and obstruct fluid flow, as discussed in the next section.

## 3.2 Chemical Reactions

Chemical alteration is an important process within a hydrothermal system. Dissolution can reduce cohesion and weaken a volcanic edifice. This can lead to catastrophic flank collapses and debris avalanches (Reid 2004). Such events can depressurise the magmatic system and trigger an eruption. Conversely, chemical precipitation and deposition processes can cause plugging in areas of intense mineralisation, this can promote pressurisation, which can also lead to flank collapse, as pore pressures increase within the edifice. Such pressurisation can also generate violent steam-driven explosions (Ingebritsen et al. 2010). Chemical dissolution and precipitation processes also alter the host-rock permeability. These processes are sensitive to the thermodynamic conditions and the proportions of different fluid components (Pirajno 2010). Feedbacks between physical and chemical flow behaviour within a hydrothermal system can modify the physical and chemical characteristics of its surface expression - hydrothermal fluid and fumarolic discharge. Therefore, monitoring the hydrothermal discharge can provide clues about the state of volcanic unrest and can even be used to help predict volcanic eruptions (Cronan et al. 1997).

To maximise the value of chemical analysis of hydrothermal discharge as a volcanic monitoring tool, and to fully understand the hazard presented by hydrological and magmatic interactions it is necessary to quantify the rates, spatial distribution and physical effects of chemical alteration within a hydrothermal system.
<table><caption>**Table 1** Measured permeability and porosity ranges for various rock types</caption><thead><tr><th rowspan="2">Rock type</th><th colspan="2">Permeability (m²)</th><th>Porosity (%)</th></tr><tr><th>Min</th><th>Max</th><th>Range</th></tr></thead><tbody><tr><td colspan="4"><em>Unconsolidated rocks</em></td></tr><tr><td>Gravel</td><td>10<sup>-10</sup></td><td>10<sup>-7</sup></td><td>25–40</td></tr><tr><td>Clean sand</td><td>10<sup>-13</sup></td><td>10<sup>-9</sup></td><td>5–50</td></tr><tr><td>Silty sand</td><td>10<sup>-14</sup></td><td>10<sup>-10</sup></td><td></td></tr><tr><td>Silt, loess</td><td>10<sup>-16</sup></td><td>10<sup>-12</sup></td><td>35–50</td></tr><tr><td>Unweathered clay<sup>a</sup></td><td>10<sup>-20</sup></td><td>10<sup>-15</sup></td><td>40–80</td></tr><tr><td colspan="4"><em>Consolidated rocks</em></td></tr><tr><td>Shale</td><td>10<sup>-20</sup></td><td>10<sup>-16</sup></td><td>0–10</td></tr><tr><td>Unfractured metamorphic and igneous</td><td>10<sup>-20</sup></td><td>10<sup>-17</sup></td><td>0–5</td></tr><tr><td>Sandstone</td><td>10<sup>-17</sup></td><td>10<sup>-13</sup></td><td>5–35</td></tr><tr><td>Limestone and dolomite</td><td>10<sup>-16</sup></td><td>10<sup>-13</sup></td><td>0–20</td></tr><tr><td>Fractured igneous and metamorphic</td><td>10<sup>-15</sup></td><td>10<sup>-13</sup></td><td>0–10</td></tr><tr><td>Permeable basalt</td><td>10<sup>-14</sup></td><td>10<sup>-9</sup></td><td>0–25</td></tr><tr><td>Karst limestone</td><td>10<sup>-13</sup></td><td>10<sup>-9</sup></td><td>5–50</td></tr><tr><td>Fractured basalt</td><td></td><td></td><td>5–50</td></tr><tr><td>Basalt near surface<sup>b</sup></td><td>10<sup>-14</sup></td><td>10<sup>-12</sup></td><td></td></tr><tr><td>Basalt at 1 km depth<sup>b</sup></td><td>10<sup>-18</sup></td><td>10<sup>-10</sup></td><td></td></tr><tr><td>Andesite<sup>c</sup></td><td>10<sup>-20</sup></td><td>10<sup>-18</sup></td><td>0.2–0.3</td></tr><tr><td>Thermometamorphic<sup>d</sup></td><td>10<sup>-18</sup></td><td>10<sup>-14</sup></td><td>2–17</td></tr><tr><td colspan="4"><em>Campi Flegrei trachy-phonolite</em></td></tr><tr><td>Tuff, surface<sup>e</sup></td><td>10<sup>-16</sup></td><td>10<sup>-15</sup></td><td>48–52</td></tr><tr><td>Tuff, depth<sup>e</sup></td><td>10<sup>-17</sup></td><td>10<sup>-15</sup></td><td>19–52</td></tr><tr><td>Chaotic tuff/tuffites &lt; 1 km depth<sup>d</sup></td><td>10<sup>-18</sup></td><td>10<sup>-14</sup></td><td>6–40</td></tr><tr><td>Chaotic tuff/tuffites &gt; 1 km depth<sup>d</sup></td><td>10<sup>-18</sup></td><td>10<sup>-14</sup></td><td>5–36</td></tr><tr><td>Tuffites (HT altered)<sup>f</sup></td><td>10<sup>-16</sup></td><td>10<sup>-16</sup></td><td>0.05–0.07</td></tr><tr><td>Lava<sup>d</sup></td><td>10<sup>-18</sup></td><td>10<sup>-14</sup></td><td>7–25</td></tr><tr><td colspan="4"><em>Montserrat andesite</em></td></tr><tr><td>Pyroclastic flow deposit<sup>g</sup></td><td>10<sup>-18</sup></td><td>10<sup>-13</sup></td><td></td></tr><tr><td>Lava<sup>g</sup></td><td>10<sup>-17</sup></td><td>10<sup>-13</sup></td><td></td></tr><tr><td>Lahar deposit<sup>g</sup></td><td>10<sup>-14</sup></td><td>10<sup>-13</sup></td><td></td></tr></tbody></table>

Sedimentary rocks are given for comparison. Hydrothermal systems in limestone usually develop as skarn deposit. Data from Freeze and Cherry (1979). <sup>a</sup>Neuzil (1994), <sup>b</sup>Ingebritsen et al. (2006), <sup>c</sup>Petrov et al. (2005), <sup>d</sup>Piochi et al. (2014), <sup>e</sup>Peluso and Arienzo (2007), <sup>f</sup>Giberti et al. (2006), <sup>g</sup>Hemmings et al. (2015a)

### 3.2.1 Reaction Controlling Parameters

Many factors may influence hydrothermal alteration, including temperature, pressure, rock type, fluid flux, fluid composition, and time. The relative importance of each of these has been much discussed in the literature (Gifkins et al. 2005;

Pirajno 2010) and appears to vary between different case studies.

### 3.2.2 Fluid Composition

The dominant form of chemical alteration (solution and/or precipitation) is principally a
function of the composition of the circulating hydrothermal fluids. Meteoric water and seawater are the main sources of fluid in hydrothermal systems with an additional and dynamic contribution of magmatic fluids. The composition of hydrothermal fluid critically affects the mineral-fluid equilibria and therefore the concentration of rock forming elements (RFEs) such as Silica (SiO₂), Sodium (Na), Potassium (K), Calcium (Ca) and Magnesium (Mg). The mineral-fluid equilibria and solubility of RFEs, as well as sulphate (SO₄²⁻), chloride (Cl⁻) and bicarbonate (HCO₃⁻), is affected by temperature, pressure and water/rock (W/R) ratio as well as the composition of the host rock. These factors also affect kinetic rate of chemical alteration. In addition, the relative mobility of elements depends on the characteristics of fluid flow, the number of phases (liquid and gas) and chemical condition along the flow path including pH, redox condition, sulfidation state, availability of ligands.

The chloride-sulphate-bicarbonate ternary diagram by Giggenbach and Soto (1992) provides a tool to identify water end-members (Fig. 2). It represents graphically the classification of thermal water suggested by Ellis and Mahon (1977) based on major ions, which identifies (i) neutral alkali-chloride waters which

result from extensive interaction with the reservoir rocks and may cause silica or carbonate supersaturation at surface condition; (ii) acid-sulphate waters which result from the condensation of volcanic gases into the shallower groundwater system and are often depleted in alkali and Cl⁻ but enriched in metals; (iii) bicarbonate waters which usually show thermodynamic equilibrium with the reservoir rock and are common at the edge of magmatic-hydrothermal systems (Ellis and Mahon 1977; Giggenbach and Soto 1992; Goff and Janik 2000).

### 3.2.3 Acidity of Hydrothermal Fluids

Upper regions of hydrothermal systems are often characterized by steam-heated fumarolic alteration due to the presence of acidic, sulphate-rich fluids (Rye 2005). These fluids may cause leaching of the host rocks, resulting in an increase in both rock porosity and permeability. Eventually extreme acidic fluids (pH < 2) generate the development of vuggy silica and thereby facilitate faster gas escape in the shallow zone (Mayer et al. 2016 and references therein). In the presence of abundant sulphate ions and Al-rich host rocks, within a lesser acidic environment (pH > 2), the formation of alunite dominates (alunitic alteration, Pirajno 2010).

<figure><img src="image_32.png" /><figcaption>**Fig. 2** Classification ternary diagram of thermal waters from Giggenbach and Soto (1992) based on the content of major solutes: Cl⁻, SO₄²⁻ and HCO₃⁻. The vertexes represent the alkali-chloride, sulphate and bicarbonate water type end members, whilst the arrows show major differentiation processes</figcaption></figure>
<figure><img src="image_33.png" /><figcaption>**Fig. 3** Conceptual model for the formation of near surface high-sulfidation alteration. pH and composition control the development of alteration zones with increasing distance to the main fumarolic conduit. A highly permeable, acidic core characterized by amorphous silica is laterally replaced by a zone of alunite and amorphous silica. Successive neutralization of the fluids promotes the formation of kaolinite. Rock permeability as well as the degree of alteration increase toward the center of the hydrothermal activity (Mayer et al. 2016)</figcaption></figure>

Often the fluids undergo progressive neutralization as they flow away from the degassing vents. This results in a sequence of alteration facies (Fig. 3) from silicic to advanced argillic to intermediate argillic (Fulignati et al. 1998).

In similar environments the distribution of kaolinite and alunite may also be affected by the presence of groundwater. Alunite preferentially forms at or above the groundwater table where atmospheric oxygen could oxidize H₂S to H₂SO₄, which is required for the formation of alunite (Mutlu et al. 2005).

### 3.2.4 Water/Rock Ratio

The amount of water and the rock surface area available for reactions are two of the primary controls on alteration. Therefore, the Water/Rock (W/R) ratio and rock porosity and permeability will determine the type and extent of alteration. W/R ratios range between 0.1 and 0.4 (Henley and Ellis 1983) while porosity can vary from 0 to ~80% (Freeze and Cherry 1979; Neuzil 1994). Static systems, with low porosity and low

W/R ratios, are termed “closed systems”, or “rock dominated systems”. In this case, the secondary minerals depend nearly entirely on temperature and are of similar chemical composition to the original rocks, although possibly in a hydrated form (typical of propylitic alteration, see below). The alteration minerals often resemble those due to metamorphism (Giggenbach 1984).

Most hydrothermal systems are, however, “open systems”, or “liquid dominated systems”, which are characterised by high W/R ratios. In these cases, the fluid composition has a greater importance. The minerals that precipitate in open systems are the result of alteration by mobile fluids of constantly changing composition. In these systems permeability is highly influential and systematic spatial patterns of alteration zoning are common.

### 3.2.5 Rock Type

Many studies support the assumptions that the chemical and mineralogical composition of the original rock will change the composition of the
equilibrium solution and therefore the rate of individual dissolution/precipitation reactions (e.g. Pirajno 2010). Consequently, various investigations have attempted to assess the most easily altered minerals in the host rocks (e.g. Browne 1984). Glass, followed by olivine are the least stable phases at surface conditions, as such basalts are likely to alter more rapidly than felsic rocks. Numerous studies of basalt dissolution have shown that the presence of glass can increase dissolution rates (Wolff-Boenisch et al. 2006; Berger et al. 1994; Stefansson and Gislason 2001; Zakharova et al. 2007; Hu et al. 2010; Gudbrandsson et al. 2011). However, experimental results on the alteration of volcanic materials are biased by the dominant use of basalt as starting material.

Even though basaltic glass dissolves relatively rapidly, basalts are still low-silica magmas, and therefore silica concentrations remain higher in felsic rocks. Browne (1978) showed that, at temperatures above 280 °C, the host rock composition has a negligible effect on alteration minerals. Indeed, he gathered evidence of the same stable alteration assemblages in basalts, sandstones, rhyolites and andesites. Most accessible hydrothermal systems, however, are at temperatures below 280 °C and in such systems Browne (1978) reports high-silica zeolites in rhyolitic volcanoes, and low-silica zeolites in basaltic and andesitic systems.

### 3.2.6 Pressure

Pressure generally has a secondary role in hydrothermal alteration (Robb 2005). An important exception is the role of pressure in controlling boiling in hydrothermal environments. Boiling at depth leads to low-salinity vapour and high-salinity brine. This phase separation is responsible for transport and deposition of elements that are key to ore mineralization (Henley and Berger 2013). In the upper ~400 m, pressure is due to the weight of the hot, possibly vapour rich, water column leading to pressure gradient below hydrostatic. At the margin of the hydrothermal system mixing between cold and hot water is enhanced by the pressure difference (Henley 1985). At greater depth, pressures exceed hydrostatic, feeding the upper reservoir (Henley

1985). Sharp pressure gradients can occur between high and low permeability portions of a hydrothermal system, for example within the flow system feeding fumaroles. Major processes occur at the interface of liquid-gas phases, such as massive precipitation of minerals (Lu and Kieffer 2009). In addition, high pressures can cause rock compaction, thus reduce permeability and drive pressure solution. Conversely, rapid increase in fluid pressure can promote fracturing leading to increases in permeability.

### 3.2.7 Temperature

Temperature, on the other hand, controls the general alteration patterns of hydrothermal systems because it is the main control on mineral solubility (Giggenbach 1988; Oelkers et al. 2009). For example, metal chlorides and alkaline minerals are more soluble at high temperatures, while gypsum, anhydrite, calcite and dolomite show retrograde solubility below ~100 °C (Frazer 2014). Silica solubility increases as temperatures rise, until ~300 °C. With further temperature increases, silica solubility decreases (Fournier 1985). These types of thermodynamic relationships in single-phase hydrothermal systems are relatively well constrained, and are reviewed in detail by Oelkers et al. (2009).

## 4 Hydrothermal Systems and Unrest

Many of the conditions that control fluid flow and chemical alteration are modified by the re-activation of the magmatic system, and evolve during volcanic unrest. For example, the introduction of fresh magma into the deep portions of an active hydrothermal system can critically change the pressure and temperature conditions within the system, thus leading to the development of gas pockets in the subsurface (Jasim et al. 2015). This can rapidly lead to phreatic eruptions. Thus it is crucial to expand unrest tracking to include monitoring of non-magmatic hazards (Sandri et al. 2017—this volume).

Seismicity, gases and ground deformation are usually monitored around active and restless
volcanoes (Sparks 2003). Gravity anomaly studies (Gottsmann et al. 2008; Coco et al. 2016) and acoustic waves (Ferrazzini and Aki 1987) also provide insight into subsurface processes. Measurements of water chemistry composition from hydrothermal manifestation such as boiling pools, crater lakes and thermal springs are also routinely conducted (Varekamp et al. 2001, 2009; Federico et al. 2002; Tassi et al. 2003). The increase of Rare Earth Elements (REE)/Cl, RFEs (e.g., Ca, Mg, K)/Cl and the increase in SiO₂ concentration are indicative of either intrusion of fresh magma within the hydrothermal reservoir or exposure to water/rock interaction of fresh rock due to hydrofracturing (Varekamp et al. 2008). A pH drop and temperature increase in spring water can also be indicative of an increase in magmatic activity. However the majority of springs in volcanic environment are fed by the regional groundwater reservoirs, thus representing the cooler water inflow of the hydrothermal system (Jasim 2016). Thermal waters are focused on fluid upwelling pathways such as faults and fractures (Curewitz and Karson 1997; Hemmings et al 2015b; Jasim et al. 2015).

The influx of magmatic fluids can manifest as changes in chemical compositions of hydrothermal discharges. In particular the strong field

ligands Cl⁻ and F⁻, from magmatic degassing, can mobilize metals and F⁻ greatly enhances the dissolution rates of aluminium silicates (Oelkers and Gislason 2001; Wolff-Boenisch et al. 2004) with detrimental effect on rock mechanical properties. However, along the upwelling flow path, mixing with surface waters and chemical reactions with the rock often occur, overprinting the magmatic signature. Magmatic gases and vapour either mix with deep circulating water (Giggenbach 1988) or they condense to form in situ thermal waters (Rye 1993). In both cases, they rapidly dissociate and form a strong acidic solution which causes cation leaching of the host rock leading to advanced argillitic alteration (Giggenbach 1988; Symonds et al. 2001). Prior to mineral precipitation, the resulting waters are enriched in Si, Na⁺, K⁺, Mg²⁺ and Ca²⁺, and other metals, proportionally to their concentrations in the host rock (rock congruent dissolution) until the solution is more or less neutralised. This is therefore called the “primary neutralisation zone”. Continued acid leaching in K-feldspar systems leads invariably to the formation of alunite, an important component of high-sulfidation epithermal systems, where it can replace entire masses of rocks. For example, alunite can be formed indirectly from K-feldspar through the formation of K-mica and kaolinite (Eqs. 8a–8c).

$$3\text{KAlSi}_3\text{O}_8 + 2\text{H}^+ \rightarrow \begin{array}{l} \text{K-feldspar} \\ \text{K-mica} \end{array} \qquad \text{K-feldspar} \qquad (8a)$$

$$\text{KAl}_3\text{Si}_3\text{O}_{10}(\text{OH})_2 + 2\text{H}^+ + 3\text{H}_2\text{O} \rightarrow \begin{array}{l} \text{K-mica} \\ \text{Kaolinite} \end{array} \qquad \text{Kaolinite} \qquad (8b)$$

$$3\text{Al}_2\text{Si}_2\text{O}_5(\text{OH})_4 + 2\text{K}^+ + 6\text{H}^+ + 4\text{SO}_4^{2-} \rightarrow \begin{array}{l} \text{Alunite} \\ \text{Kaolinite} \end{array} \qquad \text{Alunite} \qquad (8c)$$
Most field and experimental observations
imply that permeability in hydrothermal systems
tends to decrease with time. However, most of
these systems remain active for long periods of
time (typically 10³–10⁶ years, Ingebritsen et al.
2010). Mechanisms must exist by which perme-
able pathways are maintained and or developed to
allow continuing circulation of hydrothermal
fluids and ongoing alteration. Such mechanisms
include: (i) the periodic re-organisation of flow
patterns related to spatial variations in dissolution
and precipitation behaviour (Ritchie and Pritch-
ard 2011); (ii) the dissolution of minerals because
of pulses of acidic fluids (Plumlee 1999);
(iii) feedback between permeability reduction,
fluid pressure and rock mechanics resulting in
hydrofracturing, shear dislocation, mineral dis-
solution and the opening of flow pathways (Bar-
nes 2015; Weis 2015); (iv) pulsating volcanic
activity causing fracturing, periodic variations in
temperature, pressure and the composition of the
circulating fluids (Bodnar et al. 2007); (v) Cool-
ing of the magmatic sills and dikes may lead to
thermal cracking (Cathles et al. 1997) and the
thermal expansion of the rock; both of which
cause increases in fracture density (Chen et al.
1999) enhancing rock permeability; and
(vi) stressed induced fracturing (Tapponnier and
Brace 1976). Furthermore, fluid pathways often
ease the movement of magma towards the surface
as shown by the 1975–1984 volcano-tectonic
crisis at Krafla caldera (Iceland), which lead to the
emplacement of fault-controlled pseudodikes at
shallow depths (<100 m) and eruptive events
(Opheim and Gudmundsson 1989).

The evolution of a hydrothermal system
involves the interplay between a number of
mechanisms, physical and chemical, that operate
at very different timescales ranging from seconds
to hundreds of years. Where volcanic unrest
results in rapid modification of the hydrothermal
system, hazards associated with unrest can
manifest rapidly, with limited warning or pre-
cursor activity. Dynamic changes in perme-
ability, related to rapid mineral precipitation or
opening of fractures can immediately modify
flow pathways. This can result in dramatic
changes in heat and fluid flow, near surface

pressurisation, hydrothermal outflow and phrea-
tic explosions. Even slow processes such as the
incremental development of pervasive alteration
zones can manifest as a dynamic hazard in
response to continued or future unrest, as strong
crystalline rocks are hydrothermally altered into
weak secondary clays.

5 Monitoring and Signals

The dynamic hydrological and hydrothermal
response to volcanic unrest means that, bore-
holes, springs, fumaroles, crater lakes and geo-
physical imaging of the hydrological system can
provide a rare window into the state of a volcano
and the evolution of volcanic hazard. Hydrolog-
ical monitoring itself is multi-parametric; insights
can be gained from exploring physical and
chemical patterns. For instance, the effect of
groundwater on volcanic gases changes accord-
ing to their solubility. As such, a larger propor-
tion of SO₂, HCl and HF emitted from magma
remain in solution in water compared to CO₂ and
H₂S. Hence, SO₂, HCl and HF can be detected at
the surface, only during intense magmatic
activity or after drying of degassing pathways
(Symonds et al. 2001). In the absence of active
degassing, the isotopic ratio of the gases dis-
solved in groundwater2 such as ³He/⁴He (posi-
tive) and δ¹³C (negative) can be indicative of a
magmatic source (Sorey et al. 1998; Allard et al.
1997; Federico et al. 2002).

Fluctuations of the water table/spring dis-
charge have also been frequently recorded before
the onset of magmatic activity and are often
interpreted as the effect of opening and closing of
fractures during the intrusion of fresh magma
(Tanguy 1994; Shibata and Akita 2001; Newhall
et al. 2001). Alternatively, the effect of the water
phase transition from liquid to gas at relatively
shallow (<2 km) depth may also cause uplift of
the water table (Jasim et al. 2015). Water levels
in boreholes can be relatively easily monitored
and have been observed to respond to tectonic
and volcanic perturbations in a range of volcanic
settings (e.g. Usu Volcano, Japan; Kilauea Vol-
cano, Hawai'i; Koryajskii Volcano, Kamchatka).
Level changes have been attributed to thermal pressurisation, compression of water saturated rocks and opening of fractures in response to the intrusion of magma. However, the magnitude and even the sign of this hydrological response is a complex function of the nature of the thermal and mechanical perturbation, the orientation and connectivity of permeable pathways and even the design of the well itself. Thus, interpreting such signals in relation to magmatic unrest requires some prior understanding of the hydrological features involved.

Spring discharge fluctuations are harder to measure than well water level changes, especially on the flanks of volcanoes experiencing unrest, and are therefore less well documented. Decline in non-thermal spring discharge on Centre Hills, Montserrat, were observed prior to the onset of volcanic activity at the adjacent Soufrière Hills Volcano in 1995. This was followed by an increase after the cessation of the second eruptive phase in 2004 (Hemmings et al. 2015a). The mechanism behind such fluctuation is unclear, it may relate to fracture dynamics associated with magmatic pressurisation (and depressurisation). Regular temperature measurement and chemical analysis of spring systems and hydrological lakes in volcanic settings can provide insights into the differences and changes in flow pathways related to magmatic perturbation.

Chemical analysis of thermal springs and fumaroles are more common hydrological/hydrothermal monitoring strategies employed at active volcanoes. Changes in chemical composition and isotopic concentrations are often related to changes in the relative contribution of magmatic fluids to other groundwater species. Although there are general indicators for increased magmatic fluid contribution to discharging hydrothermal fluids, effective use of spring temperature, chemistry and discharge data as volcanic unrest monitoring tools requires a good understanding of the underlying composition of the hydrological and hydrothermal features and the likely sensitivity to different perturbation scenarios, in specific volcanic areas. For example, Taran et al. (2008) proposed that lower flowing, acidic springs at El Chichón

volcano, Mexico would make a better monitoring target than near-neutral, high discharge springs. Potential chemical indicators of unrest in these springs include increase in relative concentration of Mg, and increase in Cl/B and Cl/Br ratios.

In summary, the physical and chemical signals or hydrological perturbation associated with magmatic unrest are complex and site dependent. Relative contributions of different fluid components and their interaction with existing flow pathways, can determine how a system evolves during quiescent periods. This evolution dictates the likely response to thermodynamic and chemical perturbation associated with the initiation of volcanic unrest. Given the intricate feedbacks between magma, hydrology and hazards and the sudden changes of the systems involved, only high-frequency (hour-week) monitoring of temperature, pH, electrical conductivity of water, depth of the water table, REE, rock forming elements and dissolved gas coupled with geophysical monitoring can untangle the evolution of the magmatic system, the opening/closure of fractures and the seasonal groundwater dynamic.

## 6 Open Questions—Important Unknowns

We have established that circulating hydrothermal fluids are highly reactive and may result in precipitation of alteration products or dissolution of the host rock, both of which may cause porosity, and permeability changes. However, the precise nature of this alteration varies with fluid chemistry, rock mineralogy and thermodynamic conditions. This uncertainty in alteration makes predicting the impact of water/rock interaction (WRI) on porosity and permeability, and therefore on fluid flow, particularly challenging. The background fluid flow regime is a critical part of the local expression of heat-flow as well as pressure distribution that surrounds a magmatic system. As such it may exercise an important control over the dynamics of the magmatic system that is currently poorly understood. Data constraining the time scales over which hydrothermal alteration occurs, related to
data gathered from long term monitoring of coupled magmatic-hydrothermal systems, are thus crucial to inform ongoing interpretations and further predictions of areas experiencing magmatic-hydrothermal unrest.

**Acknowledgements** The authors thank Shaul Hurwitz for his constructive comments during the review of this manuscript and Pablo Palacios for carefully editing the extended abstract. This project has received funding from the European Union's Seventh Program for research, technological development, and demonstration under grant agreement no. 282759 (VUELCO). K. Mayer and B. Scheu also acknowledge the support of a PROCOPE grant (Hot Hydrothermal Volcanic Systems; project-ID 57130387), funded and implemented by the Deutscher Akademischer Austauschdienst (DAAD) in Germany, and the Ministry of Foreign and European Affairs (MAE) and the Ministry of Higher Education and Research (MESR) in France. K. Mayer and B. Scheu acknowledge the support of the ERC Advanced Investigator Grant (EVOKES—no. 247076).

## Glossary

**Hydrothermal system** A groundwater system that has an area of recharge, an area of discharge, and a heat source. When a magma supplies the heat source and volatiles, the hydrothermal system is termed a magmatic hydrothermal system

**Hydrothermal alteration** hydrothermal alteration is a complex process involving chemical, mineralogical, and textural changes, due to the interaction of hot aqueous fluids and the host rocks through which they circulate

**Permeability** Connected pore space of a rock or lithology, controlling fluid flow within a reservoir

**Porosity** Ratio of voids over the total volume of the rock with respect to a reference rock-volume

**Water/rock interaction (WRI)** The set of chemical reactions between aqueous fluids and rocks. These reactions modify both the chemistry of the circulating fluid and the mineralogy of the host rock

**Fluid** generic term for either liquid or gas or both

**Liquid** liquid state of matter (e.g., water)

**Gas** gas state of matter (e.g., vapour)

**Water table** level below which water saturation occurs

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# Experimental Simulations of Magma Storage and Ascent

C. Martel, R.A. Brooker, J. Andújar, M. Pichavant, B. Scaillet and J.D. Blundy

## Abstract

One of the key issues in utilizing precursor signals of volcanic eruption is to reliably interpret geophysical and geochemical data in terms of magma movement towards the surface. An important first step is to identify where the magma is stored prior to ascent. This can be studied through phase-equilibrium experiments designed to replicate the phase assemblage and compositions of natural pyroclasts or by measuring volatiles in melt inclusions from previous eruptions. The second crucial step is to characterize the magmatic conditions and processes that will guide the eruption style. This may be addressed through controlled dynamic decompression or deformation experiments to examine the different rates that govern the kinetics of syn-eruptive degassing, crystallization, and strain. Comparing the compositional and textural characteristics of these experimental products with the natural samples can be used to retrieve magma ascent conditions. These experimental simulations allow interpretation of direct observations and *in situ* measurements of syn-eruptive processes leading to more accurate forecasting of future eruptive scenarios.

## 1 Linking Geophysical and Geochemical Warning Signals to Magmatic Processes

A key objective in volcanology is to forecast eruptions, i.e. to establish when, where, and how an eruption will occur and what magnitude it will be. The prerequisite of such forecasting is to (i) detect reliable precursory signals of magma ascent to subsurface and (ii) anticipate the eruption style in order to inform the crisis

<footnote>C. Martel (✉) · J. Andújar · M. Pichavant ·
B. Scaillet
Institut Des Sciences de La Terre D'Orléans,
Université D'Orléans-CNRS-BRGM, Orléans,
France</footnote>

<footnote>e-mail: caroline.martel@cnrs-orleans.fr</footnote>

<footnote>R.A. Brooker · J.D. Blundy
School of Earth Sciences, University of Bristol,
Wills Memorial Building, Queens Road, Bristol BS8
1RJ, UK</footnote>
management strategy. To reach these objectives, the intensification of the geophysical and geochemical signals associated with an unrest episode has to be interpreted in terms of magma movements. This is a far from trivial task because (i) seismicity has to distinguish signals related to magma movements from those of rock fracturing and/or gas percolation (see Chapter "Volcano Seismology: detecting unrest in wiggly lines"), (ii) ground deformation has to be precisely track magma motion towards the surface (see Chapter "Volcano geodesy and multiparameter investigations"), and (iii) the flux and the specification of emitted gas at the surface has to be interpreted in terms of magma ascent and degassing (see Chapter "Volcanic gases and low temperature volcanic fluids"). For any of these monitoring signals, their interpretation in terms of impending eruption requires knowing at what depth beneath the volcano magma is stored (magma storage conditions) and how it progresses toward the surface (magma ascent conditions). A pertinent approach to investigate the conditions of magma storage and ascent consists of comparing petrological and textural studies of previously erupted products to the results of experimental simulations carried out under realistic magma conditions. During the last decades the development of powerful analytical and experimental tools has led to great advances in this type of investigation. Of course, this is an a posteriori approach (using previously erupted products) that relies on considering past eruptive behaviour of a given volcanic system as a guide to future activity. For this reason, it is necessary to understand the fundamental magmatic processes at any particular volcano and in the long term, build up a record that links the pre-eruptive signals with eruptive products. In this way we can successfully use the warning signals to forecast or even start to predict the timing and style of an imminent eruption.

## 2 Magma Storage

How and where magma is stored before an eruption are enduring and complex questions, particularly given the range of hypotheses covering single versus multiple storage regions or dyke feeder systems. Key parameters in interpreting the precursory geophysical and geochemical signals are the depth of storage and the volatile content dissolved in the magma, as the exsolution of these provides an important driving force for explosive eruptions. Magma consists principally of phenocrysts (crystals larger than 50–100 µm) coexisting with a silicate melt containing dissolved volatile species (e.g. H₂O, CO₂, S species, F, Cl, etc....). To assess the magma storage conditions, one has to determine the parameters (i.e. the pressure, temperature, redox state, volatile content) that govern equilibrium between melt and the phenocrysts. This is accomplished by comparing the phase assemblage and compositions of the natural products to those obtained by phase-equilibrium experiments where all these parameters are controlled.

### 2.1 Decoding Natural Pyroclasts

**Phenocrysts.** Mineral compositions are sensitive to various intensive (pressure, temperature, fO₂) and extensive (host liquid composition and volatile content) variables. In some cases, the compositions can be used as geothermobarometers (or hygro/oxy/chemo-meters) to retrieve first-order parameters of crystallization. For instance, we can calculate crystallization temperature from the composition of orthopyroxenoclinopyroxene pairs (e.g. Lindsley and Andersen 1983) or amphibole-plagioclase pairs (e.g. Holland and Blundy 1994), temperature and oxygen fugacity from Fe–Ti oxide pairs (e.g. Ghiorso and Evans 2008) or pressure from
amphibole composition (e.g. Ridolfi and Renzulli 2012). Such calculations require two main criteria: (i) the thermobarometers must have been calibrated experimentally for conditions and compositions relevant to those of the target samples and (ii) the analysed phenocrysts must be identified as part of the phenocryst assemblage in chemical equilibrium with the melt in the reservoir under pre-eruptive conditions. This can achieved by comparison with experiments that simulate a range of variables. Establishing different ‘equilibrium’ events also becomes crucial as magma mixing (and associated reheating prior to eruption or further crystallization outside the storage zone) very often blur the pre-intrusion/eruption equilibrium conditions. Moreover, many eruptions involve the entrainment of crystals that did not grow from the erupted magma and are consequently out of equilibrium with the melt in which they occur. These are often termed xenocrysts or antecrysts and increasingly recognised as important aspects of magma’s crystal cargo (e.g. Streck 2008; Kilgour et al 2013). The processes of mixing, reheating, and potentially decompression recorded in these crystals, may be used to provide

timescales of reservoir dynamics, such as residence times between the last magma recharge and eruption (e.g. Saunders et al. 2012). Indeed, the compositional zoning of some phenocrysts witness cycles of recharge events prior to the final eruption (e.g. Druitt et al. 2012). Diffusion chronometry provides a means to infer the crystal residence time in the reservoir prior to eruption (e.g. Costa and Morgan 2010), but again requires experimental calibration of the rates involved under various conditions.

**Glass inclusions.** Glass (or “melt”) inclusions are aliquots of melt that are trapped in crystals, usually during stages of rapid crystal growth (Fig. 1a). If trapped in phenocrysts crystallizing in the magma chamber, these inclusions will be the only witness of the composition of the melt in equilibrium with the phenocrysts prior to eruption, provided that they remained sealed after entrapment (no volatile leak, no crystallization or post-entrapment interaction with the host mineral). Indeed, after leaving the storage region, the melt initially surrounding the phenocrysts is likely to degas and crystallize microlites upon ascent, therefore deviating significantly from its

<figure><img src="image_35.png" /></figure>

**Fig. 1** Melt inclusion (MI) volatile contents from the Morne aux Diables complex (MAD), northern Dominica, as determined by ion-microprobe measurements. a Close up of inclusions shows they were either slightly vesiculated, during capture (fluid-saturated) or became so during ascent. The right hand crystal contains a very large melt inclusion around the bright oxide inclusions that has vesiculated more extensively, possibly due to cracks developing and exposing the melt to the full

decompression effect during eruption. In b the maximum amount of H₂O and CO₂ that can be dissolved in a melt at any given depth follows pressure dependent isopleths that can be determined experimentally (in this case from Tamic et al. 2001). This represents the minimum pressure (depth of entrapment of a MI). These trends are controlled by the lower solubility of CO₂ compared to H₂O, such that there is a rapid drop in CO₂ before H₂O starts to be lost (coloured arrows)
pre-eruptive composition and character. The two main objectives of studying glass inclusions in phenocrysts are (i) mineral-melt thermobarometry (clinopyroxene-melt or plagioclase-melt; e.g., Putirka 2008; Water and Lange 2015) and (ii) pre-eruption volatile contents and speciation that can be converted into gas saturation pressure using experimentally-derived volatile solubility laws and which represent an end-member in the calculations of the volcanic degassing budget (dissolved vs. released gases). This latter approach requires that the magma be demonstrably volatile-saturated at the time of melt inclusion entrapment, for example by looking at relationships between dissolved volatile contents and trace elements in inclusions (Blundy and Cashman 2008). If the magma was not saturated, the calculated pressure generally represents a minimum depth prior to volatile volatile-saturated, the calculated pressure generally represents a minimum pressure estimate. As an example, the glass inclusions in phenocrysts from pyroclasts of the last eruption of Morne aux Diables, Dominica, have been analysed by ion microprobe. The data show about 6 to 8 wt% dissolved H₂O, up to 3000 ppm CO₂, together with some chlorine and fluorine. Available H₂O–CO₂ solubility models based on experiments for comparable compositions (e.g. Tamic et al. 2001) indicate melt entrapment during phenocryst crystallization at pressures as high as 400–500 MPa (depth of <22 km) for sample DC139 and shallower depth for sample DC08 although it is also possible this magma originally contained even more CO₂ than is recorded by any of the analysed melt inclusions. (Figure 1b. Small vesicles in the inclusions of Fig. 1a suggest there was exsolution of volatiles during ascent. The possible disequilibrium between such vesicles and melt produced during very rapid ascent is discussed in Chapter “Magma degassing: the diffusive fractionation model and beyond”.

**Complexity of open-systems.** An eruption is often triggered by the injection of new magma into the reservoir, which reheats, mingles, and mixes with the resident magma. Upon ascent to the surface, the two batches may interact to varying degrees and can further crystallize and

cool. Therefore, imprudent use of geothermobarometers may yield large pressure and temperature ranges that cannot be easily reconciled with a single/specific episode of equilibrium crystallization. In order to retrieve the storage conditions of the resident magma, i.e. prior to deep magma mixing or before possible modification within the volcanic conduit, a detailed petrological study is necessary to identify precisely the different stages of perturbation and their characteristics in terms of phase assemblage and chemical composition. Experimental petrology is one of the tools that helps to unravel the various magmatic processes at work and their relative impact on magma chemistry and magmatic evolution.

## 2.2 Phase-Equilibrium Experiments

Phase-equilibrium experiments use natural (or analogue) products as starting material that are subjected to high-pressure (HP) and high-temperature (HT) in various devices under controlled conditions of pressure, temperature, oxygen fugacity, and volatile content. Such experiments are powerful tools to simulate realistic magmatic conditions for the crustal reservoirs that feed volcanic systems. Experimental equipment ranges from cold-seal pressure vessels, internally-heated pressure vessels, and piston-cylinder apparatus, depending on the investigated conditions. The principle is to reproduce the natural assemblage, proportion, and chemical compositions of the phenocrysts and equilibrium coexisting melt in the magma storage region. This is then compared with the natural samples in order to retrieve the pre-eruptive crystallization conditions (Fig. 2).

The first prerequisite for such an approach is a detailed petrological and mineralogical study of the erupted samples in order to identify the magmatic processes potentially perturbing equilibrium crystallization in the reservoir. Indeed, the relevance of the experimental study relies on accurate petrological knowledge that dictates the choice of the starting material and run procedure (Pichavant et al. 2007). The second prerequisite
<figure><img src="image_36.png" /><figcaption>**Fig. 2** Phase equilibrium experiments for the Tungurahua 2006 andesite, showing a mineral stability fields as a function of temperature and H₂O content at 200 MPa and oxidizing conditions (fO₂ = NNO + 0.8 log unit). SEM images of the experimental charges are shown in b for 1000 °C and H₂O saturation (~6.1 wt% H₂O</figcaption></figure>

is the appropriate choice of the volatile species (H₂O, CO₂, sulphur, etc.) and contents to be added to the starting material. These dissolved volatiles can impact crystallization (sequence, mineral stability fields, and phase compositions; Scaillet and Pichavant 2003; Riker et al. 2015). Where volatile measurements on melt inclusions are available, these can be used, although it is possible that the melt inclusions may no longer be representative of the initial volatile content (e.g. due to possible leakage or recrystallization). Consequently, volatile species and contents become experimental parameters that have to be varied within a range that is first inferred from the study of the glass inclusions (when available), and/or based on previous work carried out on similar bulk rock compositions. It should be remembered that entrapment of melt inclusions requires crystallisation to occur and magmas may undergo substantial volatile loss (degassing) prior to any crystallisation. For instance, Blundy

dissolved in melt), c 975 °C and ~5.7 wt% H₂O, and d 975 °C and 5.2 wt% H₂O. Note the drastic increase of crystal content with cooling or dehydration; gl for glass (L for silicate liquid), ol for olivine, cpx for clinopyroxene, opx for orthopyroxene, pl for plagioclase, amph for amphibole, mag for magnetite, and ilm for ilmenite

et al. (2010) speculate that many magmas had original CO₂ contents significantly higher than those recorded by any melt inclusions.

It is clear that an approach involving the combination of petrological study of the natural products and phase-equilibrium experiments can help to retrieve the storage conditions of magmas of a wide range of compositions. This is a pre-requisite step for the interpretation of unrest signals and construction of eruptive scenarios.

## 3 Magma Ascent

During ascent in the volcanic conduit the silicate melt around the phenocrysts degasses by exsolving its dissolved volatiles as gas bubbles. This may lower the liquidus triggering the crystallization of microlites (i.e. crystals smaller than about 50–100 µm). The residual melt is transformed both chemically, by degassing and
differentiation as microlites crystallize and physically, by an increase in melt viscosity and a change from a single liquid phase to a three-phase suspension (i.e. liquid, gas bubble, and microcrystals). Both types of transformations have drastic effects on the bulk magma flow conditions (rheology) that control ascent rate and the ductile versus brittle behaviour of the magma.

The rate of magma decompression/ascent is the key parameter that controls the kinetics of degassing and crystallization, and ultimately, the eruptive style. In silicic to intermediate systems, slow ascent rates typical of effusive eruptions such as lava flow or dome growth, i.e. cm/s to mm/s (Gardner and Rutherford 2000) yield timescales long enough for extensive degassing and crystallization. In contrast, the high ascent rates prevalent during paroxysmal Strombolian or Plinian eruptions (i.e. of the order of m/s; Gardner and Rutherford 2000) are able to generate physico-chemical disequilibria of both degassing and crystallization processes, driving gas overpressures that may be released explosively.

## 3.1 Textures of Natural Pyroclasts

Decompression-induced degassing of the magma creates gas bubbles, the number density of which has been demonstrated to correlate with the decompression rate simulated by experiments (Mourtada-Bonnefoi and Laporte 2004; Mangan et al. 2004). The growing bubbles can rapidly coalesce and form gas escape channels. In this case, the bubble number densities in the erupted/quenched pyroclasts may no longer be representative of the initial ascent rates under which nucleation was triggered. The timescale of outgassing by magma foam collapse varies from a couple of hours to about 1000 h for magmas having bulk viscosities of ~10⁴ to 10⁵.⁵ Pa.s, respectively (Martel and Iacono-Marziano 2015). This restricts the use of the degassing process to simulations of rapid magma ascent rates such as those during Plinian events. To investigate longer transit times in the conduit, one requires information from magmatic processes with timescales longer than degassing. Microlite crystallization is one of those

processes, because diffusion in the melt, that controls crystal growth, occurs on timescales ranging from hours in mafic melts, to days in silicic melts. Microlite number density, volume proportion, size and shape have all been used as markers of the undercooling (liquidus temperature minus magma temperature) that drives crystallization (e.g. Hammer et al. 1999); the higher the undercooling, the more numerous, smaller, and irregularly-shaped the crystals. With decompression (and dehydration of the melt), liquidus temperature increases (as does undercooling), so that it becomes possible to infer the depth of crystallization in the conduit by relating the textural characteristics of the microlites to undercooling and pressure (Fig. 3). This approach has been used by Melnik et al. (2011) to constrain both magma flow and reservoir shape for the 1980–86 dome-forming eruptions of Mount St. Helens (USA). Such modelling requires an accurate determination of the dependence of undercooling on pressure, which can be achieved through decompression experiments (e.g. Riker et al. 2015).

## 3.2 Dynamic Experiments

Dynamic experiments, such as decompression, deformation or shock-wave experiments, are valuable tools to investigate degassing, crystallization, strain, mixing, or fragmentation of a magma. They provide information on the physics, chemistry, and kinetics of syn-eruptive magmatic processes, which can be used in turn to decode natural pyroclast formation in order to better identify geophysical and geochemical precursory signals of an eruption. Shock-wave experiments dedicated to the fragmentation process are covered in Chapter “From unrest to eruption: Conditions for phreatic versus magmatic activity”; the discussion below concentrates on decompression and deformation experiments.

Decompression experiments performed at elevated pressure (HP) and temperature (HT) have proved useful in accurately simulating magma ascent in volcanic conduits (e.g. Hammer and Rutherford 2002). In particular, experimental decompression rates can cover the most of the perceived range of magma ascent rates during
<figure><img src="image_37.png" /><figcaption>**Fig. 3** Relationships between effective undercooling ($\Delta T_{eff}$; in green) and microlite textural characteristics in a H$_2$O-saturated rhyolitic melt (modified after Mollard et al. 2012). Plagioclase microlites crystallized at 150 MPa after an isothermal quasi-instantaneous decompression from 200 MPa, i.e. $\Delta T_{eff} = 50$ °C, are</figcaption></figure>

represented by scarce large tabular crystals. With decreasing pressure and increasing $\Delta T_{eff}$, microlites become more numerous, but smaller in size, and they show more complex shapes ranging from skeletal (hollow) to dendritic

volcanic eruptions. For instance, Plinian ascent rates of the order of m/s can be simulated experimentally by decompression durations from seconds to hours whereas the slow ascent rates recorded for dome eruptions can be reproduced by decompression durations of several days or weeks. More generally, decompression experiments can simulate different natural eruptive scenarios depending on the applied decompression rate, final pressure, and dwell time at final pressure. In basaltic H$_2$O- and CO$_2$-bearing magmas, experimental decompression in the duration range of <1–10 h has provided information on degassing processes leading to either regular or paroxysmal Strombolian eruptions (Chapter 15 “Magma degassing: the diffusive fractionation model and beyond”). In silicic melts, decompression pathways and durations from ten seconds to forty days have been investigated experimentally to evaluate the lifetime of rhyolitic foams as a function of bulk viscosity

(Martel and Iacono-Marziano 2015). Deformation experiments performed in vessels equipped with torsion or coaxial deformation modules have shown that the lifetime of such magmatic foams is drastically reduced when a differential stress field prevails, because it enhances bubble coalescence (e.g. Okumura et al. 2009). The recent implementation of HP-HT devices that allow magma deformation at pressure coupled with in situ measurements of permeability, represents a considerable step forward for investigating the explosive-effusive transition of volcanic eruptions in the laboratory under realistic conditions (Kushnir et al. 2017).

Figure 4 illustrates how timescales of degassing and crystallization during decompression can be used to decipher eruption style. Magmas from both, Plinian and dome-forming eruptions (dome, block-and-ash flows, surges), degas during ascent. However, gases in dome-forming magmas escape from the melt (leading to dense pyroclasts)
**Fig. 4** Deciphering eruption explosivity from decompression-induced degassing and crystallization experiments (see text)

[Photograph of Redoubt is from R. Clucas (USGS), Mt. Pelée is from Lacroix (1904), and Merapi comes from the website: gunungmerapi. weebly.com]

whereas Plinian foams have no time to collapse through gas escape (leading to pumiceous pyroclasts) which suggests Plinian ascent durations are limited to a couple of hours (Martel and Iacono-Marziano 2015). Furthermore, in contrast to Plinian magmas, dome-forming magmas have time to crystallize during ascent. At Mt. Pelée, the moderately-explosive block-and-ash flows in 1929–1932 may have degassed and crystallized continuously during an ascent lasting more than 3–6 days, so that little gas overpressure remains at dome level. In contrast, the devastating surges in 1902 may have resulted from rapid ascent (i.e. <3 days) that did not allow crystallization in the conduit, followed by extensive microlite crystallization at dome level (due to large effective undercooling). The exsolving gas and high overpressurization resulting from this extensive crystallization may have triggered the violent surges (Martel 2012).

<figure><img src="image_38.png" /></figure>

This application of decompression experiments highlights the possibility of forecasting the style of an eruption provided the magma ascent rate towards the surface can be determined by some remote means (seismology, gravity, geodesy, gas discharge).

## 4 Future Directions

The combination of equilibrium and dynamic experiments can simulate many of the conditions relevant to magma eruption, because realistic pressures, temperatures and rates of decompression or shear are now accessible in the laboratory. The one parameter that remains impossible to simulate is an extended timescale. Experiments last typically a maximum of weeks, or occasionally months. In practical terms, this leads to small crystal sizes compared with nature, occasional
difficulties in establishing equilibrium, or very short diffusion profiles for controlled ‘disequilibrium’ experiments. However, as new analytical techniques are developed we can start to make nanoscale measurements that allow us to measure profiles developed on laboratory timescale, providing access to ever faster natural processes (Saunders et al. 2014; Lloyd et al. 2014).

Development of *in situ* observation or measurement techniques represents a major step forward in the understanding of the magmatic processes linked to volcanic eruptions. It is becoming possible to make *in situ* observations using cameras coupled to HP-HT vessels equipped with transparent windows (e.g. Gondé et al. 2011) or 4D *in situ* X-ray tomography (e.g. Pistone et al. 2015). HP-HT vessels coupled with *in situ* analytical techniques are already capable of measuring the volatile species either dissolved in the melt under pressure by *in situ* spectroscopy techniques (Raman or Infrared) or exsolved as vapour. Laboratory simulation of magma degassing and crystallization with these new *in situ* approaches will allow the identification of potential geophysical and geochemical signals that may be used as unrest precursors.

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# Magma Chamber Rejuvenation: Insights from Numerical Models

C.P. Montagna, P. Papale, A. Longo and M. Bagagli

## Abstract

Most volcanic systems on Earth are characterized by chemically different magmas that can be found in the erupted products throughout their history. The reasons are multiple, including variations in the mantle source and/or crustal assimilation, as well as shallower processes such as fractional crystallization or mixing and mingling. Magma chamber rejuvenation indicates the processes that happen whenever a magma intrudes from the mantle to shallower depths and encounters an already established storage zone (i.e. a magma chamber or reservoir). Magmas rising from depth are typically characterized by higher temperatures, larger volatile contents and more primitive, mantle-like compositions than those residing in the shallow crust. The interaction with magmas that have already resided at shallower depths for a while (years to thousands of years) varies the physical and chemical properties of both the involved magmatic end-members. Typically, volatile-rich magmas coming from depth are lighter than degassed shallow magma; therefore, a gravitational instability sets in as the two come into contact, which generates convection and thus intense mingling and mixing among the two. These dynamic interactions cause variations in the physical and chemical properties of the magmas themselves, as well as in the stress conditions both inside the reservoir and in the host rock. The volcanic system as a whole enters an unrest scenario, that can evolve to eruption or not depending on the specific conditions. Numerical simulations of the dynamics within magmatic systems can shed light on the features of magma chamber rejuvenation, providing the time

<footnote>C.P. Montagna (✉) · P. Papale · A. Longo ·
M. Bagagli
Istituto Nazionale di Geofisica e Vulcanologia, via
U. della Faggiola 32, Pisa, Italy
e-mail: chiara.montagna@ingv.it</footnote>

<footnote>Present Address:
M. Bagagli
D-ERDW, ETH-Zuerich, Zürich, Switzerland</footnote>
scales of mixing processes and possibly of the evolution towards eruption. Coupling with models for the visco-elastic response of the host rock allows the identification of the onset of recharge processes from the analysis of geophysical signals observed at the surface.

Keywords

Magma chamber • Magma dynamics • Magma mixing

# 1 Extended English/Spanish abstract

Most volcanic systems on Earth are characterized by chemically different magmas that can be found in the erupted products throughout their history, either in synchronous eruptive episodes, or in different epochs of volcanic activity. This chemical heterogeneity can have multiple reasons, and it originates from deep in the mantle, due to variations in the source and/or crustal assimilation during ascent, to shallower crustal regions, where processes such as fractional crystallization or mixing and mingling take place. Magma chamber rejuvenation comprises some of the aforementioned processes at shallow level. Whenever a magma intrudes from the mantle to shallower depths and encounters an already established storage zone (a magma chamber or reservoir), the magma already emplaced gets rejuvenated by the incoming more primitive magma. Magmas rising from the deep regions of a volcano feeding system are typically characterized by higher temperatures, larger volatile contents and more primitive, mantle-like compositions. On the other hand, magmas that have resided at shallower depths for a while (years to thousands of years) have evolved by fractional crystallization, thus they have changed their composition towards a more felsic one, and have lost most of their gaseous phase, that can escape towards the surface. The interaction between primitive and evolved magmas varies the physical and chemical properties of both the end-members involved. Typically, volatile-rich magmas coming from depth are lighter than degassed shallow magma, albeit

having a higher liquid density: they are characterized by a much larger gas content. The light magma tends to rise inside the denser reservoir; a gravitational instability sets in as the two magmatic mixtures come into contact, and generates convection inside the reservoir. As a consequence, intense mingling and mixing are generated among the two end-members. These dynamic interactions cause variations in the physical and chemical properties of the magmas themselves, that loose their identity as initial end-members and become a more homogeneous mixture. The volcanic system as a whole enters an unrest scenario, that can evolve to eruption or not depending on the specific conditions. Numerical simulations of a magmatic system representing magma injection into a shallow reservoir show that mixing is very intense at the time of contact, and can be efficient on time scales of hours to day in homogeneizing the system. Depending on the geometry of the volcano feeding system, and even more on the volatile content of the incoming and resident magmas, the process can be suppressed or enhanced. Sills favour mixing, while more vertically elongated, dike-like reservoirs slow the dynamical interactions. As the presence of a gaseous phase is the engine of the gravitational instability that triggers the dynamics, a higher volatile content, which translates into a higher gas content, in the deep regions of the feeding system strongly accelerates the rejuvenation process. As mixing patterns are found almost ubiquitously in products from volcanoes around the world, comparison of the observed features to the model predictions can provide insights on the features of magma chamber rejuvenation, including the time
scales over which mixing processes are efficient and possibly the timings for the evolution towards an eruption or not. Coupling to models that describe the visco-elastic response of the host rock to stress variations within the magmatic system provides hints as to how to identify recharge processes at depth from the analysis of geophysical signals observed at the surface. Characteristic features of ground deformation associated to convection and mixing is the appearance of oscillation of extremely long period, on the order of hours (Ultra-Long-Period, ULP), that can be detected by instruments such as continuous tiltmeters and dilatometers. Their records can identify the onset of the interaction among different magmas, thus provide time scales for unrest duration and evolution.

## 2 Introduction

Magmas evolve in many ways during their residence time within the crust, determining whether they are going to be erupted or not. Magma chamber rejuvenation takes place whenever a magma intruding from the mantle to shallower depths encounters an already established storage zone (i.e. a magma chamber or reservoir). It can involve many different processes such as reheating and melting of the residing magmas, fractional crystallization due to changes in the pressure and temperature conditions, mingling and mixing among the different components; typically, it takes place at shallow crustal depths. Magmas rising from depth are often characterized by higher temperatures, larger volatile contents and more primitive, mantle-like compositions with respect to those that have been residing at shallow levels for a while (months, years to thousands of years). This general scenario can have a variety of declinations, depending on the specific setting and physico-chemical characteristics of the magmatic mixtures involved. The shallow magma can be highly crystalline, a mush, that can be rejuvenated by the heat from the incoming component (Bachmann and Bergantz 2003, 2008; Girard and Stix 2009; Bain et al. 2013; Till et al. 2015); or, at the other end, it can still be hot and

more fluidal, especially if injection episodes are frequent (Voight et al. 2010), giving rise to mixing and mingling phenomena (Montagna et al. 2015). The interaction among the deep and shallow components changes the physical and chemical properties of both the involved magmatic end-members, triggering an unrest phase that can evolve to eruption or not depending on the specific conditions. Evidence of chamber rejuvenation both in igneous and in intrusive rocks, manifested mostly by mingling and mixing patterns, is almost ubiquitous at volcanic systems worldwide, and it is often invoked as eruption trigger.

Magma movement at depth implies mass re-distribution, pressure changes, and pressure transients which translate into variations in the gravity field, shape and slope of the volcano flanks, and seismic signals registered at the surface. Understanding the complex relationships between quantities measured by volcano monitoring networks and shallow magma processes is a crucial step for the comprehension of volcanic processes and in evaluating more realistic hazard forecast. The ability to detect the onset of magma recharge at depth is fundamental as it can provide hints to unrest duration and evolution, and possibly eruption timings.

In this work we describe a forward-modeling approach to describe magma chamber dynamics, specifically for what concerns rejuvenation episodes, and link it to the geophysical observables that are expected as a consequence. This provides a framework for the consistent interpretation of geological and geophysical records of unrest periods at active volcanoes. This methodology allows for identification of rejuvenation episodes in ground deformation records, and possibly discrimination between those episodes that lead to eruption or not.

## 3 Numerical Simulations of Magma Chamber Rejuvenation

### 3.1 Magmatic System

We refer as an archetypal case to the Phlegraean Fields magmatic system, where seismic imaging and attenuation tomographies have identified a
huge (probably around 10 km wide) magma reservoir at a depth of around 8 km (Zollo et al. 2008; De Siena et al. 2010), while a variety of geophysical and geochemical evidence suggests that smaller (probably less than 1 km³), shallower batches of magma have been forming throughout the caldera history at virtually any depth smaller than 9 km (Arienzo et al. 2010; Di Renzo et al. 2011). These shallow magma bodies have been identified as actively involved in past eruptions, which at least in some cases shortly followed the arrival of volatile-rich, less differentiated magmas from the deep feeding system (Arienzo et al. 2009; Fourmentraux et al. 2012). Chemical compositions of erupted magmas range from shoshonitic to trachytic to phonolitic; geochemical analyses on melt inclusions suggest a variety of processes contributing to this variability, such as recharge from depth, intra-chamber mixing, syn-eruptive mingling (Arienzo et al. 2010; Fourmentraux et al. 2012). The same analyses

show that deep magmas are typically rich in gas, especially CO₂ (Mangiacapra et al. 2008), while shallow magmas are unusually crystal-poor, down to less than 3 wt% (Arienzo et al. 2009). To study the magmatic dynamics occurring as a consequence of a recharge event, we simplify the magmatic system retaining its most peculiar features. We model the injection of CO₂-rich shoshonitic magma coming from a deep reservoir into a shallower, much smaller chamber, containing more evolved and partially degassed phonolitic magma (see Table 1 for compositions). The two chambers are connected by a dyke. This idealized layout captures several first-order characteristics of prototype magmatic systems, including a composite structure, vertical extension, and heterogeneous composition, and it approximates systems composed by long-lived, interconnected multiple reservoirs believed to exist at many active volcanoes (Elders et al. 2011).

<table><caption>**Table 1** Composition of the phonolite and shoshonite magma types employed in the simulations</caption><thead><tr><th>Composition</th><th>SiO<sub>2</sub><br>(wt%)</th><th>TiO<sub>2</sub><br>(wt%)</th><th>Al<sub>2</sub>O<sub>3</sub><br>(wt%)</th><th>Fe<sub>2</sub>O<sub>3</sub><br>(wt%)</th><th>FeO<br>(wt%)</th><th>MnO<br>(wt%)</th><th>MgO<br>(wt%)</th><th>CaO<br>(wt%)</th><th>Na<sub>2</sub>O<br>(wt%)</th><th>K<sub>2</sub>O<br>(wt%)</th></tr></thead><tbody><tr><td>Phonolite</td><td>53.5</td><td>0.6</td><td>19.8</td><td>1.6</td><td>3.2</td><td>0.1</td><td>1.8</td><td>6.8</td><td>4.7</td><td>7.9</td></tr><tr><td>Shoshonite</td><td>52.5</td><td>0.9</td><td>17.6</td><td>1.9</td><td>5.7</td><td>0.1</td><td>3.6</td><td>7.9</td><td>3.4</td><td>4.3</td></tr></tbody></table>

<figure><img src="image_39.png" /><figcaption>**Fig. 1** Initial conditions for the numerical simulations of the magmatic system. On the *left*, the whole domain is shown, indicating the two magmatic end-members. On the *right*, the *upper* portion of the domain shows the three different geometries explored</figcaption></figure>
Figure 1 shows the system domain for the numerical simulations. We assume one of the horizontal dimensions of the magmatic system to be much larger than the other, so that our domain is two-dimensional. The deep chamber is elliptical, 1 km thick and 8 km wide; its top is at 8 km depth. The geometry of the shallow chamber has been varied as shown in Fig. 1, keeping its surface area fixed. In the elliptical cases, the semi-axes measure 400 and 800 m, respectively, while the circular chamber has a radius of 283 m.

The initial conditions of the system are also shown in Fig. 1. The shallow chamber hosts a differentiated, volatile-poor phonolitic magma. Its volatile content has been varied from 0.3 wt% CO₂ and 2.5 wt% H₂O to 0.1 wt% CO₂ and 1 wt% H₂O. In the feeding dyke and deep reservoir is a less evolved, basaltic shoshonite, containing 1 wt% CO₂ and 2 wt% H₂O. Such a low water content in the phonolitic end-member derives from melt inclusion data from Phlegrean Fields (Arienzo et al. 2010). Typically, more evolved magmas are expected to have a relatively larger water content (Signorelli et al. 2001; Cannatelli et al. 2007; Pappalardo et al. 2007; Mollo et al. 2015), resulting possibly in smaller density contrasts at the interface among the two magmas thus less efficient mixing dynamics.

Volatiles partitioning between gaseous and liquid phases is computed following Papale et al. (2006) as a function of composition and pressure. Pressure at time 0 consists of a depth-dependent

turn depends on pressure, thus on the depth at which the interface is placed, which is different for each geometry of the shallow chamber (Fig. 1). Temperature differences between interacting magmas are often negligible (Sparks et al. 1977), particularly at Phlegraean Fields (Mangiacapra et al. 2008; Arienzo et al. 2010), thus the system is assumed isothermal. As a result, there is no need to speculate on the thermal status of the surrounding rock, thus reducing model uncertainties. Moreover, heat transfer effects are expected to play a minor role on the short simulated time scales (hours; Di Renzo et al. 2011).

## 3.2 Magma Dynamics

Interaction among the two magmas develops as a consequence of the initial gravitational instability at the interface. We solve numerically the two-dimensional space-time evolution of the system, consisting of a mixture of two different magmatic components, each of them including a liquid (silicate melt and dissolved volatiles) and a gaseous (exsolved volatiles) fractions. The equations of motion for the mixture express conservation of mass for each component $k = 1, 2$, and momentum for the whole mixture (Longo et al. 2012a):

$$ \frac{\partial (\rho y_k)}{\partial t} + \nabla \cdot (\rho \mathbf{u} y_k) = -\nabla \cdot (\rho D_k \nabla y_k), \quad \sum_k y_k = 1 \qquad (1) $$

$$ \frac{\partial (\rho \mathbf{u})}{\partial t} + \mathbf{u} \cdot \nabla (\rho \mathbf{u}) = -\nabla p + \nabla \cdot \left\{ \mu \left[ \nabla \mathbf{u} + (\nabla \mathbf{u})^T - \frac{2}{3} \nabla \cdot \mathbf{u} \right] \right\} + \rho g. \qquad (2) $$

magmastatic contribution superimposed to the host rock confining pressure. The interface between the two magmas, at the inlet of the shallow chamber, is gravitationally unstable, the lower magma being less dense due to its higher gas content. The dynamics is solely driven by buoyancy, without any external forcing.

The density contrast at the interface varies for each simulated scenario, as it depends on both volatiles content and their partitioning between liquid and gaseous phases; volatiles exsolution in

In the above, $t$ is time; $\rho$ is mixture density, $y_k$ is mass fraction of component $k$, $\mathbf{u}$ is fluid velocity, $D_k$ is the k-th coefficient of mass diffusion, $p$ is pressure, $\mu$ is viscosity and $g$ is gravity acceleration.

The magmatic mixture is considered ideal. Its density is evaluated as weighted sum of the components' densities; for each component, density is calculated using a non-ideal equation of state for the liquid phase, real gas properties and ideal mixture laws for multiphase fluids.
Mixture viscosity is computed through standard rules of mixing for one phase mixtures and with a semi-empirical relation in order to account for the effect of non-deformable gas bubbles. Liquid viscosity is modeled as in Giordano et al. (2008), and it depends on liquid composition and dissolved water content. The assumption of Newtonian rheology is justified by the very low strain rates and the crystal-free nature of the magmas. The generalized Fick's law is used to describe mass diffusion. Volatile partitioning between gaseous and liquid phases is evaluated at every point in the space-time domain as function of mixture composition and pressure as in Papale et al. (2006). All the physical properties of the two magmas are evaluated at every point in the space-time domain depending on the local conditions of pressure, velocity and mass fractions, which are the unknowns in Eqs. (1) and (2). The equations are solved numerically using GALES, a finite element C++ code specifically designed for volcanic fluid dynamics (Longo et al. 2012a).

The evolution in space and time of the system is complex and presents a number of interesting features. Figure 2 summarizes the results regarding magma dynamics, showing the evolution of

composition in time in the shallow chamber for the five different simulation scenarios.

The initial inverse density contrast at the contact interface between the two magmas gives rise to convective mass transfer from the deeper parts of the system to shallower depths and vice versa. The unstable density contrast is solely due to the different volatile content of the two mixtures: the shoshonitic melt has an higher density than the phonolitic. The role played by volatiles is crucial, and it is exsolved gases that ultimately determine the buoyant dynamics. A Rayleigh-Taylor instability develops, which acts to bring the system to gravitational equilibrium by overturning it. The instability develops starting from the perturbed interface, with a first plume of light material that rises into the chamber. Depending on the initial density contrast as well as on the geometry of the shallow chamber, the initial plume starts developing at different times. The dynamics is strongly enhanced by higher density contrasts; geometry also plays an important role when density contrasts are similar, with horizontally elongated, sill-like chambers favouring convection with respect to more dyke-like setups (see also Fig. 2).

<figure><img src="image_40.png" /><figcaption>**Fig. 2** Snapshots of variation of composition with time in the shallower parts of the system for the different simulations. Columns correspond to different simulations; rows correspond to different times</figcaption></figure>