Velasco‑Reyes et al.
Progress in Earth and Planetary Science(2022) 9:19
https://doi.org/10.1186/s40645-022-00477-4

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open Access

Paleotsunami history of Hachinohe, northern
Japan: a multiproxy analysis and numericalmodeling approach
Erick R. Velasco‑Reyes1* , Kazuhisa Goto2, Daisuke Sugawara3, Yuichi Nishimura4, Takahiro Shinohara1 andTakashi Chiba5

Abstract
Paleotsunami studies along the Pacific coast of Tohoku, northern Japan, have been considerably developed recently,
particularly after the massive impact of the 2011 Tohoku-oki tsunami. Nevertheless, in the southernmost Shimokita
Peninsula, studies pertaining to paleotsunami are underdeveloped, leading to a vague understanding of the tsunami‑
genic sources northward of the Tohoku region, along with incomplete hazard evaluation. Paleotsunami deposits in
Shimokita can be related not only to the Japan Trench along the Sanriku coast but also to the Kuril trench along the
Pacific coast of Hokkaido. In this study, we unveiled the paleotsunami history of Hachinohe in northern Tohoku. Usinga combination of sedimentological, geochemical, paleontological, and mineralogical proxies, we characterized sevensand layers that dated from ca. 2700 to ca. 5500 yr BP based on radiocarbon (14C) ages as event deposits of marine
origin. Sedimentological and paleontological evidence coupled with ground-penetrating radar imagery revealed
a marsh environment comprising successive extinct ponds, controlling the depositional environment. Numericalmodeling ruled out the possibility of storms as genetic sources, leading to the conclusion that the presence of event
deposits with marine sediments in the study area would be associated with tsunami inundation episodes. Based on
14
C dating, the mean frequency of recurrence of tsunamis is estimated as 384 years (320–450 yr, 95% confidence inter‑val) and a coefficient of variation of 0.78 (0.68–0.99, 95% confidence interval). The previously recorded limited paleot‑
sunami evidence and absence of an estimated recurrence interval in the Shimokita Peninsula reaffirm the importanceof Hachinohe as a tsunami record site for the activity of both trenches.
Keywords: Paleotsunami deposits, Hachinohe, Japan Trench, Tsunami recurrence, Tsunami modeling, Computed
tomography, XRF
1 Introduction
1.1 Paleotsunami studies in the Pacific coast of northernJapan

The Pacific coast of northern Japan is established as oneof the regions worldwide with large and frequent earthquakes. Earthquake events occur not only in relation to
the Japan Trench, facing the Tohoku region but also to*Correspondence: velasco@dc.tohoku.ac.jp
1
Department of Earth Science, Graduate School of Science, Tohoku
University, 6‑3, Aramaki Aza Aoba, Aoba, Sendai, Miyagi 980‑8578, Japan
Full list of author information is available at the end of the article

the Kuril Trench, facing Hokkaido Island (Seno 1978;
Ide and Aochi 2013). Historical events demonstrated theremarkable tsunamigenic capacity of the Japan Trench in
869 A.D., 1611, 1896, 1933, 1968, and 2011 (Hatori 1975;
Tanioka and Satake 1996; Nagai et al. 2001; Ozawa et al.
2011; Uchida et al. 2016) (Fig. 1a). In the seventeenth
century, a massive tsunami associated with the activity of
the Kuril Trench exhibited a high capacity of inundation
along the coast of Hokkaido, leaving deposits far inlandand in high cliff areas (Ioki and Tanioka 2016).
The magnitudes of these earthquakes have attracted
the attention of the scientific community, particularly© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
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regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19b)
HOKKAIDOry

ntu

8mm/yrd)

Elevation (m)

2011

e)

To-a
To-cu

Japan

1611

Sendaic)

Trench

TOH

OK

U

10

10

H06

Pacific Plate

A

10
5

GonoheRiver
Takada et al. (2016)

Survey transect
GPR Line
0m

~5
~4
~3
<1

A’

Goto et al. (2015)
Goto et al. (2019)
Ishimura (2017)
Takada et al. (2016)
Imaizumi et al. (2010)

H13

A

Holocene Surfaces

Inoue et al. (2017)
Ishizawa et al. (2022)

500m

j-shaped
depression
A’

Takada et al. (2016)

H06

Survey area

1500
1000
500
Distance (m)

H02
H13
H07
H03

0

5

20

5

H04

40.58

Hirono20

1933

Hachinohe

50

T

40.6

Misawa

1896

100

ril

Ku

Tanigawa et al. (2014)
H09

40.59

1968

h

c
ren

Minoura et al. (1994)

40.59

t
17

40.58

e
hC

>0

e)

Oirase River

40.6

a)

Page 2 of 29

141.450m

141.46500m

141.47

Fig. 1 Map of the study area. a Location of the modeled rupture areas for the tsunamigenic earthquakes of the seventeenth century, 1611, 1896,
1933, 1968, and 2011. (Hatori 1975; Tanioka and Satake 1996; Nagai et al. 2001; Ozawa et al. 2011; Ioki and Tanioka 2016; Uchida et al. 2016); To-a andTo-Cu correspond to the isopachs of the respective tephra layers (Machida and Arai 2003). b Digital elevation model of the study area – 5 m gridsize provided by the Geospatial Information Authority of Japan. c Topographic map of the study area. Colored areas evidenced the levels of upliftedmarine terraces. d Cross-section of the elevation model along the study area, from the A to A’. e Paleotsunami studies developed along the Sanriku
Coast and the Shimokita Peninsula. The circles represent the approximate survey location for each study

after the catastrophic 2011 Tohoku-oki tsunami (TOT),
which clearly demonstrated that understanding tsunami hazard was not adequate to develop an effective
disaster risk reduction strategy and prevent casualties
due to tsunami episodes (Goto et al. 2021). To increase
the knowledge regarding the frequency and magnitudeof events from historical records, several studies evaluating the sedimentological evidence of tsunamis havebeen conducted along the Sanriku Coast (Minoura et al.
1994; Sugawara et al. 2012; Ishimura and Miyauchi 2015;
Takada et al. 2016; Inoue et al. 2017; Ishizawa et al. 2018;
Ishizawa et al. 2022) and the Pacific coast of Hokkaido(Nanayama et al. 2002, 2003, 2007; Hirakawa et al. 2005;
Ishizawa et al. 2017).
Regarding geological studies of paleotsunami deposits,
clarifying the origin of tsunami, regional stratigraphic
correlation, age, and source estimation is still a matter ofdiscussion, not only in Japan but worldwide (Goto et al.
2012; Costa et al. 2016; Jaffe et al. 2016; Ramírez-Herrera et al. 2016; Takada et al. 2016; Moreira et al. 2017;

Ishizawa et al. 2018; Castillo-Aja et al. 2019). To the bestof our knowledge, the only technique to obtain information regarding the spatial extent of tsunami deposits is
consistent survey of coastal areas, with maximum possible resolution. The sedimentological characteristicsof the tsunami deposits largely depend on the distribution and composition of sediments in the shore zone andtopography on the backshore, thereby varying the geometry and lithology over short distances (Jagodziński et al.
2009; Abe et al. 2012; Moreira et al. 2017). Between Misawa in the Shimokita Peninsula (Tanigawa et al. 2014a)
up to Hirono Town (Harashinai site), northern Sanriku
Coast (Takada et al. 2016), there are no paleotsunami
studies, rendering the region record incomplete (Fig. 1e).
Southward, along with the Sanriku coast, several studies have been done (e.g., Imaizumi et al. 2010; Goto et al.
2015; Takada et al. 2016; Inoue et al. 2017; Ishimura 2017;
Goto et al. 2019) (Fig. 1e), and the paleotsunami record isthorough. Due to the peculiar location and distance fromthe two neighboring subduction zones, the southern

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

Shimokita Peninsula helps in not only to recover the dataon the maximum northward extent of tsunami eventsfrom the Japan Trench but also to obtain paleotsunami
data that can elucidate the seismic activity of the Kuril
Trench and the flexion between the two trenches.
1.2 Objective

This study contributes toward the understanding of tsunami hazard assessment in northern Japan by identifying,
dating, and correlating paleotsunami deposits in Hachinohe. Our methodology was based on a combination of
sedimentological, geochemical, geophysical, and paleontological tools, along with numerical simulations forinundation and sediment transport.
1.3 Brief review of approaches for paleotsunami deposit
identification

Previous paleotsunami studies have demonstrated thatcharacteristics such as upward fining, landward thinning and finning, erosional sedimentary structures, andpaleocurrent directions are relevant indicators of thenature of the flow and key points to link deposit events toa tsunami source (Sugawara et al. 2008; Richmond et al.
2011; Goff and Chagué-Goff 2012; Cisternas et al. 2018).
However, in several cases, such characteristics can also
be the result of river floods (Inoue et al. 2017; Shyu et al.
2019). By performing grain size analysis, it is possible toinfer some of these textural characteristics and contribute toward the understanding of the sedimentary environment, energy, and transport media involved during
its deposition (López 2017). In addition, the characterization of the sedimentary fabric can be complemented
by identifying the relative density changes generated bylithological variations (Kain et al. 2015). X-ray computed
tomography (CT) can illustrate the compositional variations in the sedimentary sequence because it records theattenuation of X-rays on materials due to the Compton
scattering effect, the magnitude of which depends primarily on density and consequently on the mineralogy(Mees et al. 2003). For instance, Paris et al. (2019) utilized
high-resolution micro–X-ray CT to characterize the sedimentary fabric, grading, and imbrication angles, inferring tsunami as the transport agent.
In addition, geochemical characterization of paleotsunami deposits can be performed by determining relative
quantities of major elements, trace elements, and carbonates (Chagué-Goff et al. 2017). Because of the leachingeffect of rainwater and groundwater on high-mobility elements, only major and trace elements must be utilized tocharacterize deposits with long-term diagenesis, that is,
paleotsunami deposits (Goto et al. 2012; Shinozaki et al.
2015; Chagué-Goff et al. 2017; Judd et al. 2017), However,
because the characterized group of elements can possess

Page 3 of 29

different mineralogical arrangements and hence different
geological meanings, it is imperative to reinforce element
interpretation with mineralogical identification techniques such as X-ray diffraction (XRD) (Croudace andRothwell 2015). Additionally, XRF is beneficial for relative quantification of the organic matter content (Croudace et al. 2006; Chawchai et al. 2016), as demonstrated
in previous tsunami deposit studies (Chagué-Goff et al.
2016; Judd et al. 2017). The obtained data have been analyzed using multivariate statistical methods, such as principal component analysis (PCA), to find associations or
linear dependencies among the measured elements andclarify the sedimentary source (Chagué-Goff et al. 2016).
To support geological data, few studies have used diatom
analysis, and shell fragment recognition to clarify the terrestrial or marine origin of event deposits (Sawai et al.
2009; Tanigawa et al. 2014b; Chagué-Goff et al. 2015;
Judd et al. 2017; Cisternas et al. 2018).
Regarding the differentiation of the origin of thedeposit between storms or tsunamis (Morton et al. 2007;
Sugawara et al. 2008; Goff et al. 2012), recent studies havedemonstrated that numerical modeling can be suitable
for solving this problem by comparing the inundation
capacity for both phenomena (Inoue et al. 2017; Watanabe et al. 2018).

2 Study area
The study area is in the northernmost part of the coastalzone of Hachinohe City, Aomori Prefecture, Japan, surrounded by the Oirase and Gonohe rivers. A successionof uplifted both Pleistocene and Holocene marine andriver terraces resulting from the constant tectonic uplift
related to the subduction in the Japan Trench (Miyauchi
1987; Niwa and Sugai 2021) controls the stepwise morphology of the area. Based on digital elevation models(DEMs), it is possible to infer the presence of the four different surfaces (Fig. 1c). The lowest surface (1) is about
3 m high; a following ~ 4 m high surface (2) is edged by
the past lateral migration of the surrounding rivers;
next, a ~ 5 m high surface (3) (Fig. 1d). These three surfaces have been named “Holocene Lowland” by Miyauchi(1987), and include river terraces, backshore deposits,
and poorly developed marine terraces. Lastly, a 35 mhigh surface (4) corresponds to the marine terrace named
Takadate Surface by Miyauchi (1987). The survey area islocated on the surface (3).
Land use mostly includes industrial activities andhousing on surfaces 1 and 2, paddy fields on surface
3, and housing on surface 4. The presence of 5 m highmarine-surge protection structures on the beach berm
and river dikes is remarkable in the current topography. In the northern part of the 4 m surface, next to the
Oirase river, a j-shaped depression is present that can beVelasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

morphologically related both to a cutoff meander and apaleo-pond (Fig. 1c).
As a first approach to paleotsunami research in Hachinohe, Velasco et al. (2019) conducted numerical modeling for storm and tsunami surges to compare theirinundation propagation. To reproduce natural coastal
settings, artificial features, such as protection walls andbreakwaters, were removed from the DEM used in thisstudy. Extreme and improbable storm parameters wereassumed for the storm simulations—physical conditionsof the 1979 Typhoon Tip, with a pressure of 870 hPa, andconstant winds of 140 knots for 10 min; and the propagation speed was taken from the 2013 Haiyan Typhoon(Watanabe et al. 2018). For the tsunami simulation, thesource parameters of the 2011 TOT were applied. The
results showed that even under the most severe storm
conditions, erosion and sedimentation are limited toareas below terrace 2, under the assumption of the present sea level (Fig. 1). In addition, tsunami surges derived
from the 2011 TOT can inundate further inland andreach the edge of terrace 3 (Fig. 2, Velasco et al. (2019)).
In practical terms, this means that up to a height of 3 m,a)Page 4 of 29

the sedimentary environment can be affected by theoccasional occurrence of extreme events such as riverfloods, storms, and tsunamis. Moreover, in areas higher
than 3 m, the sedimentary environment can solely beaffected by either tsunamis or river floods, restraining thedifferentiation of event deposits to a marine or terrestrial
origin. As the modeled tsunami surge reached the terrace
3 edge, we selected a survey transect along such a surface(Fig. 2c).

3 Methods
3.1 Ground penetrating radar (GPR) survey and excavation
using Handy Geoslicer

As reported by Velasco et al. (2019), who performed apreliminary site survey, GPR lines were used to select thebest possible survey points by identifying paleosurfaces
with the maximum sediment preservation potential. This
study used processed GPR imagery for paleotopographic
interpretation.
We used a GPR controller GSSI SIR®-4000 (Geophysical Survey Systems, Inc., USA) with an antenna set up toa frequency of 400 MHz to achieve a maximum effective

b)

10

A

H13

2.5

H06

Elevation (masl)

A’0

Fig 2b
1.5

1 km

c)

H06

A

Fig 2b

0

5

A’

A’

H13

0

1 km

Water Level (m)

3.5

-5

Survey area:
paleotsunami domain

A’

5

Tsunami domain
Surface 30

Surface 2

Storm domain

Surface 1

Mean sea level

-3
1800

1600

1400

1200

1000
800
Distance (m)

600

400

200

Expected tsunami deposit

Main sediment source zone

Expected storm deposit

Storm reworking zone0

Fig. 2 Maximum calculated inundation areas for storm and tsunami events for the survey site (modified after Velasco et al., 2019). The calculation
area corresponds to the area of Fig. 1b. a Maximum calculated inundation by storm surge. b Maximum calculated inundation by tsunami surge. c
Schematic model comparing storm and tsunami inundation capacity

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

vertical penetration of 3 m and a maximum resolutionof 18 cm (1/4 of wavelength). GPR data were processedusing the RADAN 7 software by GSSI (Geophysical Survey Systems, Inc., USA).
A Handy Geoslicer (Takada et al. 2002) was used forcore sampling. Sediment core samples were extracted
along a transect perpendicular to the shoreline.
3.2 Non‑destructive core scanning: X‑ray CT

X-ray CT was used to identify and clarify lithologicalchanges, sedimentary structures, and sediment grading. X-ray CT was conducted using the scanner Toshiba
Aquilion Prime at the Center for Advanced Marine
Core Research facilities at Kochi University. Horos software (open source licensed under the GNU Lesser General Public License, Version 3.0 (LGPL 3.0)) was used togenerate 3D reconstructions, from axial–perpendicularslides obtained every 0.5 mm. In addition, we modified
the colors depending on the CT units to enhance the visualization of lithological changes. Image J software (public domain Java image processing and analysis program)extracted a CT value profile from the front-axial images
to be included in the log worksheet along with the X-ray
fluorescence scanning results.
In the 3D reconstruction, the original grayscale wasmodified to blue, green, and yellow green scales to facilitate the recognition of the lithological features. These
colors were manually selected on Horos for convenience
to differentiate the lithological changes. Threshold values
were determined by first identifying sand, peat, tephra
layers, and roots and further manually assigning such values, thereby allowing faster identification of composite
materials.
3.3 Non‑destructive core scanning: magnetic
susceptibility

Magnetic susceptibility (MS) was measured to identify lateral mineralogical changes linked to the tsunami
inundation trajectory. As MS values are absolute quantities, we utilized them for lateral comparison among
the measured cores. The MS of the sediment core samples was measured using the core logging sensor (Model
MS2C, Bartington, USA) with a resolution of 10 mm.
The obtained values were plotted as a vertical profile
and included in the log worksheet along with the XRF
profiles.
3.4 Non‑destructive core scanning: micro XRF (ITRAX)

We utilized XRF core-scanning by ITRAX (Löwemark
et al. 2019) to obtain the content of the major elements
and identify geochemical signatures that contribute to
the source discrimination. The equipment was facilitated by the Center for Advanced Marine Core Research

Page 5 of 29

at Kochi University. The measurements were conductedusing a molybdenum tube with acquisition parametersof 30 kV, 55 mA, exposure time of 10 s, and step size of
1 mm.
XRF raw data were further processed to identify points
with unreliable measured values due to the irregularities
in the sample or misleading measurements. This processing used the “validity profile” delivered along with theraw data by the ITRAX equipment and discarded unreliable measurements. Then, elemental spectra were normalized by the total count rate (kcps) to correct the effectrelated to sample quality and measurement parameters(Chagué-Goff et al. 2016). Also, we calculated centeredlog ratios for each element measurement (clr) to improve
the precision in correlations (Löwemark et al. 2011; Mondal et al. 2021) using the following equation:

 
Iij
)
clr Iij = ln(
gmj
where ­Ijj is the intensity of element i after normalization
at depth j, and ­gmj is the geometric mean at depth j of theraw data after normalization by kcps.
PCA and linear discriminant analysis (LDA) were utilized to identify geochemical changes among the sandlayers and their provenance. PCA has been primarily used in tsunami sedimentology to identify relationships or discriminate among sand layers (Trauth 2015;
Chagué-Goff et al. 2016). Herein, PCA was applied to
identify geochemical changes in the same sediment core.
Based on the XRF-element detection rates (Bishop 2016)
and pattern profiles, we used Sr, Ca, Mn, Ti, Fe, Cr, Ta,
Zr, Si, K, Al, Rb, and inc/coh as inputs for the PCA. As
our interest lies in the geochemical characterizationof the sand layers, we extracted the data corresponding to every sand layer. For the data processing we used
PCA code provided by Nell (2017) LDA was utilized forsand correlation among the core samples. LDA and Fischer discriminants are valuable statistical techniques
for classification and reducing components when inferring a relationship among them. Such a linear function
maximizes the distance between two classes and minimizes the variation between each class (Govindaraju andRao 2014). Herein, LDA determined the relationships
among the sand layers in every core sample. To perform
such analysis, we selected XRF-element detection rates
restricted to the sand layers in every core and comparedthem using the data visualization tool DisplayR®. In XRF
core scanning, because the quantification of major elements in counts per second (cps) largely depends on themeasuring parameters and sample conditions (ChaguéGoff et al. 2016; Löwemark et al. 2019), it is not possible to conduct direct associations of elemental values

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

among different cores, even after kcps normalization. Asa ratio is a quantitative relation between two elementsof the same core (Croudace and Rothwell 2015), it does
not depend on analytical factors and can be utilized tocompare with ratios among other samples. To conduct
such a comparison along with every sand-event sheet, weapplied LDA through the predominant elements in PCA,
normalized by Ta (for the use of Ta as normalization element, see the discussion in the body text).
3.5 Grain size analysis

Grain size analysis was performed on the sand layers to
identify vertical and lateral grading that could be used
to identify marine–related surges. Each sand layer was
divided into slices of 1 cm thick. A cubic sample of 1.5 cm
wide and 2 cm depth was extracted from every slice, with
3.5 g of dry sample on average. Prior to the grain size
analysis, samples were treated to remove organic matterby dissolution in 10% hydrogen peroxide (­ H2O2), desiccation at 60 °C for 12 h, and homogenization by mixing witha spatula for 1 min. Subsequently, we utilized a sample
splitter to obtain a suitable amount of sediment for thelaser equipment. The sample was divided in two half-lots
three times to obtain approximately 0.5 g of dry sample.
Subsequently, we utilized the sediment sieving method
to separate particles > 2000 μm to adequately characterize smaller particles with laser diffraction (Gerlach et al.
2002). We utilized a laser diffraction analyzer (Shimadzu
SALD–2300, Japan) at Tohoku University. The resolutionof the equipment ranges from 17 nm to 2500 μm. Grainsize results were processed in GRADISTAT v8 (Blott andPye 2001) to obtain the grain size distribution, mean,sorting, kurtosis, and skewness. GRADISTAT implements the graphical classification methods of moment
and Folk and Ward.
3.6 XRD analysis

XRD analysis was conducted to identify the mineralogical source of key elements, such as Ca, identified in
the XRF analysis and the presence of glauconite in thedeposit. A total of 5 g was extracted from each sand layer
for homogenization. Sampling was conducted throughout the entire layer to obtain a representative lithologicalcomposition.
XRD analyses were conducted on the powder and oriented samples extracted from the sand layers. To separate
and orientate the clay-size portion from the sedimentsand layers, we followed the sample preparation technique proposed by Moore and Reynolds (1999). First, wedisaggregated the unconsolidated minerals and homogenized the samples with a bead cell disrupter. Afterward,
2 g of the sample was dispersed by ultrasound in purewater. After the decantation of the heavier minerals, thePage 6 of 29

suspended material was centrifuged and filtered using
Millipore™. Finally, the clay-portion was transferred to
the glass slide.
The oriented samples were treated and analyzed inthree stages: air-dried, ethylene glycol solvated for smectite identification, and heated to 550 °C for 3 h for kaolinite identification. We utilized the diffractometer from the
Earth Science Department of Tohoku University (Philips
X’pert Pro MPD, USA). The measuring resolution was
0.5 min every degree, from 0° to 35° 2θ. The resulting patterns were processed and interpreted using the Match!
software (Crystal Impact GbR, Germany).
3.7 Diatom analysis and shell fragments recognition

Diatom analysis aimed to identify brackish marine–origin species. The separation and preparation method
applied for diatom analysis was based on the methodology proposed by Kosugi (1993). The sub-samples weretreated with 15% H­ 2O2, and the cleaned diatom valves
were mounted on Pleurax. For each sub-sample, at least
300 valves were identified and counted. Diatom species
were identified according to the studies of (Patrick andReimer 1966; Akiba 1986; Krammer and Lange-Bertalot
1986, 1988; Simonsen 1987; Krammer and Lange-Bertalot 1991a, b; Lange-Bertalot 1993, 2001; Lange-Bertalot
and Moser 1994; Reichart 1995, 1999; Lange-Bertalot
et al. 1996, 2003; Lange-Bertalot and Genkal 1999; Witkowski et al. 2000; Levkov et al. 2003, 2007; Nagumo
2003; Mann et al. 2004; Tanaka and Nagumo 2004; Watanabe et al. 2005; Kobayashi et al. 2006). After identification and counting, we grouped the species according totheir environment as freshwater, freshwater–brackish,and brackish–marine.
Shell fragment recognition was conducted with two
objectives: evidence of marine influence and high-energy
surges. The shell fragments were separated and identified
using a trinocular microscope.
3.8 Carbonate quantification

Carbonate quantification aimed to quantify biogenic
carbonate and complement elemental and mineralogicalinterpretations based on XRF core scanning and XRD,
respectively. Carbonate content was calculated by subtracting the initial and final weights after controlling
pH dissolution. To fix the pH to 5.2, we used a solutionof 500 ml of 2% acetic acid ­(CH3COOH) and 6.5 g ofammonium acetate (­ CH3COONH4). Each sample (200 g)
was poured into 100 ml of solution for 24 h. The sample
was further dehydrated using a 0.8 µm Millipore™ filterand desiccated at 24 °C for 24 h.

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

3.9 Tephra analysis

Tephra analysis aimed to identify the layers T1 and T6
to constrain the radiocarbon dating modeling. Samples were extracted from the tephra layers for analysis.
Kyoto Fission-Track Co., Ltd. conducted the analyses.
The petrological characteristics of the tephra layers wereobtained via mineralogy analysis by microscope, selecting
up to 200 grains of volcanic glass, light minerals, heavy
minerals, and rock fragments. Characteristics such as
heavy mineral content, volcanic glass shape, and refractive index of glass shards, hornblende, and orthopyroxene crystals were analyzed. For refractive index in T1,
81 volcanic glass shards and 60 orthopyroxene shards
were measured. For T6, 66 volcanic glass shards and 60
orthopyroxene shards were measured. The obtained
data were further correlated with standardized values ofknown tephra layers in Japan.
Layers T2, T3, T4, and T5 were not sampled for tephra
identification.
Herein, the term “tephra” is defined as any deposit
event conformed mainly by volcanic materials, from clay
up to pebbles, that are either possible fallout tephra layers or reworked tephra layers. The source and age of thelayers can be either known or unknown.
3.10 Radiocarbon dating

Bulk samples were obtained from the interbedding peatymud layers for 14C dating. Slices of 1 cm wide and 5 mm
thick were extracted. The samples were treated with anacid wash to remove the bone collagen and cellulose. Furthermore, 14C dating was conducted using accelerator
mass spectrometry (AMS) in Beta Analytic Inc.
We employed OxCal 4.4.4 (Bronk Ramsey 2021) tocalibrate dating results to calendar ages and applied thecalibration curve dataset IntCal20 (Reimer et al. 2020).
The sequence model (Lienkaemper and Ramsey 2009)
was used to calculate the estimated deposition age of thesand layers (Lienkaemper and Ramsey 2009). In addition,using OxCal, it was possible to obtain probabilistic recurrence intervals for deposition ages for every sand unit
using the “Difference ()” function.

4 Results
4.1 GPR, core sampling, and sedimentary features

Thirteen GPR lines were collected from the survey
area (Fig. 1b). Thirteen core samples (H01–H13) wereobtained. Core sample Ha8 was obtained in a preliminary
recognition of the area at the same point as H02 (Shinohara et al. 2017). These core samples were described in
the field and analyzed in the laboratory.
GPR profile images revealed two major features regarding the stratigraphic configuration (Fig. 3a): first, small
basins associated with large depths of the paleosurfaces

Page 7 of 29

and thus locations with better preservation potential of
sediments; second, the presence of two strong reflectors
in the sedimentary sequence.
The relative positions of the core samples are illustrated
in Fig. 3b. The general sedimentological characteristics of the sand layers found in core samples Ha8, H02,
H03, H04, and H06 are described in Table 1. As statedin the Sect. 3, these cores were later used for the laboratory analyses. Core samples Ha8 and H02 contained
the largest number of sand layers among the core samples taken (Fig. 3c), with ten layers. Core samples H03,
H04, and H06 contained seven, six, and two sand layers,respectively. The sedimentary record generally displayeda succession of two or three tephra layers at the top; thefollowing section comprised intercalation among sandlayers and peaty-mud, with occasional ashy tephras and a
thick tephra layer at the bottom.
As core sample H02 contained the most representative sedimentary succession (Fig. 3b and c), it can be
described as follows: the upper part of the core sample
H02 was characterized by cultivated soil, followed by asuccession of three thick tephra layers (T1, T2, and T3)along with two interbedding thin mud beds. The underlying sequence comprised the interbedding of sand andpeaty-mud, along with two tephra laminae (T4 and T5).
Within this section, it was possible to recognize eightsand layers with the naked eye, named as units S1, S2,
S4, S5, S7, S8, S9, and S10 from top to bottom. Units S3and S6 in H02 were recognized by a magnifying glass,and their boundaries were later constrained by XRF corescanning and CT images (see the Sect. 5 in the body text).
The bottommost section was formed by a thick tephra
layer (T6).
4.2 CT, MS, geochemistry, and grain size analysis

X-ray CT, MS, XRF, and grain size analyses were conducted on core samples H02, H03, H04, and H06.
The CT image reconstruction of core samples H02,
H03, and H04 is shown in Fig. 4.
In CT reconstruction, it is possible to recognize thesand units (yellow green in CT color), interbedded nonbioturbated mud (blue in CT color), and tephra layers(see color palette for CT in Fig. 4). Sedimentologicalcharacteristics such as geological contacts (planar, erosive, or transitional), intraclasts, and bioturbation, of core
H02 have also been delineated and shown in Fig. 4. Sand
layers S3 and S6 were highlighted in the CT image, whichallowed the boundaries to be constrained. The mud layerthat separate units S8 and S7 shows high mixing withsand aggregates indicating bioturbation. Also, it is noticeable there is a considerable increase in the intensity ofbioturbation, as evidenced by the heterogeneity of thesand layers. Units S2 and S3 are cut by roots (Fig. 4).

H01

H 02/Ha8

H 13

70

Soil

1

Page 8 of 29

0

218°

70

Top strong reflector

T1
T3

0
10

0

30

T6

Basal contact of T3
Top contact of T6

40

Basal strong reflector

50

Seaward

60

H02 /
Ha 8

H01 H07

H08

H03

H09

H10

H04

H11/
H12

H05/
H06

T1

60

120
130

70
80

S5

90

S8
S1
S3
S4
T4 S5
S2

130

S6
S7

140

S8

150

170

T1
(To-a)

T2

T1p
T2

T3

T3

Tephra deposit
Soil

190
200

0

210

S5
T4
S6

S6

S1
S2
S4
S5
T4
S7
S8
T5
S9

T6
(To-Cu)T6(To-Cu)S10

180

Peaty mud
Sand

S10

200

S3

S8
T5
S9
S10

150

170

S9

T6

S1
S2
S3
S4

S7

140

160

S5
T4
S6
S7
S8
S9
S10

180
190

90

110

50

160

80

100

40

120

T1
(To-a)

70

H13

30

110

Unit

10

20

100

Core sample
H02

20

3

BurialDepth(cm)

Core sample
Ha8

Depth
(cm)

Deepest point
relative to surface

2

(b)

(c)

mud
v.f. sandf. sandm. sandc. sand

(m) 0
0

(a)

(2022) 9:19

mudv.f. sandf. sandm. sandc. sand

Velasco‑Reyes et al. Progress in Earth and Planetary Science

Landward

100 m

210
TephraPeaty mud

Sand

Soil

Fig. 3 a Location of core sample H02/Ha8 and relative positions of core samples H01 and H13on the GPR profile. Note that core samples H01
and H13 are displaced laterally from the GPR line (Fig. 1b). b Relative position of horizontal core sample and sand events correlation, based on
sedimentary features and stratigraphic relationships. c Stratigraphic column of core samples Ha8 and H02, separated laterally by ± 2 m

High values of MS in core H02 can be linked to thepresence of sand layers S4 to S10 (Figs. 5 and 6).
XRF elemental patterns obtained from core H02 exhibited conspicuous changes related to the interface between
the peaty-mud and the sand layers. Si, Ca, Sr, K, Ti, andFe had higher values on sands, whereas Ta exhibited
lower values (Fig. 6). Elemental profiles also contributed
to differentiating S3 and S6 from the neighboring mud
layers. Fluctuation pattern of Sr was relatively like that of
Ca. Ti exhibited the most remarkable changes among theelements measured; every sand layer can be characterized by an abrupt change in Ti across, over, and underlying mud beds. However, high Ta values were related topeaty-mud layers. A ratio of Ca to Ta was introduced toclearly delineate the interface between sand and peaty
mud. High Mo inc/coh ratio values represented the highrelative content of organic matter in the peaty-mud layers. Although sand layers were characterized by low
Mo inc/coh intensity, the pattern behaved oppositely
to Ca, Ti, and Ca/Ta. We applied PCA for the XRF dataobtained on the cores H02, H03, H04, and H06.

The CT value profile extracted from the CT images(Fig. 6) displayed high values related to the sand layers.
As stated before, the CT images primarily reflect densitychanges; thus, this profile illustrates those related to lithological changes among sand, mud, and tephra.
In a first-order difference, vertical variations in MS(Fig. 5), elemental composition (Fig. 6), and CT color
intensity (Fig. 4) were linked to the changes in mineralcomposition and density; at the second-order difference
to grain size and compaction (Fig. 7).
Grain size analysis was conducted in cores H02, H03,
and H04 to identify vertical and landward changes ineach sample. Some sand layers (e.g., S1, S2, and S3 in
H02) were not sliced and analyzed by grain size owing to
the nominal thickness of the layer. In addition, sand layers in core sample H06 were not analyzed because of thehigh bioturbation and mixing with peaty mud. Regarding
the sample processing, although it is expected that the
sample splitter produces a minor overall inaccuracy level
among the splitting methods, some segregation could
have occurred because of the number of times the sample

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

Page 9 of 29

Table 1 General sedimentological characteristics of the sand layers found in the five main cores
Lithology

Grain size

General description

Color

Soil

Mud

Paddy field related soilBlack

Tephra 1

Lapilli

Clayey layer at the base. Upward fining

Khaki

Mud

Mud

Peaty mudBlack

Tephra 2

Lapilli

Massive tephra

Khaki

Mud

Mud

Peaty mudBlack

Tephra 3

Lapilli to ash

Thiny clay layers at the base with interlayered mud

Gray-White

Mud

Mud

Peaty mud

Black

Sand

vfSandVery thin bed of massive sand

Brown

Mud

Mud

Peaty mud

Black

Sand

vfSandVery thin bed of massive sand

Brown

Mud

Mud

Peaty mud

Black

Sand

fSand

Thin lamina of massive sand

Brown

Mud

Mud

Peaty mud

Black

Sand

vfSandMassive sand

Dark brown

Mud

Mud

Peaty mud

Black

Sand

m-fSandThin bed. Upward fining. Bottom erosive contact

Light brown

Mud

Mud

Peaty mudBlack

Tephra 4

Ash

Clay

white

Mud

Mud

Peaty mud

Black

Sand

Silt

Lenticular stratification

Brown

Mud

Mud

Peaty mud

Black

Sand

vfSand to mSand

Thin bed. Upward coarsening. Bottom transitional contact

Brown

Mud

Mud

Peaty mud

Black

Sand

Bottom subunit: vsSand to mSand
Upper subunit: mSand to fSand

Thin bed divided in two subunits: the basal one (S8a) is a dark brown very fine upward Brown
coarsening to medium sand, the upper unit (S8b) is light brown upward fining. Bot‑
tom erosive contact

Mud

Mud

Peaty mudBlack

Tephra5

Ash

Clay

Gray-White

Mud

Mud

Peaty mud

Black

Sand

vfSandTwo thin sand laminas interbeded with peaty mud including some intraclasts. Bottom
erosive contact

Brown-dark

Mud

Mud

Peaty mud

Black

Sand

fSand

Upward fining. Bottom erosive contact

Light brown

Mud

Mud

Peaty mudBlack

Tephra 6

LapilliThick tephra layer. Upward fining lapilli to ash

Gray

Soil

Mud

Paddy field related soilBlack

Tephra 1

Lapilli

Clayey layer at the base. Upward fining

Khaki

Mud

Mud

Peaty mudBlack

Tephra 2

Lapilli

Massive tephra

Khaki

Mud

Mud

Peaty mudBlack

Tephra 3

Lapilli to ash

Thiny clay layers at the base with interlayered mud

Gray-White

Mud

Mud

Peaty mud

Black

Sand

vfSandVery thin bed of massive sand

Brown

Mud

Mud

Peaty mud

Black

Sand

vfSandVery thin bed of massive sand

Brown

Mud

Mud

Peaty mud

Black

Sand

fSand

Thin lamina of massive sand

Brown

Mud

Mud

Peaty mudBlack

Core Ha8Core H02

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

Page 10 of 29

Table 1 (continued)
Lithology

Grain size

General description

Color

Sand

vfSandMassive sand

Dark brown

Mud

Mud

Peaty mud

Black

Sand

m-fSandThin bed. Upward fining. Bottom erosive contact

Light brown

Mud

Mud

Peaty mudBlack

Tephra 4

Ash

Clay

white

Mud

Mud

Peaty mud

Black

Sand

Silt

Lenticular stratification

Brown

Mud

Mud

Peaty mud

Black

Sand

vfSand to mSand

Thin bed. Upward coarsening. Bottom transitional contact

Brown

Mud

Mud

Peaty mud

Black

Sand

Bottom subunit: vsSand to mSand
Upper subunit: mSand to fSand

Thin bed divided in two subunits: the basal one (S8a) is a dark brown very fine upward Brown
coarsening to medium sand, the upper unit (S8b) is light brown upward fining. Bot‑
tom erosive contact

Mud

Mud

Peaty mudBlack

Tephra5

Ash

Clay

Gray-White

Mud

Mud

Peaty mud

Black

Sand

vfSandTwo thin sand laminas interbeded with peaty mud including some intraclasts. Bottom
erosive contact

Brown-dark

Mud

Mud

Peaty mud

Black

Sand

fSand

Upward fining. Bottom erosive contact

Light brown

Mud

Mud

Peaty mudBlack

Tephra 6

LapilliThick tephra layer. Upward fining lapilli to ash

Gray

Soil

Mud

Paddy field related soilBlack

Tephra 1

Lapilli

Clayey layer at the base. Upward fining

Khaki

Mud

Mud

Peaty mudBlack

Tephra 2Ash

Massive tephra

Khaki

Mud

Mud

Peaty mudBlack

Tephra 3

Lapilli

Massive tephra

Gray-White

Mud

Mud

Peaty mud

Black

Sand

vfSandMassive sand

Brown

Mud

Mud

Peaty mud

Black

Sand

mSand to fSand

Upward fining. Bottom erosive contact

Brown

Mud

Mud

Peaty mudBlack

Tephra 4

Ash

Clay

white

Mud

Mud

Peaty mud

Black

Sand

Silt

Thin lamina

Brown

Mud

Mud

Peaty mud

Black

Sand

mSand

Massive sand. Bottom erosive contact

OrangeMud

Mud

Peaty mud

Black

Sand

fSand

Upward fining

Dark brown

Mud

Mud

Peaty mud

Black

Sand

fSand

Two thin sand laminas interbeded with peaty mud. Bottom erosive contact

Dark brown

Mud

Mud

Peaty mud

Black

Sand

fSand

Massive sand. Top and bottom transitional contacts

Dark brown

Mud

Mud

Peaty mudBlack

Thick tephra layer. Upward fining lapilli to ash

GrayCore H03

Tephra 5/6? Lapilli to ash
Core H04
Soil

Mud

Paddy field related soilBlack

Tephra 1

Lapilli to ash

Clayey layer at the base. Upward fining

Khaki

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

Page 11 of 29

Table 1 (continued)
Lithology

Grain size

General description

Color

Mud

Mud

Peaty mudBlack

Tephra 2

Lapilli

Massive tephra

Khaki

Mud

Mud

Peaty mudBlack

Tephra 3

LapilliThiny clay layers at the base with interlayered mud

Gray-White

Mud

Mud

Peaty mud

Black

Sand

vfSandMassive sand, with bottom erosive contact

Brown

Mud

Mud

Peaty mudBlack

Tephra 4

Ash

Clay

Gray-White

Mud

Mud

Peaty mud

Black

Sand

vfSandThin lamina

Brown

Mud

Mud

Peaty mud

black

Sand

vfSandLens of sand

Brown

Mud

Mud

Peaty mud

black

Sand

vfSandFlame structures at the base

Brown

Mud

Mud

Peaty mud

Black

Sand

vfSandMassive sand. Top and bottom transitional contacts

Brown

Mud

Mud

Peaty mud

Black

Sand

vfSandMassive sand

Dark brown

Mud

Mud

Peaty mudBlack

Thick tephra layer. Upward fining lapilli to ash

GrayTephra 5/6? Lapilli to ash
Core H06
Soil

Mud

Paddy field related soilBlack

Tephra 1

Lapilli

Upward fining

Gray

Silt

Silt

Peaty mudGray-dark

Tephra 2

Lapilli

Massive tephra

Khaki

Mud

Mud

Peaty mud

Black

Sand

vfSandMassive sand, highly bioturbated

Dark brown

Mud

Mud

Peaty mud

Black

Sand

vfSandMassive sand, highly bioturbated

Dark brown

Mud

Mud

Peaty mudBlack

Tephra3/4/5/6?

Lapilli to ash

Upward fining

Gray

Note that there is not a lateral correlation among the core samples. Neither the thickness of the cells represents the thickness of the sand layers. Emphasis enhance
the stratigraphic position of tephra (bold), sand (italic), and peaty mud or soil (bold italic) layers
vfSand, Very fine sand; fSand, Fine sand; mSand, Medium sand; cSand, Coarse sand; vcSand, Very coarse sandwas divided; nonetheless, it is expected that the error
level is not greater than 2% (Gerlach et al. 2002).
Grain size analysis revealed that the sand layers aremostly composed of sand with grain sizes of 1.5–2.0 phi,
which frequently exhibit upward fining (Fig. 7). Thicker
sand layers, such as S8b and S5 at H02, typically exhibited
normal or inverse grading. In addition, the sand layers
exhibited vertical changes in mode values (Fig. 7). Some
curves exhibited multimodal grain-size distribution, primarily related to sand-mud mixing or sand–granule mixing. As observed in S4, S5, and S8, in addition to grading,sorting and skewness exhibited a vertical variation in thesand layers (Fig. 7).

In Table 2, the specific characteristics of each sand layer
with respect to geochemistry, MS, CT, and grain size aredescribed. Not all features are observed in every proxy.
4.3 PCA and linear component analysis

The first three principal components represented 59%of the total variability, with distributions of 39%, 11%,
and 9%, respectively. After using several combinationsof elements, it was not possible to separate the sandlayers in the clusters (Fig. 8). Nonetheless, although thecloud of data is averaged to the center, the data illustrated the tendency of unit S8, S9 and S10 to move from

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

Page 12 of 29

Core H02
Depth
(cm)

Unit

80

90

T3

Core H03

Gray CTvalue Image

Unit

Core H04

CT
Image

Unit

CT
Image

Color palettefor CT
Roots
Sand
Mud
T3

S1

100

S2
S3

110
S4

120
S5

130

T4
S6

140

T3

S7

Legend for
schematic profile
Roots
Sand
TephraMud
Transitional
contact
Erosivecontact
PlanarcontactFlowdirectiona Intraclast
size

150

S4

S5

S5

T4

T4

S6

S6

S7

S7

S8
S8
S9

a)

160

S8-b

S10

S8-a

b)

170

180

S9b
S9a
S10

S10

S9

Internal erosion
surface (IES)

To-Cu

To-Cu

a
c)

190

b
To-Cu

Fig. 4 CT image interpretation for the core sample H02, H03, and H04. From left to right: depth, core H02: stratigraphic column, sedimentarytexture, and structures, CT value, and CT image; cores H03 and H04: stratigraphic column and CT images. Gray tone gradient depicts sedimentgrading. a–c correspond to closer looks showing flow sedimentary structures and the size of a rip-up clast at S8 and S9

Velasco‑Reyes et al. Progress in Earth and Planetary Science

H 02
H 03
Top
sedimentary
sequence(2022) 9:19

H 04
H 06

Magnetic susceptibility (SI 10-5)
0
50
100
150
Sand 4

Not at vertical scale

Sand 5

4.4 XRD

XRD analyses were conducted on sand layers S2, S4, S5,
S7, S8, S9, and S10 in core samples H02 and H04.
Even after the separation of clay portion, plagioclase(varying compositionally from albite to andesine) andquartz were highly representative in every sample.
Kaolinite was recognized in all sand layers (see Additional file 2: Appendix 2). However, pure illite or puresmectite was not observed. Mixed-layered clay mineralssuch as kaolinite–montmorillonite, montmorillonite–chlorite, and illite–smectite (rectorite) were identified indifferent proportions among the sand layers. Glauconite
appeared in units S4, S5, S6, S7, S8, S9, and S10.

Sand 6
Sand 7
Sand 8

Sand 9Base

Page 13 of 29

Sand 10

Fig. 5 Magnetic susceptibility landward changes along with thecore samples H02, H03, H04, and H06. Note that it is a graphic
representation of the MS intensity only in the sand layers, and thethickness of the sand layers is not at scale. MS values of the mudlayers were not plotted

Sr, Al, and Ca. Units S1, S2, and S3 were gently inclined
to be associated with Rb, Cr, Ta, and Zr.
We applied LDA to the predominant elements in PCA(Si, Ca, Cr, Zr, Cr, Ti, and Fe), normalized by Ta (see thediscussion in the body text), which behaved opposite tosand-related elements in the XRF results. In essence,the LDA scatterplot shows every sand layer’s meanvalue and its relative position to the elemental ratios.
It helps to see how chemically “similar” one sand layer
to another is and how correlatable the sand layers areamong the core samples (Fig. 9). LDA results show thedata is mainly distributed from ~ − 3.0 to ~ 2.0 in thefirst linear discriminant and from ~ − 2.0 to 4.0 in thesecond linear discriminant. H02_S1 and H02_S2 show
no correlation with the other sand layers. Independent and explicit clustering of sand layers is not possible
among the remaining sand layers. However, although it
is possible to make some close associations among S3,
S4, S5, S6, and S7, there are no clear and unique geochemical signatures for these.

4.5 Paleontological analyses

Diatom analysis was conducted on 24 sub-samples
obtained from the core sample Ha8. Diatom species
related to freshwater (34 species), freshwater–brackish(3 species), and brackish–marine (5 species) environments were identified in core sample Ha8 (Fig. 10). Bothfreshwater and freshwater–brackish species were foundin sand layers, peaty mud, and tephra layers. Brackish–marine (B–M) species were identified in S5, S7, S8, S9and S10 (Table 2 and Fig. 10). The proportional amountof the B–M species with respect to the total found species was < 10% in every sample of every sand layer.
Using a trinocular microscope, a shell fragment of
the bivalve was recognized in unit S5 (Fig. 11). In theother sand layers, it was not possible to find any mollusk
fragments.
Carbonate content was identified on units S4, S5, S6,
S7, S8, S9, and S10 at H02. Units S6, S7, and S8 exhibiteda 0 wt. % carbonate content. Meanwhile, in units S4, S5,
S9 and, S10 the recorded value was < 0.3% (Table 2).
4.6 Tephra analysis

Tephra layers at the top and base of the core samples
were sampled in Ha8 for identification.
T1 is conformed in general by two layers. The basal
unit corresponds to a 1 cm thick ash layer overlayed by achaotic massive tephra deposit containing volcanic material ranging from clay to lapilli. T6 is formed by a chaotic deposit of volcanic material that ranges from clay tolapilli.
Tephra analysis revealed that both tephra layers are
related to the eruptive events of the Towada Volcano.
Based on the petrological analysis (mineralogy, heavy
mineral content, volcanic glass shape, and refractive
index of glass shards, hornblende, and orthopyroxene
crystals), T1 was identified as To–a, whose age is 915 CE

(2022) 9:19

5

4

Ca/Ta
20
30
3

10

0

Ta
-1
-0.5
0
0.5

Fe
1.2
-1.2
-0.2
0.8
1.8

0.2

-1.2
-0.7
-0.2
0.3
0.8
-1.4

Ti
-0.8

-0.6

K
0.6
-1.8

Sr

-0.4

Ca
0.4

Si

Mo inc/Mo cohPage 14 of 29

Element ratio (clr)

-0.6
-0.1
0.4
-1.6

MagSus150

CT value

56
0

16

Unit

Depth
(cm)

CT image
Validity

Velasco‑Reyes et al. Progress in Earth and Planetary Science

800
900
1000
1100
1200
1300
1400
1500
1600
1700
1800

Fig. 6 Log pane of the core sample H02. Log distribution: depth, stratigraphic units, CT image, validity, CT value profile, magnetic susceptibility, andmicro X-ray fluorescence elements result (Si, Ca, Sr, K, Ti, Fe, Ta, Ca/Ta, and Inc/Coh) vs depth. Black arrows indicate trend changes in pattern intensity(Machida and Arai 2003). In addition, basal tephra wasassociated with To–Cu, with a deposition age of 5986–
5899 cal yr BP (McLean et al. 2018). Reference values
were taken from Machida and Arai (2003) (Table 3).
14
4.7 
C dating

A total of 11 samples were obtained from core sample
Ha8 for the 14C dating. Three samples from H04 and two
from H02 were utilized for the lateral correlation. The
conventional radiocarbon ages obtained from core samples H02, Ha8, and H04 are listed in Table 4. The calibrated results are shown in Fig. 12.

5 Discussion
5.1 Sand events correlation

Based on the GPR imagethe two strong reflectors identified were interpreted from top to base as the contacts
between T3 and the underlying mud layer, and between
T6 and the overlaying mud layer (Fig. 3a). Further, theinitial lateral correlation of the sand–peaty mud sequence

was conducted based on sedimentary features, color, andstratigraphic position (Fig. 3c). The persistent presenceof tephra layer four (T4) among several cores served as amarker horizon (Fig. 3b, c; Table 5). Table 6 presents thelateral correlations among the core samples.
The correlation was supported with CT, geochemical
analysis, and radiocarbon dating.
5.1.1 Correlation between core samples H02 and Ha8

As the core sample Ha8 was the first core to be extracted,
diatom analysis, tephra analysis, and radiocarbon dating forevent recurrence were conducted on this core sample. When
conducting the field survey, the extraction of the core sample was intended to be in a 2 m radius from the Ha8 sampling site, which corresponded to the GPS device horizontal
error: ± 2 m, to assure a good correlation between them.
The correlation between Ha8 and H02 was determinedbased on the stratigraphic position of the sand layers,lithology, and sedimentary characteristics (Figs. 3, 13,
Table 1), as follows:Velasco‑Reyes et al. Progress in Earth and Planetary Science

Unit S4
H04

Mode (phi): 8.119 - 3.313 - 1.940
Skewness: Fine Skewed

H03

Mode (phi): 2.283
Skewness: Very Fine Skewed(2022) 9:19

Page 15 of 29

Unit S8
H04

Mode (phi): 1.940 - (-)0.464
Skewness: Symmetrical

Mode (phi): 1.596 - (-) 0.464
Skewness: Fine Skewed

H02
Upward kurtosis
increase(higher selection)

Mode (phi): 1.940
Skewness: Symmetrical
Mode (phi): 1.940
Skewness: Fine Skewed

H03

Mode (phi): 1.940
Skewness: Very Fine Skewed

Mode (phi): 1.940
Skewness: Symmetrical

Upward finning

Mode (phi): 1.940
Skewness: Symmetrical

Mode (phi): 1.596
Skewness: Fine Skewed

Mode (phi): 1.940
Skewness: Fine Skewed

Mode (phi): 1.596
Skewness: Fine Skewed

Upward finning

Unit S5
H04

Negative phi values (sencondary peak) related to
pumice granules

Mode (phi): 1.253 - (-) 0.464
Skewness: Very Fine Skewed

Mode (phi): 1.596
Skewness: Fine Skewed

Mode (phi): 1.253
Skewness: Fine Skewed

Unit S6
H03

Mode (phi): 1.253
Skewness: Fine Skewed

H02

Mode (phi): 2.626 - 8.119
Skewness: Very Fine Skewed

Mode (phi): 1.596
Skewness: Fine Skewed

Unit S7
H04

Mode (phi): 1.940
Skewness: Symmetrical

H02

Mode (phi): 1.253
Skewness: Fine Skewed

Mode (phi): 1.253
Skewness: Fine Skewed

Mode (phi): 0.910
Skewness: Fine Skewed

Mode (phi): 1.596
Skewness: Fine Skewed

Mode (phi): 0.910
Skewness: Fine Skewed

Mode (phi): 1.940
Skewness: Symmetrical

H03

Mode (phi): 1.596
Skewness: Fine Skewed

Mode (phi):1.253-1.940
Skewness: Symmetrical

Mode (phi):1.253
Skewness: Fine Skewed

-2

0

2

4

6

8

10

(a) Grain size (ϕ)

12

14

H02

Mode (phi): 1.253
Skewness: Fine Skewed

Upwardcoarsening

Mode (phi): 0.910
Skewness: Fine Skewed

Mode (phi): 2.283 - 6.746 - 5.030
Skewness: Very Fine Skewed

-2

0

2

4

6

8

10

Mode (phi): 0.910
Skewness: Fine Skewed

Upward coarsening

Mode (phi): 1.596
Skewness: Fine Skewed

Mode (phi): 1.940
Skewness: Fine Skewed

Mode (phi): 2.28-7.09-5.3
Skewness: Very Fine Skewed

-2

12 14

(a) Grain size (ϕ)

H04Mode (phi): 8.463 - 5.716 - 7.433
Skewness: Symmetrical

H03

Mode (phi): 8.463 - 7.433 - 5.373
Skewness: Coarse Skewed

Mode (phi): 1.940
Skewness: Very Fine Skewed

H02

Unit S10
Mode (phi): 7.089 - 5.373 - 8.463
Skewness: Symmetrical

H03

Mode (phi): 1.253
Skewness: Fine Skewed

S8a

Upward skewness tohigher phi values

Mode (phi): 1.253
Skewness: Fine Skewed
Mode (phi): 1.253
Skewness: Fine Skewed

Unit S9

Rapid landwardchange fromsand to mud

Mode (phi): 1.253
Skewness: Fine Skewed

Mode (phi): 1.940 - 4.686 - 5.373
Skewness: Fine Skewed

0

2

4

6

8

10

(a) Grain size (ϕ)

1214Mode (phi): 2.283 - 6.746- 5.373
Skewness: Very Fine Skewed

Rapid landwardchange fromsand to mud

H02Mode (phi): 1.940
Skewness: Symmetrical

S8b

Upward finning

Mode (phi): 1.940 - 5.373 - 7.089
Skewness: Very Fine Skewed

Upward finning

Mode (phi): 1.253
Skewness: Fine Skewed
Mode (phi): 2.283
Skewness: Symmetrical

H03

H02
-2

0

Mode (phi): 1.596 - (-)0.464
Skewness: Symmetrical

2

4

6

8

10

12

14

(a) Grain size (ϕ)

Fig. 7 Grain size analysis result for core sample H02, H03, and H04. Every square depicts the distribution obtained for every sand on every coresample. Every distribution corresponds to ~ 1 cm thick. The figures are not at vertical scale• T1 is present in all core samples and was correlatedusing stratigraphic and lithological characteristics.
It shows a basal ash layer (T1p) and an overlying
chaotic deposit of volcanic material. (Fig. 3c). T1p
is considered as primary tephra based on the following results: tephra identification results recognized this layer as the To–a event (915 CE); no diatom species were found that could show reworking;
14C results show the age below T1 corresponds to1184 to 1271 cal BP (Fig. 13), which means this mud
layer was deposited before the To–a; and, it fits thereported isopach map by Machida and Arai (2003),
which reports a thickness < 5 cm (Fig. 1) for the To–aevent. Consequently, the overlying chaotic deposit isconsidered the possible secondary deposit resulting
from a lahar pyroclastic flow derived from the Oirase
River, which directly drains towards the Towada caldera. On the other hand, freshwater diatom species
are mixed only at the top part, which supports theinterpretation of the deposit source (Fig. 10).
• T2 and T3 were correlated between the two coresamples using the lithological characteristics and

•
•

•

•

stratigraphic position. These are considered secondary deposits by flooding events related to the
Oirase river, supported by the layering with mud in
T3 (Table 3, Fig. 3c), and the inclusion of freshwater
diatom species into the deposit (Fig. 10) which points
out the reworking.
Sand layers S1, S2, S3, and S4, were correlated using
the relative stratigraphic position because the lithological characteristics were identical (Fig. 13).
Based on dating results, the mud layer overlying
S5 in Ha8 – sample C5 can be directly correlated
to the one overlying S5 in H02 – sample C12, withages ranging from 2973 to 3075 cal BP and 2800 to3000 cal BP, respectively (Fig. 13).
T4 was used as a marker horizon among the coresamples (Fig. 3b), based on its thickness and clayey
texture. However, T4 is considered a possible fallout
tephra layer or reworked tephra layer which source
and age are unknown.
S5 was above T4 for both Ha8 and H02. S6 was below
T4, and the sedimentary characteristics were identical.

Massive

Massive

Massive

Upwardfining(skewnessvariation andphi modechange)Constant

Upwardcoarsening
at H02

Upwardcoarsening—
massive—
upwardfining in H02
Upwardfinning in
H03 and H04

Massive

Upwardfining in the
distal coresample (H04)

S2

S3

S4

S5

S6

S7

S8

S9

S10

No change

No change

Landward
thinning

Landward
thickening
and thinning

Landward
thinning

Landward
thinning

Upwardincrease

Upwardincrease—
upwarddecrease(inclusion ofcoarse androundedgrains ofpumice)Landward
thinning

Landward
thinning

Landward
thinning

Not continu‑ous

Not continu‑ous

Not continu‑ous

No change

Upwardsortingdecrease—presence ofcoarse pum‑ice towards
the top

Upwardincrease

No change

No change

No change

Erosive

Erosive. Ero‑sive surface
bounding
the twosubunits

Erosive.Ero‑sive surface
bounding
the twosubunits

Bioturbated

Planarcontact

Erosive

Bioturbated

Bioturbated

Bioturbated

Bioturbated

4 species (Adoneis
sp.; Delphineis sp.;
Diploneis smithii;
Melosira moniliformis)

3 species (Adoneis
sp.; Fallacia sp.;
Melosira moniliformis)

Not recorded

1 specie (Fallaciasp.)/shell fragment(Fig. 11)

–

–

–

–

Not visible

2 species (Adoneis
sp.; Melosira moniliformis)

Rip-up1 specie (Adoneis
clast in thesp.)
interbedding
mud-layer,
flame struc‑ture

Rip-up clast
at the baseNot visible

Not visible

Not visible

Not visible

Not visible

Not visible

Not visible

Paleontologicalindicators: marine
diatom species/
shell fragments

0.3%

0.1%

0%

0%

0%

0.2%

0.1%

–

–

–

Carbonatecontent

Landward
decrease

Not significant
changeLandward
decrease

Landward
increase—decrease

Landward
decrease

Landward
decrease

Landward
decrease–––

Lateral change

Magnetic
susceptibility

Upwardreduction,
increase andconstant

Upwardreduction in
two pulses

Upwardincrease—
upwardreduction

Slightly
upwardincrease

Constant

Upwardreduction

Constant

No change

No change

No change

TitaniumUpwardincrease

Peak

Lower unit:
upwarddecrease
Upper unit:
constant

Upwarddecrease

Peak

Upwardincrease

Constant

Peak

Peak

Peak

Rubidium

X-ray
diffraction

Yes

–

–

–

High—
Upwardreduction

Low in thebase, moder‑ate in theupper partLow peakin thebase, rapid
increase,
constant in
the middle
part andupwardfining in thelast 1/3

Moderateupward
increase

Low-con‑
stant

Yes

No

Yes

Yes

Yes

High-upward Yes
reduction

High-flat

Low-peakHigh-PeakLow-peak

Ca/Ta
Glauconite(clastic/mud
ratio)

Micro X-ray fluorescence by ITRAX(description for the main core—H02)

4942–5522 calBP

4677–5012 calBP

4286–4784 calBP

3823–4362 calBP

3469–3859 calBP3020–3499 calBP

2769–3068 calBP

1935–2774 calBP

1935–2774 calBP

1239–1924 calBPModeleddepositionalage (2σ)(2022) 9:19

For lateral correlation of the sand layers S1 to S10, see Sect. 5 in the body text

MassiveFlowstructures

Basal
contact

ThicknesschangeGrading

Sorting
verticalchangeComputed tomography

Grain size analysis

S1

Sand unit

Table 2 Summary of sedimentological, paleontological, and geochemical analysis of the event sand layers (S1 to S10)

Velasco‑Reyes et al. Progress in Earth and Planetary Science
Page 16 of 29

Velasco‑Reyes et al. Progress in Earth and Planetary Science

Ta Zr(2022) 9:19

Si

Cr

K

Second Principal Component

Rb
5

S1
S2
S3
S4
S5
S6
S7
S8
S9
S10

Fe

Sr Ca

Al

0

Mn

−5

inc.coh

Ti

0

5

−5

First Principal Component

Fig. 8 Principal component analysis results for XRF core scanning
results in core H02

Page 17 of 29• T6 is present in all core samples and was correlatedusing stratigraphic and lithological characteristics.
There are no clear insights about the primary or
secondary character of the tephra layer. According
to the isopach map by Machida and Arai (2003),
To–Cu should be < 10 cm; however, the thicknessof the observed layer is > 40 cm. We consider thisthick layer was generated by an initial ash fallout
and an ensuing lahar or pyroclastic flow derived
from the Oirase River.
In general, the thickness of the sand layers changed
considerably between the two core samples. S3 was notrecognizable in Ha8, and S6 was significantly reduced inthickness to a medium lamina (Fig. 3b). Such strong lateral changes in thickness can occur in tsunami deposits(Nakamura et al. 2012).
5.1.2 Lateral correlation of cores H02, H03, H04 and H06
using LDA and 14C

Second linear discriminant

4.0

H02_S1
H02_S22.0

0

H03_S10Zr/TaCr/Ta

H02_S9 H03_S6

Sand layer

Fe/Ta

Si/Ta

H02_S4 H02_S5
Al/Ta
H04_S5
H03_S7
H02_S6
H03_S5
H03_S9
H04_S9
H02_S7 H04_S7
H06_S5
H02_S8Ti/Ta
H02_S3
H03_S4

H02_S10

H06_S8Ca/Ta

H04_S8

H03_S8

S4
S5
S6
S7
S8
S9
S10

H04_S6-2.0

H04_S10

-4.0

-2.0

0

2.0

First linear discriminantFig. 9 Linear discriminant analysis results for XRF core scanning
results in core H02. Elements used for discrimination: Al, Ca, Cr,
Zr, Ti, Fe, and Si, normalized by Ta. Every point corresponds to thecomponent value for every sand layer in every core. Color shades
represent correlations among sand layers• The dating result of the mud layer separating S8 andS9 in Ha8 – sample C9 did not coincide with thesame stratigraphic position in H02 – sample C13(Fig. 13). Nonetheless, the age difference is small,which could be related to the close time frame orreworking of sedimentary events during sand event
deposition. However, it supports the correlationof S8 as it was deposited after 4892 cal BP, and thesedimentary characteristics of the layers coincide.
• T5 is also considered a possible fallout tephra layeror reworked tephra layer which source and age areunknown.
• In both cores, both S9 and S10 appear as thin beds
underlying S8 (Fig. 13).

The XRF results and its analysis by LDA contributed toclearly differentiating the sedimentary facies of S1 andS2 from the other sand layers. However, LDA evidenced
a substantial geochemical similarity among the sands
S4, S5, S6, S7, S8, S9, and S10 in different cores, suggesting a similar lithological composition, and thus thesource of the sediments. However, although it was possible to cluster the sand layers, some samples are close
to different sand layers, which means that in the case of
performing clustering by using automatic algorithms
or unsupervised techniques, wrong associations can be
done. For instance, S5 in core H06 lacks a good correlation with the corresponding depth in H02, H03, and H04(Fig. 4), implying that the furthest landward sand could
correspond to a different sedimentary event and source.
Therefore, the clustering must be made carefully to avoid
the false correlation of sand layers (Fig. 9).
As for the correlation between Ha8 and H02, the 14C
dating results supported the lateral correlation of thesand layers (Fig. 13). In addition, the mud layer overlying
S10 in Ha8 can be correlated to the same stratigraphic
layer in H04, with age ranges of 4892–5034 cal BP and
4854–4960 cal BP, respectively. Calibrated ages show thatthe peaty–mud layers underlying S5 in Ha8 and H04 areof similar age. On the other hand, the dating result fromthe peat–mud underlying S7 in H04 does not correspondto the age obtained on the same stratigraphic level on
Ha8, which can be caused by modern contamination during core extraction or sampling. As stated previously, differences in dating ages among core samples H02 and Ha8
may be related to the erroneously sampling of reworked
material (Ishizawa et al. 2018), or sampling resolution(Fig. 13) (i.e., mud layers underlying S8 in H02 and Ha8).

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

Decussata placenta
Epithemia adnata
Eunotia formica
Eunotia sp.1
Eunotia sp.2
Eunotia bidence

Cymbella himaspera

Stephanodiscus spp.

Depth
(cm)

Aulacoseira sp.

Aulacoseira granulata

10 %Freshwater / Brackish /BrackishMarineEunotia bilunalis
Eunotia curtagrunowii
Gomphonema acuminatum
Gomphonema cf. micropus
Gomphonema parvulus
Gomphonema truncartum
Hantzschia amphioxys
Navicula cryptcephara
Naidium krasskei
Nitzschia crausii
Pinnularia microstauropsis
Pinnularia pseudogibba
Pinnularia acrosphera
Pinnularia borearis
Pinnularia eiferana
Pinnularia major
Pinnularia rupestris
Pinnularia nodosa
Pinnularia viridis
Pinnularia viridiformis
Pinnularia viridiformis var. minor
Placoneis negrecta
Placoneis palaelginensis
Placoneis sp.2
Nitzschia nana
Pseudostarurosira brevistriata
Rhopalodia accuminata
Adoneis sp.
Deplhineis sp.
Diploneis smithii
Fallacia sp.
Melosira moniliformis

Freshwater

Page 18 of 29

Brackish - Marine
Freshwater - Brackish
Freshwater
50 100%

40
To-a
60

80

100

T2
T3
S1
S2
S3
S4

0

30%

S5
S6
S7

120
S8
S9
140

S10

Fig. 10 Diatom analysis result for the core sample Ha 8. In the horizontal axis are described the diatom species found along with the sedimentcore: freshwater, freshwater–brackish, and brackish–marine environments. On the right side, diatom-origin proportion is described as a percentage.
A close view depicts the presence of marine species (from 0 to 30%)

5.2 Identification of tsunami origin

H02_S40

1 mm

Fig. 11 Shell fragment recovered from S5

We followed the same methodology using the XRF data
and the LDA for the lateral correlation in the remaining
cores (i.e., H13, H01, H07, H08, H09, H10, H11, H12, andH05) (Fig. 3b).

We consider brackish-marine diatom species, shell fragments, glauconite occurrence, landward thinning, andlandward reduction of MS value as strong evidence ofmarine provenance of the sand layer (Table 2). Regarding
marine provenance, a factual imprint is the presence offragments or complete structures of marine organisms,
which is, in our case, brackish-marine diatom species
and shell fragments. Complementarily, glauconite also
supports the marine origin of the sand layers by usually
being an authigenic mineral of shallow oceanic platform
environments (Huggett 2013). However, it can also be
related to reworked material from the Miocene or the
Pleistocene deposits (Kamada et al. 1991; Phillips et al.
2017).
Sand units S4, S5, S6, S7, S8, S9, and S10 can be considered as clear marine-origin events, based on attributes such as landward thinning, erosive basal contacts(Fig. 4), landward decrease in magnetic susceptibility
value – indicating landward mineral gradation during
the wave progression (Fig. 5), presence of diatoms ofVelasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

Page 19 of 29

Table 3 Results of tephra analysis for T1 and T6
Tephralayer

Volcanic glass
shards refractive
index
Range

Number of
volcanic
glass
shardsMode measured

Volcanic
glass
morphology

Orthopyroxene shards
refractive index
Range

Mode

Number oforthopyroxene
shardsmeasuredRemarks

Correlative
tephraTowada-a(To-a)

Resultsof tephraanalysis forT1 and T6
T1

1.4933–
1.49901.5023–1.50511.5055–1.5083
1.5088–1.5146
1.5227–1.5227

–
1.504
1.507
–
–

4
9
55
12
1

C, T, H, It
T, C, H, It
T, C, H > It
T, C
T

1.699–1.712 1.707–1.708 46
1.714–1716 1.715
11
1.721–1.722 –3

Heavy minerals:
Opx (Hyper‑
sthene > Bronzite)(γ = 1.699–1.722),
Opq, Cpx > GHb
Light minerals:
Pl, QzT6

1.5008–1.5112
1.5116–1.5148

–
1.513

16
60

T, C, H, It
T>C

1.699–1.703 –
1.704–1.709 1.707
1.711–1.712 –4
542

Heavy minerals:
TowadaOpx (Hypersthene) Chuseri(γ = 1.707), Opq,
(To-Cu)Cpx > ApLight minerals:
Pl, QzTo-a1.496–1.508(1.502–
1.506)

–

–

–

–

–

–

Opx: 1.706–1.708(1.707)–To-Cu

1.510–1.514(1.511–1.513)

–

–

–

–

–

–

Opx: 1.704–1.708(1.707)–

Reference values (Machida
and Arai
2003)

Glass shards morphology: H: large broken type, T: fibrous pumice type, C: intermediate from between H-type and T-type, It: irregular type. Heavy minearls: Opx:
orthopyroxene, Cpx, clinopyroxene, GHb: green hornblende, Ap: apatite, Opq: opaque minerals. Light minerals: Qz: quartz, Pl: plagioclase

Table 4 Conventional radiocarbon ages obtained from the core samples H02, Ha8 and H04
Sample ID

Type of material

Pretreatment

H02/C12

Organic sediment

Acid washes

H02/C13

Organic sediment

Acid washes

Ha8/C1

Organic sediment

Acid washes

Ha8/C2

Organic sediment

Acid washes

Ha8/C3

Organic sediment

Acid washes

Ha8/C4

Organic sediment

Acid washes

Ha8/C5

Organic sediment

Acid washes

Ha8/C6

Organic sediment

Acid washes

Ha8/C7

Organic sediment

Acid washes

Ha8/C8Organic sediment

Acid washes

Ha8/C9Organic sediment

Acid washes

Ha8/C10

Organic sediment

Acid washes

Ha8/C11

Organic sediment

Acid washes

H04/C14

Organic sediment

Acid washes

H04/C15

Organic sediment

Acid washes

H04/C16

Organic sediment

Acid washes

σ13C (‰)− 26.30− 27.04− 19.5− 25.2− 25.7− 26.2− 26.6− 26.4− 26.8− 26.6− 26.9− 27.0− 26.9− 28.00− 26.78− 27.29

Conventional
radiocarbon age
2830 ± 304400 ± 301280 ± 30

1770 ± 30

1990 ± 30

2690 ± 302900 ± 30

3270 ± 303550 ± 30

3890 ± 304210 ± 30

4390 ± 304760 ± 30

3160 ± 30

3190 ± 304300 ± 30

Beta number
525634525633458359455968455969442357531884442358442359442360442361442362442363531883525635525636

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

brackish-marine species (Fig. 10), upward fining, andglauconite occurrence (Table 2). Complementarily,
the sand layers can be categorized as tsunami deposits when using the results from numerical modelingthat suggest that only tsunami events can inundate the
5 m-uplifted terrace.
As mentioned before, Velasco et al. (2019) compared
the inundation capacity of both storms and tsunamis
in the area of this study by means of numerical modeling using the present–day topography without coastal
engineered dikes. For the storm calculation, extreme
parameters were utilized for a cyclone, which is unrealistic for Hachinohe because the trajectory of thestorm was NE to SW (counter–clockwise), opposite to
the expected Coriolis–induced SW to NE direction.
Even under such extreme boundary conditions, thestorm surge could not inundate and deposit sediments
on areas higher than 3 m in elevation (Fig. 2a). For thetsunami surge, under the source parameters of the 2011
Tohoku–oki event, the calculated wave could reachthe 5 m–uplifted terrace (Fig. 2b). Therefore, closer, ormore potent tsunami sources can inundate the study
area further inland.
According to sea-level reconstructions, sand layers S4 to S10 were deposited under a relative sea–levelhighstand about 2 m higher than the present day’s (i.e.,from ~ 6000 cal BP to ~ 2800 cal BP) (Niwa and Sugai
2021). It means, the edge of the current five-meterterrace should be three-meter high for that time. Even
though we did not perform reconstructions for the
paleo shoreline, such maximum sea level implies thateven in the worst storm scenario, its inundation capacity should still be limited by the topography. Hence, thesand units identified as a marine origin (i.e., S4, S5, S6,
S7, S8, S9, and S10) were deposited on the exclusive
tsunami domain zone (Figs. 3, 4).
Contrastingly, sand units S1, S2, and S3 lack any such
evidence; they are not related to marine-origin events;
hence cannot be considered tsunami events. Although
the lack of marine evidence does not necessarily indicate
a non-marine origin (Goff and Chagué-Goff 2012), its
association with a tsunami event is weaker than that of
the other sand units. Suppose a marine origin is assumed
for sand units S1, S2, and S3. In this case, one possible
explanation is that they were deposited close to the limitof the maximum sediment extent, where the flow conditions only allow reduced sediment content, typically represented in mica flakes and clay minerals (Yoshii et al.
2017). Consequently, the remaining thin and extentlimited sand layers were highly susceptible to leaching,weathering, and poor preservation of marine-related evidence. This hypothesis is supported by Niwa and Sugai(2021). They showed that there was a slight sea-level fall

Page 20 of 29

from ~ 2800 cal BP in the Hachinohe area, meaning thatthe inundation capacity of tsunami events was progressively reduced.
Another possible explanation is that the sand layers S1,
S2, and S3 can be associated with possible river floods,
as the survey area is bounded by the Oirase and Gonohe
rivers.5.3 Sedimentation processes.
5.2.1 Implications from XRF, XRD and CT data

The CT profile displayed bulk density changes associated with landward thickness reduction, packing, andgrading (Figs. 4 and 7). Likewise, XRF–pattern analysis is significant not only for geochemical characterization but also for sedimentological analysis by showing
elemental changes related to the mud–sand ratio change(Fig. 6 and Additional file 3: Appendix 3, Additional
file 4: Appendix 4, and Additional file 5: Appendix 5).
Such trend changes in the sand layers exhibit a close
relationship between the elemental pattern and changes
in packing, and composition (Figs. 4, 6, and 7 and Additional file 3: Appendix 3, Additional file 4: Appendix 4,and Additional file 5: Appendix 5). As the mud is mixed
into the sand layer, the clastic signal (i.e., sand fraction)
decreases with sorting and composition change (Fig. 7).
Thus, the upward trend of the decrease or increase in
Ca and Sr patterns in the sand layers can be associatedwith the sand-mud ratio change, respectively. As these
two elements (Ca and Sr) have similar atomic properties,
they can easily replace each other in the lattice (Nichols2009) and can be related to the same origin. In addition,changes in Ti display variations in the content of heavy
minerals (Croudace and Rothwell 2015) within sandy
deposits, reflecting the density–gradation by a decreasein the particle settling velocity, due to the landwardchange in MS.
Carbonate dissolution and XRD demonstrated thatthe positive calcium signal from sand layers on XRF was
related to the plagioclase content rather than the marine
origin. It undoubtedly helps to avoid misinterpretationsof the origin of the element (Additional file 1: Appendix 1). Even though small quantities of carbonate wereobtained in some sand layers (< 0.3%) (Table 2), such values can be related to analytical error instead of real mineral content. Ta has high values in the peaty–mud layers
because it tends to be dissolved by weathering and concentrated in the latest phases of fluvial sediment transport, depositing as clay minerals on floodplains (Parker
and Fleischer 1968; Fricke and Heilig 2006). Thus, Ca/Ta
represents the subtraction of the mud content from thetotal Ca (i.e., plagioclase), which means that this ratio
reflects the relative siliciclastic content along with thesediment core sample and its trend variation. As depicted

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

by the rows in Fig. 6 and Additional file 3: Appendix 3,
Additional file 4: Appendix 4, when the Ca/Ta value
decreases, the grain size also decrease (Figs. 4 and 7).
Consequently, the Ca/Ta pattern mimicked the grainsizechange. Ta was also helpful for normalizing the elements
used in the LDA. As mentioned above, it is closely related
to mud (Fig. 6 and Additional file 3: Appendix 3, Additional file 4: Appendix 4, and Additional file 5: Appendix 5), and it can be considered relatively homogeneousand abundant in the core samples. Thus, it serves as a reference for the comparison of the elements related to thesand layers.
On the other hand, CT imagery suggested a rapid landward reduction in sand content and density via a reduction in color intensity in units S8 and S5.
5.2.2 Implications from grain‑size and MS data

The grain size analysis was not entirely conclusive forbeing used as a key factor for tsunami discrimination.
For instance, the grain size of the coarsest portion of thesand layers S4, S6, S7, S8, and S9 at H03 was finer thanthat at H02 (100 m seaward of H03). Horizontal changes
in the grain size of the coarsest portion of the sand were
not evident between H03 and H04, except for the sandlayer S4 (Fig. 7). Thus, landward fining was not convincing among the core samples. This may be related to several factors, such as the paleotsunami sediment source
was mostly homogeneous in grain size, the particle size
analysis was performed with a small amount of sample,
approximately 0.5 g each, the number of core samples islimited, and particularly because the changes in grain size
are slight. For instance, when comparing the maximum
extent of the sand layer S8, approximately 200 m (Figs. 1
and 3b), to the number of core samples analyzed by grainsize, it is not precise to assert that the sand layer is reducing landward grain size. Although the laser diffraction
method offers high resolution (Shimadzu 2012), reliability, and reproductivity, the number of samples limits thequality of the results and increases the expected error forinterpretation.
On the other hand, regarding sample preparation, sample homogenization using a spatula after organic matterdissolution may not be enough for sample disaggregation,
which could leave some particle aggregates that could
mislead the measurement. Thus, to improve the precision of the results, it is desirable to increase the sample
volume and the number of sample measurements. This
reduces systematic errors.
The sand layers, S5, S6, S7, and S8 exhibit slight or
no lateral change in the distributions of the grain size.
On the other hand, S4, S9, and S10 show a remarkablevariation from sand at H02 to mud at H04. Interestingly, although S5 and S8 are the thickest sand layers andPage 21 of 29

exhibit the maximum landward extent, the landward
decrease in the mode value is not significant. This can be
explained by the homogeneity in the source’s grain sizeor greater flow speed and depth for the distance between
H04 and H02, which is only 200 m (Fig. 3). However,some sedimentary characteristics can be observed in thegrain-size distributions, which fluctuate along with thesand layer showing the particles’ behavior when they settle down. In sand layers, such as S5 in H02 and S8b in
H02 from the middle to the upper part, and S8 in H03, in
addition to upward fining, grain size skewness tends torise owing to a lower grain selection (Fig. 7). Some distribution curves exhibit multimodal distribution due to themixing of sand-mud or sand-granules mixing. Coarser
particles exhibiting negative phi values in a secondarymode peak are composed of rounded pumice grains andare distributed mostly towards the base and top of thelayer (S5 in H02 and S8 in H02), and further inland (S5and S8 in H04) (Fig. 7, Table 2). This can be explained byvertical density stratification during sediment transport
in the inundation flow and differences in particle settling
velocity.
In the mud layer interbedded with S9 in H02, the ripup clast was derived from the mud layer below S9a. The
front (Fig. 4c) and lateral (Fig. 4b) views expose differences in axial lengths, which points out the possible
transport direction perpendicular to the a-axis; hence,
perpendicular to the coastline. In the CT images fromcores H02, H03, and H04 (Fig. 4), units S6, S9, and S10
exhibit a remarkable landward decrease in thickness. In
addition to the landward decrease in magnetic susceptibility (Fig. 5), landward thinning reflects the progressive
reduction in energy and flow capacity during inundationfrom the sea (Fig. 4).
The lateral variation of MS in S7 is also different, witha maximum in the middle section of the transect (Fig. 5).
High magnetic susceptibility values can be associatedwith high-energy environments (Černý et al. 2016), suggesting a flow velocity peak reached that point is possible.
Regardless inverse grading observed in S7 is linked or not
to a marine origin, diatom evidence points it out.
The inverse grading appeared in S7 and at the base of
S8, both in H02 (Fig. 7). Three different mechanisms can
explain this phenomenon. Firstly, Sohn (1997) described
the traction-carpet model as a result of a bivariant velocity profile, which results in upward coarsening along withan increase in velocity. Such a model has been developed
based on turbidites and hyperconcentrated flow deposits (Sohn 1997), as well as previous paleotsunami studies(Moore et al. 2011; Minoura et al. 2013). Secondly, Yoshii
et al. (2017) proposed that the inverse grading at the baseof the deposit may be related to processes such as kinetic
sieving, geometrical sieving, and spatial differences inVelasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

Page 22 of 29

Table 5 Probabilistic recurrence between sand units

OxCal v4.4.4 Bronk Ramsey (2021); r:5 Atmospheric data from Reimer et al (2020)

Boundary To-a=(915 CE)
R_Date c1

S1

Mean recurrence(years, 95%
confidence
interval)

Sigma Coefficient ofvariation (CV = σ/µ)

R_Date c3

Unit S1–Unit S2

784

323

0.66

S2

Unit S2–Unit S4

559

263

0.57

R_Date c4

Unit S4–Unit S5

346

151

0.68

S4

Unit S5–Unit S6

395

169

0.80

Unit S6–Unit S7

418

179

0.74

Unit S7–Unit S8

450

192

0.77

Unit S8–Unit S9

320

147

0.99

Unit S9–Unit S10

378

177

0.71

Recurrence intervalfrom S1 to S10

456

200

0.74

Recurrence intervalfrom S4 to S10(tsunami sands)

384

169

0.78

R_Date c5

S5
R_Date c6

S6
R_Date c7

S7
R_Date c8

S8
R_Date c9

S9
R_Date c10

S10
R_Date c11

Sequence Ha8
7000

6000

5000

4000

3000

2000

1000

0

Modelled date (BP)Fig. 12 Calibrated 14C dating results and modeled dates for samples
obtained from core sample Ha8. Modeled dates are shown with aprobability of 95% (2σ). R_Date curves correspond to each calibrated
sample result. Yellow stripes denote the calculated deposition ages
for the sand layers. Sequence model is bounded by To-a tephra event

flow speed (Middleton 1970; Kneller and Branney 1995;
Dasgupta and Manna 2011), rather than traction- carpet sedimentation. Thirdly, Naruse et al. (2012) assumed
velocity changes with time, including acceleration anddeceleration cycles, which induce upward coarsening andfining, respectively, and are divided by an internal erosion
surface (IES). The IES can be defined as the sedimentary
truncation generated by the maximum shear velocity.
We interpreted the boundary between S8a and S8b as
an IES (Fig. 4). Thus, both S8a and S8b corresponded
to the same run-up event and represent a flow cycle ofacceleration–deceleration, respectively. The presence ofthe two subunits in H02 can be explained by the reduction in internal sedimentary units along with landwardthinning (Naruse et al. 2012).
All three explanations fit the inverse grading sedimentation style. Although Sohn’s model employs a logarithmic profile of flow velocity and its increase with depth
reduction and Naruse’s model assumes velocity andacceleration changes with time, Yoshii’s model includes
geometrical properties. One does not necessarily exclude
the other. In that sense, during the acceleration phase, a
traction-carpet sedimentation style can be developed togenerate inverse grading, such as in S8a.
Based on the sedimentological characteristics of thesand layers, it is possible to classify them into three typesof sedimentation: (1) normal grading with fast landward
sediment run–out, marked by a strong landward grain sizechange from sand to mud and landward thinning (S4, S9,and S10); (2) massive sands with slight vertical and lateralchanges (S6 and S7), and (3) normal grading with thicker
and broader sediment deposition (S5 and S8). Such settling
can be related to three aspects: first, the sediment–source
distribution and availability, which vary seasonally in the
shoreface (Dean and Dalrymple 2004); second, the paleosurface formed by successive ponds that cause a rapid runout of the sediment by a rapid decay in the sediment transport capacity of the wave; and third, the surge’s energy iscontrolled by the source magnitude and distance.
5.3 Sedimentary environment

GPR imagery displayed the basal and top boundaries
that limit the peaty–mud–sand–event sequence, characterized as undulating paleosurfaces (Fig. 14). The
freshwater-brackish diatom species that are present
along with the core sample and are included mainly inthe peat–mud sediment depict a sedimentary environment of wetlands controlled by the influence of freshwater-brackish water and anoxic regimes. These two
features indicate that the sedimentary environment
corresponds to a marsh complex formed by sequential
ponds (Fig. 14).

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

Page 23 of 29

Table 6 Correlation of the sand layers among the core samples
Sand layer Correlation analysis
T1

It was defined as the Towada-a event (Table 3), see the discussion in the body text. It continues throughout the core samples (Fig. 3), justbelow the paddy field soil. It is also consistent that two more tephra events were found underlying it, interbedded with peaty mud, except
in H13, H07, and H06, where there was only one, probably due to erosion

S1

It only appears in the core sample H02 (the thickest sedimentary record). Therefore, it is difficult to identify a lateral continuity, essentially
due to its thickness (Table 1, Fig. 3)

S2

It was traced among H02, H01, and H07 due to its relative stratigraphic position and lithological characteristics

S3

It only appears in the core sample H02 (the thickest sedimentary record). Therefore, it is difficult to identify a lateral continuity, essentially
due to its thickness

S4

It was traceable among H02, H01, H07, H08, and H03 (Fig. 3). Such correlation was made on the grain size, color, and relative stratigraphic
position

S5

It was traced throughout all the core samples, representing the sand with the furthest extent. It is the second thickest sand layer, after S8,
in the sedimentary sequence. Its position above the peaty-mud layer and T4 (marking horizon), upward fining, and bottom erosive con‑tact allowed its lateral correlation. The dating result obtained from the peaty-mud layer underlying S5 between Ha8 and H04 supported
the correlation (see Sect. 5 in the body text)

T4

It is a consistent ash layer, defined as a white clay with homogeneous thickness among the sand layers. Due to its consistency, lateralcontinuity, and lithological characteristics, it was used as a marker horizon to define the upper boundary of S6 and the lower boundary of
S5, considering the peaty mud interbedding between the sand and tephra layers (Table 1, Fig. 3)

S6

It is located below the peaty mud underlying T4; it was found in the core samples H02, H03 and H04. Such correlation was made based on
the characteristic lenticular stratification, silt composition, and the thickness of the layers (Table 1)

S7

It was traceable among H02, H07, H08, H03, H09, H10, and H04 (Fig. 3). Grain size, the bottom transitional or erosional contact, and therelative stratigraphic position led to its lateral correlation

S8

It was traceable throughout all the core samples between H02 and H06 and represented, in general, the thickest sand layer. Its thickness,
upward fining, and relative stratigraphic position allowed its lateral correlation

S9

It was traceable among the core samples H02, H07, H08, H03, H10, and H04. The two thin laminas interbedded with the thin peaty mud
led to the correlation between H02 and H03. The stratigraphic position allowed the correlation among the other core samples. The dating
result obtained from the peaty-mud layer underlying S9 and overlying S10 in Ha8 and H04 supported the correlation (see Sect. 5 in the
body text)

S10

It was traceable among the core samples H02, H07, H08, H03, H10, and H04. The stratigraphic position, overlying To-Cu and the peaty
mud, led to its lateral correlation. The dating result obtained from the peaty-mud layer underlying S9 and overlying S10 in Ha8 and H04
supported the correlation (see Sect. 5 in the body text)

T6

It was defined as the Towada-Cu event (Table 3), see the discussion in the body text. It continues throughout all the core samples at theend of the sequence. In all the cases, it was found as a thick lapilli layer

Such geomorphological setting not only controls thetransport and deposition of organic–rich mud sediments,
but also the maximum extent of the tsunami sand layers
while restricting the progression of inundation.
Bioturbation intensification and appearance/disappearance of diatom species after sand layers S5 and S8indicate sudden environmental changes (Figs. 4 and 10)(Dixit et al. 1992). Such alterations can be related either
to the impact of extreme events on the local ecosystemor the progressive reduction in the water sheet thickness,hence increasing oxygen content and, consequently, bioturbation (Dixit et al. 1992; Nichols 2009).
The presence of freshwater and freshwater-brackish
species related to the sand layers, mixed with marine
species, can elucidate the reworking of marine provenance sediments with terrestrial material caused by thetsunami wave. Due to the minor thickness of the sandlayers, it was not possible to determine whether the position of the marine species was related to a specific stratigraphic level. On the other hand, S8 exhibits that themarine diatoms are in the base and top of the layer. Thiscan be related to the increased mixing of marine and terrestrial materials during the progression and regressionof the wave, in which the addition of in situ material isincreased. Such behavior is not unusual because the sedimentary background environment is related to a marsh,where entrainment of freshwater species included in terrestrial sediments is expected. In addition, freshwater
diatom species have been previously reported in the lithological units surrounding the study area (Kamada et al.
1991).
5.4 Recurrence interval between 5600 cal yr BP and 1600
cal yr BP, and regional correlation of tsunami events

The modeled depositional dates for sand layers, witha probability of 95.4% (2σ), are shown in Table 2 andFig. 12. The model calculation in OxCal was constrainedby the To–a event at the top of the sedimentary sequence.
Owing to the minor thickness of S3 and its adjacentpeaty-mud layers, it was not possible to constrain its
deposition age. Even though T6 was identified as To–Cu,

Velasco‑Reyes et al. Progress in Earth and Planetary Science

Ha 8
T1

(2022) 9:19

Page 24 of 29

H02

H04

Sea

1184 - 1271 cal BP - C1
1621 - 1720 cal BP - C2

S1
S2
S3
S4
S5

1898 - 1975 cal BP - C32757 - 2842 cal BP - C42973 - 3075 cal BP - C53457 - 3557 cal BP - C63731 - 3891 cal BP - C7
4292 - 4406 cal BP - C8

S6
S7
S8

S1
S2
S3
S4
S5
S6

4656 - 4836 cal BP - C94892 - 5034 cal BP - C105474 - 5583 cal BP - C11

S9
S102800 - 3000 cal BP - C12

S5

S8

S7

S9

3395 - 3460 cal BP - C15+S94854 - 4960 cal BP - C16

S10

S8

S10

3340 - 3448 cal BP - C14S6
S74893 - 5054 cal BP - C13*
* Reworwed?

+

Modern contamination?

Fig. 13 Carbon dating results and ages correlation among core samples H02, Ha8, and H04. C# notation corresponds to the sample number
described in the dating model on Fig. 12 and Table 4a)Seaward

(m) 0

b)

H 04
5

10

15

20

25

H 04
0

5

10

15

20

25

0

Soil
1

2

To -a

To-cu

To-Cu
H04

Top sand-mud sequence
Base sand-mud sequence

Tsunami sand deposit
Peaty mudMarsh environment
Landward

Fig. 14 Paleosedimentary environment interpretation for the base of the peaty-mud sequence. a Ground penetrating radar image obtained
around H04 sampling area. Strong reflectors display the top and base of the sand/peaty-mud sequence. b Interpreted paleo environment before
the deposition of T1, T2 and T3. It was contructed by using diatom results, sedimentary characteristics, and GPR imagery

the lack of insights about its primary or secondary origin limits its usefulness as a boundary in the radiocarbon
modeling.
Regarding carbon dating sampling, it was impossible
to find fragments of organic matter in the peaty-mud layers for more precise age estimation. Hence, it is expectedthat the actual depositional ages of the sand layers canbe older than the estimated ones due to unknown events
such as local erosion and reworking.

The calibrated 14C results exhibited a mean recurrence
interval of 456 yr (320–784 yr, 95% confidence interval)
and a coefficient of variation (CoV) of 0.74 (0.57–0.99,
95% confidence interval) for all sand layers (Table 5,
Additional file 6: Appendix 6), during the period between
1200 and 5500 yr BP. However, the lack of marine evidence indicates that sand layers S1, S2, and S3 cannot
be directly correlated to tsunami events, and their inclusion in the recurrence estimation reduces the reliability

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19a) Misawa (Tanigawa et al., 2014a)b) Hachinohe - this study

S9
S10

S7

S8

S6

S5

S4

c) Harashinai (Takada et al., 2016)
TSb2 [934-1278 calBP]
TSb3 [1286-1614 calBP]
TSb4 [1710-1926 calBP]
TSb5 [2122-2337 calBP]
TSb6 [2441-2845 calBP](a) [2856-3215 calBP](b) [3008-3399 calBP](c) [3235-3641 calBP]

TSb7

TSb8 [3451-3972 calBP]
TSb9 [3986-4788 calBP]
TSb10 [4437-4872 calBP]
TSb11 (a,b,c) [5486-5901 calBP]d) Noda - Maita

Tse5
Layer 4

(Inoue et al., 2017)

6000

5000

Tse3

Tse4

(Ishizawa et al., 2022)

Layer 3

Tse2
Layer 1

Layer 2

4000

3000

2000

1000

Age (yr BP)
Fig. 15 Chronological correlation with previous studies conducted
close to Hachinohe area. a Tanigawa et al. (2014a) in Misawa,northern Aomori; b This study; c Takada et al. (2016) in Harashinai site,
TSd stands for tsunami sand; d Ishizawa et al. (2022) and Inoue et al.
(2017) in Noda. The position of each survey is plotted in the coastlineof the Tohoku Regionof the calculation. Thus, a mean recurrence interval of
384 yr (320–450 yr, 95% confidence interval) and a CoVof 0.78 (0.68–0.99, 95% confidence interval) was obtained
from S4 to S10, from ~ 2700 to ~ 5500 yr BP (Table 5,
Additional file 6: Appendix 6). This does not imply thatno tsunami affected the Hachinohe area between ~ 1200
and ~ 2700 yr BP. Instead, it suggests no tsunami evidence
was recorded or the geological conditions for such time
were different and left tsunami evidence without a clear
marine imprint. Besides, reworking and tsunami erosion
may have removed sedimentological evidence of additional tsunami events that may have occurred among theregistered events.
On the coast of Misawa city, Tanigawa et al. (2014a)
identified a tsunami deposit formed from 2900 to 4800 cal
BP, which can be broadly correlated with events S4, S5,
S6, S7, S8, and S9 in the present study (Fig. 15). Hence,
making it difficult to correctly correlate the deposit found

Page 25 of 29

in Misawa City with any of the sand layers from Hachinohe. To the south, tsunami events from Hachinohe canbe correlated with events found by (Takada et al. 2016)
in Harashinai (Hirono Town) (Fig. 15). Nonetheless, such
chronological and recurrence correlations lack sufficientaccuracy owing to the uncertainty in the dating results inHarashinai Site (Takada et al. 2016) and error ranges of
the calibrated 14C ages. Further south, in Noda (Maita),
S4 can be correlated to the sand layers “Tse5” and “Layer
4” identified by Ishizawa et al. (2022) and Inoue et al.
(2017), respectively (Fig. 15).
Anyway, the northern Sanriku Coast (i.e., Noda andHarashinai) and Hachinohe face both trenches at different angles and at different latitudes. This means that
evidence from Hachinohe can be related to the activity of the Kuril and Japan trenches and their junction,
while the evidence recorded in Noda and Harashinai canbe primarily related to the activity of the Japan Trench(e.g., Figure 7 in Tetsuka et al. (2020)). Such geologicalrecords and recurrence intervals reaffirm the importanceof Hachinohe as a suitable site for paleotsunami research.
To improve temporal correlations and recurrence estimation, it is imperative to increase the precision of thedeposition ages by conducting detailed studies at everysite. In addition, numerical modeling of the tsunami
sources will improve our understanding of the size of tsunami events that can inundate the tsunami deposit sites
examined here (e.g., Figure 7 in Tetsuka et al. 2020).

6 Conclusions
In this study, we carried out a geological investigationof paleotsunami in Hachinohe, Aomori Prefecture,northern Japan, to reconstruct the regional tsunami history using sedimentary evidence on upliftedmarine terraces. Numerical modeling allowed us todetermine the best possible survey point and rule out
the possibility of storms as a genetic factor. We identified the marine provenance of seven sand layers.
Sedimentological analysis indicated that three typesof depositional settings existed among the sand layers: (1) normal grading with fast landward sediment
run–out, marked by a strong landward grain sizechange from sand to mud and landward thinning (S4,S9, and S10); (2) massive sands with slight verticaland lateral changes (S6 and S7), and (3) normal grading with thicker and broader sediment deposition (S5and S8). Such sedimentary differences can be related
to sediment–source distribution and availability, thepaleosurface formed by successive ponds and vegetation that causes a quick run out of the sediment by a
rapid decay in the sediment transport capacity of thewave, and the surge’s energy controlled by the source

Velasco‑Reyes et al. Progress in Earth and Planetary Science(2022) 9:19

magnitude and distance. As a result, we identified
seven sand layers (S4 to S10) as tsunami origin with amean recurrence interval of 384 yr (320–450 yr, 95%
confidence interval) and a CoV of 0.78 (0.68–0.99,
95% confidence interval), from 5500 to 2700 BP. Further paleotsunami studies in the Shimokita Peninsula must be conducted to reduce uncertainty in thechronological correlation, understand the maximum
spatial extent of the paleotsunami deposits, and constrain possible sources.
Abbreviations
CT: Computed tomography; GPR: Ground penetrating radar; LDA: Linear
component analysis; PCA: Principal component analysis; To-a: Towada-a; ToCu: Towada-cu; XRF: X-ray fluorescence; XRD: X-ray diffraction; MS: Magnetic
susceptibility.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s40645-​022-​00477-4.
Additional file 1. Mineral phases identified in sand layers found in H02,
H04, and current beach. Note S1 and S3 in H02 are not characterized dueto the thickness of the layer.
Additional file 2. X-ray diffractogram of the sand S5 in the core ample
H02. (a) Diffractogram of the air dried and oriented sample: blue patterndepicts the raw spectrum; red pattern represents calculated spectrum.
(b) Diffractogram of the sample treated with ethylene-glycol: red patterndepicts the raw spectrum; brown pattern represents calculated spectrum.
(c) Diffractogram of the sample after heated to 550 °C: green patterndepicts the raw spectrum; red pattern represents calculated spectrum. It
is representative the disappearance of the Kaolinite peak at 12.5 2θ after
heat treatment.
Additional file 3. Log pane of the core sample H03. Log distribution:
depth, stratigraphic units, CT image, validity, gray value profile, magnetic
susceptibility and Micro X–Ray Fluorescence elements result vs depth.
Additional file 4. Log pane of the core sample H04. Log distribution:
depth, stratigraphic units, CT image, validity, gray value profile, magnetic
susceptibility and Micro X–Ray Fluorescence elements result vs depth.
Additional file 5. Log pane of the core sample H06. Log distribution:
depth, stratigraphic units, CT image, validity, gray value profile, magnetic
susceptibility and Micro X–Ray Fluorescence elements result vs depth.
Additional file 6. Recurrence intervals calculated from S1 to S10. The
values were obtained from OxCal V4.4.4 (Bronk and Ramsey 2021).
Acknowledgements
CT images and XRF core scanning analyses were conducted at the Center for
Advanced Marine Core Research at Kochi University, acceptance No. 18A003.
We particularly thank the landowners for their permission to conduct the fieldsurveys. We would like to thank K. Nakamura and T. Ishizawa, and M. Watanabe
for their assistance during the field trip, during laboratory analysis, and for his
collaboration with the numerical simulations, respectively. Finally, we thank Dr.
Philipp Kempf and an anonymous reviewer for their valuable comments thatconsiderably improved this manuscript.
Authors’ contributions
All authors accompanied field surveys, collected samples, and participated in
discussions during the research. Complementarily, EVs performed the experi‑ments and wrote the manuscript. KG proposed, conceived, and designed thestudy. TS conducted preliminary research and collected samples for dating

Page 26 of 29

on the core sample Ha8. TC conducted diatom analysis. All authors read andapproved the final manuscript.
Funding
This research was supported by JSPS KAKENHI Grant Numbers 17H02971 and
18H03820, by the International Research Institute of Disaster Science (IRIDeS),
and by the International Joint Graduate Program in Earth and Environmental
Sciences (GP–EES) of Tohoku University.
Availability of data and materials
The data and materials used and generated by this research can be foundin the archive of the Tsunami Engineering Laboratory at the International
Research Institute of Disaster Science, Tohoku University, and in the following
URL: https://​doi.​org/​10.​5281/​zenodo.​57691​35. The datasets supporting theconclusions of this article are included within the article and its additional files.

Declarations
Competing interests
The authors declare that they have no competing interest.
Author details
1
Department of Earth Science, Graduate School of Science, Tohoku University,
6‑3, Aramaki Aza Aoba, Aoba, Sendai, Miyagi 980‑8578, Japan. 2 Departmentof Earth and Planetary Science, Graduate School of Science, The Universityof Tokyo, Hongo 7‑3‑1, Bunkyo‑ku, Tokyo 113‑0033, Japan. 3 International
Research Institute of Disaster Science, Tohoku University, Aramaki Aza‑Aoba
468‑1, Aoba‑ku, Sendai 980‑8570, Japan. 4 Institute of Seismology and Volcan‑
ology, Graduate School of Science, Hokkaido University, Kita 10 Nishi 8, Kita‑ku,
Sapporo 060‑0810, Japan. 5 College of Agriculture, Food and Environment Sci‑
ences, Department of Environmental and Symbiotic Science, Rakuno Gakuen
University, 582, Bunkyodai‑Midorimachi, Ebetsu, Hokkaido 069‑8501, Japan.
Received: 14 October 2021 Accepted: 16 March 2022

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