— V11E-09 —Detecting Small Volcanic Tsunami Signals from Kita-Ioto Caldera

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Always happy to discusseither in person at AGUor via Zoom :)Using Dense DONET Ocean-Bottom Pressure Records
Osamu SANDANBATA1, and Tatsuhiko SAITO2

X (Twitter) ➡ @osm3dan
E-mail ➡ osm3@eri.u-tokyo.ac.jp(1) Earthquake Research Institute, The University of Tokyo, Japan(2) National Research Institute for Earth Science and Disaster Resilience (NIED), Japan,

1. Introduction

2. Data

Mw 5.32010

Mw 5.22015

Mw 5.2

2017

To improve the S/N ratio of tsunami waves.
Step 1: Estimate tsunami arrival time at stations

Pressure [mm H2O]

Mw 5.22008

3. Methods: Waveform stacking(b) OBP data from 18:30:50 on 11 March 2019

Did ocean-bottom-pressure (OBP) gauges,~900 km away, detect the tsunami waves?
—In raw data, tsunami signals are unclear!

Non-double-couple earthquakes (EQs) repeat
at the Kita-Ioto submarine caldera, Japan.
—Did the 2017/2019 EQs cause tsunamis?
1992(a) OBP data from 17:48:25 on 15 June 20172019

Mw 5.2Mw 5.3Signal contaminatedby noise?

ABOBP arrayDCBCuTs

DmnaAiTime (s)Pressure [mm H2O]

Step 2: Align waveform data to tsunami arrival timeFunka
AsaneKita-IotoIslandTime (s)(a) SET-KM for the 2017 event

Fig. 1: Non-double-couple earthquakes at the KitaIoto submarine caldera, the Izu-Bonin Arc. Dots
represents locations of the repeating earthquakes.

4. Results Tsunami signals of ~1.0 mm in average became evident, with S/N-ratio increase by >~70%.
*Signal/noise (S/N) ratio ! =A"er stacking!"#!"#$%!"#$#&!'((a) Stacked OBP waveform for 2017 EQ(a) Example OBP waveform of the 2017 EQ (@KMB07)
RMSnoise

RMSsignal! = 1.55(b) SET-MR for the 2017 event

Fig. 3: Band-pass (100–500 s) filtered waveforms
after the earthquakes in (a) 2017 and (b) 2019. Bluelines indicate the estimated tsunami arrival times.
(a) SET-KM for the 2017 event

Before stacking

Step 3: Stack and take average
Meanpressure[mm H2O]

Fig. 2: OBP gauges of DONET (Dense Oceanfloor
Network system for Earthquakes and Tsunamis),located ~900 km away from the submarine caldera.

Stacking! = 2.68

Time (s)Fig. 4: Schematic illustration of waveform stacking.
(b) SET-MR for the 2017 event

5. Discussion & Conclusions

Stacked tsunami waveforms help distinguish a difference in the ruptured fault location.

l The synthesis from “trapdoor faul4ng (TF)” model reproduces the stacked waveforms,
which resolves the detailed loca4ons of the ruptured fault for the 2017/2019 EQs.
Reverse faulting+68%■ West-sided trapdoor fault explains the 2017 tsunami.

Sill deformation

Fault

Tsunami signal(c) SET-KM for the 2019 event■ North-sided trapdoor fault explains the 2019 tsunami.
(b) Stacked OBP waveform for 2019 EQ(c) SET-KM for the 2019 event(b) Example OBP waveform of the 2019 EQ (@KMB07)! = 2.47

Stacking+96%

Caldera(d) SET-MR for the 2019 event

For the 2017 event, S/N ratio ! = 1.65±0.63 (16 stations)! = 1.22

Tsunami signalFault⬆Fig.
7: TF sourceof the 2008 EQ.
(d) SET-MRfor the model2019 event[Sandanbata & Saito, 2023, ESSOAr]

Caldera➡Fig. 8: Comparison between stacked waveformsof the model (blue) and the observed data (black).

l We suggest that successive fault rupture on
segmented faults in the submarine caldera
causes frequent trapdoor faul4ng recurrence,in an interval of 2–5 years, at Kita-Ioto caldera.
For the 2019 event, S/N ratio ! = 1.26±0.49 (13 stations)Fig. 5: An example of waveform & wavelet analysis
result before stacking for the (a) 2017 & (b) 2019 EQs.

Enhanced tsunami signal

Fig. 6: Waveforms and wavelet analysis after stacking. Line & shade
on top panel indicates the mean value & standard deviation, resp.

Fig. 9: Illustration of intra-caldera fault segmentations inferredfrom our analysis of stacked tsunami waveform data.

Westernsegment

Intra-caldera fault

EQ
2017

2019 EQ

Sill-likemagma reservoir

Northern
segment