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Title: Adjoint Inversion of Near‐Field Pressure Gauge Recordings for Rapid and Accurate Tsunami Source Characterization
Abstract

We explore the potential of the adjoint‐state tsunami inversion method for rapid and accurate near‐field tsunami source characterization using S‐net, an array of ocean bottom pressure gauges. Compared to earthquake‐based methods, this method can obtain more accurate predictions for the initial water elevation of the tsunami source, including potential secondary sources, leading to accurate water height and wave run‐up predictions. Unlike finite‐fault tsunami source inversions, the adjoint method achieves high‐resolution results without requiring densely gridded Green's functions, reducing computation time. However, optimal results require a dense instrument network with sufficient azimuthal coverage. S‐net meets these requirements and reduces data collection time, facilitating the inversion and timely issuance of tsunami warnings. Since the method has not yet been applied to dense, near‐field data, we test it on synthetic waveforms of the 2011Mw9.0 Tohoku earthquake and tsunami, including triggered secondary sources. The results indicate that with a static source model without noise, using the first 5 min of the waveforms yields a favorable performance with an average accuracy score of 93%, and the largest error of predicted wave amplitudes ranges between −5.6 and 1.9 m. Using the first 20 min, secondary sources were clearly resolved. We also demonstrate the method's applicability using S‐net recordings of the 2016Mw6.9 Fukushima earthquake. The findings suggest that lower‐magnitude events require a longer waveform duration for accurate adjoint inversion. Moreover, the estimated stress drop obtained from inverting our obtained tsunami source, assuming uniform slip, aligns with estimations from recent studies.

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NSF-PAR ID:
10482072
Author(s) / Creator(s):
 ;  ;  ;  ;  
Publisher / Repository:
DOI PREFIX: 10.1029
Date Published:
Journal Name:
Earth and Space Science
Volume:
10
Issue:
12
ISSN:
2333-5084
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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