Adaptive Tsunami Source Inversion Using Optimizations and the Reciprocity Principle

Abstract

We propose an advanced two‐step tsunami source inversion method with adaptive Green's functions by applying an optimization and a reciprocity principle. The method first reconstructs the sea surface displacement from observed tsunami waveforms based on a superposition of Gaussian‐shaped unit sources. In the first step, we optimize the unit source locations that give the best fit of waveforms to observations. This leads to nonequidistantly distributed unit sources, in which the synthetic waveforms from such sources to the observation points are generated using the reciprocity principle to save computational efforts of Green's functions. In the second step, the reconstructed sea surface displacement is used to estimate the slip on a finite fault plane. We also optimize the fault parameters that produce the closest displacement pattern to the first step result. Therefore, our method provides best fitting of waveforms and optimum fault parameters with automation for the process. The current method improves our previous studies, in terms of the construction of tsunami Green's functions using the reciprocity principle and determination of fault parameters through the optimization. We test the proposed method using tsunami data of the 2004 Kii Peninsula, Japan event generated by an Mw 7.4 intraplate earthquake. The overall waveform fit that resulted from our method shows much better agreement with the observations compared to that of the conventional approach, and the estimated fault depth is more consistent with the relocated aftershock distribution.

Effects of Depth of Fault Slip and Continental Shelf Geometry on the Generation of Anomalously Long‐Period Tsunami by the July 2020 Mw 7.8 Shumagin (Alaska) Earthquake

“…We apply a two‐step inversion in which the sea surface vertical displacement is first inverted from the tsunami waveform data, then the finite fault slip distribution is inverted in the second step from the estimated sea surface displacement (Gusman et al., 2018; Hossen et al., 2018). More specifically, we employ an Adaptive Tsunami Source Inversion that efficiently constructs the tsunami Green's function using a reciprocity principle, including optimizations of unit source locations and fault parameters (Mulia et al., 2018). However, since the uncertainty of fault parameters of megathrust earthquakes are relatively small compared to intraplate faulting, in this study, we only perform the optimization for the first step inversion.…”

“…In our previous study (Heidarzadeh & Mulia, 2021), using a simple source model with uniform slip, we attributed the long‐period tsunami of the July 2020 Shumagin event to the effect of shallow water depths over the source area. Here we consider a finite fault source model to further reveal the tsunami source characteristics using a tsunami waveform inversion approach (Mulia et al., 2018; Satake, 1989). Additionally, we also utilize static displacement data at GNSS stations in the inversion to better constrain the total moment release.…”

An Optimized Array Configuration of Tsunami Observation Network Off Southern Java, Indonesia

“…A tsunami waveform inversion has been widely used for studying the source mechanism (e.g., Gusman et al, ; Mulia, Gusman, Hossen, et al, ; Satake, ) and forecasting (e.g., Mulia et al, ; Tsushima et al, , ; Wei et al, ). In this study, for the tsunami waveform inversion analysis, we develop the Green's functions based on synthetic waveforms recorded at potential tsunameter locations originating from a unit amount of slip on the predefined subfaults.…”

An Adjoint‐State Full‐Waveform Tsunami Source Inversion Method and Its Application to the 2014 Chile‐Iquique Tsunami Event

“…However, weighted TRI images still suffer from the incorrect amplitude and the artifacts due to the unevenly distributed stations, which limits the further usage of TRI images, for example, serving as an input of tsunami simulations. Using similar reciprocity principle of Green's function explored in TRI imaging, recently, a two-step adaptive tsunami source inversion method (Mulia et al, 2018) reduces the computational cost of high-resolution inversions.…”