ABIC-based joint inversion using tsunami, GNSS and SAR data: finite fault model of the 2024 Noto Peninsula earthquake, Japan

SUMMARY Nowadays, many joint inversions are carried out to understand the earthquake source process. In the joint inversion analysis, we have to determine the relative weights among different data sets in addition to the regularization term, such as smoothing. Akaike’s Bayesian Information Criterion (ABIC) is known to be useful to find the appropriate values of such hyperparameters. This study proposes a method to jointly invert tsunami, GNSS and SAR data using ABIC to construct a finite fault model. We demonstrate our inversion scheme in the case of the 2024 Noto Peninsula earthquake, whose fault geometry is still under discussion. Since the dip angle of the fault can also be considered as a hyperparameter, we evaluate three types of dip angles and estimate an appropriate value based on ABIC. In other words, our inversion scheme utilizes ABIC to determine the dip angle, the weights among data sets and the spatial smoothness of fault slip. Our fault model indicates that (1) listric fault, varying the dip angle with depth, is the most appropriate among the ones we proposed, (2) the largest slip is on the fault under the northwestern corner of the peninsula and (3) coseismic fault slip extends to offshore faults east of the peninsula. In the case of the listric fault, ABIC values GNSS and SAR data, which improves the agreement of the on-land coseismic displacement while also reproducing tsunami data. We also find that analysing tsunami records in the frequency domain helps to obtain a robust inversion result when employing ABIC.