Dr. Benavente-Bravo, Roberto

Large earthquakes and tsunamis often occur around the Pacific Ring of Fire, in which Traveling Ionospheric Disturbances (TIDs) have been observed after these events. Sometimes (depending on seismic source features), TIDs can be observed near the epicenter of the generated earthqucovering 14ake due to the shock-acoustic wave. Additionally, TIDs can be induced by tsunamis due to the generated gravity waves and be detected several thousand kilometers away from the source. TIDs can be detected by analyzing Total Electron Content (TEC), which is calculated, indirectly, using signals from Global Navigation Satellite System (GNSS) receivers. The procedure allows for studying the ionospheric disturbance with a good spatial and temporal resolution. This study aims to identify tsunami-induced TIDs in the near- and far-fields following significant events in the Oriental and Occidental South Pacific Ocean. The selection criteria covering 14 tsunamis that occurred between 2010 and 2021 generated by earthquakes with Mw > 7.8 and depth less than 50 km. Tsunamis were modeled and compared with the TIDs obtained from TEC deviations. Near-field and far-field TIDs observed in hodochrons differ primarily in the clarity of their association with tsunamis. The simulated tidal gauges show a possible connection with the observed TEC anomalies, behaving similarly but with different delay times, showing some events even hours in advance. This potential correlation between TID parameters and tsunamis propagated in the Pacific Ocean can contribute to understanding the involved mechanisms and facilitate the development of near real-time early warning systems.

Estimating locking degree at faults is important for determining the spatial distribution of slip deficit at seismic gaps. Inverse methods of varying complexity are commonly used to estimate fault locking. Here we present an innovative approach to infer the degree of locking from surface GNSS velocities by means of supervised learning (SL) algorithms. We implemented six different SL regression methods and apply them in the Central Chile subduction. These methods were first trained on synthetic distributions of locking and then used to infer the locking from GNSS observations. We tested the performance of each algorithm and compared our results with a least squares inversion method. Our best results were obtained using the Ridge regression, which gives a root mean square error (RMSE) of 1.94 mm/yr compared to GNSS observations. The ML-based locking degree distribution is consistent with results from the EPIC Tikhonov regularized least squares inversion and previously published locking maps. Our study demonstrates the effectiveness of machine learning methods in estimating fault locking and slip, and provides flexible options for incorporating prior information to avoid slip instabilities based on the characteristics of the training set. Exploring uncertainties in the physical model during training could improve the robustness of locking estimates in future research efforts.