Andrea Chiang | Searcharxiv

arXiv2026

Crustal and upper mantle model of the Middle East based on full-waveform inversion

We present MEAD-M20, a new tomographic model of the Middle East and its surrounding regions, including Anatolia, Iran, and the Caucasus. The model is developed within a full-waveform inversion framework, based on 3D wavefield simulations and the adjoint method, after 20 iterations, utilizing an extensive dataset from permanent and temporary stations available from EarthScope and regional networks. Starting from the global FWI model GLAD-M25 on a 60{\deg}x 60{\deg} regional mesh, we invert 210 regional earthquakes recorded by 1,215 stations to obtain the P- and S-wave model with transverse isotropy in the upper mantle. For the first 12 iterations, we combine multitaper traveltime measurements of 15-50 s body waves and 50-100 s body and surface waves on three components. We use a refined crustal mesh to better sample the crust after the 12th iteration and gradually decrease the minimum surface-wave period to 30 s. MEAD-M20 provides a self-consistent P- and S-wave model ready for seismic wave simulations, which is essential for accurate earthquake location, source parameter estimation, and seismic hazard assessment in the geologically and tectonically complex region. MEAD-M20 reveals several important geodynamical and tectonic features, including local mantle plumes beneath the Arabian Plate, Jordan, and the Levant, characterized by low-velocity anomalies and likely associated with volcanism in the Harrats, Jordan, and the Karacadag regions. In addition to the active subduction and rifting in the area, the model clearly identifies remnants of the Tethys Ocean beneath Eastern Anatolia, which become progressively shallower toward the Makran region in the south, consistent with the subduction history along the Bitlis-Zagros suture zone. We also observe lithospheric-scale low-velocity anomalies associated with the North and East Anatolian faults, extending to depths of approximately 200 km.

physics.geo-ph↗

arXiv2021

Deep Convolutional Autoencoders as Generic Feature Extractors in Seismological Applications

The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea of using autoencoders as feature extractors for different seismological applications, such as event discrimination (i.e., earthquake vs. noise waveforms, earthquake vs. explosion waveforms, and phase picking). These tests involve training an autoencoder, either undercomplete or overcomplete, on a large amount of earthquake waveforms, and then using the trained encoder as a feature extractor with subsequent application layers (either a fully connected layer, or a convolutional layer plus a fully connected layer) to make the decision. By comparing the performance of these newly designed models against the baseline models trained from scratch, we conclude that the autoencoder feature extractor approach may only perform well under certain conditions such as when the target problems require features to be similar to the autoencoder encoded features, when a relatively small amount of training data is available, and when certain model structures and training strategies are utilized. The model structure that works best in all these tests is an overcomplete autoencoder with a convolutional layer and a fully connected layer to make the estimation.

physics.geo-ph↗