Earthquake Source Depth Determination Using Single Station Waveforms and Deep Learning

Abstract In areas with limited station coverage, earthquake depth constraints are much less accurate than their latitude and longitude. Traditional travel‐time‐based location methods struggle to constrain depths due to imperfect station distribution and the strong trade‐off between source depth and origin time. Identifying depth phases at regional distances is usually hindered by strong wave scattering, which is particularly challenging for low‐magnitude events. Extracting effective depth features from single or sparse stations to enhance depth constraints is a pressing challenge. Deep learning algorithms, capable of extracting various features from seismic waveforms, including phase arrivals, amplitudes, and frequency, offer promising constraints to earthquake depths. In this work, we propose a novel depth feature extraction network (named VGGDepth), which directly maps seismic waveforms to earthquake depth using single‐station three‐component waveforms. The network structure is adapted from VGG16 in computer vision. It is designed to take single‐station three‐component waveforms as inputs and produce depths as outputs. Two scenarios are considered in our model d