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 development: (a) training and testing solely on the same station, and (b) generalizing by training and testing on different seismic stations within a particular region. We demonstrate the efficacy using seismic data from the 2016–2017 Central Apennines, Italy earthquake sequence. Results demonstrate that earthquake depths can be estimated from single stations with uncertainties of hundreds of meters. These uncertainties are further reduced by averaging results from multiple stations. Our method shows strong potential for earthquake depth determination, particularly for events recorded by single or sparsely distributed stations, such as historically instrumented earthquakes.