EQConvMixer: A Deep Learning Approach for Earthquake Location From Single-Station Waveforms

We present a novel deep-learning method using the ConvMixer network for automatic earthquake location. The proposed ConvMixer network utilizes three-component waveform recordings of single stations for estimating the hypocenter location. The ConvMixer network is a patch-based architecture that combines depthwise and pointwise convolutions to extract the global and local information of the earthquake waveforms. We train and test the proposed method using the Italian seismic dataset (INSTANCE). The ConvMixer network estimates the earthquake hypocenter locations with high accuracy, reaching a mean absolute error (MAE) of 2.71 km for the epicenter distance, and 1.15 km for the depth. In addition, we use the global STanford EArthquake Dataset (STEAD) to further evaluate the performance of the ConvMixer. As a result, the ConvMixer network achieves MAEs of 2.27 and 1.19 km for the distance and the depth, respectively. The proposed ConvMixer network is compared to the benchmark methods, i.e., ResNet, AlexNet, MobileNet, and Xception, and outperforms all of them.