Rapid identification of induced seismicity using deep learning in West Texas

SUMMARY Timely identification of the triggering mechanism behind the observed seismicity in areas with multiple overlapping human activities is an important research topic that can facilitate effective measures to mitigate the seismic hazard. This task is particularly challenging when dealing with delayed operational data, uncertain focal depths or uneven seismic monitoring coverage. Here, we propose a deep learning (DL) framework to identify which human activity triggered a certain earthquake in near real-time using only seismic waveforms as input. We use an advanced architecture, the compact convolutional transformer (CCT), to extract high-level abstract features from the three-component seismograms and then use an advanced capsule neural network to link the induced seismicity in West Texas with three potential causal factors, that is, hydraulic fracturing (HF), shallow saltwater disposal (SWDsh) or deep saltwater disposal (SWDdp). The training data was prepared based on an established probabilistic approach that combined physics-based principles with both real and reshuffled injection data to hindcast past seismicity rates. In the end, each activity was assigned a confidence level for association at the 5 km spatial scale. Even though the training data include only 981 events, we obtain over 90 per cent accuracy for all three causal factors for both the single- and multistation versions of the model.