Enhanced deep learning approach for detecting and locating tectonic tremors in the Nankai subduction zone

Abstract Tectonic tremors are key indicators of slow-slip phenomena, and detecting them accurately is a challenging task. Conventional techniques often fail to detect tremors during periods of intense tremor activity. We present here a deep learning approach for detecting and locating tremors in the Nankai subduction zone that is more effective than conventional techniques. We utilized two convolutional neural networks (CNNs): a CNN for classification of seismic waveforms into noise, tremors, or earthquakes, and a CNN for regression prediction of tremor epicenters from amplitude data. The accuracy, recall, and precision of the CNN for classification all exceeded 95%. The CNN for regression used ensemble predictions to produce estimates of tremor locations with a median error of 3.2 km. When this approach was applied to continuous data, it successfully mapped key features of tremor activity and improved the detection and location of tremors, especially during high-activity periods. Graphical Abstract