<p>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&#xa0;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.</p> Graphical Abstract <p></p>