Abstract

Monitoring the activities of very small seismic events or acoustic emissions (AEs) by estimating their hypocenters is useful in investigating fracturing processes in laboratory experiments. Here, we proposed an analysis procedure to develop high-quality AE event catalogs using deep learning and similar waveform searches from the continuous records of AE sensors. The proposed routine comprised the following five steps: 1) automatically developing catalogs using a conventional procedure, where the short-term average-to-long-term average ratio detects transient signals, and arrival times are identified using an autoregressive model and the Akaike information criterion; 2) training a deep learning model for arrival time reading (neural phase picker) using datasets based on the Step 1 catalog; 3) reproducing the AE catalog by applying the trained neural phase picker to continuous waveform records; 4) applying template matching to continuous waveform records based on the template events listed in the catalog in Step 3; and 5) determining the precise hypocenters of template events and newly detected events in Step 4 using a relative location method based on the cross-correlation travel time reading technique. We applied this procedure to continuous AE waveforms recorded at 10 MHz sampling during hydraulic fracturing experiments, resulting in a catalog with 10 times the number of events compared to the Step 1 catalog. This reproduced catalog revealed new aspects of the fracturing process, such as the propagating fracture front and tremor-like AE activity. The proposed procedure eliminates the need for manual labeling, thereby facilitating a fully automated analysis of the observed continuous records. This technique is expected to enhance our understanding of AE sensor records in laboratory experiments.