Readapting PhaseNet to Laboratory Earthquakes: AEsNet, a Robust Acoustic Emission Picker Illuminating Seismic Signatures of Different Fault Gouge Materials

Laboratory acoustic emissions (AEs), resembling small-scale earthquakes, provide vital insights into frictional instability mechanics. Recent advancements in acoustic monitoring technology allow for the rapid collection of thousands of AE waveforms within minutes, highlighting the critical need for efficient detection and analysis methods. This study presents a deep learning model designed to automatically detect AEs in laboratory shear experiments.

Our dataset comprises approximately 30,000 manually identified AE waveforms obtained under varying experimental boundary conditions using two fault gouge materials: Min-U-Sil quartz gouge and glass beads. We modified the PhaseNet model, originally designed for detecting seismic phases in natural earthquakes, by optimizing its architecture and training process to develop AEsNet—an advanced AE detection model that consistently outperforms existing picking methods for Min-U-Sil quartz gouge and glass beads.

To assess the model's generalizability across different boundary conditions and materials, we employed transfer learning, examining performance relative to training dataset size and material diversity. Results indicate that while model performance remains consistent across varying boundary conditions, it is notably influenced by the specific material type due to distinct frequency characteristics inherent to each material. This sensitivity stems from the distinct frequency characteristics of AEs, reflecting the microphysical processes unique to each granular material. Consequently, models trained on one material do not transfer effectively to another.

However, rapid fine-tuning of AEsNet substantially improves its performance, outperforming a similarly fine-tuned PhaseNet model pre-trained on natural earthquakes. This highlights the necessity of tailoring models to the specific features of laboratory-generated AEs, aligning with observations in transfer learning applications for natural seismicity.

In summary, our deep learning approach effectively enhances AE detection across diverse laboratory settings, enabling the creation of reliable AE catalogs that deepen our understanding of fault mechanics. This advancement facilitates the development of reliable AE catalogs, significantly contributing to the understanding of fault mechanics in controlled experimental environments.