Dataset related to "Laboratory Insights into Fracturing Processes Using Deep Learning Acoustic Emission Catalogues"

Laboratory research has been fundamental in elucidating the fracturing processes linked to rock failure and fault preparation. This study explores how machine learning techniques can enhance our perspective on rock damage and failure through the analysis of acoustic emissions (AEs).

We developed two independent catalogues from a continuous AE record during a triaxial failure test on an intact sandstone specimen: (1) a standard catalogue utilizing traditional AE data reduction methods and (2) a "deep" catalogue employing the quakephase deep learning phase detection algorithm (Shi et al., 2024). We conducted a comparative analysis of these catalogues using advanced seismological tools applicable across scales from centimetres to kilometres.