Seismic events triggered by stress unloading during geo-energy extraction activities have become a key focus in both seismological research and engineering safety. This study presents a novel application of waveform neural networks, combining unsupervised and supervised learning techniques to classify and characterize fractures in laboratory-induced seismic events. Initially, A neural network model was initially developed that is capable of extracting time-frequency features from waveforms through unsupervised training on 1.2 million Acoustic Emission waveforms. Subsequently, this model was fine-tuned using a labeled dataset obtained from Brazilian split and uniaxial compression tests. The final result was a highly accurate model, achieving an accuracy rate of 97.6%. By applying this refined model, insights have been gained into the complex fault slip behaviors induced by geo-energy extraction activities. Our findings reveal that fluid infiltration at the onset triggers low-energy, shear-induced fractures in low-stress fault regions, which then escalate into tensile fractures during critical sliding in high-stress areas. Key precursors to fluid-induced seismicity have been identified, providing a major advance in early seismic hazard detection. These insights are essential for monitoring and early warning of induced seismicity during geo-energy extraction activities. Our work contributes significantly to improving the safety and efficiency of geo-energy extraction, including geothermal, shale gas, and conventional hydrocarbon production.
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Advances in Geo-Energy Research 2024, 14(2): 106-118
Published: 29 September 2024
Downloads:11

Journal of Intelligent Construction 2024, 2(1): 9180029
Published: 11 March 2024
Downloads:117
Total 2