Acoustic Emission Waveform Picking with Time Delay Neural Networks during Rock Deformation Laboratory Experiments
Abstract We report a new method using a time delay neural network to transform acoustic emission (AE) waveforms into a time series of instantaneous frequency content and permutation entropy. This permits periods of noise to be distinguished from signals. The model is trained in sequential batches, using an automated process that steadily improves signal recognition as new data are added. The model was validated using AE data from rock deformation experiments, using Darley Dale sandstone in fully drained conditions at a confining pressure of 20 MPa (approximately 800 m simulated depth). The model is initially trained by manual picking of five high-amplitude waveforms randomly selected from t…
Source Mechanisms of Laboratory Earthquakes During Fault Nucleation and Formation
Identifying deformation and pre-failure mechanisms preceding faulting is key for fault mechanics and for interpreting precursors to fault rupture. This study presents the results of a new and robust derivation of first motion polarity focal mechanism solutions (FMS) applied to acoustic emission (AE). FMS are solved using a least squares minimization of the fit between projected polarity measurements and the deviatoric stress field induced by dilatational (T-type), shearing (S-type), and compressional (C-type) sources. 4 × 10 cm cylindrical samples of Alzo Granite (AG, porosity <1%) and Darley Dale Sandstone (DDS, porosity ≈14%) underwent conventional triaxial tests in order to investigat…