Abstract: Machine-learning methods and apparatus are disclosed to determine critical state or other parameters related to fluid-driven failure of a terrestrial locale impacted by anthropogenic activities such as hydraulic fracturing, hydrocarbon extraction, wastewater disposal, or geothermal harvesting. Acoustic emission, seismic waves, or other detectable indicators of microscopic processes are sensed. A classifier is trained using time series of microscopic data along with corresponding data of critical state or failure events. In disclosed examples, random forests and artificial neural networks are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to live data from a fluid injection locale in order to assess a frictional state, assess seismic hazard, assess permeability, make predictions regarding a future fluid-driven failure event, or drive engineering solutions for mitigation or remediation. Variations are disclosed.

Abstract: Machine-learning methods and apparatus are disclosed to determine frictional state or other parameters in an earthquake zone or other failing medium, using acoustic emission, seismic waves, or other detectable indicators of microscopic processes. Predictions of future failures are demonstrated in different regimes. A classifier is trained using time series of acoustic emission data along with historic data of frictional state or failure events. In disclosed examples, random forests and gradient boost trees are used, and grid-search or EGO procedures are used for hyperparameter tuning. Once trained, the classifier can be applied to testing or live data in order to assess a frictional state, assess seismic hazard, or make predictions regarding a future failure event. The technology has been developed in a double direct shear apparatus, but can be widely applied to seismic faults, other terrestrial failures, or failures in man-made structures. Variations are disclosed.

Abstract: Apparatus and methods for measurement of pore pressure in rock formations through an open, or cemented and/or cased, borehole are described. Such measurements are achieved using the Dynamic Acoustic Elasticity (DAE) method for characterizing nonlinear parameters by perturbing a selected rock formation volume with a High Amplitude, Low Frequency (HALF) acoustic strain wave, and probing this volume using a Low Amplitude, High Frequency (LAHF) acoustic wave. Time reversal techniques may be employed for focusing acoustic energy Into the formation in the vicinity of the pipe or open hole.

Abstract: In some aspects of the disclosure, a method and an apparatus is disclosed for investigating material surrounding the borehole. The method includes generating within a borehole an intermittent low frequency vibration that propagates as a tube wave longitudinally to the borehole and induces a nonlinear response in one or more features in the material that are substantially perpendicular to a longitudinal axis of the borehole; generating within the borehole a sequence of high frequency pulses directed such that they travel longitudinally to the borehole within the surrounding material; and receiving, at one or more receivers positionable in the borehole, a signal that includes components from the low frequency vibration and the sequence of high frequency pulses during intermittent generation of the low frequency vibration, to investigate the material surrounding the borehole.

Abstract: In some aspects of the disclosure, a method and an apparatus is disclosed for investigating material surrounding the borehole.