Deep learning framework bridges lab and field scale microseismic focal mechanism
Abstract
Climate change is the existential challenge of our generation. Renewable energy technologies such as nuclear waste storage, geothermal, and geologic carbon sequestration promise huge potential for reductions in greenhouse gas emissions and thus mitigate climate change. However, these systems are extremely challenging to design and operate owing to the limited knowledge on variable geological conditions. Moreover, it has raised public concern that these systems can induce earthquakes.
Microseismicity, which has lower magnitude and occur more often than conventional earthquakes, is a key diagnostic tool for these sites. Where possible, moment tensor inversion of microseismic source mechanisms is particularly useful as these tensors can highlight the kinematics and kinetics of subsurface deformation. However, these inversions can be difficult to constrain and automate in field applications owing to the requirement of a seismometer array with good azimuthal coverage and signal-noise ratio. There exists a wealth of laboratory acoustic emissions (AE) data obtained under controlled laboratory conditions which can aid in the processing of these field microseismic waveform data.
Deep learning has shown increasing potential in signal processing and data mining, where the term deep refers to its numerous tunable parameters, and learning characterizes the optimization of these parameters.
Here, a framework is proposed to re-train an existing deep convolutional network learned on field data to determine first arrival polarities for the related but more difficult task of determining the polarity of volume change at its source, i.e., whether the source exhibits volume increase (ISO+), or volume decrease (ISO-). The framework leverages over 300,000 laboratory waveforms in this retraining and show that the retrain approach combining learned weights from both field and lab data achieves over 86% F-score on both previously unseen laboratory and field source mechanisms. Post analyses qualitatively suggest that, on average, ISO- waveforms in the lab exhibit a distinct short-duration low-frequency pulse, and the ISO+ waveforms exhibit a longer-duration coda at frequencies above 50 kHz. Similarly, ISO+ waveforms from the Geysers Geothermal field in Northern California exhibit a low-frequency coda whereas ISO- waveforms are shorter in duration with more high-frequency energy than ISO+ waveforms.