资源环境科技发展态势分析平台

We revisited the June 2010 to October 2011 Guy-Greenbrier earthquake sequence in central Arkansas using PhaseNet, a deep neural network trained to pick P and S arrival times. We applied PhaseNet to continuous waveform data and used phase association and hypocenter relocation to locate nearly 90,000 events. Our catalog suggests that the sequence consists of two adjacent earthquake sequences on the same fault and that the second sequence may be associated with the wastewater disposal well to the west of the Guy-Greenbrier Fault, rather than the wells to the north and the east that were previously implicated. We find that each sequence is composed of many small clusters that exhibit diffusion along the fault at shorter timescales. Our study demonstrates that machine-learning-based earthquake catalog development is now feasible and will yield new insights into earthquake behavior.

Plain Language Summary Finding small earthquake signals from long duration continuous seismic data is a time-consuming task, but machine learning algorithms have the potential to accelerate the workflow and improve the results. We reprocessed the seismic data from the area spanning Guy and Greenbrier in central Arkansas in 2010 and 2011 using a machine learning algorithm to reexamine this well-studied earthquake sequence, which is thought to be caused by injection of wastewater from unconventional hydrocarbon production into deep disposal wells. Even using conservative postprocessing steps, we were able to locate nearly 90,000 earthquake events. The improved catalog illuminates previously unseen aspects of this earthquake sequence that give new insights into its behavior.