Abstract

Earthquake detection is critical for tracking fracture networks and fault zone deformation, particularly microseismicity that produces weak ground motions. We develop deep learning models to detect seismic phase arrivals and first motion polarities. The detection model is a convolutional encoder-decoder with a multi-head attention latent space that assigns a softmax value to each data point in continuous seismic records for classifying earthquake waveforms and the phase arrivals. The multi-output classification model utilizes weighted categorical cross entropy for the different softmax predictions to account for the unbalanced number of signal points compared to noise. The model training uses a benchmark data set of global seismic waveforms and the events are augmented using various techniques to reduce the signal-to-noise ratio, simulate multiple events arrivals, and channel failures. Detected p-waves are passed through a second model to obtain the first motion polarity. The phase arrivals, first motions, arrival waveforms, and additional metrics needed for catalog development are saved in a detection table. A neural network phase associator is used with the detection table to build an event arrival table. Locations are calculated and double difference locations are produced using correlation metrics from the waveforms retained in the detection table. The analysis is wrapped in a multiprocessing workflow to efficiently analyze large data sets. As a case study the workflow is applied to southern Kansas, a region with increased seismic activity related to hydrocarbon-production and waste water injection. The deep learning seismicity and focal mechanism catalogs show immensely more seismic activity than standard processing.