Seismic Phase Identification with a Merged Deep Neural Network

Timothy J. Draelos, Stephen Heck, Jennifer Galasso, Ronald Brogan Sandia National Laboratories & ENSCO, Inc.

Seismic signals are composed of the seismic waves (phases) that reach a sensor, similar to the way speech signals are composed of phonemes that reach a listener’s ear. We leverage ideas from speech recognition for the classification of seismic phases at a seismic sensor. Seismic Phase ID is challenging due to the varying paths and distances an event takes to reach a sensor, but there is consistent structure and ordering of the different phases arriving at the sensor. Together with scalar value measurements of seismic signal detections (horizontal slowness, amplitude, Signal-to-Noise Ratio (SNR), and the time since the previous signal detection), we use the seismogram and its spectrogram of detection waveforms as inputs to a merged deep neural network (DNN) with convolutional (CNN) and recurrent (LSTM) layers to learn the frequency structure over time of different phases. The binary classification performance of First-P phases versus non-First-P (95.6% class average accuracy) suggests a potentially significant impact on the reduction of false and missed events in seismic signal processing pipelines. Other applications include discrimination between noise and non-noise detections for induced seismicity networks and for early warning of large hazards.