An Investigation of Rapid Earthquake Characterization Using Single‐Station Waveforms and a Convolutional Neural Network

Abstract

Effective early warning, emergency response, and information dissemination for earthquakes and tsunamis require rapid characterization of an earthquake’s location, size, and other parameters, usually provided by real‐time seismogram analysis using established, rule‐based, seismological procedures. Powerful, new machine learning (ML) tools analyze basic data using little or no rule‐based knowledge, and an ML deep convolutional neural network (CNN) can operate directly on seismogram waveforms with little preprocessing and without feature extraction. How a CNN will perform for rapid automated earthquake detection and characterization using short single‐station waveforms is an issue of fundamental importance for earthquake monitoring.

For an initial investigation of this issue, we adapt an existing CNN for local earthquake detection and epicentral classification using single‐station waveforms (Perol et al., 2018), to form a new CNN, ConvNetQuake_INGV, to characterize earthquakes at any distance (local to far‐teleseismic). ConvNetQuake_INGV operates directly on 50‐s three‐component broadband single‐station waveforms to detect seismic events and obtain binned probabilistic estimates of the distance, azimuth, depth, and magnitude of the event. The best performance of ConvNetQuake_INGV is obtained using a last convolutional layer with fewer nodes than the number of output classifications, a form of information bottleneck.

We show that ConvNetQuake_INGV detects very well (accuracy 87%) and characterizes moderately well earthquakes over a broad range of distances and magnitudes, and we analyze outlier results and indications of overfitting of the CNN training data. We find weak evidence that the CNN is performing more than high‐dimensional regression and pattern recognition, and is generalizing information or learning, to provide useful characterization of new events not represented in the training data. We expect that real‐time ML procedures such as ConvNetQuake_INGV, perhaps incorporating rule‐based knowledge, will ultimately prove valuable for rapid detection and characterization of earthquakes for earthquake response and tsunami early warning.