Comparison of convolutional neural networks with matched-filtering for detection of induced seismicity

The problem of extracting weak earthquake signals from continuous waveforms data recorded by networks of seismic sensors, referred to as earthquake detection, is a challenging and critical task in seismology. Waveform cross-correlation (matched-filtering) is a widely used method to detect weak earthquake signals with waveforms similar to those of known events. Even using this method, earthquake catalogs are often limited and incomplete, so there is a need for a more general detector. Because earthquakes occur infrequently, detection algorithms must be capable of quick processing of months to years of continuous data dominated by noise. To address these challenges, we propose to use convolutional neural networks, a new detection method that utilizes large seismic datasets to perform computationally efficient search to identify events in the continuous data. We set up an algorithm as a supervised machine learning problem and test its performance against matched-filtering based detector on synthetic and Kaybob-Duvernay region data. In this work, I introduce a DuverNet, which is an optimized convolutional neural network for the detection of induced seismicity in the Kaybob-Duvernay production region. First, I test two different convolutional neural network architectures: ConvNetQuake and VGG-Junior. Second, I test two different loss functions: cross-entropy loss and focal loss. My thesis is the first time focal loss is used to tackle class imbalance problem in earthquake detection. Focal loss helps tackling the problem of class imbalance and allows to achieve higher accuracy for convolutional neural network detectors. Third, I compare performance of matched-filtering and DuverNet. After allowing time for appropriate training, DuverNet performs best in computational runtime and memory use. Moreover it delivers superior detection performance compared to the matched-filtering detector. Synthetic data testing illustrates that DuverNet better generalizes to previously unseen events and is found to be better at detecting more events at lower signal-to-noise ratio compared to the matched-filtering method. Finally, my thesis introduces a novel dataset collected by the 6 UC/DSA array stations installed by Nanometrics for the University of Calgary.

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