Unsupervised Approaches for Post-Processing in Computationally Efficient Waveform-Similarity-Based Earthquake Detection

Recent improvements in computational efficiency for waveform correlation-based detections achieved by new methods such as Fingerprint and Similarity Thresholding (FAST) promise to allow large-scale blind search for similar waveforms in long-duration continuous seismic data. Waveform similarity search applied to datasets of months to years of continuous seismic data will identify significantly more events than traditional detection methods. With the anticipated increase in number of detections and associated increase in false positives, manual inspection of the detection results will become infeasible. This motivates the need for new approaches to process the output of similarity-based detection. We explore data mining techniques for improved detection post-processing. We approach this by considering similarity-detector output as a sparse similarity graph with candidate events as vertices and similarities as weighted edges. Image processing techniques are leveraged to define candidate events and combine results individually processed at multiple stations. Clustering and graph analysis methods are used to identify groups of similar waveforms and assign a confidence score to candidate detections. Anomaly detection and classification are applied to waveform data for additional false detection removal. A comparison of methods will be presented and their performance will be demonstrated on a suspected induced and non-induced earthquake sequence.