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Abstract

Detection of repeating seismic events from long duration seismic data without prior knowledge of event waveforms is performed by computing compact binary fingerprints from seismic data, generating a similarity matrix from the fingerprints, and identifying seismic events from the similarity matrix, e.g., using a thresholding condition. Each element of the similarity matrix is a value representing similarity between a pair of fingerprints, where the value is calculated by hashing fingerprints to hash buckets in multiple hash tables and counting a fraction of the multiple hash tables containing a fingerprint match in the hash buckets. The similarity matrix may be combined with similarity matrices derived from other seismic data to produce a total network similarity matrix, increasing the sensitivity of the detection. Other seismic data may include multiple components recorded at a single station or data recorded at separately located stations.

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First Claim