Systematic review of similarity criteria for identifying similar seismic events
Abstract
The identification of similar seismic events constitutes an important aspect for understanding seismic dynamics, identifying the recurrence of aftershocks and seismic swarms, and assessing geological hazards. This work presents a systematic literature review on the computational methods employed for identifying similarity between seismic waveforms, with an emphasis on decision criteria and signal processing techniques. The review was conducted based on searches in scientific databases, utilizing specific strings related to seismic event similarity measures. The analysis of the selected studies evidences that, despite the recurrent limitations associated with the dependence on empirical thresholds, sensitivity to noise, and the difficulty of ensuring consistency and reproducibility in event classification within large seismic catalogs, cross-correlation-based methods are widely adopted. It is concluded that the literature points to conceptual gaps in the modeling of similarity between seismic events, indicating the need for future mathematical and computational investigations aimed at developing more consistent and well-founded criteria for the automated curation of seismic data.
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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