Strategy for automated analysis of passive microseismic data based on S-transform, Otsu’s thresholding, and higher order statistics

Abstract

Small-magnitude seismic events, either natural or induced microearthquakes, have increasingly been used in exploration seismology with applications ranging from hydrocarbon and geothermal reservoir exploration to high-resolution passive seismic tomography surveys. We developed an automated methodology for processing and analyzing continuously recorded, single-channel seismic data. This method comprised a chi-squared-based statistical test for microseismic event detection and denoising filtering in the Stransform domain based on the Otsu thresholding method. An automatic P-phase picker based on higher order statistics criteria was used. The method was used with data from a surface seismic station. The performance of the method was tested and evaluated on synthetic and real data from a microseismic network used in a high-resolution PST survey and revealed a high level of consistency.

Development of an adaptive multi‐method algorithm for automatic picking of first arrival times: application to near surface seismic data

“…Recently, Tselentis et al . () provided an excellent classification of the common event‐detection and phase‐picking methodologies. Another exhaustive review with a new classification (i.e.…”

Adaptive STA–LTA with Outlier Statistics

“…Picking: Estimation of phase arrival times. (e.g., Allen, 1978;Baer and Kradolfer, 1987;Earle and Shearer, 1994;Leonard and Kennett, 1999;Tselentis et al, 2012) 3. Classification: Categorization of detected events, typically using seismic attributes.…”

“…Most autopicking algorithms either determine onsets from STA-LTA ratios or STA-LTA differences (e.g., Allen, 1982;Baer and Kradolfer, 1987;Earle and Shearer, 1994) or by autoregressive methods (e.g., Takanami and Kitagawa, 1988;Leonard and Kennett, 1999;Leonard, 2000). Tselentis et al (2012) gives a thorough literature review of common methods for both classes of seismic detection problem. The performance of any seismic detection algorithm depends on suitable choices for a number of variables, which are typically passed as free parameters to the algorithm itself.…”

“…The goal of any seismic detection problem is to determine when signals begin and end using fluctuations in measurable properties of earth tremor (Withers et al, 1998;Tselentis et al, 2012). Adopting nomenclature based on Allen (1982), Withers et al (1998), and Leonard (2000), and expanding the framework of Beyreuther et al (2012), seismic detection algorithms can be loosely grouped into four overlapping categories.…”

“…Event detection: Searching unprocessed records for new events (e.g., Tselentis et al, 2012;Carmichael, 2013;Hammer et al, 2013). We consider triggering algorithms a subclass of this category designed for (possible real time) transient detection (Swindell and Snell, 1977;McEvilly and Majer, 1982;Withers et al, 1998).…”