Automated seismic event location by waveform coherence analysis

Abstract

Automated location of seismic events is a very important task in microseismic monitoring operations as well for local and regional seismic monitoring. Since microseismic records are generally characterized by low signal-to-noise ratio, automated location methods are requested to be noise robust and sufficiently accurate. Most of the standard automated location routines are based on the automated picking, identification and association of the first arrivals of P and S waves and on the minimization of the residuals between theoretical and observed arrival times of the considered seismic phases. Although current methods can accurately pick P onsets, the automatic picking of the S onset is still problematic, especially when the P coda overlaps the S wave onset. In this paper, we propose a picking free earthquake location method based on the use of the short-term-average/long-term-average (STA/LTA) traces at different stations as observed data. For the P phases, we use the STA/LTA traces of the vertical energy function, whereas for the S phases, we use the STA/LTA traces of a second characteristic function, which is obtained using the principal component analysis technique. In order to locate the seismic event, we scan the space of possible hypocentral locations and origin times, and stack the STA/LTA traces along the theoretical arrival time surface for both P and S phases. Iterating this procedure on a 3-D grid, we retrieve a multidimensional matrix whose absolute maximum corresponds to the spatial coordinates of the seismic event. A pilot application was performed in the Campania-Lucania region (southern Italy) using a seismic network (Irpinia Seismic Network) with an aperture of about 150 km. We located 196 crustal earthquakes (depth < 20 km) with magnitude range 1.1 < M L < 2.7. A subset of these locations were compared with accurate manual locations refined by using a double-difference technique. Our results indicate a good agreement with manual locations. Moreover, our method is noise robust and performs better than classical location methods based on the automatic picking of the P and S waves first arrivals.

Recent Advances and Challenges of Waveform‐Based Seismic Location Methods at Multiple Scales

“…Besides the imaging operator, the PWS methods also differ in preprocessing of the input waveforms and the event detection and location detection criteria. There are various CFs adopted to improve the SNR and compensate the side effects of source radiation patterns; for example, the input data are converted to envelope (Gharti et al, ; Liao et al, ; Zeng et al, ); semblance (Furumoto et al, ; W. Zhang & Zhang, ; Staněk et al, ; C. Zhang et al, ); short‐term average to long‐term average ratio (STA/LTA; Drew et al, ; Grigoli et al, ; L. Li et al, ); kurtosis (Langet et al, ; Poiata et al, ); multichannel coherency (Shi, Angus, et al, ). …”

Full Waveform Seismological Advances for Microseismic Monitoring

“…• the energy traces (Kao & Shan, 2004, • the envelope traces (Gharti et al, 2010;Liao, Kao, Rosenberger, Hsu, & Huang, 2012;Zeng et al, 2014), • the STA/LTA traces (Drew et al, 2013;Grigoli et al, 2013Grigoli et al, , 2014, and • the kurtosis traces (after Langet et al, 2014).…”

“…Langet, Maggi, Michelini, and Brenguier (2014) proposed a kurtosis based stacking function designed to work with P phases only. Alternative stacking functions have been proposed by Withers, Aster, and Young (1999), Grigoli et al (2013Grigoli et al ( , 2014 and Drew, White, Tilmann, and Tarasewicz (2013). In their approaches, they locate seismic events by stacking the short-time average to long-time average ratio (STA/LTA) traces at all stations using both P and S phases.…”

“…Linear and planar polarizations (Flinn, 1965) have also been proposed as polarization attributes. The largest eigenvalue (Wagner & Owens, 1996;Grigoli et al, 2014) can be used to further evaluate specific signal polarization.…”

Weighted-elastic-wave interferometric imaging of microseismic source location

“…Gajewski et al (2007) and Zhebel et al (2010) applied diffraction stacking to two-dimensional (2D) and 3D source location. Grigoli et al (2013Grigoli et al ( , 2014 used SSA to develop an imaging method by stacking STA/LTA-transformed waveforms based on traveltime. Haldorsen et al (2012Haldorsen et al ( , 2013 proposed a migration-based deconvolution method for microseismic event location and suggested that semblance-weighted (energy-weighted relevance) imaging is superior to the energy correlation imaging proposed by Artman et al (2010).…”