Delineating complex spatiotemporal distribution of earthquake aftershocks: an improved Source-Scanning Algorithm

Abstract

SUMMARY Conventional earthquake location methods depend critically on the correct identification of seismic phases and their arrival times from seismograms. Accurate phase picking is particularly difficult for aftershocks that occur closely in time and space, mostly because of the ambiguity of correlating the same phase at different stations. In this study, we introduce an improved Source‐Scanning Algorithm (ISSA) for the purpose of delineating the complex distribution of aftershocks without time‐consuming and labour‐intensive phase‐picking procedures. The improvements include the application of a ground motion analyser to separate P and S waves, the automatic adjustment of time windows for ‘brightness’ calculation based on the scanning resolution and a modified brightness function to combine constraints from multiple phases. Synthetic experiments simulating a challenging scenario are conducted to demonstrate the robustness of the ISSA. The method is applied to a field data set selected from the ocean‐bottom‐seismograph records of an offshore aftershock sequence southwest of Taiwan. Although visual inspection of the seismograms is ambiguous, our ISSA analysis clearly delineates two events that can best explain the observed waveform pattern.

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).…”

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

“…Traveltime‐based location methods have been continuously improved and undoubtedly dominated the location routines during the 20th century. Phase picking required by these techniques is time consuming, error prone, and sometimes subjective, especially for weak aftershocks or microseismic data, which typically have relatively low SNR (e.g., Chambers et al, ; Duncan & Eisner, ; Liao et al, ). Though advanced and automated procedures can ensure an efficient picking process (e.g., Akram & Eaton, ; Grigoli et al, ; Ross et al, ; Withers et al, ), robust phase identification and picking are still challenging for low SNR events, and missed arrival times and picking errors may directly cause location errors.…”

“…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, ). …”

Continuous Kurtosis-Based Migration for Seismic Event Detection and Location, with Application to Piton de la Fournaise Volcano, La Reunion

“…On a regional/ local scale, a similar methodology was first proposed by Withers et al (1999), who pursued the correlation technique of Young et al (1996) and developed the local waveform correlation event detection system. Along the same lines and more recently, Kao and Shan (2004) proposed the sourcescanning algorithm (SSA) recently improved by Liao et al (2012), and Baker et al (2005) proposed the real-time Kirchhoff location method. Time reverse migration is another approach using back projection, and initial developments were made by McMechan et al (1985).…”

“…The method we describe in this paper, Waveloc, is a variant of the shift-and-stack methodologies such as SSA (Kao and Shan 2004;Liao et al, 2012) and the envelope stacking method of Gharti et al (2010), with an important difference: instead of stacking the energy of the signal envelopes within predetermined windows, we directly and continuously stack the P-wave arrival information as highlighted by kurtosis waveforms. The kurtosis is the fourth statistical moment of a distribution and has recently started to be applied in the computation of characteristic functions for automated P-wave arrival time picking (Saragiotis et al, 2002;Gentili and Michelini, 2006;Kuperkoch et al, 2010).…”