Fast first arrival picking algorithm for noisy microseismic data. Issue 2 (1st June 2017)

• Record Type: Journal Article
• Authors: Kim, Dowan; Byun, Joongmoo; Lee, Minho; Choi, Jihoon; Kim, Myungsun
• Is Part Of: Exploration geophysics. Volume 48:Issue 2(2017)
• Page Start: 131
• Page End: 136
• Publication Date: 2017-06-01
• Subjects: cross-correlation, first arrival picking, hydraulic fracturing, microseismic monitoring, stacking.; Geophysics -- Periodicals; Prospecting -- Geophysical methods -- Periodicals; 622.15
• DOI: 10.1071/EG15120
• Languages: English
• ISSNs: 0812-3985

Abstract

Abstract : Most microseismic events occur during hydraulic fracturing. Thus microseismic monitoring, by recording seismic waves from microseismic events, is one of the best methods for locating the positions of hydraulic fractures. However, since microseismic events have very low energy, the data often have a low signal-to-noise ratio (S/N ratio) and it is not easy to pick the first arrival time. In this study, we suggest a new fast picking method optimised for noisy data using cross-correlation and stacking. In this method, a reference trace is selected and the time differences between the first arrivals of the reference trace and those of the other traces are computed by cross-correlation. Then, all traces are aligned with the reference trace by time shifting, and the aligned traces are summed together to produce a stacked reference trace that has a considerably improved S/N ratio. After the first arrival time of the stacked reference trace is picked, the first arrival time of each trace is calculated automatically using the time differences obtained in the cross-correlation process. In experiments with noisy synthetic data and field data, this method produces more reliable results than the traditional method, which picks the first arrival time of each noisy trace separately. In addition, the computation time is dramatically reduced. Abstract : We have developed a new fast picking method, optimised for noisy microseismic data, using cross-correlation and stacking. In experiments with synthetic data and field data, this method produces reliable results and the computation time is dramatically reduced.