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First‐break picking for microseismic data based on cascading use of Shearlet and Stockwell transforms
Author(s) -
Cheng Yao,
Li Yue,
Zhang Chao
Publication year - 2019
Publication title -
geophysical prospecting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.735
H-Index - 79
eISSN - 1365-2478
pISSN - 0016-8025
DOI - 10.1111/1365-2478.12714
Subject(s) - microseism , signal (programming language) , noise (video) , computer science , shearlet , s transform , signal processing , frequency domain , energy (signal processing) , time domain , algorithm , pattern recognition (psychology) , artificial intelligence , geology , wavelet transform , mathematics , computer vision , seismology , statistics , image (mathematics) , telecommunications , wavelet , radar , wavelet packet decomposition , programming language
First‐break picking of microseismic data is a significant step in microseismic monitoring. There is a great error in conventional first‐break picking methods based on time domain analysis in low signal to noise ratio. S‐transform may provide a novel approach, it can extract the time–frequency features of the signal and reduce the picking error because of its high time–frequency resolution and good time–frequency clustering; however, the S‐transform is not well suited for microseismic data with high noise. For applications to array data where the weak signal has spatial coherency as well as some distinct temporal characteristics, we propose to combine the shearlet transform with a time–frequency transform. In the proposed method, the shearlet transform is used to capture spatial coherency features of the signal. The information of the signal and noise in shearlet domain is represented by shearlet coefficients. We use the correlation of signal coefficients at adjacent fine scales to give prominence to signal features to accurately discriminate the signal from noise. The prominent signal coefficients make the signal better gathered in time–frequency spectrum of the S‐transform. Finally, we can get reliable and accurate first breaks based on the change of energy. The performance of the proposed method was tested on synthetic and field microseismic data. The experimental results indicated that our method is outstanding in terms of both picking precision and adaptability to noise.