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Toward an Optimized Data Conditioning for Surface-acquired Microseismic Data
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, 78th EAGE Conference and Exhibition 2016, May 2016, Volume 2016, p.1 - 5
Abstract
Noise attenuation is a key challenge for surface-acquired microseismic processing. A number of data conditioning tools have been proposed and applied with various degrees of success to improve the signal-to-noise ratio prior to detection and location of microseismic events. Random noise attenuation, trace-by-trace correlation with a large magnitude event, and nonlinear stacking techniques have all been shown individually to improve microseismic event detectability in surface-acquired microseismic datasets. This paper demonstrates how the combination of these approaches significantly increases the number of detected microseismic events while keeping the number of false triggers to a minimum.
In particular, random noise attenuation and trace-by-trace correlation with a large magnitude event followed by nonlinear stacking at the stage of substack generation provide a data conditioning workflow that significantly attenuates the effects of statics, anisotropy, and, to some extent, 3D velocity variations. This work is a step towards an optimized data conditioning workflow for surface-acquired microseismic data.