1887

Abstract

Summary

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.

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/content/papers/10.3997/2214-4609.201601261
2016-05-30
2024-03-29
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References

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