Adaptive microseismic noise estimation and denoising


Microseismic data recorded by surface arrays are often strongly contaminated by unwanted random noise. This background noise makes the detection of small magnitude events difficult. A noise level estimation and random-noise reduction algorithm is presented for microseismic data analysis based upon minimally controlled recursive averaging and neighborhood shrinkage estimators. The method is fast and data-driven. Results from application of this algorithm to synthetic and real seismic data show that it holds great promise for improving microseismic detection.

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SEG Technical Program Expanded Abstracts