Adaptive microseismic noise estimation and denoising

Abstract

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.

Publication Title

SEG Technical Program Expanded Abstracts

Link to Full Text

Share

COinS