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.

Presentation Date: Wednesday, October 19, 2016

Start Time: 8:00:00 AM

Location: 148

Presentation Type: ORAL

Keywords:
upstream oil & gas, recursive averaging, adaptive microseismic noise estimation, seg seg international exposition, reservoir characterization, noise estimation, coefficient, algorithm, langston, estimation
Subjects:
Reservoir Characterization, Seismic processing and interpretation
This content is only available via PDF.
2016. Society of Exploration Geophysicists
You can access this article if you purchase or spend a download.