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Semblance for microseismic event detection

Publication at Faculty of Mathematics and Physics |
2015

Abstract

Microseismic monitoring from large arrays using migration-based detection and location techniques is limited by detections of false positive events, which are the interpretation of spurious/noisy signals as real events. Therefore, semblance has been considered to differentiate between false positive and true events.

However, semblance by itself is not suitable for variable signals such as those caused by shear source radiation. We present a new methodology for event detection and location using semblance of amplitudes corrected by a source mechanism.

Our method is suitable for multichannel processing of microseismic data sets acquired with large arrays. The amplitudes are corrected by the radiation pattern of the inverted source mechanism before the semblance computation.

We show that the source mechanism correction is the key factor in maximizing the value of semblance and makes the detection based on semblance superior to simple stacking. We apply this method to a data set recorded by a large surface star-like array on synthetic as well as on field data.