Automatic Microseismic Event Detection by Multi-scale Morphology Characteristic Function

Abstract

Summary Microseismic events detection is a key issue for field real-time microseismic data processing because of the low signal-to-noise ratios. Surface-acquired microseismic events are commonly unpredictable and appear as weak signals, which presents a significant challenge in microseismic data analysis and event detection. A new implementation of the multi-scale morphology characteristic function is proposed for detecting microseismic events automatically. First, the microseismic data are decomposed into different scales by multi-scale morphology decomposition. Then the multi-scale morphology characteristic function is calculated by waveform correlation approach. We have tested the technique on a surface passive seismic monitoring dataset of the microseismic events induced by hydraulic fracturing, and it is proved to be effective by simulation and real data processing. The proposed method has the advantage of suppressing the effect of Gaussian noises and is applicable to the detection of low SNR signal.