Weak signal detection using multiscale morphology in microseismic monitoring

• We propose a multiscale morphological method to enhance weak microseismic signals.
• Different scales of structure elements are used to transform the data into the scale domain.
• The method can help us detect more waves modes, like P and S-waves,
• Real multi-component microseismic data example is used to show the performance.

Microseismic events caused by hydraulic fracturing are usually very weak. The magnitude range of microseismic signals is usually from − 3 to 1 Mw. Processing techniques such as band-pass filtering, are widely adopted to improve the signal-to-noise (S/N) ratio of microseismic data, while with a degradation of signal quality. We propose a multi-scale morphological method to detect weak micro-seismic signals. This approach decomposes data set into multi-scale components based on the mathematical morphology theory using structuring element that is similar to the wavelet basis in the well-known wavelet decomposition. The method can help us obtain more information by detecting more waves, like P-wave, S-wave and their reflections, which can be much more valuable in processing and interpretation of microseismic data during microseismic monitoring. The proposed approach is not amplitude preserving and not mathematically reversible. It can offer enhancement of arrivals for picking (and thus can subsequently offer benefits for event detection and location) but at the expense of estimates of magnitude or moment-tensor inversion.