Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform
Typical microseismic data recorded by surface arrays are characterized by low signal-to-noise ratios (S/Ns) and highly nonstationary noise that make it difficult to detect small events. Currently, array or crosscorrelation-based ap-proaches are used to enhance the S/N prior to processing. We have developed an alternative approach for S/N improve-ment and simultaneous detection of microseismic events. The proposed method is based on the synchrosqueezed continuous wavelet transform (SS-CWT) and custom thresholding of sin-gle-channel data. The SS-CWT allows for the adaptive filter-ing of time-and frequency-varying noise as well as offering an improvement in resolution over the conventional wavelet transform. Simultaneously, the algorithm incorporates a de-tection procedure that uses the thresholded wavelet coeffi-cients and detects an arrival as a local maxima in a characteristic function. The algorithm was tested using a syn-thetic signal and field microseismic data, and our results have been compared with conventional denoising and detection methods. This technique can remove a large part of the noise from small-amplitudes signal and detect events as well as es-timate onset time.
Mousavi, S., Langston, C., & Horton, S. (2016). Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform. Geophysics, 81 (4), V341-V355. https://doi.org/10.1190/GEO2015-0598.1