Automatic microseismic denoising and onset detection using the synchrosqueezed continuous wavelet transform

2016/06/10 by S. Mostafa Mousavi, Charles A. Langston, Stephen P. Horton · 326 citations
Computer Science · Earth and Planetary Sciences · #Algorithm #Artificial intelligence #Computer science #Computer vision #Continuous wavelet transform #Discrete wavelet transform #Filter (signal processing) #Geology #Image (mathematics) #Microseism #Noise (video) #Noise reduction #Pattern recognition (psychology) #SIGNAL (programming language) #Seismic Imaging and Inversion Techniques #Seismic Waves and Analysis #Seismology #Seismology and Earthquake Studies #Step detection #Thresholding #Wavelet #Wavelet transform

paper · doi:10.1190/geo2015-0598.1

published in Geophysics 81(4), V341-V355 (Society of Exploration Geophysicists)

openalex publication_date 2016/06/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/26

Abstract

ABSTRACT 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 approaches are used to enhance the S/N prior to processing. We have developed an alternative approach for S/N improvement and simultaneous detection of microseismic events. The proposed method is based on the synchrosqueezed continuous wavelet transform (SS-CWT) and custom thresholding of single-channel data. The SS-CWT allows for the adaptive filtering of time- and frequency-varying noise as well as offering an improvement in resolution over the conventional wavelet transform. Simultaneously, the algorithm incorporates a detection procedure that uses the thresholded wavelet coefficients and detects an arrival as a local maxima in a characteristic function. The algorithm was tested using a synthetic 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 estimate onset time.

Citations

• Orthonormal bases of compactly supported wavelets
• A Threshold Selection Method from Gray-Level Histograms
• Ideal spatial adaptation by wavelet shrinkage

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