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

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.

An application of waveform denoising for microseismic data using polarization–linearity and time–frequency thresholding

“…; Zhang et al . ; Mousavi, Langston and Horton ). A bandpass filter is the simplest for suppressing unwanted frequencies.…”

De‐aliased and de‐noise Cadzow filtering for seismic data reconstruction

“…It first transforms the observed seismic data into certain sparsepromoting domain (in which the signal can be represented by a few basis) and then apply a thresholding or masking operator (Wang, Cao and Yang 2011;Chen et al 2018b). There are a number of mathematical transforms studied in the seismological community such as Fourier transform (Zwartjes and Gisolf 2007), wavelet transform (Mousavi, Langston and Horton 2016;Anvari et al 2017), curvelet transform (Herrmann and Hennenfent 2008;Cao, Wang and Wang 2014;Cao and Zhao 2017), Radon transform (Trad, Ulrych and Sacchi 2002;Latif and Mousa 2017;Gong, Wang and Du 2018), seislet transform (Fomel and Liu 2010;Gan et al 2016) and dreamlet transform (Wu, Geng and Wu 2011;Huang, Wu and Wang 2018).…”

A Nonparametric Method for Automatic Denoising of Microseismic Data

“…In the past few decades, microseismic monitoring has been widely used in a variety of applications, ranging from hydraulic fracturing to mining and geotechnical engineering [1,2]. The signal is crucial for microseismic monitoring, and its quality will significantly affect the results of event detection, location, and source mechanism estimation.…”

“…Gaci [21] studied soft thresholding denoising techniques based on the discrete wavelet transform to enhance the firstarrival picking. In addition to Mousavi et al [1], Mousavi and Langston [22] also used synchrosqueezed continuous wavelet transform (SS-CWT) for seismic denoising.…”