Iterative interferometry-based method for picking microseismic events

Abstract

Continuous microseismic monitoring of hydraulic fracturing is commonly used in many engineering, environmental, mining, and petroleum applications. Microseismic signals recorded at the surface, suffer from excessive noise that complicates first-break picking and subsequent data processing and analysis. This study presents a new first-break picking algorithm that employs concepts from seismic interferometry and time-frequency (TF) analysis. The algorithm first uses a TF plot to manually pick a reference first-break and then iterates the steps of cross-correlation, alignment, and stacking to enhance the signal-to-noise ratio of the relative first breaks. The reference first-break is subsequently used to calculate final first breaks from the relative ones. Testing on synthetic and real data sets at high levels of additive noise shows that the algorithm enhances the first-break picking considerably. Furthermore, results show that only two iterations are needed to converge to the true first breaks. Indeed, iterating more can have detrimental effects on the algorithm due to increasing correlation of random noise.Comment: 31 pages, 18 Figure

Iterative Interferometric Denoising Filter for Traveltime Picking

“…However, all these approaches require some parameters to adjust the noise filter threshold; inappropriate parameters will damage useful information or cause noise elimination failure. Seismic interferometry [22] has been introduced to increase the microseismic waveform signal-to-noise (S/N) ratio [23] by fixing the correct picking of a reference first break on at least one trace and then applying cross-correlation to provide picking convenience. Further, this concept has been transferred to the refraction field [24][25][26].…”

Iterative Gaussian mixture model and multi‐channel attributes for arrival picking in extremely noisy environments

“…Traditional manual picking often takes a lot of time and lowers the efficiency of the whole workflow. Therefore, many automatic picking methods were developed to accelerate this procedure (Fedorenko and Husebye, 1999; Sabbione and Velis, 2010; Mousa et al ., 2011; Blias, 2012; Mousa and Al‐Shuhail, 2012; Senkaya and Karsli, 2014; Velis et al ., 2015; Mousavi et al ., 2016; Tan and He, 2016; Iqbal et al ., 2017; Lee et al ., 2017; Khajouei and Goudarzi, 2018; Khalaf et al ., 2018; Mousavi et al ., 2019), leverage techniques relying on short‐term and long‐term average ratio (STA/LTA) (Allen, 1978; Jones and van der Baan, 2015; Vaezi and van der Baan, 2015) and attribute‐based methods (Saragiotis et al ., 2013; Forte et al ., 2016). Alvarez et al .…”

Noise Suppression Method of Microseismic Signal Based on Complementary Ensemble Empirical Mode Decomposition and Wavelet Packet Threshold

“…Because the coal rock damage is mostly micronlevel vibration intensity, the signal is very weak, and the external noise causes the received signal to always be mixed with noise, which makes the signal's effectiveness greatly reduced [5]- [9]. In order to improve the effectiveness of coal and rock microseismic signals, it is necessary to make full use of noise suppression means to suppress the signal [10]- [14]. The noise suppression of the noisy microseismic signal can better provide real and effective signals to reduce the noise signal interference for the subsequent work such as the initial phase picking, the source location, and the focal mechanism interpretation.…”