Automatic Event Detection and Event Recovery in Low SNR Microseismic Signals Based on Time‐Frequency Sparseness

Wang, Peng; Chang, Xu; Wang, Yibo; Wang, Luchen; Zhai, Hongchen

doi:10.1002/cjg2.20137

Cited by 4 publications

(4 citation statements)

References 19 publications

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“…The method is more robust than STA/LTA because no prior bandpass filtering is necessary to enhance the SNR. Similar kinds of events were also detected by Wang et al who constructed target function of event detection, which can detect and restore clean MS events simultaneously [31]. Tselentis and others put the idea of statistical Chi-squared test for event detection and automatic phase picker of primary wave based on Kurtosis criterion [32].…”

Section: Former Approaches To Microseismic Event Classificationmentioning

confidence: 76%

Review of machine learning and deep learning application in mine microseismic event classification

Wang¹,

Basnet²,

Mahtab³

2021

Min. miner. depos.

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Purpose. To put forward the concept of machine learning and deep learning approach in Mining Engineering in order to get high accuracy in separating mine microseismic (MS) event from non-useful events such as noise events blasting events and others. Methods. Traditionally applied methods are described and their low impact on classifying MS events is discussed. General historical description of machine learning and deep learning methods is shortly elaborated and different approaches conducted using these methods for classifying MS events are analysed. Findings. Acquired MS data from rock fracturing process recorded by sensors are inaccurate due to complex mining environment. They always need preprocessing in order to classify actual seismic events. Traditional detecting and classifying methods do not always yield precise results, which is especially disappointing when different events have a similar nature. The breakthrough of machine learning and deep learning methods made it possible to classify various MS events with higher precision compared to the traditional one. This paper introduces a state-of-the-art review of the application of machine learning and deep learning in identifying mine MS events. Originality.Previously adopted methods are discussed in short, and a brief historical outline of Machine learning and deep learning development is presented. The recent advancement in discriminating MS events from other events is discussed in the context of these mechanisms, and finally conclusions and suggestions related to the relevant field are drawn. Practical implications. By means of machin learning and deep learning technology mine microseismic events can be identified accurately which allows to determine the source location so as to prevent rock burst. Keywords: rock burst, MS event, blasting event, noise event, machine learning, deep learning

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Section: Former Approaches To Microseismic Event Classificationmentioning

confidence: 76%

Review of machine learning and deep learning application in mine microseismic event classification

Wang¹,

Basnet²,

Mahtab³

2021

Min. miner. depos.

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“…As discussed earlier in Sections 2.1.1 and 2.1.2, we applied both the conventional and improved polarity correction methods at 306 microseismic events identified by using automatic event detection approach [44], respectively. Then, their polarities were corrected through both the conventional and improved polarity correction methods, respectively.…”

Section: Field Data Examplementioning

confidence: 99%

A Novel Polarity Correction Method Developed on Cross Correlation Analysis for Downhole Migration-Based Location of Microseismic Events

et al. 2022

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Migration-based approaches depending on waveform stacking are generally used to locate the microseismic events in hydro-fracturing monitoring. A simple waveform stacking with polarity correction normally provides better results than any of the absolute value-based methods. However, the existing polarity estimation method based on cross correlation analysis selects only individual waveform as a reference waveform, which may affect the precision of migration-based methods. Therefore, a novel polarity correction method based on cross correlation analysis is introduced for a migration-based location in order to accurately locate the microseismic events in a borehole system. The proposed method selects all waveforms from one event having high signal-to-noise ratio (SNR) as corresponding reference waveforms, instead of only selecting a single high SNR waveform from one target event as the corresponding reference waveform. Compared with the above-mentioned conventional method, this proposed method provides a more accurate migration-based location of microseismic events with minimum error. The presented method was successfully tested on synthetic and field data acquired from a single monitoring well during a hydraulic fracturing process. Our study distinctly demonstrates that the proposed method provides more robust and reliable results, even in low SNR circumstances.

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“…With the development of information technology, more and more signal analysis and processing technologies have been applied to the study of time-frequency analysis, including the shorttime Fourier transform (STFT), continuous wavelet transform (CWT) and S transform. The methods described above are mainly used to depict seismic thin-layer (Huang et al, 2018;Wang et al, 2014). In order to take advantage of various methods, Stockwell et al (1996) adopted S transform with both STFT and CWT, and the time-frequency resolution could be adjusted adaptively (Deng et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

A seismic thin‐layer detection factor calculated by integrated S transform with non‐negative matrix factorization

Zhao,

Cao,

Yang

et al. 2024

Geophysical Prospecting

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Time–frequency analysis is one of the effective methods for seismic thin‐layer detection. Conventional time–frequency analysis technology for seismic thin‐layer detection is interfered by the energy of adjacent frequency signals, and there is information redundancy in the frequency‐domain analysis. Therefore, an S transform with improved window factor, which is based on the constrained non‐negative matrix factorization, is constructed to realize seismic thin‐layer detection. First, the seismic data is processed by the S transform of the improved window factor, and then we can obtain the frequency‐domain information with strong time–frequency focus by changing the adjustment factor and attenuation factor in the window function. Furthermore, the key frequency of the seismic data spectrum, which can also be called the key frequency characteristic factor, can be calculated by the non‐negative matrix factorization algorithm. Fortunately, the overthrust model shows a good correspondence between the key frequency characteristic factor and the thin‐layer interface. The field data example shows that this approach provides a new approach for thin‐layer detection.

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Automatic Event Detection and Event Recovery in Low SNR Microseismic Signals Based on Time‐Frequency Sparseness

Cited by 4 publications

References 19 publications

Review of machine learning and deep learning application in mine microseismic event classification

Review of machine learning and deep learning application in mine microseismic event classification

A Novel Polarity Correction Method Developed on Cross Correlation Analysis for Downhole Migration-Based Location of Microseismic Events

A seismic thin‐layer detection factor calculated by integrated S transform with non‐negative matrix factorization

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