Automatic microseismic event picking via unsupervised machine learning

Chen, Yangkang

doi:10.1093/gji/ggx420

Cited by 117 publications

(47 citation statements)

References 66 publications

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“…These existing methods have been applied in many different cases, but their applications also have many restrictions. Most of these methods are too sensitive to noise (Guan & Niu, 2017;Mousavi & Langston, 2016a, 2016b, 2017.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning methods, supervised classification or unsupervised clustering, are also used in seismic classification and arrival picking (Chen, 2017;Knapmeyer-Endrun & Hammer, 2015;McCormack et al, 1993;Muller et al, 1998;Provost et al, 2017;Rouet-Leduc et al, 2017;Shahnas et al, 2018). The Neural network is one of the mostly used machine learning methods for waveform classification and arrival picking (Akram et al, 2017;Dai & MacBeth, 1995, 1997Langer et al, 2003;Maity et al, 2014;McCormack et al, 1993;Pulli & Dysart, 1990;Zhao & Takano, 1999).…”

Section: Introductionmentioning

confidence: 99%

“…applied machine learning method to classify seismic waveforms from deep and shallow induced microearthquakes. Chen (2017) applied unsupervised fuzzy clustering method to pick arrivals for The features are all normalized to [0,1]. The red triangles denote the k-means clustering picked arrival.…”

Section: Introductionmentioning

confidence: 99%

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Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network

Chen

Zhang

Bai

et al. 2019

Earth and Space Science

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Automatic waveform classification and arrival picking methods are widely studied to reduce or replace the manual works. Machine learning based methods, especially neural networks, and clustering based methods have shown great potentials in previous studies. However, most of the existing methods are sensitive to noise. The convolution neural networks (CNNs), developed from the traditional neural networks, have been successfully applied in many different fields, but are rarely studied in seismic waveform classification. In this paper, we propose a novel antinoise CNN architecture for waveform classification and also propose to combine k‐means clustering (KC) with CNN classification to pick arrivals (CNN‐KC). Seismic data are sampled to 1‐D vectors using a specific time window. Using the trained CNN classifier, these 1‐D vectors are classified into two categories: waveform and nonwaveform. With the constraint of the first waveform, CNN‐KC can pick the arrival more accurately. We also apply the proposed methods to the synthetic microseismic data with different noise levels and the actual field microseismic data to test their robustness. CNNs perform much better than the traditional multilayer perceptron on the waveform classification of the noisy microseismic data. Based on the analysis of the CNN internal architecture, we also conclude that the main reason that CNN is insensitive to noise is the convolution and pooling layers which behave like smooth operation in some ways. The final results show that the CNN and CNN‐KC are effective and robust methods for waveform classification and arrival picking.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network

Chen

Zhang

Bai

et al. 2019

Earth and Space Science

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“…Assuming that each sample of a microseismic trace represents only noise or seismic signal -from here on referred to as signal, we consider the identification of wave arrivals as a binary clustering problem (Chen, 2017). Following this definition, we define the samples k = 1, ..., N, of a trace d(k) as a set of points X = x 1 , ..., x N , in a F-dimensional Euclidean space (Figure 1), where x k is a feature vector which elements represent the value of some feature of d(k) (e.g.…”

Section: Cfcm Assisted Aic Based Wave Arrival Pickermentioning

confidence: 99%

A fuzzy c-means assisted AIC workflow for arrival picking on downhole microseismic data

Cano

Akram

Peter

et al. 2019

SEG Technical Program Expanded Abstracts 2019

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We propose a workflow for automatic P-and S-wave arrival picking on downhole microseismic data. It uses conditional fuzzy c-means clustering to identify time intervals of possible wave arrivals. We classify the signal intervals as P-and S-wave using the first and second eigenvalues of the waveforms contained within. The Akaike information criterion (AIC) picker is then applied to the identified P-and S-wave intervals for arrival picking. Using real microseismic dataset examples, we show that the proposed workflow yields accurate arrival picks for both high and low signal-to-noise ratio waveforms. The identification of signal intervals, however, uses features based on amplitude, thus remains susceptible to high amplitude noise.

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“…Artificial neural network, entropy function, and image processing method were used to pick arrivals automatically [29][30][31]. With the development of machine learning, some unsupervised and supervised algorithms were used to detect the arrival times of microseismic events [32,33]. Since a single method may fail in accurately identifying arrival times, several hybrid methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Relative Arrival Time of Microseismic Events Based on Phase-Only Correlation

2018

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The arrival time of a microseismic event is an important piece of information for microseismic monitoring. The accuracy and efficiency of arrival time identification is affected by many factors, such as the low signal-to-noise ratio (SNR) of the records, the vast amount of real-time monitoring records, and the abnormal situations of monitoring equipment. In order to eliminate the interference of these factors, we propose a method based on phase-only correlation (POC) to estimate the relative arrival times of microseismic events. The proposed method includes three main steps:(1) The SNR of the records is improved via time-frequency transform, which is used to obtain the time-frequency representation of each trace of a microseismic event.(2) The POC functions of all pairs of time-frequency representations are calculated. The peak value of the POC function indicates the similarity of the traces, and the peak position in the time lag axis indicates the relative arrival times between the traces. (3) Using the peak values as weighting coefficients of the linear equations, consistency processing is used to exclude any abnormal situations and obtain the optimal relative arrival times. We used synthetic data and field data to validate the proposed method. Comparing with Akaike information criterion (AIC) and cross-correlation, the proposed method is more robust at estimating the relative arrival time and excluding the influence of abnormal situations.

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Automatic microseismic event picking via unsupervised machine learning

Cited by 117 publications

References 66 publications

Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network

Automatic Waveform Classification and Arrival Picking Based on Convolutional Neural Network

A fuzzy c-means assisted AIC workflow for arrival picking on downhole microseismic data

Estimation of the Relative Arrival Time of Microseismic Events Based on Phase-Only Correlation

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