Arrival-time picking of microseismic events based on MSNet

Arrival-time picking of microseismic events is a critical procedure in microseismic data processing. However, as field monitoring data contain many microseismic events with low signal-to-noise ratios (SNRs), traditional arrival-time picking methods based on the instantaneous characteristics of seismic signals cannot meet the picking accuracy and efficiency requirements of microseismic monitoring owing to the large volume of monitoring data. Conversely, methods based on deep neural networks can significantly improve arrival-time picking accuracy and efficiency in low-SNR environments. Therefore, we propose a deep convolutional network that combines the U-net and DenseNet approaches to pick arrival times automatically. This novel network, called MSNet not only retains the spatial information of any input signal or profile based on the U-net, but also extracts and integrates more essential features of events and non-events through dense blocks, thereby further improving the picking accuracy and efficiency. An effective workflow is developed to verify the superiority of the proposed method. First, we describe the structure of MSNet and the workflow of the proposed picking method. Then, datasets are constructed using variable microseismic traces from field microseismic monitoring records and from the finite-difference forward modeling of microseismic data to train the network. Subsequently, hyperparameter tuning is conducted to optimize the MSNet. Finally, we test the MSNet using modeled signals with different SNRs and field microseismic data from different monitoring areas. By comparing the picking results of the proposed method with the results of U-net and short-term average and long-term average (STA/LTA) methods, the effectiveness of the proposed method is verified. The arrival picking results of synthetic data and microseismic field data show that the proposed network has increased adaptability and can achieve high accuracy for picking the arrival-time of microseismic events.

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Automated arrival-time picking using a pixel-level network

Geophysics ◽

10.1190/geo2019-0792.1 ◽

2020 ◽

Vol 85 (5) ◽

pp. V415-V423

Author(s):

Yuanyuan Ma ◽

Siyuan Cao ◽

James W. Rector ◽

Zhishuai Zhang

Keyword(s):

Seismic Data ◽

Arrival Time ◽

Spatial Information ◽

Training Data ◽

Convolutional Network ◽

Data Set ◽

Arrival Times ◽

Data Volume ◽

Borehole Seismic ◽

Arrival Time Picking

Arrival-time picking is an essential step in seismic processing and imaging. The explosion of seismic data volume requires automated arrival-time picking in a faster and more reliable way than existing methods. We have treated arrival-time picking as a binary image segmentation problem and used an improved pixel-wise convolutional network to pick arrival times automatically. Incorporating continuous spatial information in training enables us to preserve the arrival-time correlation between nearby traces, thus helping to reduce the risk of picking outliers that are common in a traditional trace-by-trace picking method. To train the network, we first convert seismic traces into gray-scale images. Image pixels before manually picked arrival times are labeled with zeros, and those after are tagged with ones. After training and validation, the network automatically learns representative features and generates a probability map to predict the arrival time. We apply the network to a field microseismic data set that was not used for training or validation to test the performance of the method. Then, we analyze the effects of training data volume and signal-to-noise ratio on our autopicking method. We also find the difference between 1D and 2D training data with borehole seismic data. Microseismic and borehole seismic data indicate the proposed network can improve efficiency and accuracy over traditional automated picking methods.

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First arrival time picking for microseismic data based on DWSW algorithm

Journal of Seismology ◽

10.1007/s10950-018-9735-z ◽

2018 ◽

Vol 22 (4) ◽

pp. 833-840 ◽

Cited By ~ 2

Author(s):

Yue Li ◽

Yue Wang ◽

Hongbo Lin ◽

Tie Zhong

Keyword(s):

Arrival Time ◽

Microseismic Data ◽

First Arrival ◽

First Arrival Time ◽

Arrival Time Picking

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Automatic Classification of Microseismic Signals Based on MFCC and GMM-HMM in Underground Mines

Shock and Vibration ◽

10.1155/2019/5803184 ◽

2019 ◽

Vol 2019 ◽

pp. 1-9 ◽

Cited By ~ 2

Author(s):

Pingan Peng ◽

Zhengxiang He ◽

Liguan Wang

Keyword(s):

Rock Mass ◽

Gaussian Mixture ◽

Automatic Classification ◽

Microseismic Monitoring ◽

Underground Mines ◽

Basic Step ◽

Microseismic Data ◽

Safety Risks ◽

Microseismic Events

In order to mitigate economic and safety risks during mine life, a microseismic monitoring system is installed in a number of underground mines. The basic step for successfully analyzing those microseismic data is the correct detection of various event types, especially the rock mass rupture events. The visual scanning process is a time-consuming task and requires experience. Therefore, here we present a new method for automatic classification of microseismic signals based on the Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) by using only Mel-frequency cepstral coefficient (MFCC) features extracted from the waveform. The detailed implementation of our proposed method is described. The performance of this method is tested by its application to microseismic events selected from the Dongguashan Copper Mine (China). A dataset that contains a representative set of different microseismic events including rock mass rupture, blasting vibration, mechanical drilling, and electromagnetic noise is collected for training and testing. The results show that our proposed method obtains an accuracy of 92.46%, which demonstrates the effectiveness of the method for automatic classification of microseismic data in underground mines.

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First arrival time picking for microseismic data based on shearlet transform

Journal of Geophysics and Engineering ◽

10.1088/1742-2140/aa5777 ◽

2017 ◽

Vol 14 (2) ◽

pp. 262-271

Author(s):

Yao Cheng ◽

Yue Li ◽

Chao Zhang

Keyword(s):

Arrival Time ◽

Shearlet Transform ◽

Microseismic Data ◽

First Arrival ◽

First Arrival Time ◽

Arrival Time Picking

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A review and appraisal of arrival-time picking methods for downhole microseismic data

Geophysics ◽

10.1190/geo2014-0500.1 ◽

2016 ◽

Vol 81 (2) ◽

pp. KS71-KS91 ◽

Cited By ~ 75

Author(s):

Jubran Akram ◽

David W. Eaton

Keyword(s):

Arrival Time ◽

Hybrid Methods ◽

Optimal Parameter ◽

Signal To Noise Ratio ◽

Information Criterion ◽

High Signal ◽

Data Set ◽

Single Level ◽

Microseismic Data ◽

Arrival Time Picking

We have evaluated arrival-time picking algorithms for downhole microseismic data. The picking algorithms that we considered may be classified as window-based single-level methods (e.g., energy-ratio [ER] methods), nonwindow-based single-level methods (e.g., Akaike information criterion), multilevel- or array-based methods (e.g., crosscorrelation approaches), and hybrid methods that combine a number of single-level methods (e.g., Akazawa’s method). We have determined the key parameters for each algorithm and developed recommendations for optimal parameter selection based on our analysis and experience. We evaluated the performance of these algorithms with the use of field examples from a downhole microseismic data set recorded in western Canada as well as with pseudo-synthetic microseismic data generated by adding 100 realizations of Gaussian noise to high signal-to-noise ratio microseismic waveforms. ER-based algorithms were found to be more efficient in terms of computational speed and were therefore recommended for real-time microseismic data processing. Based on the performance on pseudo-synthetic and field data sets, we found statistical, hybrid, and multilevel crosscorrelation methods to be more efficient in terms of accuracy and precision. Pick errors for S-waves are reduced significantly when data are preconditioned by applying a transformation into ray-centered coordinates.

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

Energies ◽

10.3390/en11102527 ◽

2018 ◽

Vol 11 (10) ◽

pp. 2527 ◽

Cited By ~ 2

Author(s):

Peng Wang ◽

Xu Chang ◽

Xiyan Zhou

Keyword(s):

Arrival Time ◽

Linear Equations ◽

Time Lag ◽

Synthetic Data ◽

Information Criterion ◽

Peak Position ◽

Arrival Times ◽

Time Frequency ◽

Microseismic Events ◽

Microseismic Event

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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