Automatic classification of multi-channel microseismic waveform based on DCNN-SPP

Lin, Bolun; Wei, Xiangxiang; Zhang, Junjie; Zhong, Hui

doi:10.1016/j.jappgeo.2018.09.022

Cited by 21 publications

(8 citation statements)

References 10 publications

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“…There have been applications of this technique in the field of geology, as performed by foreign scholars (Murnion 1996;Suwansawat and Einstein 2006) and Chinese scholars (Xue and Pan 1999;Zhao and Gui 2005;Zou et al 2006;Yan et al 2008;Yang and Xia 2013;Zhang et al 2017;Bi et al 2019;Shi et al 2019), but BP neural networks rarely have been used in oil sand reservoir distribution prediction.…”

Section: Methodsmentioning

confidence: 99%

Application of back-propagation neural networks in the distribution of oil sand reservoirs—a case study from the western slope of the Songliao Basin, China

et al. 2021

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High-quality hydrocarbon source rocks are present in the upper Cretaceous layer in the western slope of the Songliao Basin. Oil and gas have accumulated in these rocks at the shallow edge of the basin, which has led to the formation of oil sand resources. This study uses the back-propagation (BP) neural network method to predict the distribution of oil sand reservoirs and is the first study of its kind in China. First, based on the basic data collected by core sample, well log and geochemical analyses, and the reasonable selection of samples, the cores are divided into mudstone, siltstone, fine sandstone, medium sandstone, and sand, according to lithology. Second, a three-layer BP neural network model is constructed with two hidden S-type layers and one linear output layer. Third, through a comparison of the effect of different numbers of training sessions of the sample data, we demonstrate that the accuracy of the model can be increased to 90% after training the network 100,000 times. Then, the log-derived data of rocks with unknown lithologies are input into the neural network to predict whether they contain oil sands. We show that the BP neural network method can predict the distribution of oil sand reservoirs in the target horizon of the study area, and the results are consistent with research results on the corresponding sand reservoirs and sedimentary facies. Thus, we conclude that it is feasible to use the BP neural network method to predict the distribution of oil sand reservoirs.

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Section: Methodsmentioning

confidence: 99%

Application of back-propagation neural networks in the distribution of oil sand reservoirs—a case study from the western slope of the Songliao Basin, China

et al. 2021

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“…(2020) composed an 11×50 feature matrix from the waveform data, the same data as Lin et al. (2018, 2019). This feature matrix is used as input of the CNN, which shows higher performance than conventional machine learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Lin et al. (2018, 2019) utilized a CNN‐based encoder network to extract optimized features. However, a significant loss of information was expected because the raw data consisting of numerous samples was converted into a small image (115×115).…”

Section: Introductionmentioning

confidence: 99%

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Automatic microseismic signal classification for mining safety monitoring using the WaveNet classifier

Choi

Pyun

Cheon

2023

Geophysical Prospecting

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Microseismic monitoring is a promising method for the safety monitoring of underground mines. However, it is crucial to isolate microseismic signals related to the collapse of a mine from others for successful monitoring because the monitoring system records various signals. Recently, deep learning‐based classification techniques have achieved high performance in such data classification problems. In this context, we develop an automatic signal classification technique using the modified WaveNet classifier. The main characteristic of the WaveNet structure is its ability to extract features at various frequencies from very long time‐series data, and such an advantage makes the WaveNet suitable for seismic data processing. The data imbalance problem coming from the safe condition of the monitoring target is solved by augmenting the training data with those acquired from another mine and employing class weighting. After training, an optimal classifier is chosen considering the loss function, accuracy and Fβ score. The optimal classifier shows very high accuracy and excellent performance for the test data prediction. Compared to the random forest model and another one‐dimensional convolutional neural network–based network, the suggested classifier has higher reliability in predicting microseismic signals. Even though the proposed WaveNet model has a much more complex structure than the random forest model, the actual application examples demonstrate that the proposed model achieves high efficiency without any preprocessing. The automatic signal classifier developed in this study can be directly applied to various safety monitoring problems, not only mines, to improve the efficiency and reliability of monitoring systems.

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“…Although the FAST method performs well in terms of detection sensitivity and applicability, it has considerable overhead in memory and computation [14]. With the rapid development in the field of computers, artificial intelligence technology has been widely used in seismic/microseismic processing and disaster prediction [15][16][17][18]. Xin et al (2021) [19] proposed an explainable time-frequency convolutional neural network (CNN) to provide an excellent classification performance and explainability.…”

Section: Introductionmentioning

confidence: 99%

Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering

Zhang

Zeng

et al. 2021

Sensors

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Due to the complexity of the various waveforms of microseismic data, there are high requirements on the automatic multi-classification of such data; an accurate classification is conducive for further signal processing and stability analysis of surrounding rock masses. In this study, a microseismic multi-classification (MMC) model is proposed based on the short time Fourier transform (STFT) technology and convolutional neural network (CNN). The real and imaginary parts of the coefficients of microseismic data are inputted to the proposed model to generate three classes of targets. Compared with existing methods, the MMC has an optimal performance in multi-classification of microseismic data in terms of Precision, Recall, and F1-score, even when the waveform of a microseismic signal is similar to that of some special noise. Moreover, semisynthetic data constructed by clean microseismic data and noise are used to prove the low sensitivity of the MMC to noise. Microseismic data recorded under different geological conditions are also tested to prove the generality of the model, and a microseismic signal with Mw ≥ 0.2 can be detected with a high accuracy. The proposed method has great potential to be extended to the study of exploration seismology and earthquakes.

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Automatic classification of multi-channel microseismic waveform based on DCNN-SPP

Cited by 21 publications

References 10 publications

Application of back-propagation neural networks in the distribution of oil sand reservoirs—a case study from the western slope of the Songliao Basin, China

Application of back-propagation neural networks in the distribution of oil sand reservoirs—a case study from the western slope of the Songliao Basin, China

Automatic microseismic signal classification for mining safety monitoring using the WaveNet classifier

Multi-Classification of Complex Microseismic Waveforms Using Convolutional Neural Network: A Case Study in Tunnel Engineering

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