Research on Recognition of Quiet Period of Sandstone Acoustic Emission Based on Four Machine Learning Algorithms
Aiming at solving the problem that it is difficult to recognize the quiet period of acoustic emission in rocks, four machine learning algorithms were adopted to develop and improve the recognition method of the quiet period of acoustic emission. In the process of establishing the model, the time dom...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Geofluids |
| Online Access: | http://dx.doi.org/10.1155/2022/2133607 |
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| _version_ | 1832565759354601472 |
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| author | Dong Duan Xiaojing Feng Ruizhe Zhang Xiaoyu Chen Hongzhi Zhang |
| author_facet | Dong Duan Xiaojing Feng Ruizhe Zhang Xiaoyu Chen Hongzhi Zhang |
| author_sort | Dong Duan |
| collection | DOAJ |
| description | Aiming at solving the problem that it is difficult to recognize the quiet period of acoustic emission in rocks, four machine learning algorithms were adopted to develop and improve the recognition method of the quiet period of acoustic emission. In the process of establishing the model, the time domain data of acoustic emission were standardized and processed by box diagram method, so as to clean the abnormal data and reduce the dimension, and the frequency domain data were denoised by wavelet four-layer transform and wavelet packet three-layer energy decomposition, and a group of 8 wavelet packet energy parameters were established as frequency domain characteristic parameters. Based on AE time domain data, frequency domain data, and composite data (time-frequency domain data sets), the grid search traversal parameter technique was used to obtain the optimal parameters of four machine learning models. The accuracy, precision, recall, and F1 score were used to verify and evaluate the recognition performance of the models. The study results show that the recognition effects of the models are good, the model accuracy of the frequency domain data set is the lowest, and the model accuracy of the composite data set is the highest, with an accuracy of more than 90%. The kernel support vector machine model has the best performance, and its average precision is 0.87. The random forest (RF) model is the best model for recognizing quiet period of acoustic emission. |
| format | Article |
| id | doaj-art-fc67a9991a5749a5980b437f82dbea78 |
| institution | Kabale University |
| issn | 1468-8123 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Geofluids |
| spelling | doaj-art-fc67a9991a5749a5980b437f82dbea782025-02-03T01:06:53ZengWileyGeofluids1468-81232022-01-01202210.1155/2022/2133607Research on Recognition of Quiet Period of Sandstone Acoustic Emission Based on Four Machine Learning AlgorithmsDong Duan0Xiaojing Feng1Ruizhe Zhang2Xiaoyu Chen3Hongzhi Zhang4College of Mining EngineeringCollege of Mining EngineeringCollege of Mining EngineeringCollege of Mining EngineeringCollege of Mining EngineeringAiming at solving the problem that it is difficult to recognize the quiet period of acoustic emission in rocks, four machine learning algorithms were adopted to develop and improve the recognition method of the quiet period of acoustic emission. In the process of establishing the model, the time domain data of acoustic emission were standardized and processed by box diagram method, so as to clean the abnormal data and reduce the dimension, and the frequency domain data were denoised by wavelet four-layer transform and wavelet packet three-layer energy decomposition, and a group of 8 wavelet packet energy parameters were established as frequency domain characteristic parameters. Based on AE time domain data, frequency domain data, and composite data (time-frequency domain data sets), the grid search traversal parameter technique was used to obtain the optimal parameters of four machine learning models. The accuracy, precision, recall, and F1 score were used to verify and evaluate the recognition performance of the models. The study results show that the recognition effects of the models are good, the model accuracy of the frequency domain data set is the lowest, and the model accuracy of the composite data set is the highest, with an accuracy of more than 90%. The kernel support vector machine model has the best performance, and its average precision is 0.87. The random forest (RF) model is the best model for recognizing quiet period of acoustic emission.http://dx.doi.org/10.1155/2022/2133607 |
| spellingShingle | Dong Duan Xiaojing Feng Ruizhe Zhang Xiaoyu Chen Hongzhi Zhang Research on Recognition of Quiet Period of Sandstone Acoustic Emission Based on Four Machine Learning Algorithms Geofluids |
| title | Research on Recognition of Quiet Period of Sandstone Acoustic Emission Based on Four Machine Learning Algorithms |
| title_full | Research on Recognition of Quiet Period of Sandstone Acoustic Emission Based on Four Machine Learning Algorithms |
| title_fullStr | Research on Recognition of Quiet Period of Sandstone Acoustic Emission Based on Four Machine Learning Algorithms |
| title_full_unstemmed | Research on Recognition of Quiet Period of Sandstone Acoustic Emission Based on Four Machine Learning Algorithms |
| title_short | Research on Recognition of Quiet Period of Sandstone Acoustic Emission Based on Four Machine Learning Algorithms |
| title_sort | research on recognition of quiet period of sandstone acoustic emission based on four machine learning algorithms |
| url | http://dx.doi.org/10.1155/2022/2133607 |
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