Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models
The environment for acquiring microseismic signals is always filled with complex noise, leading to the presence of abundant invalid signals in the collected data and greatly disturbing effective microseismic signals. Regarding the identification of effective microseismic signals with a low signal-to...
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MDPI AG
2024-03-01
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| Online Access: | https://www.mdpi.com/2076-3417/14/6/2243 |
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| author | Sihongren Shen Bo Wang Linfeng Zeng Sheng Chen Liujun Xie Zilong She Lanying Huang |
| author_facet | Sihongren Shen Bo Wang Linfeng Zeng Sheng Chen Liujun Xie Zilong She Lanying Huang |
| author_sort | Sihongren Shen |
| collection | DOAJ |
| description | The environment for acquiring microseismic signals is always filled with complex noise, leading to the presence of abundant invalid signals in the collected data and greatly disturbing effective microseismic signals. Regarding the identification of effective microseismic signals with a low signal-to-noise ratio, a method for identifying effective microseismic signals in a strong-noise environment by using the variational mode decomposition (VMD) and genetic algorithm (GA)-based optimized support vector machine (SVM) model is proposed. Microseismic signals with a low signal-to-noise ratio are adaptively decomposed into several intrinsic mode functions (IMFs) by using VMD. The characteristics of such IMFs are extracted and used as a basis for the determination of signal validity. The SVM model is optimized by utilizing GA to obtain the optimal penalty factor c and the kernel function parameter g. The availability of IMF components is judged by the optimized SVM model, based on which the effectiveness of microseismic signals is further identified. By applying the algorithm to the microseismic signals with artificially added noise, the effective microseismic signals and ineffective noise are discriminated, verifying the feasibility of the algorithm. After processing the microseismic records collected in the field, we effectively judge the effectiveness of microseismic signals, suppress the interfering noise in the data and greatly improve the signal-to-noise ratio of the seismic records. The results show that the method for identifying effective microseismic signals based on VMD and GA-SVM can well discriminate between effective and ineffective microseismic signals, which is very significant and provides technical support for microseismic monitoring in a strong-noise environment. |
| first_indexed | 2024-04-24T18:35:45Z |
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| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-04-24T18:35:45Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-8324f53c4c824f5eb13e5aac405aa0002024-03-27T13:19:04ZengMDPI AGApplied Sciences2076-34172024-03-01146224310.3390/app14062243Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine ModelsSihongren Shen0Bo Wang1Linfeng Zeng2Sheng Chen3Liujun Xie4Zilong She5Lanying Huang6School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, ChinaState Key Laboratory of Intelligent Construction and Healthy Operation & Maintenance of Deep Underground Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Civil Engineering, Xuzhou University of Technology, Xuzhou 221116, ChinaThe environment for acquiring microseismic signals is always filled with complex noise, leading to the presence of abundant invalid signals in the collected data and greatly disturbing effective microseismic signals. Regarding the identification of effective microseismic signals with a low signal-to-noise ratio, a method for identifying effective microseismic signals in a strong-noise environment by using the variational mode decomposition (VMD) and genetic algorithm (GA)-based optimized support vector machine (SVM) model is proposed. Microseismic signals with a low signal-to-noise ratio are adaptively decomposed into several intrinsic mode functions (IMFs) by using VMD. The characteristics of such IMFs are extracted and used as a basis for the determination of signal validity. The SVM model is optimized by utilizing GA to obtain the optimal penalty factor c and the kernel function parameter g. The availability of IMF components is judged by the optimized SVM model, based on which the effectiveness of microseismic signals is further identified. By applying the algorithm to the microseismic signals with artificially added noise, the effective microseismic signals and ineffective noise are discriminated, verifying the feasibility of the algorithm. After processing the microseismic records collected in the field, we effectively judge the effectiveness of microseismic signals, suppress the interfering noise in the data and greatly improve the signal-to-noise ratio of the seismic records. The results show that the method for identifying effective microseismic signals based on VMD and GA-SVM can well discriminate between effective and ineffective microseismic signals, which is very significant and provides technical support for microseismic monitoring in a strong-noise environment.https://www.mdpi.com/2076-3417/14/6/2243microseismic monitoringvariational mode decompositiongenetic algorithm-based optimizationsupport vector machine |
| spellingShingle | Sihongren Shen Bo Wang Linfeng Zeng Sheng Chen Liujun Xie Zilong She Lanying Huang Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models Applied Sciences microseismic monitoring variational mode decomposition genetic algorithm-based optimization support vector machine |
| title | Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models |
| title_full | Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models |
| title_fullStr | Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models |
| title_full_unstemmed | Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models |
| title_short | Methods for Identifying Effective Microseismic Signals in a Strong-Noise Environment Based on the Variational Mode Decomposition and Modified Support Vector Machine Models |
| title_sort | methods for identifying effective microseismic signals in a strong noise environment based on the variational mode decomposition and modified support vector machine models |
| topic | microseismic monitoring variational mode decomposition genetic algorithm-based optimization support vector machine |
| url | https://www.mdpi.com/2076-3417/14/6/2243 |
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