K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China)
Seismicity partitioning is an important step in geological structure interpretation and seismic hazard assessment. In this paper, seismic event location (X,Y,Z) and Euclidean distance were selected as the K-Means cluster, the Gaussian mixture model (GMM), and the self-organizing maps (SOM) input fea...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2017/5913041 |
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| author | Xueyi Shang Xibing Li A. Morales-Esteban Longjun Dong Kang Peng |
| author_facet | Xueyi Shang Xibing Li A. Morales-Esteban Longjun Dong Kang Peng |
| author_sort | Xueyi Shang |
| collection | DOAJ |
| description | Seismicity partitioning is an important step in geological structure interpretation and seismic hazard assessment. In this paper, seismic event location (X,Y,Z) and Euclidean distance were selected as the K-Means cluster, the Gaussian mixture model (GMM), and the self-organizing maps (SOM) input features and cluster determination measurement, respectively, and 1516 seismic events (M>-1.5) obtained from the Yongshaba mine (China) were chosen for the cluster analysis. In addition, a Silhouette and Krzanowski-Lai- (KL-) combined S-KL index was proposed to obtain the possible optimum cluster number and to compare the cluster methods. Results show that the K-Means cluster obtains the best cluster “quality” with higher S-KL indexes on the whole and meaningful clusters. Furthermore, the optimal number for detailed geological structure interpretation is confirmed as eleven clusters, and we found that two areas probably have faults or caves, and two faults may be falsely inferred by mine geologists. Seismic hazard assessment shows that C5 and C7 (K=11) have a high mean moment magnitude (mM) and C1, C2, C3, and C4 (K=11) have a relatively high mM, where special attention is needed when mining. In addition, C7 (K=11) is the most shear-related area with a mean S-wave to P-wave energy ratio (mEs/Ep) of 41.21. In conclusion, the K-Means cluster provides an effective way for mine seismicity partitioning, geological structure interpretation, and seismic hazard assessment. |
| format | Article |
| id | doaj-art-73ec8698f9dd446086ad841339b85477 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-73ec8698f9dd446086ad841339b854772025-02-03T01:31:56ZengWileyShock and Vibration1070-96221875-92032017-01-01201710.1155/2017/59130415913041K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China)Xueyi Shang0Xibing Li1A. Morales-Esteban2Longjun Dong3Kang Peng4School of Resources and Safety Engineering, Central South University, Changsha, ChinaSchool of Resources and Safety Engineering, Central South University, Changsha, ChinaDepartment of Building Structures and Geotechnical Engineering, University of Seville, Seville, SpainSchool of Resources and Safety Engineering, Central South University, Changsha, ChinaState Key Laboratory of Coal Mine Disaster Dynamics and Control, Chongqing University, Chongqing, ChinaSeismicity partitioning is an important step in geological structure interpretation and seismic hazard assessment. In this paper, seismic event location (X,Y,Z) and Euclidean distance were selected as the K-Means cluster, the Gaussian mixture model (GMM), and the self-organizing maps (SOM) input features and cluster determination measurement, respectively, and 1516 seismic events (M>-1.5) obtained from the Yongshaba mine (China) were chosen for the cluster analysis. In addition, a Silhouette and Krzanowski-Lai- (KL-) combined S-KL index was proposed to obtain the possible optimum cluster number and to compare the cluster methods. Results show that the K-Means cluster obtains the best cluster “quality” with higher S-KL indexes on the whole and meaningful clusters. Furthermore, the optimal number for detailed geological structure interpretation is confirmed as eleven clusters, and we found that two areas probably have faults or caves, and two faults may be falsely inferred by mine geologists. Seismic hazard assessment shows that C5 and C7 (K=11) have a high mean moment magnitude (mM) and C1, C2, C3, and C4 (K=11) have a relatively high mM, where special attention is needed when mining. In addition, C7 (K=11) is the most shear-related area with a mean S-wave to P-wave energy ratio (mEs/Ep) of 41.21. In conclusion, the K-Means cluster provides an effective way for mine seismicity partitioning, geological structure interpretation, and seismic hazard assessment.http://dx.doi.org/10.1155/2017/5913041 |
| spellingShingle | Xueyi Shang Xibing Li A. Morales-Esteban Longjun Dong Kang Peng K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China) Shock and Vibration |
| title | K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China) |
| title_full | K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China) |
| title_fullStr | K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China) |
| title_full_unstemmed | K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China) |
| title_short | K-Means Cluster for Seismicity Partitioning and Geological Structure Interpretation, with Application to the Yongshaba Mine (China) |
| title_sort | k means cluster for seismicity partitioning and geological structure interpretation with application to the yongshaba mine china |
| url | http://dx.doi.org/10.1155/2017/5913041 |
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