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...
Main Authors: | Xueyi Shang, Xibing Li, A. Morales-Esteban, Longjun Dong, Kang Peng |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2017-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2017/5913041 |
Similar Items
-
Data Field-Based K-Means Clustering for Spatio-Temporal Seismicity Analysis and Hazard Assessment
by: Xueyi Shang, et al.
Published: (2018-03-01) -
Privacy-Preserving Hierarchical-k-Means Clustering on Horizontally Partitioned Data
by: Anrong Xue, et al.
Published: (2009-01-01) -
Parallelization of K-Means Clustering Algorithm for Data Mining
by: Jiang Hao, et al.
Published: (2017-01-01) -
The Interpretation of Seismic and Geological Data of Some Structures in Albania
by: Ahmet Collaku
Published: (2010-11-01) -
Nonlinear Methodologies for Identifying Seismic Event and Nuclear Explosion Using Random Forest, Support Vector Machine, and Naive Bayes Classification
by: Longjun Dong, et al.
Published: (2014-01-01)