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...
Saved in:
Main Authors: | Xueyi Shang, Xibing Li, A. Morales-Esteban, Longjun Dong, Kang Peng |
---|---|
Format: | Article |
Language: | English |
Published: |
Wiley
2017-01-01
|
Series: | Shock and Vibration |
Online Access: | http://dx.doi.org/10.1155/2017/5913041 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
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) -
K-Means Clustering with Local Distance Privacy
by: Mengmeng Yang, et al.
Published: (2023-12-01) -
IRMAOC: an interpretable role mining algorithm based on overlapping clustering
by: Yaqi Yang, et al.
Published: (2025-01-01) -
Prospects for mine geological work in the intelligence age: Architecture of the intelligent geological guarantee technology system for mine exploitation
by: Yucheng XIA, et al.
Published: (2025-01-01) -
Stroke Subtype Clustering by Multifractal Bayesian Denoising with Fuzzy C Means and K-Means Algorithms
by: Yeliz Karaca, et al.
Published: (2018-01-01)