Sedimentary Environment Analysis by Grain-Size Data Based on Mini Batch K-Means Algorithm
Joint Authors
Su, Qiao
Zhu, Yanhui
Jia, Yalin
Li, Ping
Hu, Fang
Xu, Xingyong
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-12-02
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
During the last several decades, researchers have made significant advances in sedimentary environment interpretation of grain-size analysis, but these improvements have often depended on the subjective experience of the researcher and were usually combined with other methods.
Currently, researchers have been using a larger number of data mining and knowledge discovering methods to explore the potential relationships in sediment grain-size analysis.
In this paper, we will apply bipartite graph theory to construct a Sample/Grain-Size network model and then construct a Sample network model projected from this bipartite network.
Furthermore, we will use the Mini Batch K-means algorithm with the most appropriate parameters (reassignment ratio ϵ=0.025 and mini batch = 25) to cluster the sediment samples.
We will use four representative evaluation indices to verify the precision of the clustering result.
Simulation results demonstrate that this algorithm can divide the Sample network into three sedimentary categorical clusters: marine, fluvial, and lacustrine.
According to the results of previous studies obtained from a variety of indices, the precision of experimental results about sediment grain-size category is up to 0.92254367, a fact which shows that this method of analyzing sedimentary environment by grain size is extremely effective and accurate.
American Psychological Association (APA)
Su, Qiao& Zhu, Yanhui& Jia, Yalin& Li, Ping& Hu, Fang& Xu, Xingyong. 2018. Sedimentary Environment Analysis by Grain-Size Data Based on Mini Batch K-Means Algorithm. Geofluids،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1158668
Modern Language Association (MLA)
Su, Qiao…[et al.]. Sedimentary Environment Analysis by Grain-Size Data Based on Mini Batch K-Means Algorithm. Geofluids No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1158668
American Medical Association (AMA)
Su, Qiao& Zhu, Yanhui& Jia, Yalin& Li, Ping& Hu, Fang& Xu, Xingyong. Sedimentary Environment Analysis by Grain-Size Data Based on Mini Batch K-Means Algorithm. Geofluids. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1158668
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1158668