다변량 지구과학 데이터와 가우시안 혼합 모델을 이용한 공간 분포 추정

Translated title of the contribution: Estimation of Spatial Distribution Using the Gaussian Mixture Model with Multivariate Geoscience Data

Ho Rim Kim, Soonyoung Yu, Seong Taek Yun, Kyoung Ho Kim, Goon Taek Lee, Jeong Ho Lee, Chul Ho Heo, Dong Woo Ryu

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Spatial estimation of geoscience data (geo-data) is challenging due to spatial heterogeneity, data scarcity, and high dimensionality. A novel spatial estimation method is needed to consider the characteristics of geo-data. In this study, we proposed the application of Gaussian Mixture Model (GMM) among machine learning algorithms with multivariate data for robust spatial predictions. The performance of the proposed approach was tested through soil chemical concentration data from a former smelting area. The concentrations of As and Pb determined by ex-situ ICP-AES were the primary variables to be interpolated, while the other metal concentrations by ICP-AES and all data determined by in-situ portable X-ray fluorescence (PXRF) were used as auxiliary variables in GMM and ordinary cokriging (OCK). Among the multidimensional auxiliary variables, important variables were selected using a variable selection method based on the random forest. The results of GMM with important multivariate auxiliary data decreased the root mean-squared error (RMSE) down to 0.11 for As and 0.33 for Pb and increased the correlations (r) up to 0.31 for As and 0.46 for Pb compared to those from ordinary kriging and OCK using univariate or bivariate data. The use of GMM improved the performance of spatial interpretation of anthropogenic metals in soil. The multivariate spatial approach can be applied to understand complex and heterogeneous geological and geochemical features.

Translated title of the contributionEstimation of Spatial Distribution Using the Gaussian Mixture Model with Multivariate Geoscience Data
Original languageKorean
Pages (from-to)353-366
Number of pages14
JournalEconomic and Environmental Geology
Volume55
Issue number4
DOIs
Publication statusPublished - 2022 Aug

Bibliographical note

Publisher Copyright:
© 2022 Korean Society of Economic and Environmental Geology. All rights reserved.

ASJC Scopus subject areas

  • Environmental Science (miscellaneous)
  • Geology
  • Economic Geology

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