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FastICA and total variation algorithm for geochemical anomaly extraction.

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Title: FastICA and total variation algorithm for geochemical anomaly extraction.
Authors: Liu, Bin1,2 (AUTHOR) liubincim@163.com, Zhou, Zhongli1,2 (AUTHOR), Dai, Qilin1,2 (AUTHOR), Tong, Wei1,2 (AUTHOR)
Source: Earth Science Informatics. Mar2020, Vol. 13 Issue 1, p153-162. 10p.
Subject Terms: *Electronic data processing, *Crust of the earth, *Algorithms, *Image processing, *Metallogeny
Geographic Terms: Qinghai Sheng (China), China
Abstract: Because the ore-forming system in the crust of the Earth is a highly nonlinear system, geochemical anomaly classification is very important for improving the accuracy of metallogenic prediction. Further, since the distribution of the geochemical elements usually presents nonlinear characteristics due to the complexity and uncertainty of geology factors, the traditional linear data processing method has limited applications for an ore-forming system. The FastICA algorithm is applied to preprocessed geochemical data to reduce the interference information between elements. On the basis of obtaining the separated geochemical elements, the continuity of the spatial distribution of geochemical elements is considered, and combined with the application of the total variation (TV) in image processing; thus the total variation is introduced when processing geochemical data for anomaly analysis to eliminate the influence of singular geochemical data values. To measure the spatial distribution of geochemical elements, assays of the 1:10000 soil geochemical data in the area of Dachaidan in the Qinghai province of China are processed. The elemental anomaly zoning sequences are divided into three levels of anomaly: 85%, 90% and 95%. The anomaly isograms of Au and Cu processed by FastICA and the total variation algorithm predict the geological background of the study area better than the traditional cumulative frequency method. These results indicate that the application of the FastICA algorithm and the total variation algorithm to process geochemical data processing is valid and effective. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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Header DbId: enr
DbLabel: Energy & Power Source
An: 141807012
AccessLevel: 6
PubType: Academic Journal
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  Group: Ti
  Data: FastICA and total variation algorithm for geochemical anomaly extraction.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Bin%22">Liu, Bin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> liubincim@163.com</i><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Zhongli%22">Zhou, Zhongli</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dai%2C+Qilin%22">Dai, Qilin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tong%2C+Wei%22">Tong, Wei</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Earth+Science+Informatics%22">Earth Science Informatics</searchLink>. Mar2020, Vol. 13 Issue 1, p153-162. 10p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Electronic+data+processing%22">Electronic data processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Crust+of+the+earth%22">Crust of the earth</searchLink><br />*<searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br />*<searchLink fieldCode="DE" term="%22Metallogeny%22">Metallogeny</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Qinghai+Sheng+%28China%29%22">Qinghai Sheng (China)</searchLink><br /><searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Because the ore-forming system in the crust of the Earth is a highly nonlinear system, geochemical anomaly classification is very important for improving the accuracy of metallogenic prediction. Further, since the distribution of the geochemical elements usually presents nonlinear characteristics due to the complexity and uncertainty of geology factors, the traditional linear data processing method has limited applications for an ore-forming system. The FastICA algorithm is applied to preprocessed geochemical data to reduce the interference information between elements. On the basis of obtaining the separated geochemical elements, the continuity of the spatial distribution of geochemical elements is considered, and combined with the application of the total variation (TV) in image processing; thus the total variation is introduced when processing geochemical data for anomaly analysis to eliminate the influence of singular geochemical data values. To measure the spatial distribution of geochemical elements, assays of the 1:10000 soil geochemical data in the area of Dachaidan in the Qinghai province of China are processed. The elemental anomaly zoning sequences are divided into three levels of anomaly: 85%, 90% and 95%. The anomaly isograms of Au and Cu processed by FastICA and the total variation algorithm predict the geological background of the study area better than the traditional cumulative frequency method. These results indicate that the application of the FastICA algorithm and the total variation algorithm to process geochemical data processing is valid and effective. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s12145-019-00412-0
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 153
    Subjects:
      – SubjectFull: Electronic data processing
        Type: general
      – SubjectFull: Crust of the earth
        Type: general
      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Metallogeny
        Type: general
      – SubjectFull: Qinghai Sheng (China)
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: FastICA and total variation algorithm for geochemical anomaly extraction.
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            NameFull: Liu, Bin
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            NameFull: Zhou, Zhongli
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            NameFull: Dai, Qilin
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            NameFull: Tong, Wei
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          Dates:
            – D: 01
              M: 03
              Text: Mar2020
              Type: published
              Y: 2020
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              Value: 18650473
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              Value: 13
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              Value: 1
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            – TitleFull: Earth Science Informatics
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