Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model
Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to charac...
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| Format: | Article |
| Language: | English |
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Wiley
2015-01-01
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| Series: | International Journal of Geophysics |
| Online Access: | http://dx.doi.org/10.1155/2015/134834 |
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| author | A. Stanley Raj D. Hudson Oliver Y. Srinivas |
| author_facet | A. Stanley Raj D. Hudson Oliver Y. Srinivas |
| author_sort | A. Stanley Raj |
| collection | DOAJ |
| description | Soft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms of fuzzy logic, namely, fuzzy C-means clustering, fuzzy K-means clustering, and fuzzy subtractive clustering, were analyzed with the help of fuzzy inference system (FIS) training on synthetic data. Here in this approach, graphical user interface (GUI) was developed with the integration of three algorithms and the input data (AB/2 and apparent resistivity), while importing will process each algorithm and interpret the layer model parameters (true resistivity and depth). A complete overview on the three above said algorithms is presented in the text. It is understood from the results that fuzzy logic subtractive clustering algorithm gives more reliable results and shows efficacy of soft computing tools in the inversion of geoelectrical resistivity data. |
| format | Article |
| id | doaj-art-952e432606584b7e90ad89de53da3da8 |
| institution | Kabale University |
| issn | 1687-885X 1687-8868 |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Geophysics |
| spelling | doaj-art-952e432606584b7e90ad89de53da3da82025-02-03T05:44:55ZengWileyInternational Journal of Geophysics1687-885X1687-88682015-01-01201510.1155/2015/134834134834Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer ModelA. Stanley Raj0D. Hudson Oliver1Y. Srinivas2Department of Physics, Vel Tech University, Avadi, Chennai 600062, IndiaDepartment of Physics, Senthamarai College of Arts and Science, Palkalai Nagar, Madurai 625021, IndiaCentre for Geotechnology, Manonmaniam Sundaranar University, Tirunelveli, Tamil Nadu 627012, IndiaSoft computing based geoelectrical data inversion differs from conventional computing in fixing the uncertainty problems. It is tractable, robust, efficient, and inexpensive. In this paper, fuzzy logic clustering methods are used in the inversion of geoelectrical resistivity data. In order to characterize the subsurface features of the earth one should rely on the true field oriented data validation. This paper supports the field data obtained from the published results and also plays a crucial role in making an interdisciplinary approach to solve complex problems. Three clustering algorithms of fuzzy logic, namely, fuzzy C-means clustering, fuzzy K-means clustering, and fuzzy subtractive clustering, were analyzed with the help of fuzzy inference system (FIS) training on synthetic data. Here in this approach, graphical user interface (GUI) was developed with the integration of three algorithms and the input data (AB/2 and apparent resistivity), while importing will process each algorithm and interpret the layer model parameters (true resistivity and depth). A complete overview on the three above said algorithms is presented in the text. It is understood from the results that fuzzy logic subtractive clustering algorithm gives more reliable results and shows efficacy of soft computing tools in the inversion of geoelectrical resistivity data.http://dx.doi.org/10.1155/2015/134834 |
| spellingShingle | A. Stanley Raj D. Hudson Oliver Y. Srinivas Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model International Journal of Geophysics |
| title | Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model |
| title_full | Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model |
| title_fullStr | Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model |
| title_full_unstemmed | Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model |
| title_short | Geoelectrical Data Inversion by Clustering Techniques of Fuzzy Logic to Estimate the Subsurface Layer Model |
| title_sort | geoelectrical data inversion by clustering techniques of fuzzy logic to estimate the subsurface layer model |
| url | http://dx.doi.org/10.1155/2015/134834 |
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