The Knowledge Driven as Fundamental Analysis in The Geothermal Exploration Activity: Dieng Geothermal Field Study Area

Dwi Wahyu Hardiyanto; Agung Setianto; Agung Harijoko

doi:10.22146/ijg.102388

The Knowledge Driven as Fundamental Analysis in The Geothermal Exploration Activity: Dieng Geothermal Field Study Area

https://doi.org/10.22146/ijg.102388

Dwi Wahyu Hardiyanto⁽¹⁾, Agung Setianto^(2*), Agung Harijoko⁽³⁾

(1) Department of Geological Engineering, Faculty of Engineering, Gadjah Mada University
(2) Department of Geological Engineering, Faculty of Engineering, Gadjah Mada University
(3) Department of Geological Engineering, Faculty of Engineering, Gadjah Mada University
(*) Corresponding Author

Abstract

Geothermal exploration activities involve a high degree of uncertainty and financial risk, thereby requiring exploration analyses that can support accurate decision-making. This study presents a geothermal exploration method for determining the Geothermal Potential Index (GPI) by integrating surface geological, geophysical, and geochemical data. The main problem addressed in this study is the testing of a method for conducting geothermal exploration by integrating multiple parameters. Accordingly, this research applies a knowledge-driven approach through the Analytic Hierarchy Process (AHP) to develop a tentative Geothermal Potential Index (GPI) model. The Analytical Hierarchy Process (AHP) serves as a reliable methodological framework for assessing the Geothermal Potential Index by integrating newly obtained and pre-existing data. By incorporating suitability analysis, identifying prospective resources, and formulating geothermal resource criteria, this approach establishes a solid basis for systematic evaluation during the geothermal exploration phase. The tentative model of the geothermal potential index (GPI) for the Dieng volcanic complex has been identified into five zones: Pagerkandang Zone, Siglagah Zone, Pangonan-Merdada Zone, Campursari Zone, and Pakuwaja Zone.

Keywords

Geothermal exploration; spatial information analysis; knowledge driven analysis; Dieng

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DOI: https://doi.org/10.22146/ijg.102388

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