GIS-supported certainty factor (CF) models for assessment of geothermal potential: A case study of Tengchong County, southwest China

Jianming Li and Yanjun Zhang

Energy, 2017, vol. 140, issue P1, 552-565

Abstract: Promising geothermal areas were identified according to the relationship between geothermal emergencies and the affected surroundings in Tengchong County, China. It is expected that the study will guide further preliminary investigations performed over large areas with limited information. Publicly available datasets that were used in this analysis included earthquake epicenters, distribution of faults, Bouguer gravity anomalies, magnetic anomalies and Landsat7 ETM + images were used to generate five impact factor maps; b-value, distance to faults, distance to major grabens, magnetic anomaly, and land surface temperature, respectively. Predictor maps were produced separately from the impact factor maps using modified certainty factor, index overlay of certainty factor, and weight of certainty factor methods. The findings revealed that the modified certainty factor method showed a more accurate prediction, and the index overlay of certainty factor method can be applied in a simple and straightforward manner, and the weight of certainty factor has the advantage of objective and realistic applications. Based on the suitability maps, potential geothermal regions were discovered in Nujiang basin where have not been explored and exploited.

Keywords: GIS; Certainty factor (CF); Geothermal potential; Gutenberg-Richter rule; Kappa analysis (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544217315189
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:140:y:2017:i:p1:p:552-565

DOI: 10.1016/j.energy.2017.09.012

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().