3-D Inversion Of Borehole-To-Surface Electrical Data Using A Back-Propagation Neural Network
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Journal Article: 3-D Inversion Of Borehole-To-Surface Electrical Data Using A Back-Propagation Neural Network
Abstract
The "fluid-flow tomography", an advanced technique for geoelectrical survey based on the conventional mise-a-la-masse measurement, has been developed by Exploration Geophysics Laboratory at the Kyushu University. This technique is proposed to monitor fluid-flow behavior during water injection and production in a geothermal field. However data processing of this technique is very costly. In this light, this paper will discuss the solution to cost reduction by applying a neural network in the data processing. A case study in the Takigami geothermal field in Japan will be used to illustrate this. The achieved neural network in this case study is three-layered and feed-forward. The most successful learning algorithm in this network is the Resilient Propagation (RPROP). Consequently, the study advances the pragmatism of the "fluid-flow tomography" technique which can be widely used for geothermal fields. Accuracy of the solution is then verified by using root mean square (RMS) misfit error as an indicator.
- Author
- Trong Long Ho
- Published Journal
- DOI
- 10.1016/j.jappgeo.2008.06.002
Citation
Trong Long Ho. 2009. 3-D Inversion Of Borehole-To-Surface Electrical Data Using A Back-Propagation Neural Network. Journal of Applied Geophysics.
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