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 
Journal of Applied Geophysics, 2009





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. (!) .