3-D inversion of borehole-to-surface electrical data using a back-propagation neural network |
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| Authors: | Trong Long Ho |
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| Institution: | aGraduate School of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan |
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| Abstract: | The “fluid-flow tomography”, an advanced technique for geoelectrical survey based on the conventional mise-à-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. |
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| Keywords: | Mise-à -la-masse (MAM) 3-D MAM inversion Back-propagation neural network Takigami geothermal field “ fluid-flow tomography” method |
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