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Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks
The modular neural network (MNN) inversion method has been used for inversion of self-potential (SP) data anomalies caused by 2D inclined sheets of infinite horizontal extent. The analysed parameters are the depth (h), the half-width (a), the inclination (α), the zero distance from the origin (xo) and the polarization amplitude (k). The MNN inversion has been first tested on a synthetic example and then applied to two field examples from the Surda area of Rakha mines, India, and Kalava fault zone, India. The effect of random noise has been studied, and the technique showed satisfactory results. The inversion results show good agreement with the measured field data compared with other inversion techniques in use.
Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks
The modular neural network (MNN) inversion method has been used for inversion of self-potential (SP) data anomalies caused by 2D inclined sheets of infinite horizontal extent. The analysed parameters are the depth (h), the half-width (a), the inclination (α), the zero distance from the origin (xo) and the polarization amplitude (k). The MNN inversion has been first tested on a synthetic example and then applied to two field examples from the Surda area of Rakha mines, India, and Kalava fault zone, India. The effect of random noise has been studied, and the technique showed satisfactory results. The inversion results show good agreement with the measured field data compared with other inversion techniques in use.
Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks
Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks
Hesham M El-Kaliouby (Autor:in) / Mansour A Al-Garni (Autor:in)
Journal of Geophysics and Engineering ; 6 ; 29-34
01.03.2009
6 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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