Inversion of self-potential anomalies caused by 2D inclined sheets using neural networks

Abstract

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 (x o) 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.

Interpretation of magnetic anomalies due to dipping dikes using neural network inversion

“…It is known that the NNs are a class of universal approximators which can approximate any function in terms of its variables (Al-Garni 2009, 2013El-Kaliouby and Al-Garni 2009). Therefore, they may yield significant contributions to finding solutions to a variety of geophysical applications (Macias et al 2000;Poulton 2001;Al-Garni 2009, 2013.…”

“…In approximating functions, NN models can be more accurate than polynomial regression models, allowing mainly two imperative things: more dimensions than look-up table models and multiple outputs for a single model (Al-Garni 2009, 2013El-Kaliouby and Al-Garni 2009). NN models are developed by providing sufficient data, simulated or field data, from which they learn the underlying input/output mapping (El-Kaliouby and AlGarni 2009).…”

“…In unsupervising, where the desired network output is unknown, the network is fed only with input patterns and it finds the common features in those pattern groups (Poulton 2001). In other words, the network performs a kind of data compression such as dimensionality reduction of clustering; therefore, the NN learns the pattern distribution and makes a classification of that pattern where similar patterns are assigned to the same output clusters (Poulton 2001;Al-Garni 2009, 2013El-Kaliouby and Al-Garni 2009).…”

Inversion of residual gravity anomalies using tuned PSO

“…However, PSO has been successfully applied to many fields, such as model construction, biomedical images, electromagnetic optimization, hydrological problems, etc. (Cedeno and Agrafiotis, 2003;Wachowiak et al, 2004;Boeringer and Werner, 2004;Kumar and Reddy, 2007;Eberhart and Shi, 2001;El-Kaliouby and Al-Garni, 2009) but in the geophysical field PSO has a limited number of applications (Alvarez et al, 2006;Shaw and Srivastava, 2007).…”

Electrokinetic Techniques for the Determination of Hydraulic Conductivity

“…These studies involve surface or borehole measurements (Aubert & Atangana, 1996;Fagerlund & Heison, 2003;Finizola et al, 2003;Perrier et al, 1998;Pinettes et al, 2002), some of them have monitored self-potentials during hydraulic tests in boreholes Ishido et al, 1983;Maineult et al, 2008). Direct models Electrokinetic Techniques for the Determination of Hydraulic Conductivity (Ishido & Pritchett, 1999;Jouniaux et al, 1999;Sheffer & Oldenburg, 2007) and inverse problems (El-kaliouby & Al-Garni, 2009;Fernandez-Martinez et al, 2010;Gibert & Pessel, 2001;Gibert & Sailhac, 2008;Minsley et al, 2007;Naudet et al, 2008;Sailhac et al, 2004;Saracco et al, 2004) have been developed to locate the source of self-potential. Because of similarity between the electrical potential with pressure behavior, it has been proposed also to use SP measurements as an electrical flow-meter (Pezard et al, 2009).…”