Inversion of self-potential anomalies caused by simple-geometry bodies using global optimization algorithms

An efficient regularized inversion approach for self-potential data interpretation of ore exploration using a mix of logarithmic and non-logarithmic model parameters

“…They successfully applied this approach to four field examples from mining geophysics, and concluded that the method is robust, but can be affected by the noise level to some degree. Gokturkler and Balkaya (2012) described three global optimization approaches based on stochastic algorithms (genetic algorithm (GA), simulated annealing (SA), and particle swarm optimization (PSO)) to invert SP anomalies by some polarized bodies of simple geometries, and concluded that the inverse parameters recovered by the GA, SA and PSO are in good agreement. Fedi and Abbas (2013) developed a fast imaging technique (the so called depth from extreme points (DEXP) method) based on the upward continuation to interpret SP anomalies.…”

Inversion of Source Parameters from Magnetic Anomalies for Mineral/Ore Deposits Exploration Using Global Optimization Technique and Analysis of Uncertainty

“…Many linear and linearized inversions such as least squares, linearized least squares, normalized local wave number method, analytic signal derivatives, second-horizontal derivatives, Euler deconvolution method, simplex algorithm, fair function minimization have also been developed (McGrath and Hood 1973;Silva 1989;Salem and Ravat 2003;Salem et al 2004;Salem 2005;Salem and Smith 2005;Tlas and Asfahani 2011a, b;Abdelrahman and Essa 2015;Tlas and Asfahani 2015). Global optimization methods such as simulated annealing, very fast simulated annealing, regularized inversion, particle swarm optimization and higher-order horizontal derivative methods (Gokturkler and Balkaya 2012;Sharma and Biswas 2013;Biswas and Sharma 2014a, b;Mehanee 2014a, b;Biswas 2015;Singh and Biswas 2015;Biswas 2016;Biswas and Acharya 2016;Ekinci 2016) have been effectively applied to solve nonlinear parametric inversion problems. A combined work of various other modeling methods can be found in Abo-Ezz and Essa (2016), Abdelrahman and Essa (2005) and Abdelrahman et al (2003Abdelrahman et al ( , 2009Abdelrahman et al ( , 2012.…”

A very fast simulated annealing method for inversion of magnetic anomaly over semi-infinite vertical rod-type structure

“…The interpretation methods include curves matching (Gay 1963(Gay , 1965McGrath 1970), Fourier transform (Bhattacharyya 1965), Hilbert transforms (Mohan et al 1982), monograms (Prakasa Rao et al 1986), least squares minimization (McGrath and Hood 1973;Silva 1989), characteristic points and distance approaches (Grant and West 1965;Abdelrahman 1994), correlation factors between successive least-squares residual anomalies (Abdelrahman and Sharafeldin 1996), Henkel transform (Singh et al 2000), linearized least squares (Salem et al 2004), normalized local wave number (Salem and Smith 2005), analytic signal derivatives (Salem 2005), Euler deconvolution (Salem and Ravat 2003), Fair function minimization (Tlas and Asfahani 2011a), deconvolution technique (Tlas and Asfahani 2011b), secondhorizontal derivatives (Abdelrahman and Essa 2015), Simplex algorithm (Tlas and Asfahani 2015). Also, simulated annealing (Gokturkler and Balkaya 2012), very fast simulated annealing (Sharma and Biswas 2013a;Sharma 2014a, b, 2015;Biswas 2015), Particle swarm optimization (Singh and Biswas 2016) have been effectively used to solve similar nonlinear inversion problems of geometrically simple bodies.…”