Inversion of 2-D DC resistivity data using rapid optimization and minimal complexity neural network

Abstract

Abstract. The backpropagation (BP) artificial neural network (ANN) technique of optimization based on steepest descent algorithm is known to be inept for its poor performance and does not ensure global convergence. Nonlinear and complex DC resistivity data require efficient ANN model and more intensive optimization procedures for better results and interpretations. Improvements in the computational ANN modeling process are described with the goals of enhancing the optimization process and reducing ANN model complexity. Well-established optimization methods, such as Radial basis algorithm (RBA) and Levenberg-Marquardt algorithms (LMA) have frequently been used to deal with complexity and nonlinearity in such complex geophysical records. We examined here the efficiency of trained LMA and RB networks by using 2-D synthetic resistivity data and then finally applied to the actual field vertical electrical resistivity sounding (VES) data collected from the Puga Valley, Jammu and Kashmir, India. The resulting ANN reconstruction resistivity results are compared with the result of existing inversion approaches, which are in good agreement. The depths and resistivity structures obtained by the ANN methods also correlate well with the known drilling results and geologic boundaries. The application of the above ANN algorithms proves to be robust and could be used for fast estimation of resistive structures for other complex earth model also.

Imaging Rainfall Infiltration Processes with the Time-Lapse Electrical Resistivity Imaging Method

“…Electrical resistivity imaging (ERI) has been widely used in resource exploration, hydrogeology surveys, engineering geology surveys, and environment geology surveys (AHMED and SULAIMAN 2001;CHANG et al 2012;DE BARI et al 2011;DRAHOR et al 2011;FIKOS et al 2012;HERMANS et al 2012a, b;LEHMANN et al 2013;LONG et al 2006;MAITI et al 2012;MARTINEZ-PAGAN et al 2010;METWALY et al 2013;MUCHINGAMI et al 2012;PERRONE et al 2014;PUJARI et al 2007;REVIL et al 2010;SINGH et al 2010;SIRHAN and HAMIDI 2013;SONKAMBLE 2014;TANG et al 2007;TRAVELLETTI et al 2012). It can also be used for monitoring the groundwater flow and river water discharge patterns (COSCIA et al 2011(COSCIA et al , 2012HAYLEY et al 2009;SUZUKI and HIGASHI 2001).…”

Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image

“…The three-layer network structure was adopted in this work (see figure 2) as a single hidden layer has been proved adequate for land cover classification (Paola andSchowengerdt 1995, Huang et al 2002). Baum and Haussler (1989) had suggested an approach to calculate the number of nodes in the hidden layer, but actually there are no rules existing for determining the exact number of neurons in a hidden layer (Singh et al 2010). We have taken 30 neurons in our application.…”

ANN based inversion of DC resistivity data for groundwater exploration in hard rock terrain of western Maharashtra (India)

“…These schemes have been widely applied to solve non-linear problems in almost all branches of geophysics (e.g., Van der Bann and Jutten, 2000;Poulton, 2001). For example: (1) for seismic event classification (Dystart and Pulli, 1990), (2) well log analysis (Aristodemou et al, 2005;Maiti et al, 2007;Tiwari, 2009, 2010b), (3) first arrival picking (Murat and Rudman, 1993), (4) earthquake time series modeling (Feng et al, 1997), (5) inversion (Raiche, 1991;Devilee et al, 1999), (6) parameter estimation in geophysics (Macias et al, 2000), (7) prediction of aquifer water level (Coppola et al, 2005;Tsanis et al, 2008), (8) magneto-telluric data inversion (Spichak and Popova, 2000), (9) magnetic interpretations (Bescoby et al, 2006), (10) signal discrimination (Maiti and Tiwari, 2010a), (11) DC resistivity inversion (Qady and Ushijima, 2001;Singh et al, 2010;Maiti et al, 2011). There are, however, several limitations in conventional neural network approaches (Bishop, 1995;Maiti and Tiwari, 2009).…”