Inversion of Schlumberger resistivity sounding data from the critically dynamic Koyna region using the Hybrid Monte Carlo-based neural network approach

Abstract

Abstract. Koyna region is well-known for its triggered seismic activities since the hazardous earthquake of M = 6.3 occurred around the Koyna reservoir on 10 December 1967. Understanding the shallow distribution of resistivity pattern in such a seismically critical area is vital for mapping faults, fractures and lineaments. However, deducing true resistivity distribution from the apparent resistivity data lacks precise information due to intrinsic non-linearity in the data structures. Here we present a new technique based on the Bayesian neural network (BNN) theory using the concept of Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) simulation scheme. The new method is applied to invert one and two-dimensional Direct Current (DC) vertical electrical sounding (VES) data acquired around the Koyna region in India. Prior to apply the method on actual resistivity data, the new method was tested for simulating synthetic signal. In this approach the objective/cost function is optimized following the Hybrid Monte Carlo (HMC)/Markov Chain Monte Carlo (MCMC) sampling based algorithm and each trajectory was updated by approximating the Hamiltonian differential equations through a leapfrog discretization scheme. The stability of the new inversion technique was tested in presence of correlated red noise and uncertainty of the result was estimated using the BNN code. The estimated true resistivity distribution was compared with the results of singular value decomposition (SVD)-based conventional resistivity inversion results. Comparative results based on the HMC-based Bayesian Neural Network are in good agreement with the existing model results, however in some cases, it also provides more detail and precise results, which appears to be justified with local geological and structural deCorrespondence to: S. Maiti (saumen maiti2002@yahoo.co.in) tails. The new BNN approach based on HMC is faster and proved to be a promising inversion scheme to interpret complex and non-linear resistivity problems. The HMC-based BNN results are quite useful for the interpretation of fractures and lineaments in seismically active region.

Geoelectric investigation to delineate groundwater potential and recharge zones in Suki river basin, north Maharashtra

“…This method provides the apparent resistivity distribution against depth. The depth of penetration of electrical signal is generally found to be approximately one-third of the distance between the electrode separations (Dahlin 2000;Maiti et al 2011). This method is carried out to decipher problems of aquifer in hard formation such as the Deccan Trap region.…”

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

“…In view of the established utility of resistivity method in locating and demarcating the fractured and weathered zones especially in the Tenduli-Vengurla, Konkan region, it was worth to conduct electrical resistivity experiment over the area. The Direct Current (DC) resistivity sounding method has extensively been used for solving hydrological, geothermal, environmental and engineering problems (Zohdy, 1989;Gupta et al, 2010b;Maiti et al, 2011). The method provides the apparent resistivity distribution against depth.…”

“…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).…”

Thin-sheet electromagnetic modeling of magnetovariational data for a regional-scale study

“…The MCMC inversion technique has previously been applied for geo-electromagnetic data in relatively simple 1D models with satisfactory results, for example, magnetotelluric (MT; Grandis et al 1999;Guo et al 2011), DC resistivity (Schott et al 1999;Maiti et al 2011), and Controlled-Source Audio-frequency MT (CSAMT; Grandis and Sumintadiredja 2013) studies. Despite the simplicity of the 1D models in geoelectromagnetics, their inversions involve highly nonlinear problems demanding non-linear or global search approaches to avoid fundamental limitations of the linearized approach (Sen and Stoffa 1996;Sambridge and Mosegaard 2002).…”