3D gravity inversion of basement relief — A depth-dependent density approach

Abstract

We present a 3D gravity inversion technique, based on the Marquardt algorithm, to analyze gravity anomalies attributable to basement interfaces above which the density contrast varies continuously with depth. The salient feature of this inversion is that the initial depth of the basement is not a required input. The proposed inversion simultaneously estimates the depth of the basement interface and the regional gravity background. Applicability and efficacy of the inversion is demonstrated with a synthetic model of a density interface. We analyze the synthetic gravity anomalies (1) solely because of the structure, (2) in the presence of a regional gravity background, and (3) in the presence of both random noise and regional gravity background. The inverted structure remains more or less the same, regardless of whether the regional background is simulated with a second-degree polynomial or a bilinear equation. The depth of the structure and estimated regional background deviate only modestly from the assumed ones in the presence of random noise and regional background. The analyses of two sets of real field data, one over the Chintalpudi subbasin, India, and another over the Pannonian basin, eastern Austria, yield geologically plausible models with the estimated depths that compare well with drilling data.

Estimation of multiple density-depth parameters from gravity inversion: Application to detached hanging wall systems of strike limited listric fault morphologies

“…Archaean gneisses (mean density 2.67 g/cm 3 ) form the basement for the Gondwana sequence within the sub-basin and towards the east the basin margin is associated with the well-known Aswaraopet master fault, which is exposed at the surface and strikes NNW-SSE over a length of 20 km (Figure 6a). Kaila et al (1990) have carried out Deep Seismic Sounding (DSS) investigations drilled a borehole (Figure 6a and 6c) within the basin and encountered Archaean basement density contrast-depth data measured from this borehole is shown in Figure 6b (Chakravarthi, (Figure 6c) was analyzed by Chakravarthi and Sundararajan (2007) for its basement structure using a 3D inversion.…”

Regularization parameter selection in the 3D gravity inversion of the basement relief using GCV: A parallel approach

“…The northern sub-basin has an elongated rhombohedral geometry northward and is limited by steep grades corresponding to normal faulting, whereas the southern sub-basin is a typical rift basin known as half-graben, with its eastern edge defined by a fault in the northwest-southeast direction. We adopted a first a priori model for which the depth is defined by a theoretical formula (Chakravarthi and Sundararajan, 2007), assuming that the value of the measured gravity at the geometrical center on each prism top is given by a horizontal infinite slab with parabolically varying density contrast. The value of the measured gravity is obtained through interpolation as the geometrical center on each prism top do not match any observation point.…”

“…In this study, we focus on the inversion methods for the second group. Chakravarthi and Sundararajan (2007), for example, have assumed that the density contrast decays with the depth according to a parabolic law and have developed an inversion scheme based on the Levenberg-Marquardt algorithm to estimate the regional gravity anomaly and the depth of the 3D basement relief of the sedimentary basins. In addition, within the second group, Martins et al (2010) have presented a gravity inversion technique to simultaneously estimate the 3D basement relief of the sedimentary basin and the parameters defining an assumed parabolic decrease in the density contrast with the depth, imposing the following two constraints: the smoothness on the basement and the proximity between the estimated and the known a priori depth values from the boreholes.…”

Three-Dimensional Gravity Inverse Modeling for Basement Depth Estimation Integrating Maximum Difference Reduction (MDR), Trend Surface Analysis (TSA) and Total Variation Regularization

“…In fact, the observed gravity data are the Bouguer anomaly and are usually separated into regional and residual anomalies before the inversion process. Trend surface analysis (TSA) is the regional-residual anomaly separation method that is commonly used to obtain the residual anomaly with appropriate magnitude and shape for modeling [11]. We included TSA in the inversion scheme by assuming that the regional anomaly is represented by a low-order (i.e.…”

“…The MDR method for 3D sedimentary basins was developed by employing the 3D gravity forward modeling from Blakely [2]. We reformulated the MDR3D algorithm in the form of the Gauss-Newton inversion method to allow the implementation of trend surface analysis (TSA) to solve the subjectivity problem during regionalresidual anomaly separation [11,12]. In addition, we also incorporated constraints as well as the total variation regularization concept in the same inversion scheme.…”