Gravity inversion of basement relief constrained by the knowledge of depth at isolated points

Abstract

We present an interpretation method for the gravity anomaly of an arbitrary interface separating two homogeneous media. It consists essentially of a downward continuation of the observed anomaly and the division of the continued anomaly by a scale factor involving the density contrast between the media. The knowledge of the interface depth at isolated points is used to estimate the depth [Formula: see text] of the shallowest point of the interface, the density contrast Δρ between the two media, and the coefficients [Formula: see text] and [Formula: see text] of a first‐order polynomial representing a linear trend to be removed from data. The solutions are stabilized by introducing a damping parameter in the computation of the downward‐continued anomaly by the equivalent layer method. Different from other interface mapping methods using gravity data, the proposed method: (1) takes into account the presence of an undesirable linear trend in data; (2) requires just intervals for both Δρ (rather than the knowledge of its true value) and coefficients [Formula: see text] and [Formula: see text]; and (3) does not require the knowledge of the average interface depth [Formula: see text]. As a result of (3), the proposed method does not call for extensive knowledge of the interface depth to obtain a statistically significant estimate of [Formula: see text]; rather, it is able to use the knowledge of the interface depth at just a few isolated points to estimate [Formula: see text], Δρ, [Formula: see text], and [Formula: see text]. Tests using synthetic data confirm that the method produces good and stable estimates as far as the established premises (smooth interface separating two homogeneous media and, at most, the presence of an unremoved linear trend in data) are not violated. If the density contrast is not uniform, the method may still be applied using Litinsky’s concept of effective density. The method was applied to gravity data from Recôncavo Basin, Brazil, producing good correlations of estimated lows and terraces in the basement with corresponding known geological features.

Distribution of subglacial sediments across the Wilkes Subglacial Basin, East Antarctica

“…While depth to magnetic basement and radar roughness estimates can be utilized to constrain vertical and horizontal subglacial sediment distribution models, respectively, inversion of gravity anomalies is inherently a nonunique solution with sizeable trade‐offs between density and geometry. Using a nonlinear approach to solving the inverse problem, density contrasts were established between geologic interfaces and the inversion constrained with depths at isolated points along the interface [ Leao et al ., ]. Despite this, gravity inversion equations remain ill posed, such that instead of presenting a finite solution uncertainty estimates were quantified to assess the validity of the inverse problem.…”

Estimating free parameters in 2D inversion: example of gravity inversion in a rifted basement

“…Figure shows the regional Bouguer anomaly map of the Recôncavo Basin, presented by Leão et al . (). The original survey configuration data have been not available.…”

Reconstruction of geologic bodies in depth associated with a sedimentary basin using gravity and magnetic data

“…This difficulty is caused not only by the spectral overlap between the gravity or magnetic responses of the shallow and deep sources but also by the arbitrary cut‐off frequency of the low‐pass or high‐pass filter. As a result, no spectral‐based approach can overcome this hindrance: 2) regional‐residual separation methods, which include: a) filters based on the spectral content of the anomaly (Syberg 1972) and b) polynomial fitting (Rao, Radhakrishna Murthy and Visweswara Rao 1975; Beltrão, Silva and Costa 1991; Leão et al 1996).…”

“…Robust polynomial fitting (Beltrão et al 1991), on the other hand, minimizes this effect by imposing that the residual anomalies have predominantly a single signal (either positive or negative). The incorporation of this additional a priori information about the residual anomaly greatly improves the performance of the standard polynomial fitting by allowing complex, high‐order polynomials to approximate the regional anomaly (e.g., Leão et al 1996).…”