Articles written in Journal of Earth System Science
Volume 130 All articles Published: 19 April 2021 Article ID 0073 Research Article
An automatic 3D spatial domain inversion technique is developed to estimate basement depths of sedimentary basins from observed gravity anomalies using a prescribed exponential mass density contrast. A collage of vertical polygonal cross-sections, each one with unit thickness, in which the density contrast differs exponentially with depth describes the model space. The proposed technique estimates the optimum depth ordinates of the vertices of polygonal cross-sections from a given set of gravity anomalies following predefined convergence criteria. Initial depths to basement interface at plurality of observations are calculated presuming that the density contrast within the Bouguer slab at each observation is also varying exponentially with depth. A previously reported algorithm that make use of both analytic and numeric approaches to compute the gravity response of such 3D model space with exponential mass density contrast is adopted for forward modelling. The proposed inversion is efficient even when the gravity anomalies are available at non-uniform spatial grid intervals. Recovery of basement depths with modest error from a set of gravity anomalies attributable to a synthetic model in the presence of pseudorandom noise and also the fact that the estimated depth structure of the Almazán Basin in NE Spain correlates reasonably well with the information derived from seismic data demonstrates the applicability of the proposed inversion method. The snags associated with other existing density models in the analysis of gravity anomalies are demonstrated on both synthetic and real field anomalies.
$\bullet$ It is a 3D inversion technique to analyse the gravity anomalies of sedimentary basins.
$\bullet$ Density contrast variation is automatically ascribed by an exponential function in the algorithm.
$\bullet$ The interpretation technique does not require initial model specification to start with.
$\bullet$ The algorithm is fully automatic.
Volume 131, 2022
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