A MATLAB-based inversion program, b-Spline Polynomial Approximation using Differential Evolution Algorithm (SPODEA), is introduced to recover the concealed basement geometry under heterogeneous sedimentary basins. Earlier inversion techniques used the discretized subsurface interface topography into a grid of juxtaposed elementary prisms to estimate the basement depth of a basin. Such discretization leads to the failure of depth profile continuity and requires a higher number of inversion parameters for achieving the desired accuracy. The novel approach of SPODEA overcomes such limitations of earlier inversion techniques. SPODEA is based on the segment-wise b-spline optimization technique to estimate the basement depth by using high-order polynomials.Moreover, it can achieve an optimal misfit with a minimal parametric information, which reduces the computational expense. The proposed inversion approach uses the differential evolution (DE) algorithm that provides real parametric optimization and uses b-splines for an accurate estimation of continuous depth profiles. The efficiency of the proposed algorithm is illustrated with two complex synthetic sedimentary basin models comprised of constant and depth varying density distributions. Furthermore, the uncertainty analysis of the proposed inversion technique is evaluated by incorporating white Gaussian noise into the synthetic models. Finally, the utility of SPODEA is illustrated by inverting gravity anomalies for two different real sedimentary basins. It produces geologically reasonable outcomes that are in close agreement with basement structures from previously reported results.
A Matlab‐based optimization algorithm is introduced for inverting fault structures from observed gravity anomalies. A convenient graphical user interface is also presented for incorporating the input parameters without any technical complexity to any users. The inversion code uses particle swarm optimization, and all control parameters are tuned initially for faster convergence. There is no requirement of prior choice of an initial model, that is the advantage of using global optimization. The optimization technique is versatile enough to handle any depth‐varying density distributions. The maximum number of iterations and stopping criterion is fixed initially for getting the best optimized solution. The inverted model's output in terms of fault structure, observed and inverted gravity anomalies and dip, and vertex location of fault plane can be viewed in the graphical user interface at the end of the optimization process. The optimization algorithm is applied to different synthetic models with fixed and depth‐varying density contrasts. All synthetic models are further contaminated with white Gaussian noise for sensitivity analysis, and detailed uncertainty appraisal was also performed for the reliability estimation. Finally, the optimization is implemented for fault structure inversion of the Aswaraopet boundary fault, India, and found that the optimized solution provides a good agreement with the previously published literature. Optimized results indicate that this novel optimization approach demonstrates a robust implementation of fault inversion for any depth‐varying density distributions.
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