2D Basement Relief Inversion using Sparse Regularization

Basement relief gravimetry is crucial in geophysics, especially for oil exploration and mineral prospecting. It involves solving an inverse problem to infer geological model parameters from observed data. The model represents basement relief with constant-density prisms, and the data reflect gravita...

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Bibliographic Details
Published in:arXiv.org 2024-10
Main Authors: Barboza, Francisco Márcio, Arthur Anthony da Cunha Romão E Silva, Motta de Carvalho, Bruno
Format: Article
Language:English
Subjects:
Anomalies
Basements
Constraints
Discrete cosine transform
Discrete Wavelet Transform
Error analysis
Genetic algorithms
Geophysics
Inverse problems
Oil exploration
Prisms
Regularization
Regularization methods
Smoothness
Wavelet transforms
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Summary:Basement relief gravimetry is crucial in geophysics, especially for oil exploration and mineral prospecting. It involves solving an inverse problem to infer geological model parameters from observed data. The model represents basement relief with constant-density prisms, and the data reflect gravitational anomalies from these prisms. Inverse problems are often ill-posed, meaning small data changes can lead to large solution variations. To mitigate this, regularization techniques like Tikhonov's are used to stabilize solutions. This study compares regularization methods applied to gravimetric inversion, including Smoothness Constraints, Total Variation, Discrete Cosine Transform (DCT), and Discrete Wavelet Transform (DWT) using Daubechies D4 wavelets. Optimization, particularly with Genetic Algorithms (GA), is used to find prism depths that best match observed anomalies. GA, inspired by natural selection, selects the best solutions to minimize the objective function. The results, evaluated through fit metrics and error analysis, show the effectiveness of all regularization methods and GA, with the Smoothness constraint performing best in synthetic models. For the real data model, all methods performed similarly.
ISSN:2331-8422