Discrete cosine transform for parameter space reduction in bayesian electrical resistivity tomography
نویسندگان
چکیده
Electrical resistivity tomography is a non-linear and ill-posed geophysical inverse problem that usually solved through gradient-descent methods. This strategy computationally fast easy to implement but impedes accurate uncertainty appraisals. We present probabilistic approach two-dimensional electrical in which Markov chain Monte Carlo algorithm used numerically evaluate the posterior probability density function fully quantifies affecting recovered solution. The main drawback of approaches related considerable number sampled models needed achieve assessments high-dimensional parameter spaces. Therefore, reduce computational burden inversion process, we employ differential evolution chain, hybrid method between optimization sampling, exploits multiple interactive chains speed up sampling. Moreover, discrete cosine transform reparameterization employed dimensionality space removing high-frequency components model are not sensitive data. In this framework, unknown parameters become series coefficients associated with retained basis functions. First, synthetic data inversions validate proposed demonstrate benefits provided by compression. To end, compare outcomes implemented those running full, un-reduced space. Then, apply invert field acquired along river embankment. results yielded also benchmarked against standard local algorithm. Bayesian provides mean agreement predictions achieved gradient-based inversion, it uncertainties, can be for penetration depth resolution limit identification.
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ژورنال
عنوان ژورنال: Geophysical Prospecting
سال: 2021
ISSN: ['1365-2478', '0016-8025']
DOI: https://doi.org/10.1111/1365-2478.13148