Bayesian Methods Applied to Survey Data From Archeological Magnetometry

“…Details of the data modelling process, and the definition of blur matrix H in particular, can be found in Aykroyd et al (2001) and Aykroyd and Al-Gezeri (2014).…”

Section: Introductionmentioning

confidence: 99%

Spatially adaptive Bayesian image reconstruction through locally-modulated Markov random field models

Al-Gezeri¹,

Aykroyd²

2019

Braz. J. Probab. Stat.

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“…Apparent-susceptibiity mapping ͑e.g., Urquhart and Strangway, 1985͒ consists of reduction to the pole of the total field, downward continuation, and recovery of the distribution of the apparent magnetization by dividing by the shape factor of the prism. The 2D deconvolution approach consists of the convolution of the total field with an optimum filter operator ͑Tsokas and Papazachos, 1992͒ or a stochastic approach ͑Aykroyd et al, 2001;Glazunov and Efimova, 2003͒. In both cases, some lowpass filtering must be applied to suppress high-frequency noise.…”

Section: Introductionmentioning

confidence: 99%

Iterative deconvolution and semiblind deconvolution methods in magnetic archaeological prospecting

et al. 2009

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In archaeological magnetic prospecting, most targets can be modeled by a single layer of constant burial depth and thickness. With this assumption, recovery of the magnetization distribution of the buried layer from magnetic surface measurements is a 2D deconvolution problem. Because this problem is ill posed, it requires regularization techniques to be solved. In analogy with image reconstruction, the solution showing the resolved subsoil features can be considered a focused version of the blurred and noisy magnetic image. Exploiting image deconvolution tools, two iterative reconstruction methods are applied to minimize the least-squares functional: the standard projected Landweber method and a proposed modification of the iterative space reconstruction algorithm. Different regularization functionals inject a priori information in the optimization problem, and the split-gradient method modifies the algorithms. Numerical simulations in the case of perfect knowledge of the impulse response functions demonstrate that the edge-preserving, total-variation functionals give the best results. An iterative semiblind deconvolution method to estimate the burial depth of the source layer was used with a real data set to test the effectiveness of the method.

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Bayesian Methods Applied to Survey Data From Archeological Magnetometry

Cited by 9 publications

References 13 publications

The identification of shipwreck sites: a Bayesian approach

The identification of shipwreck sites: a Bayesian approach

Spatially adaptive Bayesian image reconstruction through locally-modulated Markov random field models

Iterative deconvolution and semiblind deconvolution methods in magnetic archaeological prospecting

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