Bayesian methods applied to survey data from archeological magnetometry

doi:10.1198/016214501750332983

Bayesian methods applied to survey data from archeological magnetometry

2001-03-01

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DOI：10.1198/016214501750332983

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Abstract

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A model is presented for the interpretation of magnetometer data in terms of archeological features beneath the ground. it describes the detector's response to the assemblage of buried features, incorporating both a spread function and a statistical error process as well as appropriate prior beliefs about the nature of archeological features. The problem is to estimate the magnetic susceptibility of the buried features at each horizontal location. A Monte Carlo Markov chain approach is used to estimate magnetic susceptibilities and all prior parameters. This requires estimation of the normalization constant of the Gibbs prior distribution. The approach is illustrated with both simulated data and measurements from an archeological rite. In the latter case, the reconstruction of the buried features corresponds well with the archeologist's observations during subsequent excavation.

Keywords:

archeology

geophysics

image reconstruction

inverse data problems

metropolis-hasting algorithm

Monte Carlo Markov chain

normalizing constant

Bayesian methods applied to survey data from archeological magnetometry

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