Enhanced interpretation of magnetic survey data from archaeological sites using artificial neural networks

Bescoby, David J., Cawley, Gavin C. ORCID: https://orcid.org/0000-0002-4118-9095 and Chroston, P. Neil (2006) Enhanced interpretation of magnetic survey data from archaeological sites using artificial neural networks. Geophysics, 71 (5). pp. 45-53. ISSN 1942-2156

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The use of magnetic surveys for archaeological prospecting is a well-established and versatile technique, and a wide range of data processing routines are often applied to further enhance acquired data or derive source parameters. Of particular interest in this respect is the application of artificial neural networks (ANNs) to predict source parameters such as the burial depths of detected features of interest. Within this study, ANNs based upon a multilayer perceptron architecture are used to perform the nonlinear mapping between buried wall features detected within the magnetic data and their corresponding burial depth for surveys in the ancient city of Butrint in southern Albania, achieving a greater level of information from the survey data.Suitable network training examples and test data were generated using forward models based upon ground-truth observations. The training procedure adopts a supervised learning routine that is optimized using a conjugate gradient method, while the learning algorithm also prunes network elements to prevent overregularization by reducing model complexity. Data processing was further enhanced by introducing rotational invariance using Zernike moments and by utilizing the combined output of a number, or committee, of networks. When applied to a section of survey data from Butrint, the ANN routine successfully predicted the burial depth of a number of detected wall features, with an rms error on the order of 0.20 m, and provided a coherent map of the buried building foundations. The neural network approach offered advantages in terms of efficiency and flexibility over more conventional data-inversion techniques within the context of the study, giving fast solutions for large, complex data sets while having high noise tolerance.

Item Type: Article
Faculty \ School: Faculty of Science > School of Environmental Sciences
Faculty of Arts and Humanities > School of Art History and World Art Studies (former - to 2014)
Faculty of Science > School of Computing Sciences

UEA Research Groups: Faculty of Science > Research Groups > Data Science and Statistics
Faculty of Science > Research Groups > Computational Biology
Faculty of Science > Research Groups > Centre for Ocean and Atmospheric Sciences
Faculty of Science > Research Groups > Geosciences
Depositing User: EPrints Services
Date Deposited: 01 Oct 2010 13:42
Last Modified: 06 Jun 2023 00:08
URI: https://ueaeprints.uea.ac.uk/id/eprint/3892
DOI: 10.1190/1.2231110

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