Selection of geophysical methods based on matter-element analysis with analytic hierarchy process

Abstract

The selection of the most appropriate method(s) for a specific engineering geophysical site characterisation can be challenging due to the complexity and potential ambiguity of the factors influencing the utility and cost-effectiveness of the methods. This paper, using matter-element analysis techniques with analytic hierarchy process (AHP), proposes a demonstration matter-element model for selecting the most appropriate geophysical methods, and establishes the general flow and calculation process for geophysical method selection. On the basis of the analysis of the factors influencing the utility and cost-effectiveness of geophysical methods, an evaluation criteria system is constructed. To demonstrate the application of this process, the proposed matter-element geophysical methods selection mode is applied to the selection of geophysical methods for an engineering geophysical site characterisation project that ultimately involved the acquisition of electrical resistivity tomography (ERT), multichannel analysis of surface wave data (MASW) and ground penetrating radar (GPR). The optimal results, based on the matter-element model, indicate the recommended geophysical methods are consistent with the field methods ultimately employed. The research proposes a new quantitative and objective computing approach to the selection of geophysical methods. The matter-element model can minimise subjective influences to a certain extent, and is conducive to enhancing the utility and cost-effectiveness of a geophysical survey.

KEYWORDS:

• GPR
• electrical resistivity
• optimisation
• surface wave

Acknowledgements

Evgeniy performed the experiments and collected the data. Yunsen analysed data and wrote the paper. Neil supervised the project. All authors read and approved the final manuscript. This work was supported by the National Key R&D Program of China (2017YFC0602905), Fundamental Research Funds for the Central Universities (N150104007).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

All data used to support the findings of this study are included within the article.

Additional information

Funding

This work was supported by the National Key R&D Program of China (2017YFC0602905), Fundamental Research Funds for the Central Universities (N150104007).