Study on the detectability of CSRMT in archaeological exploration: taking the ancient city wall remains as an example

Abstract

Although electromagnetic methods have made significant advances in archaeological exploration, there remains a lack of a method that offers both high operating efficiency and high resolution. This study introduces a novel approach derived from the Controlled Source Radio Magnetotelluric (CSRMT) method. The CSRMT method combines the efficient configuration of the Controlled Source Audio-magnetotelluric (CSAMT) method with enhanced resolution capabilities. To evaluate the efficacy of the new method in archeological exploration, we designed a series of models based on existing references related to ancient city wall remains. Using the 3D contraction integral equation method, we numerically simulated the high-frequency component of the CSRMT method, and then on, the Laterally Constrained Inversion technique is used to invert the data. The effectiveness of the method was assessed qualitatively and quantitatively through analysing the electromagnetic field and apparent resistivity of models with varying widths, heights, top burial depths, and resistivity. Our analysis revealed that models with shallower burial depth and greater height of the underground city wall exhibited higher resolution. The findings demonstrate that the CSRMT method can efficiently and accurately identify buried remains, making it a highly promising tool for archaeological exploration and warranting further development.

KEYWORDS:

• CSRMT
• archaeology exploration
• ancient city wall
• underground remains
• numerical simulation

Acknowledgements

We would like to thank the CEMI group of Utah University for allowing us to use their integral equation method program to complete this research. Special thanks to Dr Liang Pengfei, Dr Tu Xiaolei and Dr Wang Dezhi for improving the language.

Disclosure statement

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

Data availability statement

The authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.

Additional information

Funding

This work was supported by the National Key R&D Program of China [grant numbers 2022YFF0706200, 2020YFC1521900], and supported by the National Natural Science Fund [grant number 42374170].