Rapid in-field processing of telluric electric field frequency selection method (TEFSM) data and its practical applications

Maoyue Zhu; Tianchun Yang; Yawar Hussain; Qin Qin

doi:10.65819/tes.2026.v5i2.71

Authors

Maoyue Zhu School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, 411201 Xiangtan, China
Tianchun Yang School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, 411201 Xiangtan, China
Yawar Hussain Earth Science Department, Research Centre for Geological Risks-CERI, University of Rome “Sapienza”, Rome, Italy
Qin Qin School of Earth Sciences and Spatial Information Engineering, Hunan University of Science and Technology, 411201 Xiangtan, China

DOI:

https://doi.org/10.65819/tes.2026.v5i2.71

Keywords:

Frequency selection method, signal processing approach, groundwater, Seepage, Magnetotellurics

Abstract

The telluric electric field frequency selection method (TEFSM) is an electromagnetic exploration technique that utilizes the telluric electric field and the static effects to image the subsurface electrical structures. Currently, research on TEFSM has primarily focused on instrument development and practical applications, while theoretical algorithms have received limited attention. As a result, the interpretation of field data remains highly dependent on the quality of measurements and subjective judgment based on individual experience. In this paper, we propose an in-field signal processing approach to enhance anomaly resolution. The method involves logarithmic normalization of the measured data by dividing each value by the minimum data point in the logarithmic domain, thereby amplifying weak signals and improving contrast. Firstly, we applied this approach to process the data generated by a theoretical forward model. Subsequently, we validated its effectiveness through two field cases of groundwater prospecting—conducted in quartz sand-stone and granite areas—and one case of seepage detection at a reservoir. The applications show improved drilling success rates by enabling high-resolution delineation of target subsurface geological structures. Moreover, logarithmic normalization effectively high-lights potential leakage zones in the reservoir. The proposed approach is efficient, rapid, and cost-effective for groundwater exploration and related subsurface investigations.

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