Application of controlled-source audio-frequency magnetotellurics (CSAMT) for goaf detection: a case study in the Fangzi coal mine China

The subsidence or collapse of coal mine goaf has a great impact on the construction of various large-scale projects and the environment, and can threaten the safety of people’s lives. In the present study, CSAMT was used to detect coal mine goaf in China’s Fangzi mine. Anti-interference data acquisition and processing strategies were adopted to enhance and classify electrical characteristics to improve goaf detection accuracy under strong interference. Experiments were first performed under strong interference, based on the geological and geophysical goaf characteristics. To acquire high-precision CSAMT data, the effects of emission parameters, electromagnetic high-voltage interference, and other factors were investigated. Data were postprocessed using multilevel low-pass filtering approaches, including static correction, threshold segmentation, and the power function method, to reduce data noise, remove shallow abnormal interferences, and enhance the abnormal electrical characteristics of deep goaf. Finally, k-means clustering was used to categorize CSAMT electrical characteristics and quantitatively analyze the resistivity values. Results showed that the proposed method performs effectively for the identification of coal mine goaf.