Application research of autoregressive and moving average power spectrum in mine ground‐penetrating radar structure detection data analysis

AbstractThe mine roadway built in the same thick rock stratum will not change the lithologic medium in front of the roadway under the influence of the small structure of the mine, which makes the mine radar face many difficulties in structural interpretation. In the coal measure strata in southwest China, the coal seam roof is the weak aquifer of the Kayitou (T1k) group. The mine structure is the excellent drainage boundary of an aquifer, resulting in the water content of the structural belt being different from that of the surrounding rock. This paper proposes a method to identify hidden structures in mine roadways using the autoregressive and moving average power spectrum energy enveloping medium water content inversion technique. Based on the actual water content of strata in the study area, 48 roadway models are constructed. The finite‐difference time‐domain algorithm was used to obtain the forward data and calculate the volumetric water content of the model. The structure of the roadway model is accurately identified by the water content difference. In the numerical simulation study, the segmentation frequency of high‐frequency and low‐frequency envelope of the autoregressive and moving average spectrum of 100‐MHz radar antenna is determined to be 155 MHz, and the value range of water content calculation regulation parameter k is determined. The high‐ and low‐frequency envelope inversion characteristics of the autoregressive and moving average spectrum of different tunnel media models are summarized and analysed. To solve the calculation problem of energy envelope selection of autoregressive and moving average power spectrum of engineering data, the k‐nearest neighbour machine learning algorithm was used to learn and classify the power spectrum features of different tunnel models. The detection application is analysed through the whole‐rock medium and coal and rock medium roadway in the study area. The results show that the method can effectively identify the location and strike characteristics of the hidden structures in front of the roadway.