The overlap winding system of coils in a multiturn small loop device in the transient electromagnetic method makes the mutual inductance coefficient large, and the measurement data are seriously affected by the turn-off effect. The conical source has the advantages of low mutual inductance effects and strong secondary fields. This study conducts an inversion of an improved particle swarm optimization (PSO) algorithm for this device. The new PSO algorithm improvement strategy (PSO-GIS algorithm) has the advantages of high success rate of optimization, fast convergence speed, high calculation accuracy, and low calculation elapsed time. The inversion results of the theoretical model and the measured mine data show that the PSO-GIS algorithm does not depend on the initial model setting, and the inversion of the layered full-space geoelectric model has a fast convergence speed and enhanced calculation accuracy. The PSO-GIS algorithm inversion is applied to the measured data of coal mine wells, which can achieve the effect of abnormal separation of roof and floor. The reflection of low-resistivity anomalous bodies is refined, and the drilling verification results agree well with the inversion interpretation. The PSO-GIS algorithm inversion in this article can be applied to the processing of real measured mine data to improve the interpretation effect of full-space inversion of mines.

Subsurface electromagnetic sensing is used in the mining, tunneling, environmental, groundwater, energy, and geoscience research sectors of the global economy. For underground applications, these methods require focused, compact antenna. We introduce the concept of a ferrite core transmitting coil to time-domain electromagnetic sensing, with an emphasis of forward scanning detection of subsurface hazards from a borehole. First, we develop an analytical expression for mutual coupling between a ferrite core coil transmitting antenna coupled with an air core receiver coil to demonstrate the focusing potential gained by adding a ferrite core. Next, we complete 3-D numerical simulations for a scanning borehole transient electromagnetic (BTEM) device. These simulations include radial scanning and demonstrate the improved resolution achievable with a ferrite core coil transmitting antenna. Finally, a ferrite core coil radial scanning in-hole device was constructed, and field experiments were completed in a shallow borehole. A small high-conductivity object was hidden in the subsurface and then detected with the device within radial plots of subsurface resistivity. Our combined numerical and field experiments present the ferrite core coil antenna as a method for focusing electromagnetic fields with one applicant being hazard prediction ahead of a borehole extended from a tunneling face. We hope these electromagnetic scanning methods find many Earth science and engineering applications.

Mining stress induces deformation and fracture of the overlaying rock, which will result in water filling the separation layer if the aquifer finds access to abscission space along the fracture channels. Accurate detection is crucial to prevent water hazards induced by water-bearing fractures. The 3-D time-domain finite-difference method with Yee’s grid was adopted to calculate full-space transient electromagnetic response; meanwhile, a typical geologic and geophysical model with a water-bearing block in an separation layer was built according to regional tectonics and stratigraphic developments. By using numerical simulation, the induced voltage and apparent resistivity for both vertical and horizontal components were acquired, and then an approximate inversion was carried out based on the “smoke ring” theory. The results indicate that the diffusion velocity of induced voltage is significantly affected by the water-bearing body in the fracture, and the horizontal velocity of induced voltage is lower than the vertical one. The induced voltage curves indicate that the horizontal response to an anomaly body is stronger than the vertical one, leading to a high apparent resistivity resolution of conductivity contrast and separation layer boundary in the horizontal direction. The results of 3-D simulation making use of a measured data model also demonstrate that the horizontal component of apparent resistivity can reflect the electrical structure in a better way; however, its ability to recognize the concealed and fine conductor is rather weak. Accordingly, the observation method or numerical interpolation method needs to be further improved for data processing and interpretation.

Bearing defect detection is essential for industrial equipment condition monitoring and predictive maintenance, yet identifying subtle, multi-scale, and morphologically diverse defects remains challenging. This study proposes MSF-YOLO (Multi-Scale Fusion YOLO) , an improved YOLOv8-based model enhanced by three core architectural innovations: a multi-scale convolutional module (MSPConv) for cross-scale feature extraction, a spatial-channel collaborative attention module (C2f_SCSA) for focusing on defect-critical regions, and a learnable-weight feature fusion module (L_FFM) for adaptive hierarchical aggregation of multi-scale features. Ablation experiments on a self-built bearing defect dataset show that successive integration of these modules consistently improves performance compared to the baseline YOLOv8n. The final MSF-YOLO achieves precision (P) 92.2% , recall (R) 83.1% , mAP50 88.1% , and mAP95 53.1% , representing 4.0%, 8.1%, 6.3%, and 6.4% improvements, respectively. These results confirm the effectiveness of the proposed multi-scale extraction, attention, and fusion strategies, providing a high-precision solution for industrial visual inspection of bearing defects. https://github.com/Hankerzao/MSF-YOLO-Bearings-Defect-Base-on-YOLOv8-.git