Development and Prospects of Transparent Working Face Construction Technology for Intelligent Coal Mining

Background: As global energy demand continues to rise, coal remains a critical energy resource. However, traditional coal mining methods face significant challenges such as low safety, environmental degradation, and resource wastage. In addition, to address these issues, intelligent coal mining technologies have emerged, with the construction of transparent working faces receiving significant attention as a key component. Objective: This paper aims to explore and summarize the key technologies and methods involved in the construction of transparent working faces in coal mining, with a focus on enhancing mining safety, efficiency, and resource utilization. Methods: The research comprehensively reviews the principles and applications of various geological exploration technologies, including surface 3D seismic techniques, channel wave seismic exploration, mine resistivity methods, directional drilling, borehole transient electromagnetics, and borehole seismic CT. Additionally, it discusses critical technologies such as intelligent sensing, multi-source heterogeneous data fusion, and multi-attribute dynamic visualization modeling. Results: The study finds that surface 3D seismic techniques are effective for detecting geological structures within the working face, while mine geophysical prospecting methods like channel wave seismic and resistivity methods offer high precision and efficiency. Directional drilling and borehole techniques provide high detection accuracy for concealed structures and anomalies. Furthermore, the integration of multi-source data significantly enhances the accuracy and real-time capabilities of geological modeling. Conclusion: The construction of transparent working faces plays a crucial role in the sustainable development of the coal industry by improving mining safety, efficiency, and resource utilization. Future advancements will focus on algorithm optimization, the application of machine learning, and the development of refined multi-source data integration platforms.