3D seismic data

As coal mining extends to greater depths, accurately detecting coal seam floor undulations, identifying coal thickness variations, and recognizing complex geological features such as collapse columns has become increasingly essential. These challenges raise higher demands for safety and efficiency in mining operations. This study proposes a dynamic interpretation method for intelligent mining faces based on 3D seismic data to enhance the accuracy of detecting coal seam geological structures. The method comprehensively applies target processing and dynamic interpretation to conduct an accurate analysis of the coal seam floor elevation and average velocity field. By dynamically updating the data model and combining surface drilling and roadway data, significantly enhancing model accuracy and reliability. The study shows that the improved data correction method significantly enhances model accuracy and reliability. The accuracy of channel wave detection in structural prediction reaches 75%, while maintaining the maximum absolute error for floor profile prediction within 1.33 m. The random forest model was improved by combining gray correlation and particle swarm optimization (PSO) algorithms, further revealing the complex relationship between coal seam floor elevation and two-way travel time (TWTT). The method proposed not only enhances the precision and efficiency of intelligent face detection but also offers reliable geological support for safe coal mining operations. As geological data are continuously and dynamically updated, the method enables real-time optimization of mining decisions and reduces the risk of geological hazards.