###### Abstract  
Existing goaves (e.g., shafts and roadways) in mines represent important hidden dangers during the production of underlying coal seams. In this view, the accurate identification, analysis, and delimitation of the scope of goaves have become important in the 3D seismic exploration of mines. In particular, an accurate identification of the boundary swing position of goaves for 3D seismic data volumes within a certain depth interval is key and difficult at the same time. Here, a wide-band and wide-azimuth observation system was used to obtain high-resolution 3D seismic data. The complex structure of a mine was analyzed, and a seismic double processing system was applied to verify the fine processing effect of a goaf and improve the resolution of the 3D seismic data. Based on the seismic attribute identification characteristics of the goaf structure, we decided to adopt multi-attribute comprehensive identification and data fusion technologies to accurately determine the position of the goaf and of its boundary. Combining this information with the mine roadway engineering layout, we verified the accurateness and correctness of the goaf boundary location. Our study provides a good example of the accurate identification of the 3D seismic data of a roadway goaf. goaf; 3D seismic exploration; recognition; dual processing system +
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and multi-means processing technologies is the key to accurately determine the position of goaf boundaries [10; 11]. The scale of the goaves in a coal mine will influence the stress and strain on the roof and on the floor of the coal seam and, consequently, the characteristics of the failure, collapse, and deformation zones. Physical parameters tend to vary in different sections of the mine, following a certain gradient [12; 13; 14; 15], hindering the identification of goaf structures through 3D seismic exploration techniques. Although many scholars have carried out laboratory and simulation studies on the physical parameters of goaves, there are still deficiencies in the detection and identification of goaves in coal roadways within a certain depth interval, especially concerning the vertical and lateral resolutions and the detection of their boundaries. Notably, the disturbances caused by mining activities create a collapse zone around each goaf, leading to a variety of fracture distributions. These fracture zones have an impact on the propagation of seismic waves [16]. High-resolution 3D seismic exploration techniques are required in such settings, especially to avoid or reduce the blind-area problem: a special observation system needs to be used for field data acquisition [17; 18]. In this study, we focused on the roadway formed during mining activities in the Xinyuan Mine (Yangquan, China) and adopted a high-resolution 3D seismic fine exploration technology involving multiple dimensions and components. In the data acquisition stage, we aimed at obtaining high-resolution and high-quality data by selecting the right engineering layout, acquisition equipment, and acquisition parameters. In particular, a high-quality seismic data volume was obtained by using a dual seismic data processing system software and applying multi-method comprehensive static correction, multi-domain multi-method combination denoising, pre-stack time migration, 5D interpolation, OVT (Offset Vector Tile) domain regularization, and other targeted data processing. To accurately identify the boundary position of a small goaf, a seismic spectrum decomposition technology was used and the seismic signal response characteristics of a certain time window for different frequencies (in relation to different main frequencies) were obtained. When considering the same layers, the spectral decomposition slices of different frequencies maintained a high overall consistency but showed quite different energy intensities. From low to high frequencies, the energy ranges became more concentrated, and anomalies became more obvious.