Abstract: Traditional tunnel seismic advance detection technology is limited by the use of explosives, the need for mining stoppages, prediction speed, detection range, and capabilities, making it challenging to meet the high-precision geological structure detection requirements of intelligent tunneling faces. In response, the seismic-while-tunneling technique utilizes the vibration of the roadheader cutting the coal wall as the seismic source. This method employs real-time data upload, automatic processing, and dynamic imaging. By leveraging information technology and software support, and utilizing real-time continuous super-saturation stacking supported by massive data, the imaging precision of geological anomalies ahead of the mining face is significantly enhanced. This study, conducted in Wangpo Coal Mine in Shanxi, involved deploying a seismic-while-tunneling monitoring system in two driving tunnels to perform detailed exploration of geological structures. Data accumulated within 48 hours after the commencement of monitoring can image geological anomalies within a 200-meter range ahead of the mining face. In the return airway of the 3217 working face, the reflected anomaly of the coal gangue compresssion zone 97 meters ahead of the mining face was successfully detected with a prediction error of 1 meter. In the transport tunnel of the 3303 working face, a collapse column 39 meters ahead of the mining face was identified with a prediction error of 2 meters. Additionally, in the same transport tunnel of the 3303 working face, two abnormal areas of coal seam fracture zones were detected. One located 148 meters ahead of the mining face had a prediction error of 1 meter, while the other, located beyond 211 meters ahead, despite being obstructed by a collapse column and a coal seam fracture zone, still produced relatively strong imaging results with a prediction error of 3 meters. These findings demonstrate that the seismic-while-tunneling monitoring technique can provide significant technical support for the prevention of gas disasters. The application results suggest that this technology is convenient for construction operations, with high detection accuracy, long detection distance, and strong detection capability, providing reliable geological support for intelligent and safe tunnel excavation in coal mines.