Seismic imaging is the most effective geophysical method and has been extensively implemented to detect potential geological hazards in tunnels during construction. The coupling of geophones and the design of geometry in tunnels are the two major challenges. To ensure successful coupling, a high-sensitivity semi-automatic coupling geophone with a broadband was designed. In practice, this geophone is attached with a wheel and two springs. Once inserted into the borehole, an automatic coupling action occurs. This semi-automatic coupling design within the geophone not only guarantees good coupling, but reduces the time and costs usually required to install a traditional geophone. In the use of geophones for tunnel seismic detection, we propose two new two-dimensional (2D) seismic geometries based on the two commonly used geometries. A test to assess the effectiveness of the qualities of imaging from four geometries was completed by comparing the results of the forward modeling of sandwich models. The conclusion is that the larger the horizontal offset of the layout geometry, the higher the resolution of the imaging; the larger the vertical offset, the weaker the mirror image. The vertical offset is limited due to the narrow tunnel condition. Therefore, the mirror effect cannot be entirely eliminated; however, it can be further suppressed by constructing 2D geometry. The two newly proposed 2D geometries caused the imaging arc of the inter-layer, but suppressed the mirror image. The mirror image added a significant number of errors to the data, which could misguide tunnel construction; therefore the new 2D geometries are more reasonable than the two most commonly used. We applied one of the two new 2D geometries that was more practical to an actual project, the Chongqing Jinyunshan Tunnel in China, and acquired high-quality seismic data using two semi-automatic coupling geophones. The detection results were essentially consistent with the excavation conclusions.
This is an open access article distributed under the Creative Commons Attribution License
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited