复杂地质条件下深埋长隧洞超前地质预报技术综合应用研究——以滇中引水工程玉溪段为例

    COMPREHENSIVE APPLICATION RESEARCH ON ADVANCED GEOLOGICAL PREDICTION TECHNOLOGY FOR DEEP-BURIED LONG TUNNELS UNDER COMPLEX GEOLOGICAL CONDITIONS: A CASE STUDY OF YUXI SECTION OF MIDDLE ROUTE OF YUNNAN WATER DIVERSION PROJECT

    • 摘要: 针对西南高山区深埋长引水隧洞地质构造复杂、单一超前预报方法存在多解性的难题,本文依托滇中引水工程玉溪段,开展了基于多物理场耦合的综合超前地质预报技术应用研究。构建了“长距离TSP宏观控制、中距离TEM重点探水、短距离GPR精细刻画”的综合探测体系。结合扯那苴、小扑等典型工点的实测与开挖揭露数据,分析了涌泥涌砂、围岩塌方及隐伏岩溶的地球物理响应特征。研究结果表明,多源信息融合可有效弥补单一方法的探测盲区并降低多解性;在此基础上,建立了以正常围岩背景场为基准的“波速比-电阻率-介电常数”多参数协同判识准则。其中:纵横波速比异常升高且伴随视电阻率陡降是识别富水涌泥涌砂带的显著特征;纵波呈现低速槽且雷达同相轴错断主要对应围岩破碎塌方风险;纵波速度骤降且雷达波呈现强反射及多次震荡则有效指示隐伏岩溶空腔。工程实践表明,该综合预报体系实现了对掌子面前方不良地质体的有效定性判识与空间定位,为深埋长隧洞的安全高效掘进提供了可靠的技术支撑。

       

      Abstract: To address the challenges posed by complex geological structures and the ambiguity inherent in single advanced prediction methods for deep-buried long diversion tunnels, this study presents a comprehensive advanced geological prediction technology based on multi-physics coupling, using the Yuxi section of the Central Yunnan Water Diversion Project as a case study. A comprehensive prediction system was constructed, characterized by long-distance macro-control via Tunnel Seismic Prediction(TSP), medium-distance water content identification via the Transient Electromagnetic Method(TEM), and short-distance fine characterization via Ground Penetrating Radar(GPR). By combining field data from typical construction sites such as Chenaju and Xiaopu, the geophysical response characteristics of mud and sand inrush, surrounding rock collapse, hidden karst, and structural water-rich zones were deeply analyzed. The results indicated that multi-source information fusion effectively eliminated detection blind spots. Specifically, the ratio of longitudinal to transverse wave velocity in TSP was sensitive to the mechanical state of the rock mass; the apparent resistivity in TEM was sensitive to the volume effect of water bodies; and GPR offered high resolution for near-field structural interfaces. Furthermore, disaster identification criteria based on multi-parameter constraints of wave velocity ratio, resistivity, and dielectric constant were established, i.e., "high wave velocity ratio+steep resistivity drop gradient" as a significant feature for identifying mud and sand inrush;"low velocity trough+disordered radar waveform" mainly corresponding to surrounding rock collapse; and"abrupt drop in wave velocity+strong diffraction wave" accurately corresponding to karst cavities. Engineering practice demonstrates that this comprehensive prediction system effectively achieves qualitative identification and precise localization of adverse geological bodies ahead of the tunnel face, significantly reducing the risk of water and mud inrush during the construction of deep-buried long tunnels.

       

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