Prediction Model for Advanced Detection of Water-Rich Faults Using 3D Anisotropic Resistivity Modeling and Monte Carlo Methods
Prediction Model for Advanced Detection of Water-Rich Faults Using 3D Anisotropic Resistivity Modeling and Monte Carlo Methods
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使用 3D 各向异性电阻率建模和蒙特卡罗方法高级检测富水断层的预测模型
DOI：
10.1109/access.2021.3053861
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发表时间：
2021
期刊：
影响因子：
3.9
通讯作者：
Xiaoping Wu
中科院分区：
文献类型：
--
作者：
Daiming Hu;Xiaodong Yang;Mingxin Yue;Yong Li;Xiaoping Wu
隧道开挖过程中，断层，特别是陡峭富水断层的水害严重威胁着一些复杂山区的安全施工，导致这些地区的经济增长和发展缓慢。直流电阻率法对低阻体具有较高的分辨率和灵敏度，被广泛应用于隧道工作面前方含水构造的预测。目前的预测模型都是基于电性各向同性介质，但由于岩石裂隙的存在，含水断层的电性往往是各向异性的。忽略各向异性的预测模型显然不准确，给安全施工带来了潜在的威胁。利用非结构有限元方法建立了各向异性介质的三维电阻率模型。通过对一个全空间模型的数值计算结果与解析解的比较，证明了该算法的准确性。另一个经典的各向异性模型也从物理角度证明了我们代码的可靠性。在此基础上，通过有限元模拟，提出了预测巷道工作面前方具有各向异性电阻率的垂直断层位置的预测方程。用并行蒙特卡罗方法对10000个随机垂直断层模型进行了模拟计算，结果表明85.36%的预测结果在误差10%以内。此外，对于倾角为75°的随机断层，93.17%的预测结果在15%的误差范围内，这表明我们的预测模型可以有效地预测急倾斜富水断层或断裂带。
During tunnel excavation, water hazards in faults, especially steep water rich faults, pose a serious threat to safe construction in some complex mountains, which leads to low economic growth and development in these areas. Direct current resistivity method, which has high resolution and sensitivity to the low resistivity body is widely used to predict the water-bearing structures in the front of tunnel face. The current prediction models are based on the resistivity isotropic medium, however, the resistivity of water bearing fault is often anisotropic due to rock fracture. The prediction model neglecting the anisotropy is obviously inaccurate, which brings potential threats to safe construction. We develop a three-dimensional resistivity modeling for anisotropic media using unstructured finite element method. The algorithm is proved to be accurate by comparison of numerical results and analytical solutions for a whole-space model. Another classical anisotropic model also demonstrated the reliability of our code from a physical point of view. Then we propose a prediction equation to predict the position of a vertical fault with anisotropic resistivity in the front of tunnel face by the finite element simulations. The parallel Monte Carlo method is used to test and evaluate the quality of our prediction equation by simulations of 10000 random vertical fault models, results counted by the histogram showed 85.36% of the results are predicted within 10% of the error. Besides, 93.17% of the results are predicted within 15% of the error using the equation for random faults with 75 degree dip angle, which shows that our prediction model can effectively forecast steeply dipping water-rich faults or fracture zones.
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