高陡复杂露天矿边坡地应力场分区非线性反演分析

地应力是边坡稳定性分析的重要因素。在杏山铁矿露天高陡边坡实测地应力数据的基础上，采用多元线性回归方法，在充分考虑岩体自重与构造应力影响的情况下，对5类11种边界条件工况进行了有限差分FLAC3D的模拟加载，反演得出研究区域内的地应力场。鉴于该方法只能对地应力场进行线性和全区反演，这对内部结构极其复杂、矿区内各个区域岩性和地形差异较大的金属矿山矿体显然是不够准确的。为了克服以上缺陷，采用了非线性的神经网络反演方法，并根据岩体的岩性分布和地形起伏将整个矿区划分为5个分区，通过引入侧压力系数k0实现分区域反演，从而得到整个矿区的地应力场。研究表明，在复杂地质条件下，神经网络方法反演出的初始地应力分布更加合理。

Sub-regional nonlinear in-situ stress inversion analysis of complex high steep slope of open pit

In-situ stress is an important factor in stability analysis of slope. Based on the measured stress data in the high and steep slope of Xingshan iron open pit, the inversion of in-situ stress field in the slope is performed by using multivariate linear regression method considering the impact of the gravity of rock mass and tectonic stress, in which 11 kinds of boundary conditions are included in five categories. In terms of the limits of linear and the whole region inversion, this method cannot accurately inverse the stress field of the metal ore body, which has extremely complex internal structure and big differences in lithology and topography within the mining area. In order to overcome the above defects, nonlinear neural network inversion method is used. According to the differences in lithology and topography of the rock mass, the mine is divided into five zones. Sub-regional inversion of the in-situ stress field of the mine is achieved by introducing a lateral pressure coefficient k0. The study results show that under a complex geological condition, the inversion of initial in-situ stress by neural network method can give a rise to a more reasonable result.