基于地质统计学反演的透明化矿山岩性建模参数研究及应用

为提供煤炭精准开采地质保障前期高分辨率的岩性地质建模基础数据，针对煤系地层的煤层厚度和存储条件，基于地质统计学反演理论建立楔形煤层的纵波阻抗模型，对获取的地震数据通过多组反演实验分析不同概率密度函数、横向变程参数对煤层厚度反演的影响，并进一步讨论了不同数量和位置的井约束条件下反演的效果，最后基于煤田地震数据进行了实际应用。结果表明，地质统计学反演参数对反演结果影响较大，合理的概率密度函数、横向变程和合理的约束井选取可以提高反演的准确性。实际应用表明地质统计学反演能够预测出1 m左右的薄煤层，反演的煤层厚度与测井数据结果误差范围为1.82 % ~12.24 %。研究结果对薄煤层厚度预测具有一定的可行性，可以为透明化矿山前期岩性地质建模提供有价值的建模数据。

Research and application of inversion parameters based on geological statistics inversion in transparent mines rock major modeling

In order to provide the basic data of lithology modeling with high resolution in the early stage of coal precise mining geological guarantee, this paper established the p-wave impedance model of wedge-shaped coal seam according to the thickness and storage conditions of coal measure strata based on geostatistical inversion theory. The influence of different probability density functions and transverse range parameters on thickness inversion of coal seam are analyzed through multiple inversion experiments. The effect of inversion under different amount and location constraints is further discussed. Experimental results show that geostatistical inversion parameters have a great influence on the inversion results. Reasonable probability density function, lateral range and constrained well selection can improve the accuracy of inversion. Results shows that geostatistical inversion can predict about 1 m thin coal seam, and the error range between the inversion coal seam thickness and the logging data is 1.82 % ~12.24 %. It is therefore feasible to predict the thickness of thin coal seam, and can provide valuable modeling data for the early lithologic geological modeling of "transparent mine".