基于多变量LSTM网络的K_(2)灰岩富水区预测——以阳泉泊里矿区为例

Predicting the water-yield properties of K_(2) limestones based on multivariate LSTM neural network:A case study of the Poli mining area in Yangquan

在山西阳泉泊里矿区,太原组K_(2)灰岩是15号煤层上部主要的含水层,查明其富水分布特征对上下组煤层安全开采至关重要。为了准确得到K_(2)灰岩的富水分布区域,首先,利用常规的波阻抗反演获取精确的K_(2)灰岩空间展布特征。然后,结合皮尔逊相关系数法与交叉验证−逐步回归法优选出9种地震属性,构成网络的训练数据。此外,引入适合于时序数据处理且能够捕捉测井曲线前后相关性的长短期记忆神经网络(LSTM),构建智能化、多变量LSTM视电阻率预测模型,以精确地预测研究区视电阻率进而得到地层富水性分布特征。同时,分别利用常规多属性回归算法与多变量LSTM模型在井点位置建立电阻率测井曲线与地震属性井旁道之间的映射关系。最后,将井点处训练好的网络模型推广至无井区得到全区视电阻率体,根据视电阻率值的高低、矿区地质构造与陷落柱发育情况圈定灰岩富水区。实际数据的测试结果表明:与常规多属性回归算法相比,多变量LSTM模型预测误差小,与测井相关系数高,说明多变量LSTM模型可以更加精确地预测出工区视电阻率,在含煤地层的富水性预测中有较好的应用价值。

The Taiyuan Formation K_(2) limestones are the main aquifer in the upper part of the No.15 coal seam in the Poli mining area,Yangquan City.Therefore,determining the water yield properties of K_(2) limestones is critical to the safe mining of coal seams in the upper and lower formations.To determine the exact distribution of areas with high water-yield properties of the K_(2) limestones,this study determined the accurate spatial distribution of K_(2) limestones using the conventional wave impedance inversion firstly.Then,nine optimal seismic attributes were selected using the Pearson correlation coefficient method and the cross-validation method in stepwise regression in order to form the training data.By introducing the long short-term memory(LSTM)neural network,which is applicable for processing time-series data and is capable of capturing the correlation with log curves,this study established a multivariate LSTM neural network-based intelligent model for apparent resistivity prediction(also referred to as the multivariate LSTM-based prediction model).The purpose is to accurately predict the apparent resistivity of the study area and further obtain the water yield properties of K_(2) limestones.Moreover,this study established the mapping relationship between resistivity log curves of the well locations and the seismic attributes of near-well seismic traces using the conventional multivariate regression al-gorithm and the multivariate LSTM-based prediction model,respectively.Finally,the multivariate LSTM-based predic-tion model trained using the data on the well locations were extended to the areas without wells to obtain the apparent resistivity volume of the whole study area.Subsequently,the areas with high water yield properties in the limestones were delineated according to the apparent resistivity values,as well as the development of the geological structures and collapse columns in the mining area.As shown by the test results of actual data,compared to the conventional multivari-ate regression algorithm,the multivariate LSTM-based prediction model yielded smaller prediction errors and higher correlation coefficients with logs.Therefore,the multivariate LSTM-based prediction model can accurately predict the apparent resistivity of a survey area and is of high application value in predicting the water-yield properties of coal-bearing strata.