• 论文
主办单位:煤炭科学研究总院有限公司、中国煤炭学会学术期刊工作委员会
基于稀少样本数据的地应力场反演重构方法
  • Title

    Back-analysis and reconstruction method of in-situ stress field based on limited sample data

  • 作者

    李飞周家兴王金安

  • Author

    LI Fei1 ,ZHOU Jiaxing1 ,WANG Jin’an1,2

  • 单位

    北京科技大学 土木与资源工程学院金属矿山高效开采与安全教育部重点实验室

  • Organization
    1. School of Civil and Resource Engineering,University of Science and Technology Beijing,Beijing  100083,China; 2. State Key Lab of Education Ministry for High Efficient Mining and Safety of Metal Mines,Beijing  100083,China
  • 摘要

    地应力测量昂贵的成本限制了测点数量,稀少样本实测数据难以对区域地应力场进行全面的描述与表达。如何依据稀少样本地应力实测数据准确地构建地质体内部地应力场分布状态,一直是岩土工程关心的重点问题,尤其随着深部工程的建设,掌握地应力场的分布规律是进行工程安全设计和防灾工作的基础。提出的GMDH(批数据处理)神经网络算法,结合地应力场分布随埋深的非线性特征及局部地质构造处地应力场的非连续性特点,构建出形成复杂地质体地应力场的边界条件模式,并基于现场的稀少样本测点数据进行复杂边界条件的生成,拟合出边界载荷非线性表达式。通过Matlab编程构建GMDH神经网络算法平台,该平台具有结构最优性和全局性等优势,克服了传统神经网络方法假设过多(网络结构假设)和网络结构过于简单等缺点,实现复杂地质体边界载荷表达式与实测点应力值的非线性映射,从而获取了较为合理的地应力场分布。为了验证算法的有效性,构建二维急倾斜地层地质区域模型,分别选取15 个、12 个、9 个及6 个测点数据进行地应力场反演。结果表明:随测点数量的减少,GMDH神经网络算法反演精度均大于83%,特别是6个测点稀少样本数据中,GMDH神经网络算法反演精度为84%,BP神经网络算法反演精度为76%,说明GMDH神经网络算法在稀少样本测点数据下具有较高的反演精度。另外,对稀少样本测点数据下的杏山铁矿地应力场进行反演和重构,结果显示,GMDH神经网络算法反演精度达到84%,绝大多数测点应力分量反演误差小于10%。因此,GMDH神经网络算法在稀少样本测点数据的反演计算中,具有良好的泛化性和非线性数据预测性,可为今后日益复杂的工程设计和施工提供有效的理论依据。


  • Abstract
    The high cost of in-situ stress measurement limits the number of measuring points. It is difficult to describe and express regional in-situ stress field comprehensively with limited sample measured data. How to accurately con- struct the distribution state of the in-situ stress field within the geological body based on the limited samples of meas- ured in-situ data has always been a key issue of geotechnical engineering concern. Especially with the construction of deep engineering project,the understanding on the distribution law of in-situ stress field is the basis of engineering safety design and disaster prevention work. The GMDH (Group Modeling of Data Handing) neural network algorithm proposed in this paper combined the non-linearity of in-situ stress field distribution with burial depth and the disconti- nuity of in-situ stress field in local geological structure,constructed a boundary condition model for the formation of complex geological body in-situ stress field,and generated complex boundary conditions based on a limited sample of measured data in the field,and fitted the non-linear expression of boundary load. The GMDH neural network algorithm platform was constructed by MATLAB programming,which has the advantages of structural optimization and global op- timization,and it overcame the shortcomings of traditional neural network method such as too many assumptions (net- work structure assumptions) and too simple network structure. It realized the non-linear mapping between the boundary load expression of complex geological body and the stress values of measured points,so as to obtain a more reasonable distribution of in-situ stress field. In order to verify the effectiveness of the method,a two-dimensional geological re- gional model of steeply inclined layer was constructed. Fifteen,twelve,nine and six measuring points were selected for in-situ stress field inversion. The results showed that with the decrease of the number of measuring points,the inversion accuracy of GMDH neural network algorithm was more than 83% . Especially in the limited sample data of six measur- ing points,the inversion accuracy of GMDH neural network algorithm was 84% ,and BP neural network algorithm was 76% . It showed that GMDH neural network algorithm has a higher inversion accuracy in the limited sample data. In addition,the in-situ stress field of Xingshan iron mine was inversely calculated and reconstructed based on limited sample data. The results showed that the inversion accuracy of GMDH neural network algorithm reached 84% ,and the inversion error of stress component of most measuring point was less than 10% . Therefore,GMDH neural network algo- rithm has a good generalization and non-linear data predictability in the inversion calculation of limited sample measur- ing data,which could provide an effective theoretical basis for future increasingly complex engineering design and con- struction.
  • 关键词

    地质体GMDH神经网络算法小样本数据地应力场反演

  • KeyWords

    geological body; GMDH neural network algorithms; limited sample data; inverse calculation of in-situ stress field

  • 基金项目(Foundation)
    中国博士后基金资助项目(2017M610047);中央高校基本科研业务费专项基金资助项目(FRF-TP-16-075A1)
  • DOI
  • Citation
    LI Fei,ZHOU Jiaxing,WANG Jinan. Back-analysis and reconstruction method of in-situ stress field based on limited sample data[J]. Journal of China Coal Society,2019,44(5):1421-1431.
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