基于EWOA-VMD的钻孔弹性波处理与煤岩识别方法  

Drilling elastic wave processing and coal-rock identification method based on EWOA-VMD

作　　者：武强 王显辉[1,2,3] 曾一凡 刘春生 WU Qiang;WANG Xianhui;ZENG Yifan;LIU Chunsheng(National Engineering Research Center of Coal Mine Water Hazard Controlling,China University of Mining and Technology(Beijing),Beijing 100083,China;China University of Mining and Technology(Beijing)Inner Mongolia Research Institute,Ordos,Inner Mongolia 017000,China;Key Laboratory of Mine Water Control and Resources Utilization,National Mine Safety Administration,Beijing 100083,China;Wuhan Changsheng Coalmine Safety Technology Co.,Ltd.,Wuhan,Hubei 430312,China)

机构地区：[1]中国矿业大学(北京)国家煤矿水害防治工程技术研究中心,北京100083 [2]中国矿业大学(北京)内蒙古研究院,内蒙古鄂尔多斯017000 [3]矿山水防治与资源化利用国家矿山安全监察局重点实验室,北京100083 [4]武汉长盛煤安科技有限公司,湖北武汉430312

出　　处：《中国矿业大学学报》2024年第5期857-871,共15页Journal of China University of Mining & Technology

基　　金：国家自然科学基金项目(42027801);国家重点研发计划项目(2021YFC2902004);鄂尔多斯市新型研发机构建设项目(IMRI23001)。

摘　　要：聚焦于当前煤炭智能化开采中对煤岩界面识别装备和技术的迫切需求,围绕当前煤岩识别技术受煤矿地质条件和复杂施工环境影响导致的识别精度低、效率差等难题,提出一种利用钻孔空间进行弹性波探测确定煤层厚度和煤岩界面层位的煤岩识别技术.针对弹性波回波信号中煤岩界面反射回波特征信息受限于井下环境中多种噪声干扰难以准确识别的关键问题,首先在优化原鲸鱼优化算法(WOA)中3种搜索策略和引入池化机制的基础上提出了增强型鲸鱼优化算法(EWOA),以最小包络熵作为EWOA中的适应度函数自适应求解变分模态分解(VMD)所需关键参数组合(k,α)的最优化问题,结合相关系数阈值法识别本征模态函数(IMF)集合中包含煤岩界面特征信息的分量,构建了一种基于EWOA-VMD的组合优化算法;其次根据钻孔弹性波信号特征构建仿真信号,采用该算法、集合经验模态分解(EEMD)和互补集合经验模态分解(CEEMD)方法进行处理,通过对比均方根误差、波形相似系数和信噪比等评价指标验证该算法的有效性;最后将该算法应用于室内模型试验所得复杂弹性波回波信号的处理.结果表明:该算法可在实测复杂回波中准确识别出煤岩界面反射回波信号,所得煤层厚度误差在12 mm之内,煤岩界面位置平均误差为2.5%.该算法可为非稳态、非线性弹性波复杂回波信号的处理提供参考,并推进钻孔弹性波煤岩界面识别技术的进步.Focused on the urgent demand for equipment and techniques for coal-rock interface identification in current intelligent coal mining,this paper proposes a coal-rock identification technique that utilizes borehole space for elastic wave detection to determine coal seam thickness and coal-rock interface position.This technology aims to address the challenges faced by current coal rock recognition technology,including low recognition accuracy and efficiency due to the influence of coal mine geological conditions and complex construction environments.In response to the critical issue of limited identification of characteristic information of coal-rock interface reflection echo in elastic wave echo signals due to various noise interferences in the underground environment,this paper presents a combined optimization algorithm based on enhanced whale optimization algorithm(EWOA)and variational mode decomposition(VMD).Initially,EWOA is proposed,building upon the optimization of three search strategies and the introduction of a pooling mechanism in the original WOA.The EWOA employs the minimum envelope entropy as its fitness function to adaptively solve the optimization problem of the critical parameter combination(k,α)required for VMD.The correlation coefficient threshold method is integrated to identify components within the intrinsic mode function(IMF)set that contain coal-rock interface feature information.Subsequently,simulation signals are constructed based on the characteristics of borehole elastic wave signals.This algorithm,along with the ensemble empirical mode decomposition(EEMD)and complete ensemble empirical mode decomposition(CEEMD)methods,is utilized for signal processing.The effectiveness of the proposed algorithm is validated through the comparison of evaluation metrics such as root mean square error,normalized correlation coefficient and signal-to-noise ratio.Finally,the algorithm is applied to process complex elastic wave echo signals obtained from indoor model experiments.The results indicate that the a

关 键 词：钻孔弹性波探测 煤岩界面识别 信号处理 变分模态分解 

分 类 号：P631.8[天文地球—地质矿产勘探]