Coal Geology & Exploration


The average-velocity method is the most commonly-used time-to-depth conversion technology for the seismic survey of coalbed. But for the steeply-inclined coalbed, the average velocity changes rapidly laterally, and the interpretation accuracy varies greatly, so that the intelligent coal mining could not be assured effectively. Herein, the accuracy of various average-velocity-interpolation methods was discussed by analyzing a steeply-inclined coalbed model and a practical 3D seismic mining area and appropriate improvement methods were proposed accordingly. The calculation results of a forward model show that the bottom elevation accuracy of coalbed is greatly affected by the locations of interpolation points in the case of direct interpolation using Kriging and Polynomial methods, which cannot meet the high-accuracy requirements of time-to-depth conversion for steeply-inclined coalbeds. Therefore, referring to the stack-velocity derived average velocity, an improved interpolation method of time-to-depth conversion applicable to the steeply-inclined coalbed was proposed by integrating the polynomial, Kriging and Support Vector Machine (SVM) methods, with reference to the average velocity calculated by stacking velocity. By applying the relevant methods to the 2D model data and the 3D example of mining area, it is found that the accuracy of average velocity generated by the improved interpolation method is significantly improved, with the interpolation error much smaller than the requirement of the industrial standard. Hence, the proposed method is suitable for large-scale popularization and application.


seismic survey, inclined coalbed, time-to-depth conversion, average velocity, interpolation accuracy




[1] 王国法,范京道,徐亚军,等. 煤炭智能化开采关键技术创新进展与展望[J]. 工矿自动化,2018,44(2):5−12

WANG Guofa,FAN Jingdao,XU Yajun,et al. Innovation progress and prospect on key technologies of intelligent coal mining[J]. Industry and Mine Automation,2018,44(2):5−12

[2] 程建远,刘文明,朱梦博,等. 智能开采透明工作面地质模型梯级优化试验研究[J]. 煤炭科学技术,2020,48(7):118−126

CHENG Jianyuan,LIU Wenming,ZHU Mengbo,et al. Experimental study on cascade optimization of geological models in interlligent mining transparency working face[J]. Coal Science and Technology,2020,48(7):118−126

[3] 高有进,杨艺,常亚军,等. 综采工作面智能化关键技术现状与展望[J]. 煤炭科学技术,2021,49(8):1−22

GAO Youjin,YANG Yi,CHANG Yajun,et al. Status and prospect of key technologies of intelligentization of fully–mechanized coal mining face[J]. Coal Science and Technology,2021,49(8):1−22

[4] 赵镨,杨艳珍,艾劲松. 煤炭三维地震勘探时深转换误差分析及对策[J]. 中国煤田地质,2006,18(4):49−52

ZHAO Pu,YANG Yanzhen,AI Jinsong. Time−depth conversion error analysis and countermeasures in coalfield 3D seismic prospecting[J]. Coal Geology of China,2006,18(4):49−52

[5] 陈同俊,崔若飞,郎玉泉,等. 煤田采区三维地震精细构造解释方法[J]. 地球物理学进展,2007,22(2):573−578

CHEN Tongjun,CUI Ruofei,LANG Yuquan,et al. Detail structural interpretation methods of coal 3–D seismic[J]. Progress in Geophysics,2007,22(2):573−578

[6] 赵长征. 精细处理解释技术在复杂地质构造中的应用[J]. 能源技术与管理,2021,46(3):181−183

ZHAO Changzheng. Application of fine processing and interpretation technology in complex geological structure[J]. Energy Technology and Management,2021,46(3):181−183

[7] 许玉莹. 基于OVT域地震资料的煤田精细构造解释方法研究 [D]. 太原:太原理工大学,2020.

XU Yuying. Study on fine structure interpretation method using OVT domain seismic data of coalfield[D]. Taiyuan:Taiyuan University of Technology,2020.

[8] 田忠斌,李娟,申有义,等. OVT域处理技术在沁水盆地深部煤层气勘探中的应用[J]. 煤田地质与勘探,2020,48(6):93−102

TIAN Zhongbin,LI Juan,SHEN Youyi,et al. The application of OVT domain technology in deep CBM exploration in Qinshui Basin[J]. Coal Geology & Exploration,2020,48(6):93−102

[9] 印兴耀,张洪学,宗兆云. OVT数据域五维地震资料解释技术研究现状与进展[J]. 石油物探,2018,57(2):155−178

YIN Xingyao,ZHANG Hongxue,ZONG Zhaoyun. Research status and progress of 5D seismic data interpretation in OVT domain[J]. Geophysical Prospecting for Petroleum,2018,57(2):155−178

[10] 赵立明,崔若飞. 全数字高密度三维地震勘探在煤田精细构造解释中的应用[J]. 地球物理学进展,2014,29(5):2332−2336

ZHAO Liming,CUI Ruofei. Application of digital high–density seismic exploration in fine structural interpretation in coalfield[J]. Progress in Geophysics,2014,29(5):2332−2336

[11] KTENAS D,HENRIKSEN E,MEISINGSET I,et al. Quantification of the magnitude of net erosion in the southwest Barents Sea using sonic velocities and compaction trends in shales and sandstones[J]. Marine and Petroleum Geology,2017,88:826−844.

[12] 李军. 三维地震资料解释中时深转换方法对比分析及应用[J]. 海洋石油,2014,34(1):36−40

LI Jun. Analysis and applications of time–depth transform method in 3D seismic interpretation[J]. Offshore Oil,2014,34(1):36−40

[13] 陶天生,李春峰,李珂迪,等. 东海深部地层时深转换关系的分段优化拟合[J]. 海洋学研究,2020,38(3):65−75

TAO Tiansheng,LI Chunfeng,LI Kedi,et al. Segmented fitting in time–depth conversion of deep strata in the East China Sea[J]. Journal of Marine Sciences,2020,38(3):65−75

[14] KEHO T,SAMSU D. Depth conversion of Tangguh gas fields[J]. The Leading Edge,2002,21(10):966−971.

[15] AL−CHALABI M. Time−depth relationships for multilayer depth conversion[J]. Geophysical Prospecting,1997,45(4):715−720.

[16] 龚幸林,林建东. 煤田三维地震资料解释的时深转换方法研究[J]. 中国煤田地质,2002,14(2):65−67

GONG Xinglin,LIN Jiandong. Study on time–depth transform method of three–dimensional seismic information interpretation in coal field[J]. Coal Geology of China,2002,14(2):65−67

[17] 孙希杰. 巨厚松散层煤田三维地震勘探时深转换方法探讨[J]. 中国矿业,2018,27(增刊1):412−414

SUN Xijie. Probe into coalfield 3D seismic prospecting time–depth conversion under thick soil layer[J]. China Mining Magazine,2018,27(Sup.1):412−414

[18] ADAMS S L. Method of estimating geological formation depths by converting interpreted seismic horizons from the time domain to the depth domain:US20060047429A1[P]. 2006-03-02.

[19] 刘文明,高耀全,蒋必辞,等. 回采工作面煤层动态标定预测技术应用研究[J]. 煤田地质与勘探,10/25/2022,50(1):31−35

LIU Wenming,GAO Yaoquan,JIANG Bici,et al. Application research on dynamic calibration and prediction technology of coal seam in coalmine working face[J]. Coal Geology & Exploration,10/25/2022,50(1):31−35

[20] SHERIFF R E. Encyclopedic Dictionary of Applied Geophysics [M]. Society of Exploration Geophysicists,2002.

[21] LI Wan,CHEN Tongjun,SONG Xiong,et al. Reconstruction of critical coalbed methane logs with principal component regression model:A case study[J]. Energy Exploration & Exploitation,2020,38(4):1178−1193.

[22] REMY N,BOUCHER A,WU J. Applied Geostatistics with SGeMS:A User’s Guide[M]. Cambridge:Cambridge University Press,2009.

[23] 陈雷,孟祥武. 克里金插值模型在地面沉降监测中的应用[J]. 北京测绘,2020,34(5):691−695

CHEN Lei,MENG Xiangwu. Application of Kriging interpolation model in ground subsidence monitoring[J]. Beijing Surveying and Mapping,2020,34(5):691−695

[24] CHANG C C,LIN C J. LIBSVM:A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology,2011,2(3):1−27.

[25] 陈同俊,王新,管永伟. 基于SVR和地震属性的构造煤厚度定量预测[J]. 煤炭学报,2015,40(5):1103−1108

CHEN Tongjun,WANG Xin,GUAN Yongwei. Quantitative prediction of tectonic coal seam thickness using support vector regression and seismic attributes[J]. Journal of China Coal Society,2015,40(5):1103−1108



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