吉林大学学报(地球科学版) ›› 2020, Vol. 50 ›› Issue (4): 1197-1210.doi: 10.13278/j.cnki.jjuese.20190123

• 地球探测与信息技术 • 上一篇    

基于深度加权的多分量重力梯度数据联合相关成像方法

郑玉君1,2, 侯振隆1,2, 巩恩普1,2, 张永利1,2   

  1. 1. 东北大学资源与土木工程学院, 沈阳 110819;
    2. 东北大学深部金属矿山安全开采教育部重点实验室, 沈阳 110819
  • 收稿日期:2019-06-20 发布日期:2020-07-29
  • 通讯作者: 侯振隆(1988-),男,博士后,主要从事位场勘探数据处理解释、地球物理反演与并行算法等方向研究,E-mail:houzhenlong@mail.neu.edu.cn E-mail:houzhenlong@mail.neu.edu.cn
  • 作者简介:郑玉君(1994-),男,硕士研究生,主要从事位场勘探数据处理解释研究,E-mail:15935125158@163.com
  • 基金资助:
    国家重点研发计划（2017YFC1503100）；中国博士后科学基金项目（2017M621151）；中央高校基本科研业务专项资金项目（N180104020）

Correlation Imaging Method with Joint Multiple Gravity Gradiometry Data Based on Depth Weighting

Zheng Yujun1,2, Hou Zhenlong1,2, Gong Enpu1,2, Zhang Yongli1,2   

  1. 1. School of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China;
    2. Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines, Northeastern University, Shenyang 110819, China
  • Received:2019-06-20 Published:2020-07-29
  • Supported by:
    Supported by National Key R & D Program of China (2017YFC1503100), China Postdoctoral Science Foundation (2017M621151) and Fundamental Research Funds for the Central Universities (N180104020)

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摘要： 针对重力勘探中相关成像存在纵向分辨率较低的问题，提出基于深度加权的多分量重力梯度数据联合相关成像方法。与重力异常数据相比，重力梯度数据具有更高的信噪比、包含更多的频率信息。因此，本文在相关成像原理基础上联合多分量重力梯度数据，引入基于先验信息的深度加权函数，通过对研究区域进行划分，进一步提高深度加权效果。利用长方体组合理论模型确定了最佳梯度数据组合，验证了深度加权函数能够提高纵向成像效果；提出的方法还被证明具有抗噪性。将该方法应用于文顿盐丘区域的实测重力梯度数据，结果能够清晰地显示盖岩的位置。

关键词: 重力梯度数据, 数据联合, 深度加权, 相关成像

Abstract: Aiming at the problem of low vertical resolution of correlation imaging in gravity exploration, the authors propose a correlation imaging method with joint multiple gravity gradiometry data based on depth weighting. Compared with gravity anomaly, the gravity gradiometry data have a higher signal-to-noise ratio, and contain more frequency information. Combined with multiple tensors of gravity gradiometry data, based on correlation imaging principle, a depth weighting function is introduced with the prior information. Using the theoretical model of the prism combination, the optimal gradiometry data group is determined, and the improvement of the vertical imaging results is verified by depth weighting function. Through dividing research area, the effect of depth weighting is further improved. The proposed method is also proved to be anti-noised. The method is applied to the measured gravity gradiometry data of Vinton Dome, and the results clearly show the location of the cap rock.

Key words: gravity gradiometry data, data combination, depth weighting, correlation imaging

中图分类号: 

  • P631.1
[1] Hou Zhenlong, Wei Xiaohui, Huang Danian. Fast Density Inversion Solution for Full Tensor Gravity Gradiometry Data[J]. Pure and Applied Geophysics, 2016, 173(2):509-523.
[2] Oruc B, Sertcelik I, Kafadar O, et al. Structural Interpretation of the Erzurum Basin, Eastern Turkey, Using Curvature Gravity, Gradient Tensor and Gravity Inversion of Basement Relief[J]. Journal of Applied Geophysics, 2013, 88(4):105-113.
[3] 袁园,黄大年,余青露.利用加强水平方向总水平导数对位场全张量数据进行边界识别[J].地球物理学报,2015,58(7):2556-2565. Yuan Yuan, Huang Danian, Yu Qinglu. Using Enhanced Directional Total Horizontal Derivatives to Detect the Edges of Potential-Field Full Tensor Data[J]. Chinese Journal of Geophysics, 2015, 58(7):2556-2565.
[4] 高秀鹤,黄大年,孙思源,等.重力梯度数据协克里金三维反演确定岩脉倾向[J].吉林大学学报(地球科学版),2017,47(2):589-596. Gao Xiuhe, Huang Danian, Sun Siyuan,et al. Identify the Dip Angle of the Dipping Dike Model Based on Cokriging Inversion of Gravity Gradient Data[J]. Journal of Jilin University (Earth Science Edition), 2017, 47(2):589-596.
[5] Patella D. Introduction to Ground Surface Self-Potential Tomography[J]. Geophysical Prospecting, 1997, 45(4):653-681.
[6] Mauriello P, Monna D, Patella D. 3D Geoelectric Tomography and Archaeological Applications[J]. Geophysical Prospecting, 1998, 46(5):543-570.
[7] Mauriello P, Patella D. Resistivity Anomaly Imaging by Probability Tomography[J]. Geophysical Prospecting, 1999, 47(3):411-429.
[8] Mauriello P, Patella D. Localization of Maximum Depth Gravity Anomaly Sources by a Distribution of Equivalent Point Masses[J]. Geophysics, 2001, 66(5):1431-1437.
[9] Iuliano T, Mauriello P, Patella D. Looking Inside Mount Vesuvius by Potential Fields Integrated Probability Tomographies[J]. Journal of Volcanology and Geothermal Research, 2002, 113(3):363-378.
[10] 王绪本,毛立峰,高永才.电磁导数场相关成像方法研究[J]. 成都理工大学学报(自然科学版),2004,31(6):679-684. Wang Xuben, Mao Lifeng, Gao Yongcai. Probability Tomography of Electromagnetic Field-Derivative Method[J]. Journal of Chengdu University of Technology, 2004, 31(6):679-684.
[11] 毛立峰,王绪本,高永才.大地电磁概率成像的效果评价[J]. 地球物理学报,2005,48(2):429-433. Mao Lifeng, Wang Xuben, Gao Yongcai. Appraisement on the MT Probability Temography[J]. Chinese Journal of Geopysics, 2005, 48(2):429-433.
[12] 郭良辉,孟小红,石磊.磁异常ΔT 三维相关成像[J]. 地球物学报,2010,53(2):435-441. Guo Lianghui, Meng Xiaohong, Shi Lei. 3D Correlation Imaging for Magnetic Anomaly ΔT Data[J]. Chinese Journal of Geophysics, 2010, 53(2):435-441.
[13] 郭良辉,孟小红,石磊,等.重力和重力梯度数据三维相关成像[J].地球物学报,2009,52(4):1098-1106. Guo Lianghui, Meng Xiaohong, Shi Lei, et al. 3-D Correlation Imaging for Gravity Gradiometry Data[J]. Chinese Journal of Geophysics, 2009, 52(4):1098-1106.
[14] 马国庆,杜晓娟,李丽丽.改进的位场相关成像方法[J]. 地球科学:中国地质大学学报,2013,38(5):1121-1127. Ma Guoqing, Du Xiaojuan, Li Lili. Improved Potential Field Correlation Imaging Method[J]. Earth Science:Journal of China University of Geosciences,2013,38(5):1121-1127.
[15] 石磊,郭良辉,孟小红. 磁总场异常垂直梯度三维相关成像[J]. 地球物理学进展,2012,27(4):1609- 1614. Shi Lei, Guo Lianghui, Meng Xiaohong. 3D Correlation Imaging of the Vertical Gradient of Magnetic Total Field Anomaly[J]. Progress in Geophys, 2012, 27(4):1609-1614.
[16] 侯振隆,王恩德.基于泰勒级数的重力异常数据快速相关成像[J]. 东北大学学报(自然科学版),2019,40(4):563-568. Hou Zhenlong, Wang Ende. Fast Probability Tomography of Gravity Anomaly Data Based on Taylor Series[J]. Journal of Northeastern University (Natural Science Edition), 2019, 40(4):563-568.
[17] Li Yaoguo, Oldenburg W D. 3-D Inversion of Magnetic Data[J]. Geophysics, 1996, 61(2):394-408.
[18] Federico Cella, Maurizio Fedi. Inversion of Potential Field Data Using the Structural Index as Weighting Function Rate Decay[J]. Geophysical Prospecting, 2012, 60(2):313-336.
[19] Commer M. Three-Dimensional Gravity Modeling and Focusing Inversion Using Rectangular Meshes[J]. Geophysical Prospecting, 2011, 59(5):966-979.
[20] Commer M, Newman G A, Williams K H, et al. 3D Induced-Polarization Data Inversion for Complex Resistivity[J]. Geophysics, 2011, 76(3):157-171.
[21] Chen Z, Meng X, Guo L, et al. GICUDA:A Parallel Program for 3D Correlation Imaging of Large Scale Gravity and Gravity Gradiometry Data on Graphics Processing Units with CUDA[J]. Computers & Geosciences, 2012, 46(5):119-128.
[22] Li Y, Oldenburg D W. 3-D Inversion of Gravity Data[J]. Geophysics, 1998, 63:109-119.
[23] Li Y. 3-D Inversion of Gravity Gradiometer Data[C]//SEG Int'l Exposition and Annual Meeting. San Antonio:[s. n.], 2001:1470-1473.
[24] 秦朋波. 重力和重力梯度数据联合反演方法研究[D]. 长春:吉林大学,2016. Qin Pengbo. A Study on Integrated Gravity and Gravity Gradient Data in Inversion[D]. Changchun:Jilin University, 2016.
[25] Ennen C. Mapping Gas-Charged Fault Blocks Around the Vinton Salt Dome, Louisiana Using Gravity Gradiometry Data[D].Houston:University of Houston, 2012.
[26] Ennen C, Hall S. Structural Mapping of the Vinton Salt Dome, Louisiana, Using Gravity Gradiometry Data[C]//2011 SEG Annual Meeting.[S. l.]:Society of Exploration Geophysicists, 2011:15.
[27] 侯振隆. 重力全张量梯度数据的并行反演算法研究及应用[D]. 长春:吉林大学,2016. Hou Zhenlong. Research and Application of the Parallel Inversion Algorithms Based on the Full Tensor Gravity Gradiometry Data[D].Changchun:Jilin University, 2016.
[28] 耿美霞. 基于地质统计学的重力梯度全张量数据三维反演方法研究[D]. 长春:吉林大学,2015. Geng Meixia. The Study of 3D Inversion of Full Tensor Gravity Gradiometry Data Based on Geostatistics Method[D]. Changchun:Jilin University, 2015.
[29] Geng M, Huang D, Yang Q, et al. 3D Inversion of Airborne Gravity Gradiometry Data Using Cokriging[J]. Geophysics, 2014, 79(4):G37-G47.
[30] Thompson S A, Eichelberger O H. Vinton Salt Dome, Calcasieu Parish, Louisiana[J]. AAPG Bulletin, 1928, 12(4):385-394.
[31] Marfurt K J, Zhou H W, Sullivan E C. Development and Calibration of New 3-D Vector VSP Imaging Technology:Vinton Salt Dome, LA[R]. Texas:University of Houston, 2004.
[32] Oliveira V C, Barbosa V C F.3-D Radial Gravity Gradient Inversion[J]. Geophysical Journal International, 2013, 195(2):883-902.
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