Side-by-side Chinese-English


【摘要】 全张量磁梯度数据具有高精度、高分辨率、多参量的优点, 能更加清晰地刻画地质体的分布特征, 综合利用磁张量梯度数据准确地获得地质体水平位置和深度信息是解释的主要目的.磁张量数据的方向解析信号具有减小倾斜磁化干扰的优点, 常被用来圈定磁源体的水平位置, 但解析信号强度随着地质体埋深的增加急剧衰减, 难以有效识别较深的地质体.张量数据均衡边界识别技术, 利用不同方向解析信号的比值函数, 能有效地均衡不同深度地质体的响应, 同时显示不同深度地质体的边界, 提高了对较深地质体的分辨率.磁张量数据深度成像技术根据实测张量数据与假定模型张量数据的相关系数来给定地质体的深度, 综合利用多参量数据联合反演提高了反演结果的准确性, 且无需进行复杂的反演运算, 是大数据量张量数据解释的有效方法.理论模型试验证明:磁张量数据均衡边界识别技术可清晰和准确地识别地质体的水平范围, 受倾斜磁化干扰小;磁张量数据深度成像技术可准确地获得地质体的深度信息, 具有较强的抗噪性.将上述方法应用于铁矿区实测航磁张量梯度数据解释, 获得了铁矿体水平分布与埋深, 深度结果与张量欧拉反褶积法计算结果一致.

【关键词】 磁张量;方向解析信号;均衡边界识别;深度成像;


【基金资助】 国家自然科学基金重点项目 (41430322) ; 国家自然科学基金项目 (41604098) ; 国家重点研发计划课题 (2017YFC0602203, 2017YFC0601606, 2017YFC0601305) ; 国家科技重大专项子任务 (2016ZX05027-002-003) 联合资助;

Balanced edge detection and depth imaging technique for the interpretation of full-tensor magnetic gradient data

【Abstract】 Full-tensor magnetic gradient data have the advantages of high precision, high resolution, and multiple parameters, permitting to better characterize geological bodies. The primary purpose of data interpretation is to accurately determine the horizontal location of the geological body and its depth by integrated use of magnetic gradient tensor data. The directional analytical signal of magnetic tensor data can reduce interference of oblique magnetization, which is often used to delineate the horizontal position of the source. But this signal attenuates rapidly with burial depth of the geological body, making it difficult to identify the deep body. To solve this problem, we propose the balance edge detection, which uses ratio functions of analytical signals of different directions to balance responses of geological bodies at varied depths. Meanwhile it can indicate boundaries of geological bodies at different depths, thus enhancing the resolution of the survey. We also propose a depth imaging method which determines the depth of a geological body based on the correlation coefficient between measured and assumed tensor data. This method is a joint inversion of multiple parameter data, which can enhance the accuracy of inversion. Besides, it does not need complex calculation, and is effective in interpretation of great-amount tensor data. Tests on synthetic models show that the balanced edge detection method can identify the horizontal extent of the geological body clearly and accurately, and suffers little effect of oblique magnetization. The depth imaging method of magnetic tensor data is able to obtain depth information of the geological body, and has stronger anti-noise ability. We apply these methods to interpretation of aeromagnetic tensor data from an iron mine, and delineate the horizontal distribution and depth of iron ore, of which the depth is consistent with that from calculation using the Euler deconvolution method.

【Keywords】 Magnetic tensor; Directional analytic signal; Balanced edge detection; Depth imaging;


【Funds】 Key Program of the National Natural Science Foundation of China (41430322); National Natural Science Foundation of China (41604098); National Key Research and Development Program of China (2017YFC0602203, 2017YFC0601606, 2017YFC0601305); National Science and Technology Major Project of the Ministry of Science and Technology of China (2016ZX05027-002-003);

Download this article

    Beiki M, Pedersen L B, Nazi H. 2011. Interpretation of aeromagnetic data using eigenvector analysis of pseudogravity gradient tensor. Geophysics, 76 (3): L1–L10.

    Bell R E, Anderson R, Pratson L. 1997. Gravity gradiometry resurfaces. The Leading Edge, 16 (1): 55–60.

    Doll W E, Gamey T J, Beard L P, et al. 2006. Airborne vertical magnetic gradient for near-surface applications. The Leading Edge, 25 (1): 50–53.

    Gamey T J, Starr T, Doll W E, et al. 2004. Initial design and testing of a full-tensor airborne SQUID magnetometer for detection of unexploded ordnance.// 74th Ann. Internat Mtg., Soc. Expi. Geophys. Expanded Abstracts.

    Jekeli C. 1993. A review of gravity gradiometer survey system data analyses. Geophysics, 58 (4): 508–514.

    Luo Y, Wu M P, Wang P, et al. 2015. Full magnetic gradient tensor from triaxial aeromagnetic gradient measurements: Calculation and application. Applied Geophysics, 12 (3): 283–291.

    Ma G Q, Du X J, Li L L. 2012. Comparison of the tensor local wavenumber method with the conventional local wavenumber method for the interpretation of total tensor data of potential fields. Chinese J. Geophys. (in Chinese), 55 (7): 2450–2461, doi: 10.6038/j.issn.0001-5733.2012.07.029.

    Mikhailov V, Pajot G, Diament M, et al. 2007. Tensor deconvolution: A method to locate equivalent sources from full tensor gravity data. Geophysics, 72 (5): 161–169.

    Qruç B. 2010a. Depth estimation of simple causative sources from gravity gradient tensor invariants and vertical component. Pure and Applied Geophysics, 167 (10): 1259–1272.

    Qruç B. 2010b. Location and depth estimation of point-dipole and line of dipoles using analytic signals of the magnetic gradient tensor and magnitude of vector components. Journal of Applied Geophysics, 70 (1): 27–37.

    Roest W R, Verhoef J, Pilkington M. 1992. Magnetic interpretation using the 3-D analytic signal. Geophysics, 57 (1): 116–125.

    Schmidt P W, Clark D A. 2000. Advantages of measuring the magnetic gradient tensor. Preview, 85: 26–30.

    Schmidt P W, Clark D A. 2006. The magnetic gradient tensor: Its properties and uses in source characterization. The Leading Edge, 25 (1): 75–78.

    Shu Q, Zhou J X. 2006. The brief introduction of the development of airborne magnetometer (in Chinese). Proceedings of the 22th Annual Conference of the Chinese Geophysical Society, 2006: 185.

This Article


CN: 11-2074/P

Vol 61, No. 04, Pages 1539-1548

April 2018


Article Outline


  • 0 Introduction
  • 1 Methodology
  • 2 Model test
  • 3 Application of actual data
  • 4 Conclusions
  • References