舒晴^1,2,,
马国庆^1,,,
刘财¹,
李丽丽¹
1. 吉林大学地球探测科学与技术学院, 长春 130021
2. 中国地质调查局国土资源航空物探遥感中心, 北京 100083

基金项目: 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）和国家科技重大专项子任务（2016ZX05027-002-003）联合资助


详细信息

作者简介: 舒晴, 男, 1979年生, 博士研究生, 主要从事航空重磁数据处理及解释方面的研究.E-mail:shuqing_agrs@163.com

通讯作者: 马国庆, 男, 1984年生, 副教授, 主要从事重磁及张量数据处理及解释方面的研究.E-mail:maguoqing@jlu.edu.cn

中图分类号: P631

收稿日期:2017-11-27
修回日期:2018-01-10
上线日期:2018-04-05

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

SHU Qing^1,2,,
MA GuoQing^1,,,
LIU Cai¹,
LI LiLi¹
1. College of Geoexploration Science and Technology, Jilin University, Changchun 130021, China
2. China Aero Geophysical Survey and Remote Sensing Center for Land and Resources, Beijing 100083, China


More Information

Corresponding author: MA GuoQing,E-mail:maguoqing@jlu.edu.cn

MSC: P631

--> Received Date: 27 November 2017
Revised Date: 10 January 2018
Available Online: 05 April 2018


摘要
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

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 tensor gradient 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.
Key words:Magnetic tensor/
Directional analytic signal/
Balanced edge detection/
Depth imaging



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