杜威^1,,
吴贺宇²,
陈祥忠^3,,,
刘俊成²,
张阳阳⁴,
惠梦琳³
1. 中国科学院地球化学研究所矿床地球化学重点研究实验室, 贵阳 550081
2. 吉林大学地球探测科学与技术学院, 长春 130026
3. 北京桔灯地球物理勘探股份有限公司, 北京 102200
4. 安徽省勘查技术院, 合肥 230031

基金项目: 国家青年科学基金项目(41904129)资助


详细信息

作者简介: 杜威, 女, 1990年生, 博士后, 主要从事重磁场数据处理及解释方面的研究.E-mail: duwei0505@yeah.net

通讯作者: 陈祥忠, 男, 1982年生, 博士, 主要从事物探综合处理方法技术的研究.E-mail: 6447316@163.com

中图分类号: P631

收稿日期:2020-12-03
修回日期:2021-06-12
上线日期:2021-09-10

An improved edge detection method for full gravity gradient tensor data

DU Wei^1,,
WU HeYu²,
CHEN XiangZhong^3,,,
LIU JunCheng²,
ZHANG YangYang⁴,
HUI MengLin³
1. State Key Laboratory of Ore Deposit Geochemistry, Institute of Geochemistry, Chinese Academy of Sciences, Guiyang 550081, China
2. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China
3. Beijing Orangelamp Geophysical Exploration Company Limited, Beijing 102200, China
4. Geological Exploration Technology Institute of Anhui Province, Hefei 230031, China


More Information

Corresponding author: CHEN XiangZhong,E-mail:6447316@163.com

MSC: P631

--> Received Date: 03 December 2020
Revised Date: 12 June 2021
Available Online: 10 September 2021


摘要
摘要:边界识别是重力资料解释中的一项重要任务.随着重力梯度测量技术的迅速发展，重力梯度张量数据在边界识别中的应用越来越广泛.本文重点研究了随着深度的增加，边界识别能力下降，正负异常中出现假边缘的问题.另外，有些边缘检测方法对走向不同的地质体识别能力有所差异.本文对基于重力梯度张量的水平方向Theta法进行改进，通过选择合适的阈值来减少虚假异常，提出了一种改进的重力梯度全张量数据的边界识别方法(IED).通过模型试验的对比，证明该方法不再受地质构造走向的影响，对不同深度的地质体边缘检测清晰、连续，且正负异常之间无虚假边界.最后，将该方法应用于加拿大圣乔治湾的重力梯度张量资料，其结果显示了更多的地质细节.
关键词: 重力梯度全张量/
边界识别/
改进的水平方向Theta法/
阈值

Abstract:Edge detection is an essential task in interpretations of gravity and magnetic data. With the rapid development of gravity gradient measurement technology, gravity gradient tensor data has been increasingly used in edge detection. This article focuses on the problem that with the depth increase, the ability to recognize the edge decreases and false edges occur in positive and negative anomalies when using some edge detection methods. In addition, some methods have different recognition abilities for geological bodies with different strikes. Here we propose a new edge detection method which is based on the improved horizontal Theta method of gravity gradient tensors and choosing an appropriate threshold to reduce false anomalies. Comparison of model tests demonstrates that the proposed method is no longer affected by the strike of geological structures. The edges of geological bodies at different depths detected are clear and continuous, and there are no false boundaries between positive and negative anomalies. Finally, this method is applied to the real gravity gradient tensor data in St. Georges Bay, Canada, revealing more geological details.
Key words:Gravity gradient tensor/
Edge detection/
Improved horizontal Theta method/
Threshold



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