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物探与化探  2019, Vol. 43 Issue (2): 308-319    DOI: 10.11720/wtyht.2019.1112
  方法研究·信息处理·仪器研制 本期目录 | 过刊浏览 | 高级检索 |
正则化方法在比值类位场边缘识别方法中的研究
何涛1,2,3, 王万银1,2,3, 黄金明4, 张明华4, 杨敏1,2,3,5
1. 长安大学 重磁方法技术研究所,陕西 西安 710054
2. 长安大学 地质工程与测绘学院,陕西 西安 710054
3. 长安大学 西部矿产资源与地质工程教育部重点实验室,陕西 西安 710054
4. 中国地质调查局发展研究中心,北京 100037
5. 纽芬兰纪念大学 地球科学系,加拿大
The research of the regularization method in the ratio methods of edge recognition by potential field
Tao HE1,2,3, Wan-Yin WANG1,2,3, Jin-Ming HUANG4, Ming-Hua ZHANG4, Min YANG1,2,3,5
1. Gravity & Magnetic Institute of Chang’an University,Xi’an 710054,China;
2. College of Geology Engineering and Geomatics,Chang’an University,Xi’an 710054,China;
3. Key Laboratory of Western China’s Mineral Resources and Geological Engineering,Ministry of Education,Chang’an University,Xi’an 710054,China;
4. China Geological Survey Development Research Center, Beijing 100037, China
5. Department of Earth Sciences, Memorial University of Newfoundland, St. Johns, NF, Canada
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摘要 

位场边缘识别方法有很多种,其中归一化标准差法(NSTD)、倾斜角法(Ta)和Theta Map(cosθ)等方法属于比值类方法。比值类方法在计算过程中会出现分母接近于0或者等于0这种情况,致使计算结果不稳定,并产生错误的边缘识别结果。为此,对比值类边缘识别方法计算公式中的分母加一个大于零的正则化因子,不但解决了比值类方法的数值计算稳定性问题,而且提高了部分比值类边缘识别方法识别结果的精度。通过理论模型和实际资料检验了新方法的稳定性、精度以及有效性。正则化因子的引入同样可以改善以比值类方法为基础构建的二阶导数类边缘识别方法的识别效果,如倾斜角总水平导数(Ta-THDR)的识别效果。正则化这一思想不但可以解决比值类位场边缘识别方法的数值计算问题,而且可以解决比值类方法的数值计算问题。

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何涛
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黄金明
张明华
杨敏
关键词 边缘识别比值类方法正则化方法稳定性精度    
Abstract

The ratio methods are one sort of edge recognition methods by using potential field, which contains the Normalized Standard Deviation method (NSTD), Tilt Angle method (Ta) and Theta Map (cosθ). If the denominator of ratio method closes or even equals to zero in the process of calculation, the result obtaining from ratio methods is unstable and may even bear little resemblance to the true geology. In order to relief this problem, a regularization factor, which is greater than zero, is added in the denominator of the ratio methods’ formula, which not only enhances the numerical stability of the ratio methods but also improves the accuracy of some ratio edge recognition methods. The stability, accuracy and effectiveness of the new method is verified by testing synthetic models and calculating real data. Also, the introduction of the regularization factor also can improve the effect of recognizing edge by the second-derivative edge recognition methods, which is based on the ratio methods, such as the Total Horizontal Derivative of the Tilt Angle (Ta-THDR). The idea of regularization can not only solve the numerical calculation problem of the ratio methods of edge recognition for potential field, but also solve the numerical calculation problem of all ratio methods.

Key wordsedge recognition    ratio methods    regularization method    stability    accuracy
收稿日期: 2018-12-03      出版日期: 2019-04-15
ZTFLH:  P631  
基金资助:国家重点研发计划项目2017YFC0602200之课题“航空地球物理综合处理解释方法研究及软件开发”(2017YFC0602202);中国地质调查局发展研究中心“智能地质调查系统开发与推广”项目(121201004000150014)
作者简介: 何涛(1994-),男,在读硕士研究生,研究方向是重、磁方法理论及应用。Email: hetaochd@163.com
引用本文:   
何涛, 王万银, 黄金明, 张明华, 杨敏. 正则化方法在比值类位场边缘识别方法中的研究[J]. 物探与化探, 2019, 43(2): 308-319.
Tao HE, Wan-Yin WANG, Jin-Ming HUANG, Ming-Hua ZHANG, Min YANG. The research of the regularization method in the ratio methods of edge recognition by potential field. Geophysical and Geochemical Exploration, 2019, 43(2): 308-319.
链接本文:  
http://www.wutanyuhuatan.com/CN/10.11720/wtyht.2019.1112      或      http://www.wutanyuhuatan.com/CN/Y2019/V43/I2/308
模型类别 形体个数 长/m 宽/m 埋深/m 间隔/m 模型设计目的
模型A 重力模型 1 160 80 10~50 测试比值类方法加入正则
化因子后的适用性
模型B 重力模型 2 160 90 10~50 20 测试比值类方法加入正则化
因子后,对于横向分辨率
的影响
160 90 10~50
模型C 重力模型 3 160 40 10~50 90 测试比值类方法加入正则
化因子后,对于不同埋深地
质体边缘识别的影响
160 40 25~65
160 40 40~80
模型D 磁力模型 1 160 80 10~50 测试比值类方法加入正则化因子
后,对于磁力异常的适用性
模型E 加噪(1%)
重力模型
1 160 80 10~50 测试比值类方法加入正则
化因子后的稳定性
Table1  使用模型参数说明
Fig.1  模型A重力异常
Fig.2  A模型比值类方法边缘识别结果对比
Fig.3  A模型倾斜角总水平导数边缘识别结果对比
Fig.4  模型B重力异常
Fig.5  B模型比值类方法边缘识别结果对比
Fig.6  B模型倾斜角总水平导数边缘识别结果对比
Fig.7  模型C重力异常
Fig.8  C模型比值类方法边缘识别结果对比
Fig.9  C模型倾斜角总水平导数边缘识别结果对比
Fig.10  模型D磁力异常图与化极磁异常a—原始磁力异常;b—化极磁力异常
Fig.11  D模型比值类方法边缘识别结果对比
Fig.12  D模型倾斜角总水平导数边缘识别结果对比
Fig.13  模型E重力异常
Fig.14  E模型比值类方法边缘识别结果对比
Fig.15  E模型倾斜角总水平导数边缘识别结果对比
Fig.16  实测重力异常
Fig.17  实测重力异常比值类方法边缘识别结果对比
Fig.18  实测重力异常倾斜角总水平导数边缘识别结果对比
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