小波结合幂次变换方法在边界识别中的应用

doi:10.13278/j.cnki.jjuese.20170266

吉林大学学报(地球科学版) ›› 2018, Vol. 48 ›› Issue (2): 420-432.doi: 10.13278/j.cnki.jjuese.20170266

• 地球物理数据处理与解释技术 • 上一篇下一篇

小波结合幂次变换方法在边界识别中的应用

谭晓迪, 黄大年, 李丽丽, 马国庆, 张代磊

吉林大学地球探测科学与技术学院, 长春 130026

收稿日期:2017-09-29 出版日期:2018-03-26 发布日期:2018-03-26
通讯作者: 李丽丽(1983-),女,副教授,博士,主要从事重磁数据处理与解释研究,E-mail:lilili@jlu.edu.cn E-mail:lilili@jlu.edu.cn
作者简介:谭晓迪(1994-),女,硕士研究生,主要从事重磁数据处理和解释研究,E-mail:tanxd16@mails.jlu.edu.cn
基金资助:
国家重点研发计划项目（2017YFC0602203，2017YFC0601606）；国家科技重大专项（2016ZX05027-002-003）；国家自然科学基金项目（41604098，41430322）

Application of Wavelet Transform Combined with Power Transform Method in Edge Detection

Tan Xiaodi, Huang Danian, Li Lili, Ma Guoqing, Zhang Dailei

College of GeoExploration Science and Technology, Jilin University, Changchun 130026, China

Received:2017-09-29 Online:2018-03-26 Published:2018-03-26
Supported by:
Supported by National Key Research and Development Program of China (2017YFC0602203,2017YFC0601606),National Science and Technology Major Project (2016ZX05027-002-003) and National Natural Science Foundation of China (41604089, 41430322)

摘要/Abstract

摘要： 在位场数据边界识别传统方法理论的基础上，前人研究了各种场源边界增强技术来提高信噪比和定位精度，但仍然存在受噪声影响大、场源边界不够收敛等问题。本文在传统边界识别方法的基础上进行改进，利用小波变换与传统方法相结合来增强对噪声的压制能力，并且提出了幂次变换法对识别出的边界进行有效收敛。将改进方法与传统方法应用于地质体的边界识别；同时选取3种传统数值计算方法，结合模型数据、四川盆地重力异常数据及朱日和地区磁异常数据进行对比分析。结果表明：小波结合幂次变换法能够有效识别出研究区域内地质体的边界，能很好地起到压制噪声的作用；并且识别出的边界收敛，提高了边界识别的精度，在边界识别中取得了良好的效果。

关键词: 边界识别, 小波变换, 幂次变换, 压制噪声, 边界收敛

Abstract: On the basis of the traditional methods for edge detection of potential-field data, various field source edge enhancement techniques have been studied to improve signal-to-noise ratio (SNR) and localization accuracy. But problems still exist, such as noise interference and divergence of source edges. The authors made an improvement based on the traditional methods of edge detection using wavelet combined with traditional methods to enhance the ability of noise suppressing, and put forward power transform to make edges convergent effectively. In this paper, the principle was introduced and the method was applied to edge detection. Three commonly used numerical methods for edge detection were selected and compared with the modified methods in edge detection effect with the model data, gravity anomalies of Sichuan basin, and the magnetic anomalies in Zhurihe area. The results showed that the method of wavelet combined with power transform could effectively detect the edges of geological bodies in the study area. It plays a good role in noise suppressing, and can be used to identify the edges with convergence. The proposed method improves the accuracy of edge detection and obtains satisfactory results.

Key words: edge detection, wavelet transform, power transform, noise suppression, edge convergence

中图分类号:

P631.1

谭晓迪, 黄大年, 李丽丽, 马国庆, 张代磊. 小波结合幂次变换方法在边界识别中的应用[J]. 吉林大学学报(地球科学版), 2018, 48(2): 420-432.

Tan Xiaodi, Huang Danian, Li Lili, Ma Guoqing, Zhang Dailei. Application of Wavelet Transform Combined with Power Transform Method in Edge Detection[J]. Journal of Jilin University(Earth Science Edition), 2018, 48(2): 420-432.

参考文献

[1] 杨高印. 位场数据处理的一项新技术:小子域滤波法[J]. 石油地球物理勘探,1995, 30(2):240-244. Yang Gaoyin. A New Technique for Potential-Field Data Processing:Small Sub-Domain Filtering[J]. Oil Geophysical Prospecting, 1995, 30(2):240-244.
[2] Cooper G R J, Cowan D R. Edge Enhancement of Potential-Field Data Using Normalized Statistics[J]. Geophysics, 2008, 73(3):1-4.
[3] Hood P, McClure D J. Gradient Measurements in Ground Magnetic Prospecting[J]. Geophysics, 1965, 30(3):403-410.
[4] Cordell L. Gravimetric Expression of Graben Faulting in Santa Fe Country and the Espanola Basin, New Mexico[C]//New Mexico Geological Society Guidebook, 30th Annual Fall Field Conference. New Mexico:New Mexico Geological, Society, 1979:59-64.
[5] Nabighian M N. The Analytic Signal of Two-Dimen-sional Magnetic Bodies With Polygonal Cross-Section:Its Properties and Use for Automated Anomaly Interpretation[J]. Geophysics, 1972, 37(3):507-517.
[6] Miller H G, Singh V. Potential Field Tilt:A New Concept for Location of Potential Field Sources[J]. Journal of Applied Geophysics, 1994, 32(2/3):213-217.
[7] Wijins C, Perez C, Kowalczyk P. Theta Map:Edge Detection in Magnetic Data[J]. Geophysics, 2005, 70(4):39-43.
[8] 马国庆,杜晓娟,李丽丽. 利用水平与垂直导数的相关系数进行位场数据的边界识别[J]. 吉林大学学报(地球科学版),2011, 41(增刊1):345-348. Ma Guoqing, Du Xiaojuan, Li Lili. Edge Detection of Potential Filed Data Using Correlation Coefficients of Horizontal and Vertical Derivatives[J]. Journal of Jilin University (Earth Science Edition), 2011, 41(Sup.1):345-348.
[9] 马国庆,黄大年,于平,等. 改进的均衡滤波器在位场数据边界识别中的应用[J]. 地球物理学报,2012, 55(12):4288-4295. Ma Guoqing, Huang Danian, Yu Ping, et al. Application of Improved Balancing Filters to Edge Identification of Potential Field Data[J]. Chinese Journal of Geophysics, 2012, 55(12):4288-4295.
[10] 徐亚,郝天珧,周立宏,等. 位场小波变换研究进展[J]. 地球物理学进展,2006, 21(4):1132-1138. Xu Ya, Hao Tianyao, Zhou Lihong, et al. Review of Wavelet Transform in Potential Field[J]. Progress in Geophysics, 2006, 21(4):1132-1138.
[11] 刘彩云. 基于小波变换的位场场源识别与异常分离方法研究[D]. 北京:中国地质大学,2014. Liu Caiyun. The Research on Methods of Edge Detection and Anomaly Separation of Potential Field Source Based on Wavelet Transform[D]. Beijing:China University of Geosciences, 2014.
[12] 张郝. 基于小波变换的图像去噪方法研究[D].北京:北京交通大学,2008. Zhang Hao. The Research on Image Denoising Based on Wavelet Transform[D].Beijing:Beijing Jiaotong University, 2008.
[13] 严奉霞. 小波理论及其在图像边缘检测中的应用[D]. 长沙:国防科学技术大学,2003. Yan Fengxia. Wavelet Theory and Its Application to The Edge Detection of Images[D].Changsha:National University of Defense Technology, 2003.
[14] 张代磊,黄大年,张冲. 基于遗传算法优化的BP神经网络在密度界面反演中的应用[J]. 吉林大学学报(地球科学版),2017, 47(2):580-588. Zhang Dailei, Huang Danian, Zhang Chong. Application of BP Neural Network Based on Genetic Algorithm in the Inversion of Density Interface[J]. Journal of Jilin University (Earth Science Edition), 2017, 47(2):580-588.
[15] 王明,郭志宏,何辉,等. 基于反双曲正切的位场边界识别技术[J]. 物探与化探,2013, 37(4):655-663. Wang Ming, Guo Zhihong, He Hui, et al. Edge Detection of Potential Field Data Using Inverse Hyperbolic Tangent[J]. Geophysical and Geochemical Exploration, 2013, 37(4):655-663.
[16] 冷眉. 重磁资料异常分离与构造边界识别方法的研究及应用[D]. 成都:成都理工大学,2014. Leng Mei. Research and Application of the Method of Separating Anomalous and the Structure Boundary Identification with the Data of Gravity and Magnetic[D].Chengdu:Chengdu University of Technology, 2014.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

小波结合幂次变换方法在边界识别中的应用

Application of Wavelet Transform Combined with Power Transform Method in Edge Detection

PDF (PC)

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

Metrics

本文评价

推荐阅读 0

[1]	赵利, 李理, 董大伟. 基于小波变换的盆地沉积-构造波动分析[J]. 吉林大学学报(地球科学版), 2015, 45(4): 1227-1236.
[2]	李海山, 吴国忱, 印兴耀. 形态分量分析在去除地震资料随机噪声中的应用[J]. J4, 2012, 42(2): 554-561.
[3]	韩念龙, 刘闯, 庄立, 张巍. 基于不同小波变换与同态滤波结合的CBERS-02B卫星CCD图像的薄云去除[J]. J4, 2012, 42(1): 275-279.
[4]	卢文喜, 陈社明, 王晨子, 刘磊, 贾洪玮, 吕德全. 基于小波变换的大安地区年降水量变化特征[J]. J4, 2010, 40(1): 121-127.
[5]	李红星，陶春辉，刘财，邓显明,周建平，张金辉，顾春华，何拥华. 多频海底声学原位测试信号消除干扰研究[J]. J4, 2007, 37(5): 1034-1037.
[6]	房文静，范宜仁，李霞，邓少贵. 基于测井数据小波变换的准层序自动划分[J]. J4, 2007, 37(4): 833-0836.