Curvelet域GPR数据噪声压制与高频补偿方法

doi:10.12068/j.issn.1005-3026.2019.06.026

东北大学学报:自然科学版 ›› 2019, Vol. 40 ›› Issue (6): 903-907.DOI: 10.12068/j.issn.1005-3026.2019.06.026

Curvelet域GPR数据噪声压制与高频补偿方法

程浩¹，王德利²，王恩德¹，付建飞¹

(1. 东北大学深部金属矿山开采教育部重点实验室，辽宁沈阳110819; 2. 吉林大学地球探测科学与技术学院，吉林长春130026)

收稿日期:2018-05-15 修回日期:2018-05-15 出版日期:2019-06-15 发布日期:2019-06-14
通讯作者: 程浩
作者简介:程浩(1988-)，男，辽宁沈阳人，东北大学师资博士后；王恩德(1957-)，男，辽宁盖州人，东北大学教授，博士生导师.
基金资助:
国家自然科学基金资助项目(41804103)；国家重点研发计划项目(2016YFC0801603，2017YFC1503101)；中央高校基本科研业务费专项资金资助项目(N160103001).

Noise Suppression and High-Frequency Compensation of GRP Data in Curvelet Domain

CHENG Hao¹， WANG De-li²， WANG En-de¹， FU Jian-fei¹

1. Key Laboratory of Ministry of Education on Safe Mining of Deep Metal Mines， Northeastern University， Shenyang 110819， China; 2. College of Geo-Exploration Science and Technology， Jilin University， Changchun 130026， China.

Received:2018-05-15 Revised:2018-05-15 Online:2019-06-15 Published:2019-06-14
Contact: CHENG Hao
About author:-
Supported by:
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摘要/Abstract

摘要： 提出GPR数据Curvelet域随机噪声压制与高频补偿同步处理方法.首先，将GPR数据变换到Curvelet域，结合其多角度、多尺度的稀疏性，给出随尺度和角度变化的自适应阈值函数进行随机噪声的压制;其次，根据电磁波在完全弹性介质中的传播规律，结合Curvelet的多尺度多角度特性，求取时变补偿因子，倒数加权对应的尺度、角度，补偿高频损失;最后，进行Curvelet反变换，获得随机噪声压制与高频补偿以后的GPR数据.该方法属于完全数据驱动，克服了传统方法人为因素的影响.

关键词: GPR数据, Curvelet变换, 随机噪声, 高频衰减, 去噪方法, 吸收补偿

Abstract: This paper proposed a synchronous processing method using random noise suppression and high-frequency compensation for GPR data in curvelet domain. First， the GPR data were transformed into curvelet domain in order to use its multi-angle and multi-scale sparsity. The random noise thus could be suppressed by the adaptive threshold function changed with angle and scale. Second， according to the propagation laws of electromagnetic waves in elastic medium and the curvelet multi-scale feature， the time-varying compensation factor could be calculated so as to inverse weighting the corresponding scale and angle for compensation of the high-frequency absorption. Finally， inverse curvelet transformation was conducted thus the GPR data after random noise suppression and high-frequency compensation could be obtained. This method is totally a data-driven method thus can overcome the influence from artificial factors in traditional method.

Key words: GPR data, curvelet transform, random noise, high-frequency attenuation, de-noising method, absorption-compensation

中图分类号:

P631

程浩，王德利，王恩德，付建飞. Curvelet域GPR数据噪声压制与高频补偿方法[J]. 东北大学学报:自然科学版, 2019, 40(6): 903-907.

CHENG Hao， WANG De-li， WANG En-de， FU Jian-fei. Noise Suppression and High-Frequency Compensation of GRP Data in Curvelet Domain[J]. Journal of Northeastern University Natural Science, 2019, 40(6): 903-907.

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Curvelet域GPR数据噪声压制与高频补偿方法

Noise Suppression and High-Frequency Compensation of GRP Data in Curvelet Domain

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