Curvelet域GPR数据噪声压制与高频补偿方法

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.

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

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.