Residual magnetic error remains after standard levelling process.The weak non-geological effect,manifesting itself as streaky noise along flight lines,creates a challenge for airborne geophysical data processing and i...Residual magnetic error remains after standard levelling process.The weak non-geological effect,manifesting itself as streaky noise along flight lines,creates a challenge for airborne geophysical data processing and interpretation.Microleveling is the process to eliminate this residual noise and is now a standard areogeophysical data processing step.In this paper,we propose a two-step procedure for single aerogeophysical data microleveling:a deep convolutional network is first adopted as approximator to map the original data into a low-level part with nature geological structures and a corrugated residual which still contains high-level detail geological structures;second,the mixture of Gaussian robust principal component analysis(MoG-RPCA)is then used to separate the weak energy fine structures from the residual.The final microleveling result is the addition of low-level structures from deep convolutional network and fine structures from MoG-RPCA.The deep convolutional network does not need dataset for training and the handcrafted network serves as prior(deep image prior)to capture the low-level nature geological structures in the areogeophysical data.Experiments on synthetic data and field data demonstrate that the combination of deep convolutional network and MoG-RPCA is an effective framework for single areogeophysical data microleveling.展开更多
针对沙漠地震记录中包含大量复杂噪声降低信噪比的问题,提出一种将变分模态分解(VMD:Variational Mode Decomposition)与混合高斯鲁棒主成分分析(MoG-RPCA:Mixture of Gauss-Robust Principal Component Analysis)相结合的自适应秩收敛...针对沙漠地震记录中包含大量复杂噪声降低信噪比的问题,提出一种将变分模态分解(VMD:Variational Mode Decomposition)与混合高斯鲁棒主成分分析(MoG-RPCA:Mixture of Gauss-Robust Principal Component Analysis)相结合的自适应秩收敛去噪算法。首先利用VMD对含噪记录进行分解,将分解得到所有模态重排成一个新的信号矩阵,并对其进行MoG-RPCA低秩分解,当分解误差满足预设要求时提取有效低秩分量,最后将低秩矩阵中每一道信号的所有模态叠加并与含噪记录作差得到最终去噪结果。该方法既规避了VMD模态取舍问题,同时对传统低秩分解进行自适应秩收敛,从而无需多次调整秩数大小。模拟实验和实际数据处理表明,该算法可以有效压制低频噪声,对有效信号幅度保持均能到达85%以上。展开更多
文摘Residual magnetic error remains after standard levelling process.The weak non-geological effect,manifesting itself as streaky noise along flight lines,creates a challenge for airborne geophysical data processing and interpretation.Microleveling is the process to eliminate this residual noise and is now a standard areogeophysical data processing step.In this paper,we propose a two-step procedure for single aerogeophysical data microleveling:a deep convolutional network is first adopted as approximator to map the original data into a low-level part with nature geological structures and a corrugated residual which still contains high-level detail geological structures;second,the mixture of Gaussian robust principal component analysis(MoG-RPCA)is then used to separate the weak energy fine structures from the residual.The final microleveling result is the addition of low-level structures from deep convolutional network and fine structures from MoG-RPCA.The deep convolutional network does not need dataset for training and the handcrafted network serves as prior(deep image prior)to capture the low-level nature geological structures in the areogeophysical data.Experiments on synthetic data and field data demonstrate that the combination of deep convolutional network and MoG-RPCA is an effective framework for single areogeophysical data microleveling.
文摘针对沙漠地震记录中包含大量复杂噪声降低信噪比的问题,提出一种将变分模态分解(VMD:Variational Mode Decomposition)与混合高斯鲁棒主成分分析(MoG-RPCA:Mixture of Gauss-Robust Principal Component Analysis)相结合的自适应秩收敛去噪算法。首先利用VMD对含噪记录进行分解,将分解得到所有模态重排成一个新的信号矩阵,并对其进行MoG-RPCA低秩分解,当分解误差满足预设要求时提取有效低秩分量,最后将低秩矩阵中每一道信号的所有模态叠加并与含噪记录作差得到最终去噪结果。该方法既规避了VMD模态取舍问题,同时对传统低秩分解进行自适应秩收敛,从而无需多次调整秩数大小。模拟实验和实际数据处理表明,该算法可以有效压制低频噪声,对有效信号幅度保持均能到达85%以上。
