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
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.
