The airwave effect greatly influences the observational data from controlledsource electromagnetic exploration in shallow seas, which obscures the abnormal effects generated by exploration targets and, hence, affects ...The airwave effect greatly influences the observational data from controlledsource electromagnetic exploration in shallow seas, which obscures the abnormal effects generated by exploration targets and, hence, affects the accuracy of the late exploration data interpretation. In this study, we propose a method to separate the main part from the anomalous field of marine controlled-source electromagnetic method （MCSEM） data based on Stratton-Chu integral transforms to eliminate the airwave effect, which dominates observed electromagnetic （EM） response in shallow seawater. This method of separating the main part from the anomalous field is a type of finite impulse response filter based on a discrete data set. Theoretical analysis proved that the method is stable and able to effectively depress noise. A numerical test indicated that the method could successfully eliminate the airwave effect from the observed EM signals generated by an air water interface and a seawater layer. This technique is applicable for seawater models with either flat or rough seabeds.展开更多
Least-squares migration （LSM） is applied to image subsurface structures and lithology by minimizing the objective function of the observed seismic and reverse-time migration residual data of various underground refl...Least-squares migration （LSM） is applied to image subsurface structures and lithology by minimizing the objective function of the observed seismic and reverse-time migration residual data of various underground reflectivity models. LSM reduces the migration artifacts, enhances the spatial resolution of the migrated images, and yields a more accurate subsurface reflectivity distribution than that of standard migration. The introduction of regularization constraints effectively improves the stability of the least-squares offset. The commonly used regularization terms are based on the L2-norm, which smooths the migration results, e.g., by smearing the reflectivities, while providing stability. However, in exploration geophysics, reflection structures based on velocity and density are generally observed to be discontinuous in depth, illustrating sparse reflectance. To obtain a sparse migration profile, we propose the super-resolution least-squares Kirchhoff prestack depth migration by solving the L0-norm-constrained optimization problem. Additionally, we introduce a two-stage iterative soft and hard thresholding algorithm to retrieve the super-resolution reflectivity distribution. Further, the proposed algorithm is applied to complex synthetic data. Furthermore, the sensitivity of the proposed algorithm to noise and the dominant frequency of the source wavelet was evaluated. Finally, we conclude that the proposed method improves the spatial resolution and achieves impulse-like reflectivity distribution and can be applied to structural interpretations and complex subsurface imaging.展开更多
基金supported by the National Natural Science Foundation of China(No.41574067)863 Program(No.2012AA09A404)
文摘The airwave effect greatly influences the observational data from controlledsource electromagnetic exploration in shallow seas, which obscures the abnormal effects generated by exploration targets and, hence, affects the accuracy of the late exploration data interpretation. In this study, we propose a method to separate the main part from the anomalous field of marine controlled-source electromagnetic method （MCSEM） data based on Stratton-Chu integral transforms to eliminate the airwave effect, which dominates observed electromagnetic （EM） response in shallow seawater. This method of separating the main part from the anomalous field is a type of finite impulse response filter based on a discrete data set. Theoretical analysis proved that the method is stable and able to effectively depress noise. A numerical test indicated that the method could successfully eliminate the airwave effect from the observed EM signals generated by an air water interface and a seawater layer. This technique is applicable for seawater models with either flat or rough seabeds.
基金supported by the National Natural Science Foundation of China(No.41422403)
文摘Least-squares migration （LSM） is applied to image subsurface structures and lithology by minimizing the objective function of the observed seismic and reverse-time migration residual data of various underground reflectivity models. LSM reduces the migration artifacts, enhances the spatial resolution of the migrated images, and yields a more accurate subsurface reflectivity distribution than that of standard migration. The introduction of regularization constraints effectively improves the stability of the least-squares offset. The commonly used regularization terms are based on the L2-norm, which smooths the migration results, e.g., by smearing the reflectivities, while providing stability. However, in exploration geophysics, reflection structures based on velocity and density are generally observed to be discontinuous in depth, illustrating sparse reflectance. To obtain a sparse migration profile, we propose the super-resolution least-squares Kirchhoff prestack depth migration by solving the L0-norm-constrained optimization problem. Additionally, we introduce a two-stage iterative soft and hard thresholding algorithm to retrieve the super-resolution reflectivity distribution. Further, the proposed algorithm is applied to complex synthetic data. Furthermore, the sensitivity of the proposed algorithm to noise and the dominant frequency of the source wavelet was evaluated. Finally, we conclude that the proposed method improves the spatial resolution and achieves impulse-like reflectivity distribution and can be applied to structural interpretations and complex subsurface imaging.
