Regularized least-squares migration of simultaneous-source seismic data with adaptive singular spectrum analysis

Simultaneous-source acquisition has been recog- nized as an economic and efficient acquisition method, but the direct imaging of the simultaneous-source data produces migration artifacts because of the interference of adjacent sources. To overcome this problem, we propose the regularized least-squares reverse time migration method （RLSRTM） using the singular spectrum analysis technique that imposes sparseness constraints on the inverted model. Additionally, the difference spectrum theory of singular values is presented so that RLSRTM can be implemented adaptively to eliminate the migration artifacts. With numerical tests on a fiat layer model and a Marmousi model, we validate the superior imaging quality, efficiency and convergence of RLSRTM compared with LSRTM when dealing with simultaneoussource data, incomplete data and noisy data.

Huber inversion-based reverse-time migration with de-primary imaging condition and curvelet-domain sparse constraint

Least-squares reverse-time migration(LSRTM) formulates reverse-time migration(RTM) in the leastsquares inversion framework to obtain the optimal reflectivity image. It can generate images with more accurate amplitudes, higher resolution, and fewer artifacts than RTM. However, three problems still exist:(1) inversion can be dominated by strong events in the residual;(2) low-wavenumber artifacts in the gradient affect convergence speed and imaging results;(3) high-wavenumber noise is also amplified as iteration increases. To solve these three problems, we have improved LSRTM: firstly, we use Hubernorm as the objective function to emphasize the weak reflectors during the inversion;secondly, we adapt the de-primary imaging condition to remove the low-wavenumber artifacts above strong reflectors as well as the false high-wavenumber reflectors in the gradient;thirdly, we apply the L1-norm sparse constraint in the curvelet-domain as the regularization term to suppress the high-wavenumber migration noise. As the new inversion objective function contains the non-smooth L1-norm, we use a modified iterative soft thresholding(IST) method to update along the Polak-Ribie re conjugate-gradient direction by using a preconditioned non-linear conjugate-gradient(PNCG) method. The numerical examples,especially the Sigsbee2 A model, demonstrate that the Huber inversion-based RTM can generate highquality images by mitigating migration artifacts and improving the contribution of weak reflection events.

Research progress on seismic imaging technology

High-precision seismic imaging is the core task of seismic exploration,guaranteeing the accuracy of geophysical and geological interpretation.With the development of seismic exploration,the targets become more and more complex.Imaging on complex media such as subsalt,small-scale,steeply dipping and surface topography structures brings a great challenge to imaging techniques.Therefore,the seismic imaging methods range from stacking-to migration-to inversion-based imaging,and the imaging accuracy is becoming increasingly high.This review paper includes:summarizing the development of the seismic imaging;overviewing the principles of three typical imaging methods,including common reflection surface(CRS)stack,migration-based Gaussian-beam migration(GBM)and reverse-time migration(RTM),and inversion-based least-squares reverse-time migration(LSRTM);analyzing the imaging capability of GBM,RTM and LSRTM to the special structures on three typical models and a land data set;outlooking the future perspectives of imaging methods.The main challenge of seismic imaging is to produce high-precision images for low-quality data,extremely deep reservoirs,and dual-complex structures.