Estimating primaries by sparse inversion of the 3D Curvelet transform and the L1-norm constraint

In this paper, we built upon the estimating primaries by sparse inversion （EPSI） method. We use the 3D curvelet transform and modify the EPSI method to the sparse inversion of the biconvex optimization and Ll-norm regularization, and use alternating optimization to directly estimate the primary reflection coefficients and source wavelet. The 3D curvelet transform is used as a sparseness constraint when inverting the primary reflection coefficients, which results in avoiding the prediction subtraction process in the surface-related multiples elimination （SRME） method. The proposed method not only reduces the damage to the effective waves but also improves the elimination of multiples. It is also a wave equation- based method for elimination of surface multiple reflections, which effectively removes surface multiples under complex submarine conditions.

Diffraction separation and imaging based on double sparse transforms

Reflection imaging results generally reveal large-scale continuous geological information,and it is difficult to identify small-scale geological bodies such as breakpoints,pinch points,small fault blocks,caves,and fractures,etc.Diffraction imaging is an important method to identify small-scale geological bodies and it has higher resolution than reflection imaging.In the common-offset domain,reflections are mostly expressed as smooth linear events,whereas diffractions are characterized by hyperbolic events.This paper proposes a diffraction extraction method based on double sparse transforms.The linear events can be sparsely expressed by the high-resolution linear Radon transform,and the curved events can be sparsely expressed by the Curvelet transform.A sparse inversion model is built and the alternating direction method is used to solve the inversion model.Simulation data and field data experimental results proved that the diffractions extraction method based on double sparse transforms can effectively improve the imaging quality of faults and other small-scale geological bodies.

Comparison between several seismic inversion methods and their application in mountainous coal fields of western China

With the objective of establishing the necessary conditions for 3D seismic data from mountainous areas in western China, we compared the application results of wave impedance technology in the lithology and exploration of coal fields. First, we introduce principles and features of three kinds of inversion methods. i.e., Model-Based Inversion, Constrained Sparse Spike Inversion (CSSI) and Geology-Seismic Feature Inversion. Secondly, these inversion methods are contrasted in their application to 3D seismic data from some coalfields in western China. The main information provided by the research includes: improving the vertical resolution of coal deposit strata, inferring lateral variation of the lithology and predicting coal seams and their roof lithology. Finally, the comparison between the three methods shows that the model-based inversion has the higher resolution, while CSSI inversion has better waveform continuity. The geology-seismic feature inversion requires information from a large number of wells and many types of logging curves of good quality. All three methods can meet the requirements of seismic exploration for lithological exploration in coal fields.

Using Walkaway VSP technology to improve inversion resolution

Walkaway VSP technology is commonly seen in the seismic development stage in the middle and late stages of the oilfield.Its advantage over conventional zero-offset VSP and non-zero well-source distance VSP is mainly due to the higher coverage of the Walkaway VSP acquisition process and the larger acquisition range.The resolution and signal-to-noise ratio are higher.Walkaway VSP technology has a very good application effect on solving complex structural problems and thin interbed reservoir problems.This paper mainly introduces a VSP constrained sparse spike inversion method based on high-precision VSP data.For the data acquired by the eight-azimuth Walkaway VSP in a work area of Bohai Gulf,a new method of 3D seismic-VSP joint seismic inversion is established.In the application of the actual work area,good inversion results based on Walkaway VSP data were obtained,and a new R24 small layer above the top boundary of the NmRIll oil group was depicted.This result meets the needs of development seismic technology and is used to solve thin interbed reservoirs exploration problems that have very important practical significance.

Projection Improved SPAI Preconditioner for FGMRES

Krylov subspace methods are widely used for solving sparse linear algebraic equations,but they rely heavily on preconditioners,and it is difficult to find an effective preconditioner that is efficient and stable for all problems.In this paper,a novel projection strategy including the orthogonal and the oblique projection is proposed to improve the preconditioner,which can enhance the efficiency and stability of iteration.The proposed strategy can be considered as a minimization process,where the orthogonal projection minimizes the energy norm of error and the oblique projection minimizes the 2-norm of the residual,meanwhile they can be regarded as approaches to correct the approximation by solving low-rank inverse of the matrices.The strategy is a wide-ranging approach and provides a way to transform the constant preconditioner into a variable one.The paper discusses in detail the projection strategy for sparse approximate inverse(SPAI)preconditioner applied to flexible GMRES and conducts the numerical test for problems from different applications.The results show that the proposed projection strategy can significantly improve the solving process,especially for some non-converging and slowly convergence systems.

Layer-Wise Pre-Training Low-Rank NMF Model for Mammogram-Based Breast Tumor Classification

Image-based breast tumor classification is an active and challenging problem.In this paper,a robust breast tumor classification framework is presented based on deep feature representation learning and exploiting available information in existing samples.Feature representation learning of mammograms is fulfilled by a modified nonnegative matrix factorization model called LPML-LRNMF,which is motivated by hierarchical learning and layer-wise pre-training(LP)strategy in deep learning.Low-rank(LR)constraint is integrated into the feature representation learning model by considering the intrinsic characteristics of mammograms.Moreover,the proposed LPML-LRNMF model is optimized via alternating direction method of multipliers and the corresponding convergence is analyzed.For completing classification,an inverse projection sparse representation model is introduced to exploit information embedded in existing samples,especially in test ones.Experiments on the public dataset and actual clinical dataset show that the classification accuracy,specificity and sensitivity achieve the clinical acceptance level.