Deblending is a data processing procedure used to separate the source interferences of blended seismic data,which are obtained by simultaneous sources with random time delays to reduce the cost of seismic acquisition....Deblending is a data processing procedure used to separate the source interferences of blended seismic data,which are obtained by simultaneous sources with random time delays to reduce the cost of seismic acquisition.There are three types of deblending algorithms,i.e.,filtering-type noise suppression algorithm,inversion-based algorithm and deep-learning based algorithm.We review the merits of these techniques,and propose to use a sparse inversion method for seismic data deblending.Filtering-based deblending approach is applicable to blended data with a low blending fold and simple geometry.Otherwise,it can suffer from signal distortion and noise leakage.At present,the deep learning based deblending methods are still under development and field data applications are limited due to the lack of high-quality training labels.In contrast,the inversion-based deblending approaches have gained industrial acceptance.Our used inversion approach transforms the pseudo-deblended data into the frequency-wavenumber-wavenumher(FKK)domain,and a sparse constraint is imposed for the coherent signal estimation.The estimated signal is used to predict the interference noise for subtraction from the original pseudo-deblended data.Via minimizing the data misfit,the signal can be iteratively updated with a shrinking threshold until the signal and interference are fully separated.The used FKK sparse inversion algorithm is very accurate and efficient compared with other sparse inversion methods,and it is widely applied in field cases.Synthetic example shows that the deblending error is less than 1%in average amplitudes and less than-40 dB in amplitude spectra.We present three field data examples of land,marine OBN(Ocean Bottom Nodes)and streamer acquisitions to demonstrate its successful applications in separating the source interferences efficiently and accurately.展开更多
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 r...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.展开更多
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 fra...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.展开更多
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 exploratio...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.展开更多
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 d...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.展开更多
B and C sands of the Lower Goru Formation of Cretaceous are proven reservoirs in different parts of the Middle and Lower Indus Basin,Pakistan.Most of the discoveries in this basin have been made in structural traps.Ho...B and C sands of the Lower Goru Formation of Cretaceous are proven reservoirs in different parts of the Middle and Lower Indus Basin,Pakistan.Most of the discoveries in this basin have been made in structural traps.However,in Sawan gas field;structural inversion,deep burial depth and heterogeneity of reservoir intervals make it difficult to demarcate the sweetness zones through conventional seismic analysis.In this work,different data sets have been integrated through constrained sparse spike inversion to mark sweetness zones in B and C sands of this formation.C sand contains four sweetness zones;the main sweetness zone is located towards the east while three subtle sweetness zones were identified towards the west of Sawan fault.The location of producing and nonproducing wells within the sweetness and outside of sweetness zones confirms the credibility of this work.B sand includes three sweetness zones located towards the west of Sawan fault.Moreover,inverted porosity results not only show good agreement with the porosity log of blind well(Sawan-02)but also show good matching with the core porosities.Hence integration of different data sets leads to demarcate the accurate location,size and extent of the sweetness zones.展开更多
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 avail...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.展开更多
基金supported by National Science and Technology Major Project(Grant No.2017ZX05018-001)。
文摘Deblending is a data processing procedure used to separate the source interferences of blended seismic data,which are obtained by simultaneous sources with random time delays to reduce the cost of seismic acquisition.There are three types of deblending algorithms,i.e.,filtering-type noise suppression algorithm,inversion-based algorithm and deep-learning based algorithm.We review the merits of these techniques,and propose to use a sparse inversion method for seismic data deblending.Filtering-based deblending approach is applicable to blended data with a low blending fold and simple geometry.Otherwise,it can suffer from signal distortion and noise leakage.At present,the deep learning based deblending methods are still under development and field data applications are limited due to the lack of high-quality training labels.In contrast,the inversion-based deblending approaches have gained industrial acceptance.Our used inversion approach transforms the pseudo-deblended data into the frequency-wavenumber-wavenumher(FKK)domain,and a sparse constraint is imposed for the coherent signal estimation.The estimated signal is used to predict the interference noise for subtraction from the original pseudo-deblended data.Via minimizing the data misfit,the signal can be iteratively updated with a shrinking threshold until the signal and interference are fully separated.The used FKK sparse inversion algorithm is very accurate and efficient compared with other sparse inversion methods,and it is widely applied in field cases.Synthetic example shows that the deblending error is less than 1%in average amplitudes and less than-40 dB in amplitude spectra.We present three field data examples of land,marine OBN(Ocean Bottom Nodes)and streamer acquisitions to demonstrate its successful applications in separating the source interferences efficiently and accurately.
基金supported by the National Science and Technology Major Project (No.2011ZX05023-005-008)
文摘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.
基金supported by National Natural Science Foundation of China(41974166)Natural Science Foundation of Hebei Province(D2019403082,D2021403010)+1 种基金Hebei Province“three-threethree talent project”(A202005009)Funding for the Science and Technology Innovation Team Project of Hebei GEO University(KJCXTD202106)
文摘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.
基金part of an ongoing project of the National Important Industry Technological Development Project (High Precision 3D Seismic Technology of Coal Resources of Western China)the financial support from the National Basic Research Program of China (No.2009CB 219603)the National Key Scientific and Technological Project of China (No.2008ZX05035-005-003HZ)
文摘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.
基金supported by the Natural Science Foundation of China(41974124)the China Scholarship Council(201906440068).
文摘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.
文摘B and C sands of the Lower Goru Formation of Cretaceous are proven reservoirs in different parts of the Middle and Lower Indus Basin,Pakistan.Most of the discoveries in this basin have been made in structural traps.However,in Sawan gas field;structural inversion,deep burial depth and heterogeneity of reservoir intervals make it difficult to demarcate the sweetness zones through conventional seismic analysis.In this work,different data sets have been integrated through constrained sparse spike inversion to mark sweetness zones in B and C sands of this formation.C sand contains four sweetness zones;the main sweetness zone is located towards the east while three subtle sweetness zones were identified towards the west of Sawan fault.The location of producing and nonproducing wells within the sweetness and outside of sweetness zones confirms the credibility of this work.B sand includes three sweetness zones located towards the west of Sawan fault.Moreover,inverted porosity results not only show good agreement with the porosity log of blind well(Sawan-02)but also show good matching with the core porosities.Hence integration of different data sets leads to demarcate the accurate location,size and extent of the sweetness zones.
基金This work was supported in part by the National Natural Science Foundation of China(No.11701144)National Science Foundation of US(No.DMS1719932)+1 种基金Natural Science Foundation of Henan Province(No.162300410061)Project of Emerging Interdisciplinary(No.xxjc20170003).
文摘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.
