Optical-resolution photoacoustic microscopy is a novel imaging technique that combines the advantages of optical and ultrasound imaging,enabling high-resolution visualization of biological tissues at the micrometer sc...Optical-resolution photoacoustic microscopy is a novel imaging technique that combines the advantages of optical and ultrasound imaging,enabling high-resolution visualization of biological tissues at the micrometer scale.However,the divergence of the excited Gaussian beam limits the depth-of-field of the system to less than 100μm,which hinders accurate three-dimensional imaging of living tissues and restrictsits applicability in biological research.Therefore,there is an urgent need for an effective method to enhance the depth-of-field without altering the hardware configuration.This paper presents a photoacoustic microscopy depth-of-field extension method and system based on three-dimensional continuity and sparsity deconvolution.This method utilizes a depth-varying point spread function and incorporates continuity and sparsity con-straints into the deconvolution process to mitigate the effect of background noise,enhancing the stability and accuracy of the depth-of-field extension.Experimental results using tungsten wire phantoms suggest that the depth-of-field of system can be extended to 650 pm,which is 7.2 times greater than conventional system,while improving the resolution of the defocused region by an average factor of 3.5.Furthermore,experiments on zebrafish and nude mouse ears with irregular topologies demonstrate that the proposed method successfully overcomes image blurring and the loss of structural information due to limited depth-of-field.All the results suggest that the system with higher lateral resolution and enhanced depth-of-field has significant potential for a wide range of practical biomedical applications.展开更多
Alzheimer’s disease(AD)is the most common origin of sporadic dementia.Rare familial forms have identified a central role for toxicity based on aggregation of peptide fragments generated from amyloid precursor protein...Alzheimer’s disease(AD)is the most common origin of sporadic dementia.Rare familial forms have identified a central role for toxicity based on aggregation of peptide fragments generated from amyloid precursor protein(APP),named amyloid-beta(Aβ),which exists in two common forms,Aβ_(1-40)(Aβ_(40))and Aβ_(1-42)(Aβ_(42)).The latter is more neurotoxic.A common clinical biomarker measured in blood is the ratio Aβ_(42)/Aβ_(40).展开更多
Here we present a simple yet effective gas chromatography-mass spectrometry(GC-MS)identification approach for the detection of heteroatom-containing compounds(HACCs)in petroleum fractions.The MS/AMDIS(Automated Mass S...Here we present a simple yet effective gas chromatography-mass spectrometry(GC-MS)identification approach for the detection of heteroatom-containing compounds(HACCs)in petroleum fractions.The MS/AMDIS(Automated Mass Spectral Deconvolution and Identification System)program was used to identify parts per million(ppm)HACC concentrations in petroleum fractions in place of traditional techniques(extraction and standard injection).Polycyclic aromatic sulfur heterocycles(S-PAHs)were used as model compounds to confirm the validity of the AMDIS identifiers,which were compared with extracted results using the off-line X-calibur software.AMDIS was able to identify ppm concentrations of S-PAHs in oil condensate.There was good agreement between experimental and AMDIS identification results for S-PAHs in oil condensate.AMDIS was also used to detect nitrogen-containing compounds(NCCs)and alkylphenols in oil condensate.Our results confirmed the presence of 2-methylbenzothiazole,carbazole,and 2,4-ditertbutyl phenol.In a crude oil sample,AMDIS identification of m/z=191 biomarkers wa s consistent with empirical results.Therefore,AMDIS can help to reduce the number of experimental steps in identification protocols.展开更多
Proposes an H_∞ deconvolution design for time-delay linear continuous-time systems. We first analyze the general structure and innovation structure of the H_∞ deconvolution filter. The deconvolution filter with inno...Proposes an H_∞ deconvolution design for time-delay linear continuous-time systems. We first analyze the general structure and innovation structure of the H_∞ deconvolution filter. The deconvolution filter with innovation structure is made up of an output observer and a linear mapping, where the latter reflects the internal connection between the unknown input signal and the output estimate error. Based on the bounded real lemma, a time domain design approach and a sufficient condition for the existence of deconvolution filter are presented. The parameterization of the deconvolution filter can be completed by solving a Riccati equation. The proposed method is useful for the case that does not require statistical information about disturbances. At last, a numerical example is given to demonstrate the performance of the proposed filter.展开更多
The optimum state filter and fixed-interval smoother and the optimum deconvolution algorithm for system with multiplicative noise are derived upon the condition that the dynamic noise correlates itself in one-step and...The optimum state filter and fixed-interval smoother and the optimum deconvolution algorithm for system with multiplicative noise are derived upon the condition that the dynamic noise correlates itself in one-step and correlates with the measurement noise at the present step as well as one past step, and the multiplicative noise is white and statistically independent of the dynamic noise and the measurement noise. A simulation example demonstrates the effectiveness of the above-mentioned deconvolution algorithm.展开更多
A decentralized parallel one-pass deconvolution algorithm for multisensor systems with multiplicative noises is proposed. Comparing with the conventional deconvolution algorithm, it avoids the computational overload a...A decentralized parallel one-pass deconvolution algorithm for multisensor systems with multiplicative noises is proposed. Comparing with the conventional deconvolution algorithm, it avoids the computational overload and the high storage requirement. The algorithm is optimal in the sense of linear minimum-variance. The simulation results illustrate the validity of the proposed algorithm.展开更多
Recently we have developed an eigenvector method (EVM) which can achieve the blind deconvolution (BD) for MIMO systems. One of attractive features of the proposed algorithm is that the BD can be achieved by calculatin...Recently we have developed an eigenvector method (EVM) which can achieve the blind deconvolution (BD) for MIMO systems. One of attractive features of the proposed algorithm is that the BD can be achieved by calculating the eigenvectors of a matrix relevant to it. However, the performance accuracy of the EVM depends highly on computational results of the eigenvectors. In this paper, by modifying the EVM, we propose an algorithm which can achieve the BD without calculating the eigenvectors. Then the pseudo-inverse which is needed to carry out the BD is calculated by our proposed matrix pseudo-inversion lemma. Moreover, using a combination of the conventional EVM and the modified EVM, we will show its performances comparing with each EVM. Simulation results will be presented for showing the effectiveness of the proposed methods.展开更多
Traditional deconvolution methods based on single-channel inversion do not consider the spatial structural relation between channels,and hence,they yield high-resolution results with the existing transverse inconsiste...Traditional deconvolution methods based on single-channel inversion do not consider the spatial structural relation between channels,and hence,they yield high-resolution results with the existing transverse inconsistency or discontinuity.Therefore,in this study,the local dip angle was used to obtain the structural information and construct the spatial structurally constraint operator.This operator is then introduced into multichannel deconvolution as a regularization operator to improve the resolution and maintain the transverse continuity of seismic data.Model tests and actual seismic data processing have demonstrated the effectiveness and practicability of this method.展开更多
The conventional nonstationary convolutional model assumes that the seismic signal is recorded at normal incidence. Raw shot gathers are far from this assumption because of the effects of offsets. Because of such prob...The conventional nonstationary convolutional model assumes that the seismic signal is recorded at normal incidence. Raw shot gathers are far from this assumption because of the effects of offsets. Because of such problems, we propose a novel prestack nonstationary deconvolution approach. We introduce the radial trace （RT） transform to the nonstationary deconvolution, we estimate the nonstationary deconvolution factor with hyperbolic smoothing based on variable-step sampling （VSS） in the RT domain, and we obtain the high-resolution prestack nonstationary deconvolution data. The RT transform maps the shot record from the offset and traveltime coordinates to those of apparent velocity and traveltime. The ray paths of the traces in the RT better satisfy the assumptions of the convolutional model. The proposed method combines the advantages of stationary deconvolution and inverse Q filtering, without prior information for Q. The nonstationary deconvolution in the RT domain is more suitable than that in the space-time （XT） domain for prestack data because it is the generalized extension of normal incidence. Tests with synthetic and real data demonstrate that the proposed method is more effective in compensating for large-offset and deep data.展开更多
The Robinson convolution model is mainly restricted by three inappropriate assumptions, i.e., statistically white reflectivity, minimum-phase wavelet, and stationarity. Modern reflectivity inversion methods（e.g., spa...The Robinson convolution model is mainly restricted by three inappropriate assumptions, i.e., statistically white reflectivity, minimum-phase wavelet, and stationarity. Modern reflectivity inversion methods（e.g., sparsity-constrained deconvolution） generally attempt to suppress the problems associated with the first two assumptions but often ignore that seismic traces are nonstationary signals, which undermines the basic assumption of unchanging wavelet in reflectivity inversion. Through tests on reflectivity series, we confirm the effects of nonstationarity on reflectivity estimation and the loss of significant information, especially in deep layers. To overcome the problems caused by nonstationarity, we propose a nonstationary convolutional model, and then use the attenuation curve in log spectra to detect and correct the influences of nonstationarity. We use Gabor deconvolution to handle nonstationarity and sparsity-constrained deconvolution to separating reflectivity and wavelet. The combination of the two deconvolution methods effectively handles nonstationarity and greatly reduces the problems associated with the unreasonable assumptions regarding reflectivity and wavelet. Using marine seismic data, we show that correcting nonstationarity helps recover subtle reflectivity information and enhances the characterization of details with respect to the geological record.展开更多
In seismic data processing, blind deconvolution is a key technology. Introduced in this paper is a flow of one kind of blind deconvolution. The optimal precondition conjugate gradients (PCG) in Kyrlov subspace is als...In seismic data processing, blind deconvolution is a key technology. Introduced in this paper is a flow of one kind of blind deconvolution. The optimal precondition conjugate gradients (PCG) in Kyrlov subspace is also used to improve the stability of the algorithm. The computation amount is greatly decreased.展开更多
Sparsity constrained deconvolution can improve the resolution of band-limited seismic data compared to conventional deconvolution. However, such deconvolution methods result in nonunique solutions and suppress weak re...Sparsity constrained deconvolution can improve the resolution of band-limited seismic data compared to conventional deconvolution. However, such deconvolution methods result in nonunique solutions and suppress weak reflections. The Cauchy function, modified Cauchy function, and Huber function are commonly used constraint criteria in sparse deconvolution. We used numerical experiments to analyze the ability of sparsity constrained deconvolution to restore reflectivity sequences and protect weak reflections under different constraint criteria. The experimental results demonstrate that the performance of sparsity constrained deconvolution depends on the agreement between the constraint criteria and the probability distribution of the reflectivity sequences; furthermore, the modified Cauchy- constrained criterion protects the weak reflections better than the other criteria. Based on the model experiments, the probability distribution of the reflectivity sequences of carbonate and clastic formations is statistically analyzed by using well-logging data and then the modified Cauchy-constrained deconvolution is applied to real seismic data much improving the resolution.展开更多
The predictive deconvolution algorithm （PD）, which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this a...The predictive deconvolution algorithm （PD）, which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this assumption in practice. Since the seismic data （primaries and multiples） have a non-Gaussian distribution, in this paper we present an improved predictive deconvolution algorithm （IPD） by maximizing the non-Gaussianity of the recovered primaries. Applications of the IPD method on synthetic and real seismic datasets show that the proposed method obtains promising results.展开更多
In wave-equation migration and demigration,the cross-correlation imaging/forwarding step implicitly injects an additional copy of the source wavelet,so that the amplitude spectrum of the wavelet is applied redundantly...In wave-equation migration and demigration,the cross-correlation imaging/forwarding step implicitly injects an additional copy of the source wavelet,so that the amplitude spectrum of the wavelet is applied redundantly(effectively imposing a wavelet-spectrum weighting,often akin to an amplitude-squared bias).This redundancy degrades structural fidelity and amplitude balance yet is frequently overlooked.We(i)formalize the mechanism by which cross-correlation duplicates the source-wavelet amplitude effect in both migration and demigration,and(ii)introduce a source-equalized operator that removes the redundancy by deconvolving(or dividing by)the wavelet amplitude spectrum in the imaging condition and its demigration counterpart,while leaving phase/kinematics intact.Using a band-limited Ricker wavelet on a two-layer model and on Marmousi,we show that,if unmanaged,the redundant wavelet spectrum broadens main lobes,introduces ringing,and suppresses vertical resolution in migrated images,and inflates spectrum mismatches between demigrated and observed data even when peak times agree.With our correction,images recover observed-data-consistent bandwidth and sharpened interfaces,and demigrated data also exhibit improved spectrum conformity and reduced amplitude misfit.The results clarify when source amplitudes matter,why cross-correlation makes them redundantly matter,and how a lightweight spectral correction restores physically meaningful amplitude behavior in wave-equation migration/demigration.展开更多
Deconvolution is widely used to increase the resolution of seismic data.To compare the resolution ability of conventional spectrum whitening deconvolution to thin layers with that of welldriven deconvolution,a complex...Deconvolution is widely used to increase the resolution of seismic data.To compare the resolution ability of conventional spectrum whitening deconvolution to thin layers with that of welldriven deconvolution,a complex sedimentary geological model was designed,and then the simulated seismic data were processed respectively by each of the two methods.The amplitude spectrum of seismic data was almost white after spectrum whitening,but the wavelet resolution was low.The amplitude spectrum after well-driven deconvolution deviated from white spectrum,but the wavelet resolution was high.Further analysis showed that if an actual reflectivity series could not well satisfy the hypothesis of white spectrum,spectrum whitening deconvolution had a potential risk of wavelet distortion,which might lead to a pitfall in high resolution seismic data interpretation.On the other hand,the wavelet after well-driven deconvolution had higher resolution both in the time and frequency domains.It is favorable for high resolution seismic interpretation and reservoir prediction.展开更多
Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement.However,the trace-by-trace processing strategy is applied and ignores the spati...Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement.However,the trace-by-trace processing strategy is applied and ignores the spatial connection along seismic traces,which gives the deconvolved result strong ambiguity and poor spatial continuity.To alleviate this issue,we developed a structurally constrained deconvolution algorithm.The proposed method extracts the refl ection structure characterization from the raw seismic data and introduces it to the multichannel deconvolution algorithm as a spatial refl ection regularization.Benefi ting from the introduction of the reflection regularization,the proposed method enhances the stability and spatial continuity of conventional deconvolution methods.Synthetic and field data examples confi rm the correctness and feasibility of the proposed method.展开更多
Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf (WH) equation is constructed to solve the filter and elimina...Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf (WH) equation is constructed to solve the filter and eliminate the correlated components of the seismic records, attenuate multiples, and improve seismic resolution. However, in practice, the primary refl ectivity series of fi eld data rarely satisfy the white noise sequence assumption, with the result that the correlated components of the primary reflectivity series are also eliminated by traditional deconvolution. This results in signal distortion. To solve this problem, we have proposed an improved method for deconvolution. First, we estimated the wavelet correlation from seismic records using the spectrum-modeling method. Second, this wavelet autocorrelation was used to construct a new autocorrelation function which contains the correlated components caused by the existence of multiples and avoids the correlated components of the primary reflectivity series. Finally, the new autocorrelation function was brought into the WH equation, and the predictive fi lter operator was calculated for deconvolution. In this paper, we have applied this new method to simulated and field data processing, and we have compared its performance with that of traditional predictive deconvolution. Our results show that the new method can adapt to non-white refl ectivity series without changing the statistical characteristics of the primary reflection coefficient series. Compared with traditional predictive deconvolution, the new method reduces processing noise and improves fidelity, all while maintaining the ability to attenuate multiples and enhance seismic resolution.展开更多
Visual perception of humans penetrating turbid medium is hampered by scattering.Various techniques have been prompted recently to recover optical imaging through turbid materials.Among them,speckle correlation based o...Visual perception of humans penetrating turbid medium is hampered by scattering.Various techniques have been prompted recently to recover optical imaging through turbid materials.Among them,speckle correlation based on deconvolution is one of the most attractive methods taking advantage of high imaging quality,robustness,eas-of-use,and ease-of-integration.By exploiting the point spread function(PSF)of the scattering system,large Field-of-View,extended Depth-of-Field,noninvasiveness and spectral resoluation are now available as successful solutions for high quality and multifunctional image reconstruction.In this paper,we review the progress of imaging through a scattering medium based on deconvolution method,including the principle,the breakthrough of the limitation of the optical memory ffect,the improvement of the deconvolution algorithm and innovative applications.展开更多
To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the freque...To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the frequency domain,the influence of Gaussian as well as outlier noise on the convergence of the algorithm is effectively avoided.In other words,the proposed algorithm avoids data noise effects by implementing the calculations in the frequency domain.Moreover,the computational efficiency is greatly improved compared with the conventional method.Generalized cross validation is introduced in the solving process to optimize the regularization parameter and thus the algorithm is equipped with strong self-adaptation.Different theoretical models are built and solved using the algorithms in both time and frequency domains.Finally,the proposed and the conventional methods are both used to process actual seismic data.The comparison of the results confirms the superiority of the proposed algorithm due to its noise resistance and self-adaptation capability.展开更多
基金supported by the National Key R&D Program of China[Grant No.2022YFC2402400]the National Natural Science Foundation of China[Grant No.62275062]+2 种基金Project of Shandong Innovation and Startup Community of High-end Medical Apparatus and Instruments[Grant Nos.2023-SGTTXM-002 and 2024-SGTTXM-005]the Shandong Province Technology Innovation Guidance Plan(Central Leading Local Science and Technology Development Fund)[Grant No.YDZX2023115]the Taishan Scholar Special Funding Project of Shandong Province,and the Shandong Laboratory of Advanced Biomaterials and Medical Devices in Weihai[Grant No.ZL202402].
文摘Optical-resolution photoacoustic microscopy is a novel imaging technique that combines the advantages of optical and ultrasound imaging,enabling high-resolution visualization of biological tissues at the micrometer scale.However,the divergence of the excited Gaussian beam limits the depth-of-field of the system to less than 100μm,which hinders accurate three-dimensional imaging of living tissues and restrictsits applicability in biological research.Therefore,there is an urgent need for an effective method to enhance the depth-of-field without altering the hardware configuration.This paper presents a photoacoustic microscopy depth-of-field extension method and system based on three-dimensional continuity and sparsity deconvolution.This method utilizes a depth-varying point spread function and incorporates continuity and sparsity con-straints into the deconvolution process to mitigate the effect of background noise,enhancing the stability and accuracy of the depth-of-field extension.Experimental results using tungsten wire phantoms suggest that the depth-of-field of system can be extended to 650 pm,which is 7.2 times greater than conventional system,while improving the resolution of the defocused region by an average factor of 3.5.Furthermore,experiments on zebrafish and nude mouse ears with irregular topologies demonstrate that the proposed method successfully overcomes image blurring and the loss of structural information due to limited depth-of-field.All the results suggest that the system with higher lateral resolution and enhanced depth-of-field has significant potential for a wide range of practical biomedical applications.
文摘Alzheimer’s disease(AD)is the most common origin of sporadic dementia.Rare familial forms have identified a central role for toxicity based on aggregation of peptide fragments generated from amyloid precursor protein(APP),named amyloid-beta(Aβ),which exists in two common forms,Aβ_(1-40)(Aβ_(40))and Aβ_(1-42)(Aβ_(42)).The latter is more neurotoxic.A common clinical biomarker measured in blood is the ratio Aβ_(42)/Aβ_(40).
文摘Here we present a simple yet effective gas chromatography-mass spectrometry(GC-MS)identification approach for the detection of heteroatom-containing compounds(HACCs)in petroleum fractions.The MS/AMDIS(Automated Mass Spectral Deconvolution and Identification System)program was used to identify parts per million(ppm)HACC concentrations in petroleum fractions in place of traditional techniques(extraction and standard injection).Polycyclic aromatic sulfur heterocycles(S-PAHs)were used as model compounds to confirm the validity of the AMDIS identifiers,which were compared with extracted results using the off-line X-calibur software.AMDIS was able to identify ppm concentrations of S-PAHs in oil condensate.There was good agreement between experimental and AMDIS identification results for S-PAHs in oil condensate.AMDIS was also used to detect nitrogen-containing compounds(NCCs)and alkylphenols in oil condensate.Our results confirmed the presence of 2-methylbenzothiazole,carbazole,and 2,4-ditertbutyl phenol.In a crude oil sample,AMDIS identification of m/z=191 biomarkers wa s consistent with empirical results.Therefore,AMDIS can help to reduce the number of experimental steps in identification protocols.
基金Spsonsored by the National Natural Science Foundation of China (Grant No.60274058).
文摘Proposes an H_∞ deconvolution design for time-delay linear continuous-time systems. We first analyze the general structure and innovation structure of the H_∞ deconvolution filter. The deconvolution filter with innovation structure is made up of an output observer and a linear mapping, where the latter reflects the internal connection between the unknown input signal and the output estimate error. Based on the bounded real lemma, a time domain design approach and a sufficient condition for the existence of deconvolution filter are presented. The parameterization of the deconvolution filter can be completed by solving a Riccati equation. The proposed method is useful for the case that does not require statistical information about disturbances. At last, a numerical example is given to demonstrate the performance of the proposed filter.
文摘The optimum state filter and fixed-interval smoother and the optimum deconvolution algorithm for system with multiplicative noise are derived upon the condition that the dynamic noise correlates itself in one-step and correlates with the measurement noise at the present step as well as one past step, and the multiplicative noise is white and statistically independent of the dynamic noise and the measurement noise. A simulation example demonstrates the effectiveness of the above-mentioned deconvolution algorithm.
基金This work was supported by the Science&Technology Research Key Projects of Ministry of Education of China.
文摘A decentralized parallel one-pass deconvolution algorithm for multisensor systems with multiplicative noises is proposed. Comparing with the conventional deconvolution algorithm, it avoids the computational overload and the high storage requirement. The algorithm is optimal in the sense of linear minimum-variance. The simulation results illustrate the validity of the proposed algorithm.
文摘Recently we have developed an eigenvector method (EVM) which can achieve the blind deconvolution (BD) for MIMO systems. One of attractive features of the proposed algorithm is that the BD can be achieved by calculating the eigenvectors of a matrix relevant to it. However, the performance accuracy of the EVM depends highly on computational results of the eigenvectors. In this paper, by modifying the EVM, we propose an algorithm which can achieve the BD without calculating the eigenvectors. Then the pseudo-inverse which is needed to carry out the BD is calculated by our proposed matrix pseudo-inversion lemma. Moreover, using a combination of the conventional EVM and the modified EVM, we will show its performances comparing with each EVM. Simulation results will be presented for showing the effectiveness of the proposed methods.
基金supported by the basic and forward-looking project(No.2023YQX302)。
文摘Traditional deconvolution methods based on single-channel inversion do not consider the spatial structural relation between channels,and hence,they yield high-resolution results with the existing transverse inconsistency or discontinuity.Therefore,in this study,the local dip angle was used to obtain the structural information and construct the spatial structurally constraint operator.This operator is then introduced into multichannel deconvolution as a regularization operator to improve the resolution and maintain the transverse continuity of seismic data.Model tests and actual seismic data processing have demonstrated the effectiveness and practicability of this method.
基金financially supported by the National Science and Technology Major Project of China(No.2011ZX05023-005-005)the National Natural Science Foundation of China(No.41274137)
文摘The conventional nonstationary convolutional model assumes that the seismic signal is recorded at normal incidence. Raw shot gathers are far from this assumption because of the effects of offsets. Because of such problems, we propose a novel prestack nonstationary deconvolution approach. We introduce the radial trace （RT） transform to the nonstationary deconvolution, we estimate the nonstationary deconvolution factor with hyperbolic smoothing based on variable-step sampling （VSS） in the RT domain, and we obtain the high-resolution prestack nonstationary deconvolution data. The RT transform maps the shot record from the offset and traveltime coordinates to those of apparent velocity and traveltime. The ray paths of the traces in the RT better satisfy the assumptions of the convolutional model. The proposed method combines the advantages of stationary deconvolution and inverse Q filtering, without prior information for Q. The nonstationary deconvolution in the RT domain is more suitable than that in the space-time （XT） domain for prestack data because it is the generalized extension of normal incidence. Tests with synthetic and real data demonstrate that the proposed method is more effective in compensating for large-offset and deep data.
基金funded by the National Basic Research Program of China(973 Program)(Grant No.2011CB201100)Major Program of the National Natural Science Foundation of China(Grant No.2011ZX05004003)
文摘The Robinson convolution model is mainly restricted by three inappropriate assumptions, i.e., statistically white reflectivity, minimum-phase wavelet, and stationarity. Modern reflectivity inversion methods（e.g., sparsity-constrained deconvolution） generally attempt to suppress the problems associated with the first two assumptions but often ignore that seismic traces are nonstationary signals, which undermines the basic assumption of unchanging wavelet in reflectivity inversion. Through tests on reflectivity series, we confirm the effects of nonstationarity on reflectivity estimation and the loss of significant information, especially in deep layers. To overcome the problems caused by nonstationarity, we propose a nonstationary convolutional model, and then use the attenuation curve in log spectra to detect and correct the influences of nonstationarity. We use Gabor deconvolution to handle nonstationarity and sparsity-constrained deconvolution to separating reflectivity and wavelet. The combination of the two deconvolution methods effectively handles nonstationarity and greatly reduces the problems associated with the unreasonable assumptions regarding reflectivity and wavelet. Using marine seismic data, we show that correcting nonstationarity helps recover subtle reflectivity information and enhances the characterization of details with respect to the geological record.
基金With the support of the key project of Knowledge Innovation, CAS(KZCX1-y01, KZCX-SW-18), Fund of the China National Natural Sciences and the Daqing Oilfield with Grant No. 49894190
文摘In seismic data processing, blind deconvolution is a key technology. Introduced in this paper is a flow of one kind of blind deconvolution. The optimal precondition conjugate gradients (PCG) in Kyrlov subspace is also used to improve the stability of the algorithm. The computation amount is greatly decreased.
基金supported by the Major Basic Research Development Program of China (973 Program)(No.2013CB228606)the National Science foundation of China (No.41174117)+1 种基金the National Major Science-Technology Project (No.2011ZX05031-001)Innovation Fund of PetroChina (No.2010D-5006-0301)
文摘Sparsity constrained deconvolution can improve the resolution of band-limited seismic data compared to conventional deconvolution. However, such deconvolution methods result in nonunique solutions and suppress weak reflections. The Cauchy function, modified Cauchy function, and Huber function are commonly used constraint criteria in sparse deconvolution. We used numerical experiments to analyze the ability of sparsity constrained deconvolution to restore reflectivity sequences and protect weak reflections under different constraint criteria. The experimental results demonstrate that the performance of sparsity constrained deconvolution depends on the agreement between the constraint criteria and the probability distribution of the reflectivity sequences; furthermore, the modified Cauchy- constrained criterion protects the weak reflections better than the other criteria. Based on the model experiments, the probability distribution of the reflectivity sequences of carbonate and clastic formations is statistically analyzed by using well-logging data and then the modified Cauchy-constrained deconvolution is applied to real seismic data much improving the resolution.
基金National 863 Foundation of China(No.2006AA09A102-10)National Natural Science Foundation of China(No.40874056)NCET Fund
文摘The predictive deconvolution algorithm （PD）, which is based on second-order statistics, assumes that the primaries and the multiples are implicitly orthogonal. However, the seismic data usually do not satisfy this assumption in practice. Since the seismic data （primaries and multiples） have a non-Gaussian distribution, in this paper we present an improved predictive deconvolution algorithm （IPD） by maximizing the non-Gaussianity of the recovered primaries. Applications of the IPD method on synthetic and real seismic datasets show that the proposed method obtains promising results.
基金supported by the National Natural Science Foundation of China(42430303)Strategy Priority Research Program(Category B)of the Chinese Academy of Sciences(XDB0710000)+2 种基金National Natural Science Foundation of China(42288201)the National Key R&D Program of China(2023YFF0803203)the IGGCAS start-up funding(Grant No.E251510101).
文摘In wave-equation migration and demigration,the cross-correlation imaging/forwarding step implicitly injects an additional copy of the source wavelet,so that the amplitude spectrum of the wavelet is applied redundantly(effectively imposing a wavelet-spectrum weighting,often akin to an amplitude-squared bias).This redundancy degrades structural fidelity and amplitude balance yet is frequently overlooked.We(i)formalize the mechanism by which cross-correlation duplicates the source-wavelet amplitude effect in both migration and demigration,and(ii)introduce a source-equalized operator that removes the redundancy by deconvolving(or dividing by)the wavelet amplitude spectrum in the imaging condition and its demigration counterpart,while leaving phase/kinematics intact.Using a band-limited Ricker wavelet on a two-layer model and on Marmousi,we show that,if unmanaged,the redundant wavelet spectrum broadens main lobes,introduces ringing,and suppresses vertical resolution in migrated images,and inflates spectrum mismatches between demigrated and observed data even when peak times agree.With our correction,images recover observed-data-consistent bandwidth and sharpened interfaces,and demigrated data also exhibit improved spectrum conformity and reduced amplitude misfit.The results clarify when source amplitudes matter,why cross-correlation makes them redundantly matter,and how a lightweight spectral correction restores physically meaningful amplitude behavior in wave-equation migration/demigration.
基金supported by National 973 Key Basic Research Development Program(No.2007CB209608)National 863 High Technology Research Development Program(No.2007AA06Z218)
文摘Deconvolution is widely used to increase the resolution of seismic data.To compare the resolution ability of conventional spectrum whitening deconvolution to thin layers with that of welldriven deconvolution,a complex sedimentary geological model was designed,and then the simulated seismic data were processed respectively by each of the two methods.The amplitude spectrum of seismic data was almost white after spectrum whitening,but the wavelet resolution was low.The amplitude spectrum after well-driven deconvolution deviated from white spectrum,but the wavelet resolution was high.Further analysis showed that if an actual reflectivity series could not well satisfy the hypothesis of white spectrum,spectrum whitening deconvolution had a potential risk of wavelet distortion,which might lead to a pitfall in high resolution seismic data interpretation.On the other hand,the wavelet after well-driven deconvolution had higher resolution both in the time and frequency domains.It is favorable for high resolution seismic interpretation and reservoir prediction.
基金National Key R&D Program of China(No.2018YFA0702504)the National Natural Science Foundation of China(Nos.42074141,41874141)the Strategic Cooperation Technology Projects of CNPC and CUP(ZLZX2020-03).
文摘Seismic deconvolution plays an important role in the seismic characterization of thin-layer structures and seismic resolution enhancement.However,the trace-by-trace processing strategy is applied and ignores the spatial connection along seismic traces,which gives the deconvolved result strong ambiguity and poor spatial continuity.To alleviate this issue,we developed a structurally constrained deconvolution algorithm.The proposed method extracts the refl ection structure characterization from the raw seismic data and introduces it to the multichannel deconvolution algorithm as a spatial refl ection regularization.Benefi ting from the introduction of the reflection regularization,the proposed method enhances the stability and spatial continuity of conventional deconvolution methods.Synthetic and field data examples confi rm the correctness and feasibility of the proposed method.
基金supported by Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(No.2017RCJJ034)
文摘Conventional predictive deconvolution assumes that the reflection coefficients of the earth conform to an uncorrelated white noise sequence. The Wiener-Hopf (WH) equation is constructed to solve the filter and eliminate the correlated components of the seismic records, attenuate multiples, and improve seismic resolution. However, in practice, the primary refl ectivity series of fi eld data rarely satisfy the white noise sequence assumption, with the result that the correlated components of the primary reflectivity series are also eliminated by traditional deconvolution. This results in signal distortion. To solve this problem, we have proposed an improved method for deconvolution. First, we estimated the wavelet correlation from seismic records using the spectrum-modeling method. Second, this wavelet autocorrelation was used to construct a new autocorrelation function which contains the correlated components caused by the existence of multiples and avoids the correlated components of the primary reflectivity series. Finally, the new autocorrelation function was brought into the WH equation, and the predictive fi lter operator was calculated for deconvolution. In this paper, we have applied this new method to simulated and field data processing, and we have compared its performance with that of traditional predictive deconvolution. Our results show that the new method can adapt to non-white refl ectivity series without changing the statistical characteristics of the primary reflection coefficient series. Compared with traditional predictive deconvolution, the new method reduces processing noise and improves fidelity, all while maintaining the ability to attenuate multiples and enhance seismic resolution.
基金supported by National Natural Science Foundation of China(Nos.61705035,61575223,11534017 and 61475038)the Project of Department of Education of Guangdong Province(No.2018KTSCX241)+1 种基金State Key Laboratory of Optoelectronic Materials and Technologies(Sun Yat-sen University)STU Scienti¯c Research Foundation for Talents.
文摘Visual perception of humans penetrating turbid medium is hampered by scattering.Various techniques have been prompted recently to recover optical imaging through turbid materials.Among them,speckle correlation based on deconvolution is one of the most attractive methods taking advantage of high imaging quality,robustness,eas-of-use,and ease-of-integration.By exploiting the point spread function(PSF)of the scattering system,large Field-of-View,extended Depth-of-Field,noninvasiveness and spectral resoluation are now available as successful solutions for high quality and multifunctional image reconstruction.In this paper,we review the progress of imaging through a scattering medium based on deconvolution method,including the principle,the breakthrough of the limitation of the optical memory ffect,the improvement of the deconvolution algorithm and innovative applications.
基金supported by the National Natural Science Foundation of China(No.NSFC 41204101)Open Projects Fund of the State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(No.PLN201733)+1 种基金Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.2015051)Open Projects Fund of the Natural Gas and Geology Key Laboratory of Sichuan Province(No.2015trqdz03)
文摘To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the frequency domain,the influence of Gaussian as well as outlier noise on the convergence of the algorithm is effectively avoided.In other words,the proposed algorithm avoids data noise effects by implementing the calculations in the frequency domain.Moreover,the computational efficiency is greatly improved compared with the conventional method.Generalized cross validation is introduced in the solving process to optimize the regularization parameter and thus the algorithm is equipped with strong self-adaptation.Different theoretical models are built and solved using the algorithms in both time and frequency domains.Finally,the proposed and the conventional methods are both used to process actual seismic data.The comparison of the results confirms the superiority of the proposed algorithm due to its noise resistance and self-adaptation capability.
