Since regional paleostress controls the development of faults and fractures，an accurate understanding of regional paleostress is of great significance for oil and gas exploration and development. Based on the Wallace -Bott hypothesis，fault slip vectors combined with the four-dimensional search method can be used to accurately calculate regional paleostress. At present，the slip vectors of a fault are generally represented by the fault slip data of the surface outcrop. However，there are differences between the underground stress state and the surface stress state reflected by the fault slip data. Therefore，a new method is introduced to calculate the fault slip vectors directly with 3D seismic data. First，a vertical or horizontal seismic profile is constructed，and the apparent strike slip and apparent oblique slip are read. The fault slip vectors are calculated by the mathematical method. Then，the four-dimensional search method is used to calculate the regional paleostress. Taking the M block of the gas reservoir from the 2nd member of Xujiahe Formation in Xinchang Gasfield as an example，this study calculates 18 fault slip vectors of two faults and randomly divides them into 5 groups. After that，it calculates the regional paleostress of each group by using the four-dimensional search method and evaluates the deviation（RUP value）between the shear stress direction of the regional paleostress tensor on the cross section and the actual fault slip direction. The results show that the average RUP values of the five schemes are less than 50%，which indicates that the method is accurate in calculating regional paleostress and can be applied to similar areas.

Huaibei coalfield is one of the energy resource bases planned by China，which has a great potential of Carboniferous-Permian coal measure gas resources. However，the exploration and development of coal measure gas is still at the initial stage in there. Due to the influence of magma intrusion，nappe structure，and thick cover strata，it is difficult to prospect coal measure gas. The combination of gravity，magnetic，and electric exploration is one of the green，high -efficiency，and low-cost exploration methods，especially for deep coal measure gas resources. The Carboniferous -Permian strata in the study area are characterized by complex lithology，including coal，clastic rock，carbonate rock，and magmatic rock. However，there is a lack of systematic research on their gravity，magnetic，and electrical properties and the changing rules. This severely restricts the overall exploration and development process of Carboniferous-Permian coal measure gas in Huaibei coalfield. Based on the density， susceptibility，resistivity，and polarizability of 906 samples，this paper studies the differences of gravity，magnetic，and electrical properties among Carboniferous -Permian strata，sedimentary rock，and magmatic rock. The results show that the Carboniferous-Permian strata have lower density and resistivity than the Sinian-Ordovician strata in the Huaibei coalfield. The coal has a significantly small density with an average value of 1. 47 g/cm³ and is basically non-magnetic，whereas its resistivity is obviously high with an average value of 1. 806×10⁴ Ω· m. The mudstone has a medium density，non -magnetic or weakly magnetic properties，and significantly low resistivity with an average value of several hundred Ω·m. The densities of sandstone，limestone，and diorite are high，whose average values exceed 2. 70 g/cm³. They have medium resistivity with average values of several thousand Ω·m. The susceptibility of diorite is large with an average value of 3285. 96×10^-5 SI. It is suggested that joint exploration with gravity，magnetic，and electromagnetic（time-frequency electromagnetic method，complex resistivity method，etc. ）methods should be carried out to detect coal measure strata and concealed rock bodies during the comprehensive exploration of Carboniferous-Permian coal measure gas in the Huaibei coalfield. This research can provide reference for accurate exploration of coal measure gas in Huaibei coalfield.

As oil and gas exploration in China continues to advance and the geological conditions in exploration scenarios become increasingly complex，the need for applying the spectral element method as a seismic exploration technology in oil and gas exploration becomes more urgent. In forward modeling，the spectral element method imposes high demands on the quality of quadrilateral finite element meshes. Generating high quality quadrilateral mesh models for complex geological structures is a critical technical challenge for the successful application of the spectral element method in seismic exploration. Optimize the processes of generating triangular meshes and merging triangles to generate quadrilateral meshes using Frontal -Delaunay algorithm and Blossom Quad algorithm，respectively. However，for complex geological models，the generated initial quadrilateral meshes still suffer from issues such as topological errors and concave quadrilaterals. Therefore，it is necessary to study a quadratic optimization algorithm for further optimizing the initial quadrilateral network. This algorithm focuses on distorted nodes in the mesh，determining their solution space and optimizing loss function. Overall optimization of the quadrilateral mesh can be achievedby initializing a particle swarm in the solution space and iteratively adjusting the positions of the distorted nodes. Experiments with complex geological models indicate that the secondary optimization algorithm can completely eliminate topological errors present in the initial quadrilateral meshor degenerate quadrilaterals. Additionally，the optimized quadrilateral edges become more uniform，and the minimum edge length of the mesh is improved，which significantly enhances the efficiency of the forward modeling algorithm. This lays a solid foundation for the further application of the spectral element method in seismic exploration technology.

Quantitative saturation prediction from seismic data is important for reservoir evaluation and cost -effective development. However，elastic parameters are not sensitive to fluid saturation，which results in low accuracy of saturation inversion driven by elastic rock physics model. Therefore，this paper proposes a fluid saturation seismic inversion method driven by an attenuated rock physics model. Firstly，the sensitive parameters of fluid saturation are determined by forward and inverse analysis of this model. Then，a high -precision time -frequency analysis method is selected to construct a saturation sensitive attribute with pre stack seismic data. Lastly，the joint inversion of reservoir porosity and saturation is realized with the multi-parameter petrophysical joint inversion method based on the Bayesian theory. Taking a tight gas bearing clastic rock reservoir in western China as an example，the paper quantitatively analyzes the sensitivity of different parameters to saturation and selects the gradient of peak energy with incident angle as the pre-stack sensitive attribute to fluid. The application shows that the saturation seismic inversion method based on the attenuated rock physics model can effectively decouple solid phase and liquid phase and improve the accuracy of saturation inversion.

The traditional single difference positioning model based on a round trip acoustic path does not take into account the time delay error of the transducer. Considering the positional change of the transducer at the moments of transmitting and receiving acoustic signals，this paper proposes a global navigation satellite system-acoustic（GNSS-A）double-difference positioning method based on a round-trip acoustic path for seafloor control points. Taking the total round-trip time of the signal as the observation value，the method performs the first difference between the seafloor control points and then the second difference between observation epochs，in order to reduce the influence of the system error on positioning. Simulated and measured data are used for validation. The results show that compared with the round-trip undifferenced and round-trip single-difference methods，the proposed method has a simulated positioning accuracy improved by 0. 282~0. 363 m and 0. 022~0. 737 m and a measured positioning accuracy improved by 0. 029 m and 0. 012 m，respectively，which has a better positioning performance.

In seismic exploration，forward modeling of the wave equation is constrained by computational and storage capabilities，which limits simulations to finite spatial domains and thereby necessitates the implementation of boundary conditions. The perfectly matched layer（PML）is a widely applied boundary condition that requires a specific number of layers. However，excessive layer thickness can reduce forward modeling efficiency and increase memory usage. In this study，a novel boundary condition，PML-IGVL，is proposed by integrating PML with an improved gradient viscoelastic layer（IGVL）. To minimize truncation errors and numerical dispersion，the PML -IGVL boundary condition is incorporated into the viscous acoustic wave equation for numerical simulations with the use of compact staggered grid. Numerical results of wavefield simulations in homogeneous media and the Marmousi model demonstrate that compared to the PML boundary condition，the PML IGVL boundary condition requires thinner boundary layers，achieves better absorption of reflected waves under the same small number of boundary layers，reduces memory consumption，and enhances forward modeling efficiency. These findings validate the effectiveness and superiority of the PML IGVL boundary condition，proving it to be an efficient boundary absorption method in forward modeling.

To solve the problem that the P-wave data collected from land explorationcannot meet the increasing demand for elastic multi -parameter model prediction，this paper proposes a new wave equation of the acousticelastic equation coupled mode in elastic media，which is derived by decomposing the constitutive equation in classical elastodynamics into dilatation and shear tensors. The equation can perfectly match P wave data，and waveform information contains P and S -wave velocity and density responses，which can be used to invert elastic multi parameter models. Then，the paper establishes a theoretical framework for elastic reflection travel time inversion based on P -wave data. Since full waveform inversion is a highly nonlinear method，accurate initial velocity modeling is particularly important. Therefore，this paper introduces reflection waveform inversion to recover the middle and low wavenumber information in the background model by imaging along the wave path，which can improve imaging accuracy and avoid the inversion failure caused by periodic skipping. To verify the applicability and effectiveness of the proposed method，a homogeneous model is used to compare the wave propagation characteristics of the new equation，acoustic wave equations，and elastic wave equations under the excitation of different sources，and the verification test of radiation pattern is also carried out. Finally， the effectiveness of the inversion method in recovering the background velocity model is verified using a Sigsbee 2A resampling model.

The total organic carbon（TOC）content is an important evaluation index for shale gas exploration and development. Logging data can efficiently assess TOC，but it cannot be used for TOC prediction in inter well areas. The TOC -sensitive factors extracted from seismic data can achieve three -dimensional（3D）prediction. However，due to the thin thickness and strong heterogeneity of shale reservoirs，it is difficult to achieve the required resolution by relying solely on seismic data. Therefore，it is necessary to comprehensively use multiple data sources to improve the accuracy of TOC assessment. For this purpose，a high -precision quantitative prediction method for shale TOC based on a convolutional neural network（CNN）is proposed. Firstly，the correlation analysis between the measured TOC data of the core from drilling and multiple logging characteristic curves is conducted on the Longmaxi Formation shale in southern Sichuan，and the most representative and sensitive features are selected. Secondly，based on the identified sensitive parameters，a CNN prediction model is constructed. The measured TOC samples and the training samples constructed by sensitive logging parameters are divided into datasets at a ratio of 7：3 for model training and validation. Finally，the high-resolution sensitive parameter inversion results obtained by simulation of seismic waveform indication are used as the feature input for 3D TOC content prediction. The sensitive parameters are rearranged，reorganized，and then input into the CNN model to achieve 3D TOC content prediction. The research results show that CNN has more advantages than multiple regression and back propagation（BP）neural networks in fitting the nonlinear relationship between TOC content and sensitive parameters. The average absolute error and root mean square error are both less than 0. 6 between the predicted TOC data and the measured values from drilling. The prediction results are consistent with the actual situation. This method has high accuracy and obvious advantages in 3D TOC content prediction of thin shale reservoirs.

When directly applied to seismic data in the depth domain，the time -frequency analysis method does not take into account the velocity dependency of the seismic wavelet and thus fails to eliminate spectral decom -position anomalies caused by velocity. As a result，it is difficult to distinguish wavenumber changes due to the dispersion attenuation factor. To address these issues，a novel approach called depth-domain generalized hyperbolic S -transform is proposed in this study. This method eliminates the velocity dependency of the seismic signal in the depth domain by using a constant velocity depth domain transformation technique and enhances the resolution of depth -wavenumber spectral decomposition results through an asymmetric analysis window. Synthetic signal tests demonstrate that compared with using S -transform in the depth domain and directly adopting generalized hyperbolic S -transform for the time domain，the improved method achieves a higher resolution and better energy focusing for depth -wavenumber spectral decomposition. Furthermore，when applied to actual depth-domain seismic data for low-wavenumber shadow detection，this method effectively predicts the hydrocarbon potential of reservoirs，which is conducive to addressing the multi-solution problem in prediction.

Spectrum inversion is an effective means to study non -stationary seismic signals and plays an important role in seismic signal processing，analysis，and comprehensive interpretation. This paper proposes a weighted least square spectrum inversion method based on the swarm vortex search（SVS）algorithm for optimizing shaping regularization parameters（WLSSI-SVSOSR）. This method starts from the theoretical formula of the general forward problem，obtains the Fourier series coefficients of the seismic signal，and then introduces shaping regularization into the weighted least square spectrum inversion. Based on the spectrum inversion method，a shaping regularization operator（SRO）is constructed. The swarm vortex search algorithm is used to optimize shaping regularization parameters，which can overcome the problems of a slow convergence and poor stability in the inversion process，and the stable time-frequency distribution characteristics of seismic signals can be obtained. The results of model testing and actual data processing show that this method has a high time -frequency resolution and better energy accumulation and can identify the dominant frequency range of oil-gas reservoirs. The horizontal distribution range of oil-gas reservoirs can be basically determined using the instantaneous amplitude characteristics of the dominant frequency，so as to achieve fine characterization and description of oilgas reservoirs.

The Pinghu Formation in the Pingbei slope belt of the Xihu Sag is greatly affected by tidal action， and thin coal seams are widely developed，which have strong amplitude interference effects on the seismic reflections of the underlying strata and thus make the traditional reservoir prediction methods unable to meet the needs of fine description of the sand body. To effectively improve reservoir prediction accuracy under coal -rich strata，a reservoir prediction method based on the far -offset-trace quasi-Poisson attribute is proposed. Firstly， in view of the development location and distribution characteristics of coal seams in the study area，wave equations are used to finely simulate the seismic response characteristics of sandstone reservoirs under different coal measure strata. Secondly，with the help of the change rule of sand-coal-mud coupling pre-stack gather，the sensitive attribute information that can reduce the influence of the coal seams is preferred. Finally，through the intersection analysis of the multi-parameter information，this paper proposes the far-offset-trace quasi-Poisson attribute，which can effectively reduce the interference of coal measure strata in the reservoir prediction of under lying strata. The actual data confirms that the far-offset-trace quasi-Poisson attribute can integrate the advantageous information of far offset trace and gradient，well eliminate the influence of strong reflection of coal seams，effectively depict the spatial distribution characteristics of sandstone reservoirs，and provide effective technical support for high-precision reservoir prediction under the strong amplitude shielding of coal-rich strata.

Multiples reduce the signal -to -noise ratio of seismic data，affecting the identification of primaries， thereby increasing the difficulty of seismic processing ，reducing the authenticity and reliability of seismic imaging，and even forming geological illusions，which affect subsequent seismic exploration and development. The multiple suppression method based on wave theory can better adapt to complex media，which mainly consists of two steps，multiple prediction and adaptive matching subtraction. Both steps impact the final accuracy of multiple suppression. The paper compares three adaptive matching subtraction method，respectively based on minimum energy principle，pattern recognition，and deep learning. The advantages，disadvantages，and adaptability conditions of each method are also analyzed. The test results of model data containing surface -related multiples and field data with internal multiples show that the adaptive subtraction algorithm based on the principle of minimum energy principle assumes wavelet consistency，while the pattern recognition based adaptive subtraction technique requires high lateral consistency of seismic data. Compared with the two traditional methods，adaptive matching subtraction based on deep learning can avoid assumed conditions and effectively protect primaries， achieving higher computational accuracy.

In the field of oil exploration，multiple attenuation is an important means to improve the migration imaging quality of seismic data，especially for marine streamer data that is strongly disturbed by free -surface-related multiples. To better improve the multiple attenuation effect and data processing efficiency，this paper proposes a free surface related multiple attenuation method using an image translation technology based on deep learning. Firstly，the seismic data processing task is regarded as an image translation task in deep learning，and the Pix2Pix network is used to process the seismic data converted into the image form. Secondly，by improving the target data set of the conventional single form to the combining form，this study carries out multi -task training to improve the output effect of the Pix2Pix network. According to the correlation before and after data processing，an additional loss function is designed to further constrain and improve the output effect of the network. Finally，a layered model and a complex model are established for numerical testing，and additional interference items are added to the input data for quantitative testing. The numerical test results show that the proposed method can achieve multiple identification and attenuation by exploring the common features of data，improve the clarity of migrated images，and more accurately identify horizon information with high computational efficiency in data processing.

Model order reduction algorithms have been widely employed in the field of 3D transient electromagnetic（TEM）forward modeling due to their efficient computational performance. However，previous studies focus on the analytical expressions of the electromagnetic field for model order reduction，requiring the substitution of the forward system with a diagonal matrix composed of a large range of eigenvalues and precomputing the Krylov subspace order and optimal shift γ. Nevertheless，this approach often leads to a high Krylov subspace order，resulting in additional computational costs. This paper proposes a new model order reduction algorithm to address this issue. Firstly，a finite element method based on an unstructured tetrahedral mesh is utilized to discretize the spatial domain of the off-time transient electromagnetic control equations. Then，the shift-and-invert Krylov（SAI-Krylov）subspace is constructed utilizing the coefficient matrix of the control equations and the initial field vector. Finally，the reduced-order control equations can be obtained by projecting the original control equations onto the SAI-Krylov subspace，and transient electromagnetic responses can be rapidly computed by solving the lower-dimensional control equations. This algorithm does not require specifying the Krylov subspace order in advance. The additional computational costs caused by excessively high Krylov subspace order can be avoided by selecting the optimal shift γ and residual threshold tol. The numerical results of typical geoelectric models indicate that under the condition of meeting numerical accuracy requirements，the proposed method requires a lower Krylov subspace order. Even for transient electromagnetic forward modeling problems at the million-element level，rapid solutions at the level of minutes can be achieved.

The sedimentary water body of Enping Formation in Huibei Area of the Pearl River Mouth Basin is relatively shallow. It is mainly in a sedimentary environment of shallow water delta-lacustrine swamp-shore and shallow lake facies，among which the floodplain swamps of the delta plain and lacustrine swamps are the main sedimentary facies belts where coal measures source rocks develop. Peripheral drilling has confirmed that a large number of coal seams have developed in Enping Formation. The maximum thickness of a single coal seam is 4. 5 m，and the minimum thickness is 0. 5 m，which is far less than the minimum thickness that can be identified by conventional seismic inversion. Therefore，how to predict the development scale of thin coal seams is the key to the evaluation of the coal measures source rocks in this area. Thus，based on a comprehensive analysis of the geological and sedimentary backgrounds of coal measures source rocks，a“three -step method”is explored to achieve the prediction of thin coal seams in coal measures source rocks. Firstly，through forward modeling and analysis，it is confirmed that thin coal seams in different sedimentary environments all have the characteristics of strong amplitude seismic reflection，while sandstone and mudstone show weak amplitude characteristics. The root mean square amplitude attribute is extracted as a sensitive attribute to distinguish the development range of coal seams from that of sandstone and mudstone. Secondly，the frequency band of seismic data is broadened through high resolution seismic processing techniques to obtain broadband seismic data，which provides a high -precision data foundation for seismic inversion. Finally，by using the root -mean -square amplitude attribute，a stepwise fusion inversion method of low -，medium -，high frequency dominant frequency bands is adopted to gradually characterize coal seams of different thicknesses. Finally，the prediction of the development range of thin coal seams in coal measures source rocks is completed. The actual application results in Block L of Huibei Area show that the predicted development range of coal seams have a relatively high degree of consistency with the drilling results，and this method can effectively improve inversion accuracy.

The interbedding structure of Bohai M oilfield is well-developed. Due to the interference of the over-lying reservoir and the limitation of seismic data resolution，the seismic response of the underlying sand body with“one valley and one peak”becomes a single wave peak，making it impossible to characterize the underlying sand body through -90° phase shift data. Forward simulation shows that improving the resolution of seismic data is an effective means to eliminate the interference effect and restore the true seismic response of the reservoir. It is found that the M oilfield reflection coefficient is non -white -noise in the effective frequency band of seismic data，which means the traditional statistic deconvolution technology under the assumption of white -noise reflection coefficient is not suitable for high -resolution processing of the seismic data in the M oilfield. To solve this problem，this paper proposes a high -resolution processing technology in frequency domain that can adapt to the non -white -noise reflection coefficient. This technology utilizes the overall trend of the amplitude spectrum of the reflection coefficient to constrain the energy of different frequency components during the frequency band expansion process of seismic data，achieving the goal of aligning the overall trend of the seismic amplitude spectrum and the reflection coefficient amplitude spectrum after frequency band expansion. In addition，systematic analysis is conducted on the reasons why white -noise statistic deconvolution restricts the resolution improvement of seismic data under the non -white -noise reflection coefficient condition. After processing with the proposed technology，the seismic data resolution of the M oilfield has been significantly improved，comparable to the synthetic record of Ricker wavelet with the dominant frequency of 58 Hz，the interference effect between adjacent reservoirs is effectively eliminated. This technology can provide reliable high -resolution seismic data for the fine characterization of the reservoir.

Dense drilling platforms，coral reefs，docks，pipelines，and other obstacles lead to the necessity of obstacle avoidance for source and receiver points in the design of marine seismic data acquisition by the ocean bottom node（OBN）. Planning a path with obstacle avoidance is a key to the success of marine seismic exploration. Reasonable obstacle avoidance method and path planning scheme can minimize the influence of obstacles on bin attributes in the construction area，avoid the invalid work，and reduce the construction risk. Traditional obstacle avoidance methods generally move the source and receiver points in the obstacle area laterally to the outside without considering the influences of the turning radius of the source vessel and the width of the expander. The manual obstacle avoidance method leads to technical problems such as low obstacle avoidance efficiency，a large error，serious loss in the coverage area，and potential safety hazards caused by inaccurate turning radius design of vessel. This paper proposes an intelligent obstacle avoidance method for complex sea area observation system. The shot line of the observation system is fitted by the trajectory theory，which is then com bined with algorithms of multi-obstacle tangent circle against trajectory，obstacle merging，and dynamic feedback correction of trajectory. In this way，the paper forms an intelligent obstacle avoidance technique based on a backtracking algorithm and real-time calculation and adjustment of various parameters，greatly improving the efficiency and accuracy of obstacle avoidance. The production practice shows that the method can improve the efficiency and accuracy of exploration in complex sea areas and provide technical support for safe and efficient exploration in complex marine obstacle areas.

Microseismic monitoring is an essential technology in the field of unconventional oil and gas reservoirs exploration. It has been widely used in hydraulic fracturing fracture monitoring，CO₂ storage，and so on. However，the microseismic signal is weak in energy and easy to be polluted by noise. Its low signal-to-noise ratio makes it difficult to obtain good results in subsequent processing. Therefore，microseismic data denoising is a highly important processing step. The denoising effect has a key impact on the accuracy of subsequent source location and the reliability of focal mechanism inversion results. In this paper，a Monte Carlo non -negative dictionary learning（MCNDL）method is proposed for microseismic data denoising. The Monte Carlo block can obtain the initial dictionary containing relatively many effective signal features in a small amount of time. In the process of dictionary updating，non -negativity constraints are used to ensure the sparsity of data transformation and reduce the solution space，thus reducing the computational cost and improving denoising accuracy. This study evaluates the performance of the proposed method by using both synthetic and real-world microseismic datasets，comparing it with band -pass（BP）filtering，frequency-wavenumber（F-K）filtering，and K -singular value decomposition（KSVD）techniques. The findings highlight the superior denoising effect and efficiency of the proposed approach.

CO₂ flooding plays an important role in improving oil recovery and reducing greenhouse gas emissions，and is an effective means to achieve the goals of carbon peak and carbon neutrality，for which the seismic monitoring technology is the key. This paper analyzes and summarizes the research status and progress of seismic monitoring technology for CO₂ flooding at home and abroad，including time lapse seismic feasibility analysis，consistency processing technology and comprehensive interpretation. The application of seismic monitoring technology for CO₂ flooding in Gao89 block is also discussed. Feasibility analysis is an important prerequisite for time lapse seismic monitoring in the study area. Only when reservoir geological conditions，petrophysical conditions and seismic conditions are met，time-lapse seismic monitoring can be carried out effectively. In order to realize reservoir dynamic monitoring，it is particularly important to deal with the consistency between basic seismic and monitoring seismic（time lapse seismic），and it is necessary to carry out the matching filtering of time difference，amplitude，frequency，phase and other factors. Time-lapse seismic comprehensive interpretation is helpful to predict accurately the CO₂ plume. The pre-stack method is mainly AVO attribute analysis method. Post-stack difference analysis based on basic seismic data and monitoring seismic data is still the main method， and frequency domain information such as frequency division processing，velocity dispersion，low frequency shadows and so on is worthy of use. The prediction method of the CO₂ plume based on deep learning is in the ascendancy，but its operational efficiency and generalization ability need to be further improved. Finally，the paper provides an outlook on the development potential of time-lapse seismic technology in improving monitoring accuracy，developing monitoring methods and expanding application market.

With the development of offshore oil and gas exploration and development，the application of dualsensor ocean bottom receivers is becoming more and more extensive. As a key technology of dual -sensor seismic data processing，the separation of the up-going and down-going wavefields determines data processing quality. In response to the failure of conventional methods to complete separation of up going and down going wavefields（up going wavefields separated include some of the down going wavefields，and the down going wavefields separated also include some of the up -going wavefields），this paper proposes a separation method of up going and down going wavefields for dual sensor seismic data based on direct wave calibration. First，the spatial weighted function of the dual sensor seismic data is calculated by using the dual sensor direct waves in the frequency-space domain. Then the calibration filter operator of the dual-sensor seismic data is directly com puted by using the dual sensor seismic data with and without direct waves in the time space domain. Finally， the dual -sensor seismic data is calibrated，and up -going and down -going wavefields are processed. The data examples demonstrate the effectiveness and practicality of the proposed method. The separated up-going wavefield data not only eliminates the interference of the ghost multiples but also improves the signal -to -noise ratio and resolution of seismic data. The method provides high -fidelity up -going and down -going wavefield data for subsequent joint deconvolution and migration imaging processing.