Fault interpretation is one of the key steps for seismic data interpretation.The rapid development of deep learning，represented by neural networks，has greatly improved the efficiency and accuracy of seismic fault interpretation.The neural networks are trained by stochastic gradient descent optimization.The parameters of the network model are updated iteratively by using the loss function to measure the error of the model.The selection of the loss function is crucial for the seismic fault interpretation.In this paper，to interpret 3D seismic fault，we use the 3D U-Net model as the network structure and Adam as the optimizer to train the network with 3D synthetic samples.In terms of fault interpretation effects，we compare 10 loss functions including Balanced Cross-Entropy （BCE），Dice，Focal，Cosine，Log-Cosh Dice，Tversky，Focal-Tversky，Wasserstein，BCE-Dice，and BCE-Cosine.Normalization and data augmentation are applied to the trained data to mitigate the discrepancy between synthetic and field data.With the same network model，training parameters，and stopping criteria，we compare the convergence speed，calculation efficiency，and anti-noise performance of the 10 loss functions on 3D U-Net and analyze the fault prediction effect by using actual seismic data of the F3 field from offshore Netherlands.The experimental results show that 3D U-Net trained by Tversky and focal-Tversky loss functions can predict fault with better continuity.When crossed or parallel faults are densely distributed，and adjacent fault features can influence each other，the 3D U-Net prediction faults trained by BCE，BCE-dice，and BCE-cosine loss functions are complete，clear，and rich in detail.The research can provide a reference for selecting appropriate loss functions in different scenarios for seismic fault interpretation.

Microseismic monitoring is a common means to guide hydraulic fracturing operations and evaluate fracturing effects in shale gas extraction. The signal collected by ground monitoring has weak energy and low signal-to-noise ratio， which makes it difficult to identify microseismic events， and seriously affects the accuracy of positioning. Aiming at the ground microseismic monitoring data with low signal-to-noise ratio， a new noise cancellation method is proposed by combining synchronoussqueezing S-transform， spectral decomposition and τ-p transform. Firstly， the time difference correction is carried out on the monitoring data， and the in-phase axis of the microseismic signal is leveled. Then， the synchrosqueezing S-transform was applied to decompose the leveled data to obtain single-frequency slices. Then， the τ-p transform is performed on each single-frequency slice， and the microseismic signal position is obtained according to the results of the τ-p transform. Finally， noise cancellation is completed in the time-frequency domain according to the position of the signal. The processing results of ground microseismic monitoring data with low signal-to-noise ratio show that the new method can obtain ideal noise cancellation results.

 

The challenge faced by seismic velocity inversion methods based on deep neural networks is that the weak semantic mapping correspondence between seismic data in the time domain and model information in the spatial domain leads to a high degree of multiplicity.Additionally， neural networks lack effective guidance in mapping seismic data to velocity models， making them susceptible to noise interference and thus affecting inversion accuracy.Therefore， a seismic velocity inversion method based on feature enhancement U-Net is proposed.Firstly， by integrating the features of multi-shot seismic data， the spatial relationship between the seismic time series signal input to the network and the corresponding velocity model becomes more apparent.Subsequently， based on the concept of multi-scale feature fusion， modules with convolutional kernels of varying sizes are designed to bolster the network’s capacity for learning effective features.Next， attention gates are used to guide the network and enhance the features that the network focuses on.Finally， based on the bottleneck residual and pre-activation， a pre-activation bottleneck residual is incorporated into the network， to avoid gradient disappearance and network degradation.The experiment shows that this method has higher accuracy in seismic velocity inversion and performs well in noise testing.It has a certain generalization ability.

Scholte waves propagate along the seafloor fluid-solid interface and exhibit typical dispersion characteristics. The dispersion properties of Scholte waves can be utilized for the inversion of the shear wave velocity of the shallow seabed， making it an effective tool for seabed shear wave velocity modeling. It is of critical importance to establish the theoretical model of Scholte wave dispersion. In this study， a horizontal layered model of seawater-seabed elasticity is developed based on a real seawater-seabed environment. The dispersion equation and displacement equation for Scholte waves in this model are derived using the continuity conditions of boundary stress and displacement. The influence of seawater depth and seabed property parameters on the dispersion characteristics of Scholte waves is analyzed. Experimental results using a 6-layer seawater–seabed model show that： ①Regardless of the seabed is hard or soft， Scholte waves exhibit distinct dispersion characteristics. In the case of a hard seabed， the energy of Scholte waves is mainly concentrated in the seawater， with the fundamental mode having the weakest energy and the second mode having the strongest energy. As the seawater depth increases， the dispersion characteristics of Scholte waves weaken. For the soft seabed model， the energy of Scholte waves on the solid seabed significantly increases， and the seawater depth has little effect on Scholte wave dispersion characteristics. ②Compared with deep-water environments， Scholte waves have stronger energy and more pronounced dispersion characteristics in shallow water. Therefore， utilizing Scholte waves for inverting the shallow seabed shear wave velocity in shallow water environments yields higher accuracy and reliability. Finally， based on an actual seabed elastic geological model of a working area in the East China Sea， multi-component seismic data are simulated， and dispersion curves are extracted. A comparison analysis between the theoretically calculated dispersion curves and the extracted dispersion curves shows a good agreement， confirming the accuracy of our theoretical approach.

Amplitude-versus-angle （AVA） inversion plays an important role in the prediction of reservoir elastic parameters. The angles of the AVA equation （physical prior knowledge） are not easily determined， and the ill-posed nature of the large sparse matrix will make the conventional prestack inversion procedure unstable. For this reason， a prestack decoupled stepwise inversion method harmonized with physical and data prior knowledge is proposed. Firstly， a non-sparse forward framework is established based on physical knowledge to increase the stability of parameter inversion and lay the foundation for decoupled stepwise inversion. Next， this paper takes well data as data prior knowledge and harmonizes it with physical and data prior knowledge to decouple the prestack seismic data for more accurate prestack seismic attributes. Finally， inversion of decoupled prestack seismic attributes is performed to get the reservoir elastic parameters. This method can correct the inversion procedure by well logging prior information， which can avoid the errors caused by the inaccurate angles in the physical prior knowledge. The actual data test results show that the inversion results of the proposed method have higher accuracy compared with the industrial AVA inversion method. In addition， the inversion results of this method have higher accuracy， in which the accuracy of the lame parameter， shear modulus， and density is improved by 14.1%， 13.6%， and 11.9%， respectively.

Most of the projection onto convex sets （POCS） algorithms widely used for seismic signal reconstruction use linear or exponential threshold models，which have high computational efficiency，but have poor reconstruction effect due to the difficulty in eliminating the noise caused by the leakage of missing signals. Therefore，this paper proposes a POCS seismic signal reconstruction method based on the regional threshold model，which transforms the numerical threshold into a regional threshold，and the regional filtering window is iteratively updated as the threshold. The core idea is to reserve the transform coefficients of effective signal as much as possible by selecting a rectangular or a sectorial region of fixed size as a threshold according to a certain law in each POCS reconstruction iteration based on the frequency-wavenumber （F-K） spectrum distribution range of missing seismic signal in the spatiotemporal domain，and reserving and zeroing the transform coefficients inside and outside the region respectively. The rectangular and sectorial threshold models for POCS reconstruction of seismic signals are thus constructed. The numerical results demonstrated that compared with the POCS reconstruction of the exponential threshold model in the F-K domain，the regional threshold model in the F-K domain has a higher reconstruction accuracy for continuous missing signals. Compared with the sectorial region threshold model，the reconstruction accuracy and computational efficiency of the rectangular region threshold model are slightly higher. Compared with the exponential threshold model reconstructed by POCS in the curvelet domain，the reconstruction accuracy of the regional threshold model in the F-K domain is similar，but the computational efficiency is increased by about 90%.

The deep carbonate fractured-vuggy reservoir in Tarim Basin is characterized by rapid lateral changes，strong heterogeneity，and complex contact relationship between fractures and caves，which seriously restrict the subsequent exploration and development.Thus，this paper studies a comprehensive prediction method of fractured-vuggy reservoirs based on pre-stack and post-stack seismic multi-attribute analysis.Based on the post-stack seismic data，we firstly select the multispectral geosteering coherence attributes as the sensitive attributes for large fault and cave prediction through multi-attribute comparative analysis.Secondly，based on pre-stack wide-azimuth seismic data，we propose an anisotropic strength characterization method based on the wide-azimuth geosteering phase attributes.The singular value decomposition （SVD） is used to calculate the ratio of the maximum singular value to the sum of singular values from different azimuth geosteering phase attributes.The obtained results can be used to characterize the distributions of anisotropic strength in the target reservoir and taken as underground fracture prediction results.Finally，the post-stack large fault and cave characterization results are combined with the pre-stack fracture prediction results for analysis，and the results can be applied to the prediction of carbonate fractured-vuggy reservoirs in Tarim Basin.The contact relationship between fractures and caves can be clearly characterized，which is more consistent with the real drilling results，thus providing a favorable basis for subsequent exploration and development.

Fracture prediction is a crucial aspect of unconventional reservoir forecasting.The fracture density not only reflects the degree of fracture development but also serves as a significant parameter influencing fracture porosity and permeability.In this study， a fracture density inversion method is proposed， which uses weighted sine and cosine components of Fourier coefficients of P-wave reflection amplitude.Drawing upon the linear sliding crack equivalent medium theory， relationships between Fourier coefficients of the HTI （Horizontal Transversely Isotropic） medium P-wave reflection amplitude and fracture density are derived separately for oil-bearing and gas-bearing conditions.Additionally， the second-order Fourier coefficients are employed to predict the azimuthal angle of the fracture symmetry axis.The research investigates the variation patterns of the signal-to-noise ratio of the second-order Fourier coefficients’sine and cosine components with respect to the azimuthal angle of the fracture symmetry axis when seismic data contain noise.An inversion method for fracture density is then proposed using weighted sine and cosine components of second-order Fourier coefficients.Model simulation results indicate that the proposed fracture density inversion method exhibits strong noise resistance and stability.Application of the method to actual seismic data demonstrates that the predicted fracture density aligns with well log information， thus validating the effectiveness of the approach.

A set of key techniques of seismic exploration is proposed for the identification of deep carbonate fracture-cavity bodies and small faults in the Yingxiongling structural belt of the Qaidam Basin. First of all， from the aspect of data acquisition， the imaging accuracy of small faults can be improved by expanding the observation orientation， using a small combination of excitation and reception， and adopting an even arrangement of source and receiver points. From the aspect of seismic data processing， OVT domain multi-dimensional fidelity noise suppression technology and thick surface Q compensation processing technology can be used to improve the signal-to-noise ratio and fidelity and effectively increase the bandwidth and resolution of seismic data. From the aspect of seismic data interpretation， the sub-azimuth data volume and artificial intelligent fault identification technology can be used to identify small faults effectively，and the anisotropic strength attribute can be used to identify the fracture-cavity body effectively.The techniques can be used for reference in other similar areas.

Hydraulic fracturing is an important tool to improve the production of stratigraphic oil and gas reservoirs， and is of significance to the exploration and development of oil and gas fields. Hydraulic fractures are the main see-page channels and effective storage space for oil and gas reservoirs， and fracture identification is an important link in exploration and development. Scholars have proposed many practical technical solutions for hydraulic fracture detection by electromagnetic methods. Firstly， this paper classifies and summarizes the main geometric shapes of hydraulic fractures from the perspective of spatial development， and analyzes the advantages and disadvantages of electromagnetic monitoring methods for hydraulic fractures. Based on induction logging theory， new methods and technologies for monitoring and identifying hydraulic fractures filled with conductive proppants by electromagnetic methods are introduced. Secondly， the research status and development trend of hydraulic fractures are reviewed in detail of vertical wells， horizontal wells， and multiple wells. Finally， the main problems and difficulties in electromagnetic detection research on hydraulic fractures are discussed. Currently， the main technical difficulty is that it is hard to master complex fracture morphology， and fracture morphology research is affected by many factors. Thus， multiple electromagnetic simulation methods should be combined for the distribution and characteristics of hydraulic fractures with multi-scale characteristics.

The full-waveform inversion （FWI） based on attenuation medium usually adopts the viscoacoustic wave equation，and calculates the gradient of an objective function with respect to the velocity of the media by the adjoint state method. Since both the source wavefield and adjoint wavefield are attenuated，the gradient weakens with depth resulting in reducing the modification of subsurface parameters，which slows down the convergence of the inversion. To speed up the inversion efficiency，this paper develops a viscoacoustic FWI based on the decoupled fractional Laplacian （DFL） wave equation，and provides a new gradient compensation strategy based on a stabilization factor. The new compensation strategy achieves stable compensation by setting one stabilized factor in compensation，balances the amplitudes in the recovered gradient，meanwhile maintains correct kinematics. Compared with the conventional viscoacoustic FWI，the viscoacoustic FWI with this gradient compensation strategy has faster convergence speed and higher inversion accuracy.

Brittleness is an important index to evaluate the fracturing ability of tight reservoir and is of great significance to oil and gas exploration and development. Due to the low exploration degree， large area and few exploration wells， it is difficult to describe and predict the brittleness of the dolomitic tight reservoir in the middle and lower part of Permian in the eastern Junggar basin. This paper carries out the prestack inversion and brittleness prediction of the reservoir by using the angle domain data， according to the petrophysics experiment. Prestack inversion includes the following methods. AVO inversion is applied to obtain the P+G and P×G attributes. Y（Young's modulus，E） P（Poisson's ratio，σ） D（density，ρ） and（L（λ）M（μ）R（ρ） inversions are applied to obtain the reflection coefficient of elastic parameters logging constrained inversion is utilized to get the actual value of multiple elastic parameters. Then， the brittleness is calculated with various characterization formulas， and its physical meaning and application effects are analyzed. The practical application shows that： ①There is a good fitting relationship between shear wave velocity and P-wave velocity in the study area， and Young's modulus and Poisson's ratio are distinguished from mudstone and non-mudstone. ②The dolomitic rock has the characteristic of high velocity， and the reservoir shows a double sweet spot structure. ③Using three small-angle data bodies to directly invert the reflection coefficient of elastic parameters， the YPD inversion method has a better reservoir identification effect and high resolution than LMR inversion. Especially in the density reflection coefficient data， obvious fan and channel alluvial features can be seen. ④There is a certain difference between the brittleness characterization results and the characteristics of elastic parameters. The favorable areas of the dolomitic rock characterized by brittleness are distributed in bands， while the elastic parameters basically distributed in continuous patches. ⑤ρE/σ is the best brittleness characterization formula in the study area， which can provide a reference for the prediction of brittleness in dolomitic tight reservoirs.

Glutenite bodies widely exist in steep slope zones of faulting depression basins，and they can develop various hydrocarbon reservoir types with great exploration potential.However，due to their complex spatial distribution and overlap relationship，and large velocity differences with the surrounding rocks，it is difficult to employ the conventional grid tomographic modeling methods in the imaging domain to obtain their refined models.Thus，based on the grid tomographic inversion method in the imaging domain，this paper proposes a constrained tomographic inversion method in the imaging domain to improve the modeling accuracy of glutenite bodies.The thought of layer constraints is adopted，and the spatial distribution information of glutenite bodies obtained from seismic interpretation is combined to delineate key inversion regions.Meanwhile，it introduces the logarithmic barrier function and limits the velocity variation range of the glutenite bodies by applying inequality constraints to improve the modeling accuracy of the bodies.The application results in the BS work area indicate that compared with the original grid tomographic model，the velocity model built by this method is more consistent with the glutenite body distribution in the imaging section.Compared with the results of the fusion model，it can reveal the velocity changes inside the glutenite body，and the quality of its corresponding imaging section is also improved.

At present， CNPC’s oil and gas exploration and development are expanding from the uplift area to the basin slope， lake basin center， and basin periphery. The target layer is expanding from medium and shallow layers to deep and ultra-deep layers， and the object is expanding from conventional oil and gas to unconventional oil and gas. The exploration and development are faced with more complicated exploration areas， more hidden targets， and more difficult operations. In response to the problems of seismic exploration in highly difficult exploration areas on land， CNPC has strengthened technical research on seismic acquisition， processing， and interpretation and formed a series of technologies such as ‘acquisition of wide azimuth， broadband， high density， and high fold’， processing of ‘high resolution and high fidelity’， true surface imaging processing， and quantitative prediction of pre-stack reservoirs， which have supported continuous breakthroughs in exploration of highly difficult exploration areas and long-term stable growth of reserves， and achieved stable and rising oil and gas production. The above achievements benefit from the change in the concept of geophysical exploration technology， which brings the new concept of oil and gas exploration，‘oil and gas are not only in the mind of geologists but also hidden in high-quality seismic data’. In addition， thanks to the grasp of the breakthrough of seismic exploration technology on land， the technical idea of ‘the target is located at deep layers； the problem emerges at the near-surface； the velocity serves as the key， and the refinement remains as the core’ is proposed. Benefiting from the innovation and transformation of management ideas， a complete geophysical exploration technology management system has been established， which greatly improves the quality of seismic data in each exploration area. The breakthrough in understanding and management innovation will guide the development of onshore oil and gas seismic exploration technology to the all-directional， high-density， all-digital， multi-parameter， automated， intelligent， full-waveform， full-wave field， and other directions.

As Shunbei oil and gas area and Luntan 1 well in the Tarim Basin obtain industrial oil and gas flows at a depth of more than 8200 m， carbonate rock exploration is rapidly moving towards the deep and ultra-deep fields， posing a severe challenge to seismic exploration technology. This article mainly analyzes the research progress and problems faced in the theory of ultra-deep complex wave field seismic imaging. In terms of key technologies for predicting ultra-deep reservoirs， the current status of small fault identification through seismic data structure characterization and quantitative prediction methods for pore structure based on digital cores has been analyzed. From the perspective of geological exploration requirements， this paper proposes the development trend and key research directions of deep and ultra-deep carbonate reservoir and fluid prediction technology， so as to provide a reference for the theoretical and technical research of marine carbonate rock seismic exploration， and the following understandings are obtained： ① For ultra-deep seismic data with low signal-to-noise ratios， Q-stack depth migration and TTI medium RTM technology have achieved certain results in carbonate reservoir imaging. The key research directions include interlayer multi-wave suppression based on wave theory， anisotropic Q-RTM， least squares Q-RTM， and anisotropic omnidirectional angle domain imaging technology. ② The seismic data-based prediction technology of deep and ultra-deep heterogeneous carbonate reservoirs has the problems of weak theoretical methods and low prediction accuracy， so it is urgent to strengthen the exploration of theoretical methods and technical breakthroughs. ③ The deep integration of seismic petrophysical experiment and reservoir geology， the refined seismic data-based prediction technology of sensitive reservoir properties based on the wave characteristics of two-phase media （frequency， dispersion， and attenuation）， artificially intelligent and quantitative prediction of carbonate reservoirs， and fluid detection technology are important development directions. The development trend of “reliable deep seismic data， multi-disciplinary and high-precision characterization of reservoirs， and deep learning artificial intelligence” is obvious.

Seismic records collected through distributed optical fiber acoustic sensing （DAS） typically exhibit a low signal-to-noise ratio （SNR） due to the pervasive influence of complex and intense background noise. How to effectively suppress background noise，restore weak upgoing reflection information，and substantially improve the SNR of DAS records havs become a prominent challenge in seismic data processing. To address the issue of complex DAS background noise attenuation，this paper proposes a multiscale enhanced cascade residual network （MECRN），which employs a dual-path cascade residual network structure to extract shallow information from DAS records. On this basis，dilated convolutional layers and multiscale modules are introduced to extract the multiscale features existing in DAS records. Additionally，skip connections are introduced to import shallow features，which enhances the feature extraction capability of MECRN and avoids effective feature loss. Finally，the local and global features are integrated by residual learning，and the reconstructed features are refined to improve the denoising capabilities of MECRN. The processing results from both simulated and field DAS data demonstrate that MECRN can effectively suppresses complex DAS background noise and accurately restores weak reflection signals，which enhances the processing capacity of DAS data significantly.

Quantum computing has revolutionary advances in computing due to its powerful computational capabilities.It provides a new tool for the computation of complex problems and has been applied in many disciplines and fields.In recent years， quantum computing has been applied in geophysics.The application of quantum algorithms and quantum computers provides technical support for revealing the internal structure of the earth and probing deep resources.Quantum computing can improve computation efficiency and has great potential in geophysics.To this end， the principle of quantum computing is systematically analyzed， and the development status of the quantum algorithm is summarized.The existing achievements in the fields of geophysical data acquisition， wave field simulation， and inverse problem solving are also summarized.The superiority of the quantum algorithm is verified by establishing the theoretical model and conducting the inversion.Finally， the possible research directions of quantum computing in the future are prospected.

Improving the signal-to-noise ratio is a key step in seismic data processing. The current deep learning-based noise reduction methods have achieved better results. However，these methods are carried out in the temporal-spatial domain based on the local similarity of the seismic data and the processing efficiency is low. In view of the lateral continuity of geological structure，the shot gathers are very similar. Thus， an unsupervised rank-reduction denoise method in frequency domain is proposed based on the low-rank feature of the same frequency component of 3D data. The low-rank principle in frequency domain of 3D data is expounded and the singular value decomposition theory is used to guide the establishment of autoencoding network； Considering the characteristics of random noise distribution in frequency domain，K-L（Kullback-Leibler） divergence is used to constrain the loss function to improve the denoising effect. The experiments on synthetic and field data verified the advantages of the proposed method in denoising performance and computational efficiency compared with the multichannel singular spectrum analysis （MSSA） and K-SVD （K-Singular Value Decomposition） methods.

Gravity inversion is one of the important means to obtain the spatial structure and physical properties of underground geological bodies through surface information， and each gravity gradient component represents different geological body information. Gravity inversion combined with gravity gradient components can better reflect the shape and distribution of underground abnormal bodies. In this paper， a neural network-based algorithm for gravity full tensor data inversion is proposed. The U-Rnet network is applied to three-dimensional gravity full tensor data inversion. In order to test the effectiveness of the algorithm， six representative models are used for simulation experiments， and inversion results with clear boundaries and sparsity are obtained. Firstly， by comparing the inversion results of L₂ and Tversky loss functions， it is found that the inversion results corresponding to Tversky loss functions can clearly represent the boundary position of the model. Then， by comparing the inversion results of different gradient tensor combinations， the results of four tests show diffe-rent inversion accuracy on three directions （x， y， z）， and the test 4 shows the lowest fitting error. Finally， the proposed method is applied to the FTG data of Vinton Salt Dome in Texas， USA， and the inversion results are consistent with the real geological information.

Horizon tracking is basic and important work in seismic data interpretation, but the accuracy of conventional methods makes it difficult to meet the requirements of actual production. Therefore, this paper proposes a method for high-precision multi-set horizon tracking based on U²-Net. Firstly, a method is designed to produce filling labels, which traverse each pixel of seismic data to determine the location of each pixel and divide a horizon region for it. The method can automatically search for the adjacent horizons for the fault-crossing horizons, thus realizing the production of filling labels for the seismic reflection horizons and unconformities under the complex conditions of partial horizons and faults. Then, a U²-Net model is used to train the F3 data and the M-zone seismic data by utilizing the filling labels. Compared with the U-Net+PPM model, the U²-Net model has higher prediction accuracy, better stability, stronger generalization, and shorter training time. In addition, its accuracy and Mean Intersection over Union both exceed 95% in predicting the seismic reflection horizons in complex areas. The proposed method can be better adapted to the horizon tracking of seismic data with low signal-to-noise ratio.

There are abundant oil and gas resources entrapped in the carbonate reservoirs of the Ordos Basin. However， exploration results showed that the Majiagou Formation in the Daniudi Gas Field had developed multiple kinds of faults with small fault throws due to complex origins， which brings many challenges to its exploration and development. To address these challenges， it is crucial to optimize the sensitive elastic parameters for reservoir prediction. Therefore， the relationship between seismic attributes and the velocity ratio of compressional to shear waves （v_P/v_S） in the reservoir has been established， based on the analysis of elastic-sensitive parameters in the Daniudi Gas Field. Then， a prediction method for the v_P/v_S based on the XGBoost algorithm and multiple seismic attributes is proposed. To further improve the performance and generalization ability of the model， the hyperparameters of the XGBoost algorithm are optimized by Bayesian algorithm. This approach aims to find the optimal combination of hyperparameters， ensuring improved performance of the model on both training and testing datasets. The XGBoost algorithm is applied to the Marmousi 2 model for predicting shear wave velocity， achieving a correlation coefficient between predicted and actual values exceeding 0.88. With root mean squared error and mean absolute percentage error below 6.55×10^-7 and 4% respectively， the accuracy and reliability of the proposed method are demonstrated. The method applied in the Daniudi Gas Field of the Ordos Basin has successfully identified gas-bearing reservoirs， and the results are consistent with actual dril-ling data. Both theoretical model and practical data indicate that XGBoost， as a powerful machine learning algorithm， exhibits high accuracy， which can provide an effective approach for directly predicting v_P/v_S from post-stack seismic attributes.

Seismic data migration imaging is one of the important methods for estimating the reflectivity of underground media.However，the imaging results are often affected by the wavelet，with limited wavenumber band distribution.Effectively extending the wavenumber band of the imaging results to improve the spatial resolution is a key objective in broadband reflectivity estimation.To achieve this，we firstly point out that the wavelet and the illumination of the geometry system are two important factors that affect the resolution of imaging results from an inversion imaging perspective.Then，based on convolutional neural networks （CNN），we use broadband wavelets to construct labels and employ conventional imaging results as input features to explore the mapping relationship using CNN.We also develop a corresponding deep learning algorithm，namely the wavelet shaping deconvolution method，and design a solution to the problem of inaccurate initial wavelet estimation in deconvolution by concatenating，iterating，and updating wavelets and reflectivity.Customized broadband wavelets can take into account both low-wavenumber and high-wavenumber information and can better restore broadband reflectivity during network training.Finally，we use a known model for network pre-training，extract effective wavelets based on the target data as the initial wavelets for deconvolution of the target data，carry out wavelet shaping deconvolution processing，and test the correctness and reliability of the method through thin-layer model testing.The filed data processing results indicate that this method has great potential for practical applications.

The northern section of Longmen Mountain in the Sichuan Basin is a typical overthrust nappe structure with significant potential for oil and gas exploration.In order to obtain high-quality raw seismic data，it is necessary to optimize the seismic acquisition and observation system based on the results of forward modeling of the 3D model，which depends on the construction of a 3D geophysical model.To this end，based on the interpretation results of 2D and 3D seismic data，as well as near-surface survey data，different methods are used to construct geological models according to different geological and seismic reflection characteristics in different regions.Micrologging survey results and VSP data are used to assign velocity values to the model； the accuracy of the constructed 3D geophysical model is verified by using elastic wave equation simulation.The 3D geophysical model built using this method intuitively and accurately reflects the changes in nearsurface structure and underground velocity field.The simulated single-shot records are highly consistent with the actual single-shot records，providing a model basis for the argument of geometry system parameters.

In the actual quality factor Q estimation， it is prone to have a large estimation error due to factors such as frequency band selection， wavelet superposition， noise interference， and extrinsic attenuation. Thus， the amplitude ratio average method in the frequency domain （FARA） for Q estimation based on Taylor series expansion with different orders considering extrinsic attenuation is presented. Firstly， the continuous multiplication of the amplitude ratio in the reference frequency band is utilized to eliminate the effect of extrinsic attenuation. Then， based on the 1st-4th order Taylor series expansion expression of the amplitude factor at the reference frequency point， the single-frequency point Q estimation formula for the seismic records with extrinsic attenuation is derived. Secondly， the combination of high and low reference frequency bands is adopted to weaken the impact of reference frequency bands. Finally， the average processing of all frequency points average in dominant frequency bands is leveraged to improve the algorithm’s stability. The model test shows that the combination of high and low reference frequency bands can significantly improve the Q estimation accuracy of this method， and the proposed method is less sensitive to the time difference， time window， and noise interference than the logarithmic spectral area double difference （LSADD） method. The example application shows that the Q value estimated by the FARA method with different orders has good consistency， with greater overall Q value than that of the LSADD method. This is consistent with the model test results， indicating that the Q value estimated by the FARA method is more reliable.

As degrees of oil field exploration and development deepen， most target reservoirs are found to be thin， small， deep， and fractured. High-precision identification of thin interbedding has become the focus of re-servoir prediction. At present， time-frequency analysis methods and spectral decomposition techniques commonly used in thin interbedding identification are limited by seismic data， and the processing results fail to meet the requirements of accurate characterization of thin interbedding. Due to the nonlinear characteristics of geophysical inversion， the results of high-resolution inversion have multiple solutions. In this paper， a thin interbedding identification method based on the similarity of seismic waves is proposed. Firstly， the idea of a waveform library is used to construct the waveform library of well bypass-sensitive logging curves. Secondly， the improved Manhattan distance combined linear correlation coefficient method is then used to calculate the waveform similarity. Finally the waveform similarity is taken as the only driver to establish the mathematical relationship between seismic data and high-resolution logging data， until the high-resolution processing profile is obtained. This method makes full use of the high transverse resolution of seismic data and the high longitudinal resolution of logging data， effectively identifies thin interbedding， and greatly reduces the multi-solution of inversion results. The feasibility of this method is verified by model test. The actual data processing results show that the proposed method significantly improves the longitudinal resolution and provides technical support for the identification of thin interbedding within a tuned scale.

Missing traces reconstruction is a key step for seismic data processing. In recent years，various seismic data reconstruction methods based on deep learning theory have been proposed. However，normal convolution operation can only capture local dependencies and make insufficient use of global information. Moreover，the operation of pooling also results in the loss of feature map information，which destroys detailed features of seismic reflections. Therefore， a seismic data reconstruction method based on wavelet channel attention network is proposed. The Haar wavelet transform effectively extracts multi-scale characteristics and avoids the loss of information during the up-sampling process. Efficient channel attention modules are introduced to model the correlations between feature maps of different channels，which can make full use of the global information. Experimental results on synthetic and field datasets illustrate that the wavelet channel attention network can produce more accurate reconstruction results than some representative deep learning methods.

The P-wave to S-wave velocity ratio（v_P/v_S） is a vital tool for gas reservoir identification，reservoir characterization，and lithology recognition. At present，the P-wave velocity and S-wave velocity are mainly obtained through the inversion of the reflection coefficient approximate equation，and then the v_P/v_S is calculated. However， this indirect calculation method creates a cumulative error. To obtain the v_P/v_S directly from pre-stack seismic data，this paper proposes a new generalized elastic impedance equation and further derives an approximate equation of the P-wave reflection coefficient，which is linked to the v_P/v_S，P-wave velocity，and density. To achieve high-precision inversion results，this paper proposes a prestack seismic inversion method based on the sparse constraint of the L_p quasi-norm utilizing the derived approximation equation of reflection coefficient，which is solved by the alternating direction multiplier algorithm. The proposed direct inversion method is applied to theoretical models and practical data and compared with the indirect inversion method. The results demonstrate that the direct inversion method exhibits higher inversion accuracy and clearer boundary characterization of gas-bearing reservoirs.

Electromagnetic exploration plays an important role in oil and gas exploration，deep mineral resources survey，geothermal groundwater exploration，imaging of deep earth structures，and underwater military target reconnaissance.This paper systematically summarizes the research progress on joint inversion of electromagnetic constraints combined with multiple methods and information，introduces the research status from the perspective of method application，and focuses on the construction form，existing problems，and technical difficulties of the objective function for different inversion methods.In areas with more known information such as seismic data，different types of constraint functions are constructed by utilizing priori information to improve the tectonic imaging accuracy in saline and igneous rock development areas and reduce the inversion non-uniqueness.In areas where the magnetotelluric （MT） and controlled source electromagnetic （CSEM） data have been collected，the two methods can be employed to carry out joint inversion with their respective advantages.Various forms such as joint inversion with sequential constraints and joint inversion based on a unified objective function are developed.Additionally，CSEM and MT make up for each other in-depth inversion to improve the target inversion accuracy and reduce non-uniqueness.This paper discusses the selection of objective functions and weighting factors for this type of joint inversion.According to actual application effects，the most important and potential joint inversion is the inversion of electromagnetic and seismic data.Joint inversion has gone through from seismic constrained electromagnetic inversion to physical model-based and cross-gradient method-based joint inversion.This paper also analyzes the difficulty in constructing a reasonable connection between velocity and resistivity models and explains that the weighting factor setting of velocity and resistivity models in the objective functions is still the key and difficult points.Finally，this paper proposes to combine electromagnetic joint inversion，artificial intelligence algorithms，and three-dimensional visualization analysis.It also points out that large-scale parallel computation and other new methods and technologies can be adopted in the future，and joint acquisition systems can be developed to provide more effective data volumes for the multi-physical field joint inversion.

 

Sequential stochastic simulation is generally used to characterize reservoir heterogeneity both in the iterative stochastic inversion and linear Bayesian stochastic inversion. Most sequential simulation methods rely on variograms or training images to describe the spatial correlation of model parameters. In addition， the simulation results are required to be calculated point by point， which makes parallel computing difficult and reduces computational efficiency. Therefore， a conditioned fast Fourier transform moving average （FFT-MA） is introduced into the linear inversion framework， and a prestack seismic stochastic inversion method based on spatial co-simulation is proposed. Firstly， the posterior probability distribution of elastic parameters is obtained by integrating seismic data and low-frequency well logging information under the Bayesian framework. Then， the probability field is generated according to the FFT-MA algorithm. Well logging data is taken as conditional data to conduct Bayesian posterior probability field co-simulation. High-resolution prestack stochastic inversion results of elastic parameters constrained by well logging and seismic data are thus obtained. No iteration and update of model parameters is required by the method， which greatly improves the computational efficiency of stochastic inversion. Finally， the validity of the proposed method is demonstrated by numerical model examples and practical data application cases. The numerical model examples show that the proposed method has significant advantages over conventional methods in terms of high-resolution reservoir prediction and computational efficiency. Small-scale reservoir characterizations can be explored stably and accurately. The practical data application cases show that the high-resolution inversion results obtained by the proposed method match well with well logging data. The practicality of stochastic inversion in characterizing quantitatively thin reservoirs is greatly improved.

In-situ stress seismic data-based prediction approaches of shale reservoirs usually apply the elastic parameters to calculate differential horizontal stress ratios （DHSR）. However， there are shortcomings. First， the anisotropy parameters （fracture weakness） are included in the prediction formula and cannot be solved easily， making it difficult to predict in-situ stress； second， parameters such as Young's modulus and Poisson's ratio in the prediction formula are obtained by indirect inversion， and the accuracy is low， which is difficult to meet the requirements of shale gas geoengineering integration. Therefore， an in-situ seismic data-based prediction approach of shale reservoirs based on fracture density inversion is proposed. For enhancing the prediction accuracy， a longitudinal wave azimuthal anisotropy AVO formula based on Young's modulus， Poisson's ratio， and fracture density is established to directly invert elastic parameters， and the DHSR formula expressed by Poisson's ratio and fracture density is derived. The anisotropic petrophysical modeling of shale reservoirs is carried out according to well data， and pre-stack azimuthal anisotropy inversion is performed. The inverted Poisson's ratio and fracture density are applied to the estimated DHSR. The in-situ stress properties of shale reservoirs can be evaluated by the estimated DHSR. A real example verifies that smaller DHSR indicates that more orthogonal and complex fracture networks will be generated by hydraulic fracturing in different directions， which is beneficial for modifying the physical properties and seepage channels of the reservoir. A larger volume of reservoir reconstruction is more conducive to reservoir fracturing. In addition， larger DHSR means that hydraulic fracturing will generate non-orthogonal plane fractures parallel to the maximum horizontal principal stress， forming isolated fractures that are not beneficial for volume transformation. Meanwhile， the estimated DHSR result agrees with existing well logging in-situ stress calculation， fracturing monitoring， and production testing， and it fits with the geology recognition， revealing that the approach is effective and reliable.

The Y target in the ultra deep water area of Qiongdongnan Basin is a high-level delta underwater distributary channel sedimentary reservoir controlled by multi-level fault steps. The gas and water layers revealed by drilling data are both shown as “bright spots” in seismic data and have Class Ⅲ AVO features with amplitude enhancement along offset. Conventional oil and gas detection results based on amplitude information have multiple solutions. Therefore, a seismic full information hydrocarbon detection technology that fully utilizes the amplitude, frequency, and phase information of seismic data is proposed. Firstly, this paper comprehensively analyzes the post-stack and pre-stack seismic response characteristics of sandstone reservoirs and influencing factors via seismic forward modeling. It is pointed out that poro-sity and gas layer thickness are the main reasons affecting fluid identification by using amplitude information and proves that thick gas-bearing sandstones can be identified based on amplitude information. Secondly, the seismic reflection spectrum analysis shows that the attenuation of seismic waves caused by hydrocarbon layers is the main reason for the decrease in the main frequency and bandwidth of gas reservoirs. Finally, numerical simulation shows that both thick gas sand and thin gas sand under the background of water layers exhibit obvious absorption and phase anomalies, so the frequency and phase information related to seismic wave attenuation can be used for oil and gas detection. The application in the research area shows the proposed method can effectively distinguish between gas and water layers. The detection results are consistent with the actual drilling results, and provide a reliable basis for drilling implementation as well as effectively reduce exploration risks.

Low sparsity pseudo-layers and low resolution of traditional sparse-constrained inversion lead to difficulty in thin-layer identification.To this end，we propose a prestack seismic inversion method based on reweighted L₁-norm sparse constraints，namely to combine the reflection coefficients of the formation with the elements of the reweighted matrix，and the reflection coefficients are further reweighted to optimize and construct the inversion objective function.In addition，the alternating direction method of multipliers （ADMM） is used to transform the nonlinear inversion objective function containing multiple parameters into multiple easily solvable single-parameter linear subproblems，and iterative shrinkage thresholding algorithm （ISTA） is introduced to solve the mixed norm optimal solution of the subproblems.Unlike the traditional L₁-norm sparse constraints，which only consider the position information of the reflection boundary，the reweighted L₁-norm exploits the amplitude information of the reflection boundary，which can more fully utilize the sparsity of the L₁-norm to obtain more accurate formation velocity boundary and density boundary through the prestack seismic inversion and weaken the velocity pseudo-layer phenomenon existing in the traditional L₁-norm inversion results.The model test and the application of the measured data in the field data demonstrate that the profile boundaries of P- and S-wave velocities and density obtained by the proposed method are more accurate，with higher resolution，better identification ability for thin layers，and the pseudo-layer phenomenon is greatly reduced.It can provide a more accurate data basis for the subsequent prediction of other geophysical parameters.

The conventional total variation （TV） regularization model only considers the first-order derivative information in the horizontal and vertical directions. When dealing with prestack seismic data with curved reflection events，it can severely damage the amplitude information and cause “staircase effects” by suppressing the lateral gradient characteristics of the amplitude. The local dip information of seismic data is often applied to improve the amplitude-preserving ability of the TV model. However，the calculation of local dip information itself will be impacted by noise. To address this issue，this paper proposes a high-order TV regularization model to suppress random noise in prestack seismic data in the domain of normal moveout（NMO）. This method first transforms the prestack seismic data into the NMO domain，NMO is robust to noise and avoids the calculation of the local dip angle. In the NMO domain，the curved event is flattened，and then high-order TV denoising is performed. Finally，the prestack seismic data are restored through inverse NMO. Taking the second-order derivative as an example，a high-order TV regularization inversion denoising objective function is constructed，and a fast optimization method is derived under the split Bregman optimization framework. The processing results of synthetic seismic data and actual seismic data show that this method can not only effectively suppress random noise but also eliminate amplitude distortion caused by curved reflection events and “staircase effects”， improving the amplitude preservation performance of the TV denoising method.

With the deepening of oil and gas exploration and development， thin and lithologic reservoirs have gradually become important exploration targets， which also leads to higher requirements for the resolution of seismic data. This paper presents a high-resolution seismic data processing method of U-net based on an adaptive attention mechanism. This method first uses the U-net structure to learn the feature representation of seismic data， extracts the abstract features of seismic data through the encoder of the down-sampling process， and then reconstructs and refines the features through the decoder of the up-sampling process. The attention mechanism is introduced in the process of up-sampling， which is utilized to adjust the attention of the network to diffe-rent seismic features. Therefore， the network can capture more details and features of the seismic data more effectively. The experimental results of synthetic seismic records of the Marmousi model and real data show that the new network has less error and is more stable than the original U-net， as it can effectively improve the prediction accuracy and realize the high-resolution processing of seismic data.

Five-dimensional （5D） seismic data can better analyze the changes in attributes such as travel time， speed， amplitude， frequency， and phase of seismic waves propagating in anisotropic media with azimuth angle. Moreover， the offset information is related to the scale， stratigraphic lithology， and fluid composition of the target geological body， while the azimuth angle information is related to the development characteristics of stratigraphic faults and fractures. Therefore， this paper proposes a technique for volcanic structure characterization and lithofacies identification based on 5D seismic data. By considering that the underground structure responds more obviously in the direction perpendicular to the strike direction， the paper constructs an azimuth analysis window to extract the dominant azimuth information and uses dip imaging to enhance the processed seismic data， so as to predict the volcanic structure. By using changes in dip and azimuth angles， the paper calculates the similarity of adjacent channels， improves the lateral signal-to-noise ratio of seismic data， and clarifies the macroscopic distribution range of volcanic structures. By defining the azimuth time window and combining the seismic trace inverse distance weighting algorithm， the paper extracts the most sensitive information about faults at each azimuth angle， improves the accuracy of volcanic structure characterization， and obtains a clearer volcanic morphology. Combined with kernel principal component analysis （KPCA）， the dominant attributes are fused to predict the favorable lithofacies of volcanic rocks. The proposed method accurately predicts the favorable zones of the third phase of volcanic rocks in the KM1 well area of the Junggar Basin， laying the foundation for the exploration and development of volcanic rock reservoirs in this area.