As oil and gas explorations progressively advance towards deeper and more complex geological formations, the imperative for precise characterization of subsurface structures has become increasingly prominent. The efficacy of noise suppression is a critical determinant for the quality of subsequent inversion and imaging processes. In recent years, deep learning methodologies have garnered significant attention and widespread application in seismic denoising, primarily due to their inherent data-driven advantages. While conventional deep learning implementations have achieved notable denoising performance, they are confronted with inherent limitations, including incomplete noise reduction and potential signal degradation. To address these challenges, this study proposes an innovative multi-round SCU-Net (MR-SCU) denoising approach. The MR-SCU methodology based on SCU-Net employs noise as labeled data to generate an initial denoised outcome in the first round. Denoising results are used as input while utilizing the residuals between the labeled and predicted data as labels for subsequent denoising round. Multiple rounds are iteratively repeated to achieve more thorough denoising effect while preserving effective signals from being compromised. The incorporation of SSIM (Structural Similarity Index Measure) as the loss function further enhances the method’s precision in detail-oriented denoising tasks. Numerical experiments conducted on synthetic data and field data acquired from a specific region in western China substantiate the efficacy of the MR-SCU, demonstrating its capability to deliver superior denoising performance while optimally preserve valuable seismic information.

Wave equation-based traveltime inversion is a method that uses traveltime information to obtain the low-wavenumber components of subsurface velocity models. This helps create a reliable initial model for full waveform inversion (FWI). However, this method usually requires identifying the first arrival waves or specific seismic events to calculate the traveltime differences between the observed and synthetic data. When working with multiple seismic events, such as those from elastic wavefields or simultaneous sources-based seismic data, it becomes difficult to obtain the low-wavenumber components of velocity models by traveltime inversion. In this letter, we propose a simultaneous source-based elastic wave local-scale traveltime inversion (SS-ELTI) method. This method utilizes both P-wave and S-wave data, along with local-scale traveltimes from various seismic events generated by simultaneous sources. This approach enables the simultaneous inversion of the low-wavenumber components of both P-wave and S-wave velocity parameters. Numerical tests demonstrate that the proposed SS-ELTI method can effectively reduce the computational costs and mitigate the cycle-skipping problem of elastic full waveform inversion (EFWI).

For deep reservoir exploration, precise inversion of the deep target is important. However, the resolution of full waveform inversion (FWI) in the deeper region may not be as fine as in the shallow region due to the acquisition geometry and complex local structure. Besides, the cycle-skipping problem is critical and significantly influences the accuracy of the inversion in FWI. In order to increase the resolution for the deep region and reduce the cycle-skipping problem, we propose a local angle domain-based inversion method. We decompose the incident and scattered wavefields around a local target into the local angle domain. Then, by the simultaneous construction of the local angle filter and the local resolution function based on the wavefield decomposition, a local angle domain multiscale inversion method with illumination compensation is conducted. Based on the convergence criterion, we construct an auto-transitional misfit function that can avoid manual intervention for the multiscale inversion process. Numerical tests proved the feasibility of the proposed strategy.

Tunnel seismic prediction (TSP) is essential for guaranteeing the safety of tunnel construction. Reverse time migration (RTM) plays a vital role in providing precise visualization of the geology located in front of the tunnel. However, anomalies like karst caves cause signal reflection and attenuation, leading to blurred images and artifacts. The point spread function (PSF) characterizes the blurring effect of a specific observing system on an imaging point, and the migration result can be viewed as the convolution of the true reflectance model with the PSF. Thus, the ambiguity of the migration result can be eliminated by using the inverse of the PSF. In this paper, we utilize the PSF in the context of TSP. First, the wavefields from the source and receiver sides are broken down into angle domain components through the Poynting vector approach. Then, the PSF operator is obtained by calculating the local illumination matrix (LIM) and is further applied to image correction. We designed various models to simulate the complex geology in front of the tunnel. Numerical experiments show that the application of PSF can improve the imaging accuracy of complex structures in TSP. And the test results of actual tunnel seismic data also demonstrate the effectiveness of this method.

Precise modeling of the attenuation parameter Q is important to confirm the robust and diagnostic attenuation characteristics of seismic waveforms in oil and gas reservoirs. For this purpose, the nearly constant Q viscoacoustic wave equation is beneficial. Compared with attenuation models such as standard linear solid (SLS), the wave equation corresponding to the nearly constant Q model contains an explicit Q, which is conducive to inversion and can provide a more effective parameterization. This parameterization facilitates the initial suppression of parameter crosstalk in multiparameter inversion. We derive the adjoint equation containing auxiliary variables, compare the sensitive kernels of different parameterizations, and explain the superiority of the constructed parameterization. The truncated Gauss-Newton (GN-TRN) method is introduced to suppress parameter crosstalk further. The GN-TRN method updates the model parameters by calculating the Hessian vector product and iteratively solving the approximation of the Newton gradient directions. The test results of the theoretical model and field data verify the effectiveness and stability of the inversion method.

Although full waveform inversion (FWI) is widely recognized as one of the state-of-the-art techniques in geophysical exploration, there remain several aspects of FWI that require further improvements, specifically in resolution and modeling efficiency. To address this, we introduce an inverse scattering approach to frequency-domain seismic FWI by utilizing a regularized objective function. Different from traditional adjoint methods, the scattering theory allows us to derive the sensitivity kernel explicitly through two Greens’ functions and transforms the nonlinear inverse scattering problem into a series of linear inverse scattering problems, thereby facilitating the calculation of the gradient and Hessian. To mitigate the computational cost when calculating the background and actual wavefields, the fast Fourier transform (FFT) combined with the Krylov subspace method is used to solve the Lippmann-Schwinger (L-S) integral equation (IE) iteratively. Additionally, we incorporate minimum support (MS) stabilizing functional as an extra model misfit term alongside the traditional data misfit function, for a better recovery of the shape structure within the model. Furthermore, the inversion framework is enhanced by integrating an improved limited memory Broyden-Fletcher–Goldfarb-Shanno algorithm, with the regularized Hessian serving as a preconditioner. To demonstrate the efficacy of our method, numerical tests on Marmousi and BP models are presented to validate the performance and robustness of the proposed approach.

Seismic exploration is an important tool for the detection of road diseases. However, since engineering seismic exploration usually deals with near-surface problems, its detection is complex and difficult. Time-frequency analysis is an important seismic attribute extraction method, which can provide hidden information that is difficult to obtain from seismic profiles, which can effectively help to identify subsurface structures and various types of disease. The S-transform is an important linear time-frequency analysis method, but the window function is fixed during its time-frequency feature extraction, resulting in a shift of the spectrum to higher frequencies, which reduces the accuracy of the time-frequency analysis. The unscaled S-transform, which removes the linear frequency term in the window function, overcomes the above problem to some extent, but affects the temporal resolution of the spectrum in the low-frequency region. To this end, we propose a modified frequency-domain unscaled S-transform method (MFUST) to perform the time-frequency decomposition of seismic signals, and the proposed method adds additional parameters to its window function, which ensures the time-frequency accuracy while realizing the improvement of the spectrum in terms of temporal resolution through the adjustment of the parameters. The effectiveness of the proposed method is verified using synthetic numerical experiments and a real data test.

The seismoelectric effect, characterized by the coupling of seismic and electromagnetic (EM) waves in fluid-saturated porous media, offers a promising avenue for subsurface exploration and earthquake seismology. However, the complexity of the Earth’s subsurface, particularly the presence of anisotropic velocity and attenuation, poses challenges for accurate modeling and interpretation. Here, we introduce a novel approach to model seismoelectric wave propagation in viscoelastic anisotropic porous media by incorporating the nearly constant Q model into the seismoelectric wave equations. Using a series of 2-D and 3-D models, we analyze the influence of both velocity and attenuation anisotropy on the propagation of seismoelectric waves. Our results show that both velocity and attenuation anisotropies contribute to the attenuation of the seismoelectric wavefield. Spatial changes in the elastic and electrical parameters of the subsurface media generate interface response of EM waves, although these are weaker compared to coseismic electric fields. Our extended seismoelectric models provide a robust description of wave propagation in poro-viscoelastic anisotropic media, which can allow for enhancing the current understanding of the Earth’s interior.

Full waveform inversion (FWI) is a powerful means for accurately reconstructing subsurface velocity models at high resolution. Yet it is nevertheless a nonlinear and ill-posed problem. Physics-driven FWI methods employ gradient-based optimization algorithms to minimize the error between the observed seismic data and the synthetically generated seismic data. The solution may converge to a local rather than global minimum. The cycle-skipping problem occurs when the synthetic data exceed a half-wavelength shift relative to the observed data. FWI relies on an accurate initial velocity model to mitigate the cycle-skipping problem. Moreover, due to the increasing size and desired resolution of seismic data, FWI costs a great deal of computational time. To obviate these problems, we present a data-driven FWI scheme based on a deep learning architecture called U-Net. The network consists of the ringed residual unit, which integrates residual propagation and residual feedback. It beneficially achieves correspondence between the seismic data domain and the velocity model domain. The features of the shallow layers are connected with the deep layers by a skip connection to facilitate seismic data spatial information propagation and utilization. They improve inversion accuracy and make the network more generalizable and robust. We utilize the Society of Exploration Geophysicists (SEGs)/European Association of Geoscientists and Engineers (EAGE) overthrust and salt models to verify our proposed method’s impressive performance. The experimental results clearly demonstrate that the proposed method can produce high-quality velocity models. Compared with the conventional physics-informed FWI, it has advantages in both computational time and initial model dependence.

Seismic inversion is one of the effective techniques to obtain elastic parameters for reservoir characterization. Deep learning is widely used in seismic inversion and has yielded many satisfactory results. The performance of the existing deep learning-based seismic inversion methods mainly depends on the network structure and a large number of effective training datasets. However, due to the limitation of expensive acquisition costs, it is difficult to obtain enough effective training datasets for network training in seismic surveys. To this end, we develop a double-dual network structure that incorporates both physics and model information to alleviate the dependence of deep learning methods on training data and even enables unsupervised learning and inversion. One of the dual networks is responsible for using the physical information to constrain the inversion results and ensure the physical validity of the predictions. The other dual network is responsible for using the priori information from the model domain to constrain the inversion results and improve the stability of the predictions. Ultimately, the two dual networks are coupled by a loss function to realize labeled/unlabeled network training and inversion applications. We then implement the method in a synthetic model as well as field data. The results are compared with traditional data-driven seismic inversion method and physics-guided data-driven seismic inversion method, and it is shown that the proposed method outperforms these two methods.

The image-level label has prevailed in weakly supervised semantic segmentation tasks due to its easy availability. Since image-level labels can only indicate the existence or absence of specific categories of objects, visualization-based techniques have been widely adopted to provide object location clues. Considering class activation maps (CAMs) can only locate the most discriminative part of objects, recent approaches usually adopt an expansion strategy to enlarge the activation area for more integral object localization. However, without proper constraints, the expanded activation will easily intrude into the background region. In this paper, we propose spatial structure constraints (SSC) for weakly supervised semantic segmentation to alleviate the unwanted object over-activation of attention expansion. Specifically, we propose a CAM-driven reconstruction module to directly reconstruct the input image from deep CAM features, which constrains the diffusion of last-layer object attention by preserving the coarse spatial structure of the image content. Moreover, we propose an activation self-modulation module to refine CAMs with finer spatial structure details by enhancing regional consistency. Without external saliency models to provide background clues, our approach achieves 72.7% and 47.0% mIoU on the PASCAL VOC 2012 and COCO datasets, respectively, demonstrating the superiority of our proposed approach. The source codes and models have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/SSC.

Geophysical exploration is important for road construction and maintenance. Before road construction, geophysical exploration is required to detect the geological structure to ensure the construction of the road; after the road is completed, geophysical exploration is still required to detect diseases in time. As a traditional geophysical exploration method, seismic exploration is important in road detection for its large detection depth and high resolution. The seismic attribute information obtained from seismic data can reflect many hidden information. As an important seismic attribute extraction method, time-frequency analysis method can simultaneously describe the energy density and intensity of seismic signals at different times and frequencies, which is of great significance to geological interpretation. However, traditional time-frequency analysis methods are low resolution and insufficient focusing. In this paper, on the basis of linear time-frequency analysis, the L1 norm constraint will be introduced, and the time-frequency analysis method will be implemented from the perspective of inversion, so as to reduce the influence of the multi-solution of the method and improve the method’s resolution and focusing. In this paper, two numerical simulation data and one real seismic data of road detection are employed to test the proposed new method.

Recently, deep metric learning (DML) has achieved great success. Some existing DML methods propose adaptive sample mining strategies, which learn to weight the samples, leading to interesting performance. However, these methods suffer from a small memory (e.g., one training batch), limiting their efficacy. In this work, we introduce a data-driven method, meta-mining strategy with semiglobal information (MMSI), to apply meta-learning to learn to weight samples during the whole training, leading to an adaptive mining strategy. To introduce richer information than one training batch only, we elaborately take advantage of the validation set of meta-learning by implicitly adding additional validation sample information to training. Furthermore, motivated by the latest self-supervised learning, we introduce a dictionary (memory) that maintains very large and diverse information. Together with the validation set, this dictionary presents much richer information to the training, leading to promising performance. In addition, we propose a new theoretical framework that can formulate pairwise and tripletwise metric learning loss functions in a unified framework. This framework brings new insights to society and facilitates us to generalize our MMSI to many existing DML methods. We conduct extensive experiments on three public datasets, CUB200-2011, Cars-196, and Stanford Online Products (SOP). Results show that our method can achieve the state of the art or very competitive performance. Our source codes have been made available at https://github.com/NUST-Machine-Intelligence-Laboratory/MMSI.

Embodied question answering (EQA) is a recently emerged research field in which an agent is asked to answer the user's questions by exploring the environment and collecting visual information. Plenty of researchers turn their attention to the EQA field due to its broad potential application areas, such as in-home robots, self-driven mobile, and personal assistants. High-level visual tasks, such as EQA, are susceptible to noisy inputs, because they have complex reasoning processes. Before the profits of the EQA field can be applied to practical applications, good robustness against label noise needs to be equipped. To tackle this problem, we propose a novel label noise-robust learning algorithm for the EQA task. First, a joint training co-regularization noise-robust learning method is proposed for noisy filtering of the visual question answering (VQA) module, which trains two parallel network branches by one loss function. Then, a two-stage hierarchical robust learning algorithm is proposed to filter out noisy navigation labels in both trajectory level and action level. Finally, by taking purified labels as inputs, a joint robust learning mechanism is given to coordinate the work of the whole EQA system. Empirical results demonstrate that, under extremely noisy environments (45% of noisy labels) and low-level noisy environments (20% of noisy labels), the robustness of deep learning models trained by our algorithm is superior to the existing EQA models in noisy environments.

In geophysics, full waveform inversion rely on an efficient forward modeling method. However, a direct solver is computationally expensive and requires significant in-core memory. Furthermore, the iterative solver is hampered by the problem of the convergence. In this work, a compressive sensing (CS) solver is proposed for the frequency-domain acoustic wave modeling. Critical factors for the CS solver are the construction of a sparse transform matrix and an efficient recovery algorithm. Because of the characteristics of seismic wavefields in the frequency domain, we introduce the K-singular value decomposition (K-SVD) algorithm to construct the sparse transform matrix. Once a sparse representation is achieved, the size of the impedance matrix is strongly reduced. Numerical results obtained in homogeneous media, the Marmousi II model, and the Society of Exploration Geophysicists (SEG)/European Association of Geoscientists and Engineers (EAGE) salt model reveal that the proposed CS solver significantly reduces computation cost compared with the direct solver and the iterative solver. In addition, the comparison with the iterative solver shows that the CS solver may speed up the convergence.

The Earth is often characterized by viscoelastic rocks, porous sediments and anisotropic structures. Poro-elasticity with Biot’s theory is considered fundamental to describe the interaction between the deformation of the elastic porous solid and the flow of fluid in the porous structure. The quality factor (Q) in the theory of viscoelasticity relates seismic wave attenuation and dispersion to physical properties of the Earth’s interior, e. g. temperature, stress and composition. However, the constantQwave equation in its time-domain differential form remains difficult to solve when describing the attenuation in an explicitly specifiedQparameter. Here, we introduce the first-and second-order nearly constantQmodels capable of describing the attenuation of the solid skeleton, thereby extending the Biot and Biot-squirt (BISQ) models to poro-viscoelastic media. The bulk and shear moduli of the solid frame are represented by the modified relaxation function. By presenting examples with finite-difference time-domain (FDTD) numerical modeling for seismic wavefields in anisotropic, viscoelastic porous media including transversely isotropic media with a vertical symmetry axis (VTI) and orthorhombic meida, we demonstrate that the extended Biot and BISQ models provide good descriptions of the wave propagation in poro-viscoelastic anisotropic media and can thus help better understand the Earth’s interior.

The mixedLp-norm, 0 ≤p≤ 2, stabilization algorithm is flexible for constructing a suite of subsurface models with either distinct, or a combination of, smooth, sparse, or blocky structures. This general purpose algorithm can be used for the inversion of data from regions with different subsurface characteristics. Model interpretation is improved by simultaneous inversion of multiple data sets using a joint inversion approach. An effective and general algorithm is presented for the mixedLp-norm joint inversion of gravity and magnetic data sets. The imposition of the structural cross-gradient enforces similarity between the reconstructed models. For efficiency the implementation relies on three crucial realistic details; (i) the data are assumed to be on a uniform grid providing sensitivity matrices that decompose in block Toeplitz Toeplitz block form for each depth layer of the model domain and yield efficiency in storage and computation via 2D fast Fourier transforms; (ii) matrix-free implementation for calculating derivatives of parameters reduces memory and computational overhead; and (iii) an alternating updating algorithm is employed. Balancing of the data misfit terms is imposed to assure that the gravity and magnetic data sets are fit with respect to their individual noise levels without overfitting of either model. Strategies to find all weighting parameters within the objective function are described. The algorithm is validated on two synthetic but complicated models. It is applied to invert gravity and magnetic data acquired over two kimberlite pipes in Botswana, producing models that are in good agreement with borehole information available in the survey area.

Seismic facies analysis is a crucial foundation for basin-fill studies and oil and gas exploration. With its rapid development, CNN-assisted interpretation is becoming increasingly popular. However, CNN models are often considered "black boxes" that lack transparency. To understand how CNN models classify seismic facies and visualize the contribution of each seismic attribute to the final predictive scoring, we have investigated class activation map (CAM) techniques and an explainable tool called Shapley additive explanations (SHAP) value. Based on real seismic data collected in the Sichuan basin, we compared the visualization performances of CAM and SHAP methods and found that the SHAP tool has better visualization capabilities than CAM methods, which only produce heat maps with positive values. Using SHAP values, we identified the importance of each seismic attribute and refined redundant attributes. This approach establishes a connection between seismic attributes and sedimentary environments and is a prime example of the capability of deep learning to discover knowledge beyond human experience. We applied the selected seismic attributes to generate a refined CNN model and compared it to the original CNN model, demonstrating the superiority of our proposed strategy. When we compared the predicted seismic facies using the refined CNN model based on SHAP features, the conventional K-means, SVM and Gaussian Naive Bayes methods, it is observed that our predicted map aligns well with geological knowledge with less prediction errors, demonstrating the effectiveness and feasibility of our developed strategy.

Zero-shot video object segmentation (ZS-VOS) aims to segment foreground objects in a video sequence without prior knowledge of these objects. However, existing ZS-VOS methods often struggle to distinguish between foreground and background or to keep track of the foreground in complex scenarios. The common practice of introducing motion information, such as optical flow, can lead to overreliance on optical flow estimation. To address these challenges, we propose an encoder-decoder-based hierarchical co-attention propagation network (HCPN) capable of tracking and segmenting objects. Specifically, our model is built upon multiple collaborative evolutions of the parallel co-attention module (PCM) and the cross co-attention module (CCM). PCM captures common foreground regions among adjacent appearance and motion features, while CCM further exploits and fuses cross-modal motion features returned by PCM. Our method is progressively trained to achieve hierarchical spatio-temporal feature propagation across the entire video. Experimental results demonstrate that our HCPN outperforms all previous methods on public benchmarks, showcasing its effectiveness for ZS-VOS. Code and pre-trained model can be found at https://github.com/NUST-Machine-Intelligence-Laboratory/HCPN.

Due to its significant capability of modeling long-range dependencies, vision transformer (ViT) has achieved promising success in both holistic and occluded person re-identification (Re-ID) tasks. However, the inherent problems of transformers such as the huge computational cost and memory footprint are still two unsolved issues that will block the deployment of ViT based person Re-ID models on resource-limited edge devices. Our goal is to reduce both the inference complexity and model size without sacrificing the comparable accuracy on person Re-ID, especially for tasks with occlusion. To this end, we propose a novel attention map guided (AMG) transformer pruning method, which removes both redundant tokens and heads with the guidance of the attention map in a hardware-friendly way. We first calculate the entropy in the key dimension and sum it up for the whole map, and the corresponding head parameters of maps with high entropy will be removed for model size reduction. Then we combine the similarity and first-order gradients of key tokens along the query dimension for token importance estimation and remove redundant key and value tokens to further reduce the inference complexity. Comprehensive experiments on Occluded DukeMTMC and Market-1501 demonstrate the effectiveness of our proposals. For example, our proposed pruning strategy on ViT-Base enjoys29.4%FLOPssavings with0.2%drop on Rank-1 and0.4%improvement on mAP, respectively. Code and models have been made available athttps://github.com/NUST-Machine-Intelligence-Laboratory/AMG.

Affected by the low-frequency seismic data missing and multiple parameters coupling, elastic full waveform inversion is easy to fall into local minima. This paper attempts to solve the local minima problem from two aspects: low-frequency seismic data reconstruction and wave mode decomposition. First, by introducing the envelope into sparse constrained deconvolution, an envelope-based sparse constrained deconvolution method is proposed to overcome the problem caused by the phase shift and side lobes. However, the resolution of the envelope is insufficient to identify overlapping seismic events, which are generated by velocity models rich in thin layers. Therefore, sparse constrained deconvolution and envelope-based sparse constrained deconvolution are combined, and a dual deconvolution method is proposed: sparse constrained deconvolution is used to improve the resolution of the original seismic data, then envelope-based sparse constrained deconvolution is used to reconstruct high-precision reflection sequence. Convolve the reconstructed reflection sequence with the full-band source wavelet to obtain the full-band seismic data. Secondly, for the multi-parameter coupling problem, we use wave mode decomposition to obtain separated P- and S-wave. Finally, a multi-scale elastic full waveform inversion method based on dual deconvolution and wave mode decomposition is proposed. Numerical experiment results demonstrate the algorithm proposed in the article.

The envelope inversion (EI) is an effective method to recover low-wavenumber components, which helps to produce a good initial model for full-waveform inversion (FWI). However, when the initial model is not capable of generating reflections, it brings enormous challenges for EI to invert deep low-wavenumber components, especially for short offset data. In contrast, reflection waveform inversion (RWI) uses demigration data to fit the observed reflection, which focuses on the transmission information with short offset seismic data. However, the cycle skipping and high nonlinearity of the RWI misfit still exist when the low-frequency information is absent. In this letter, we develop a joint traditional and reflection envelope inversion (JREI) that utilizes both reflection and transmission waves with envelope low-frequencies to recover low-wavenumber components in the shallow and deep regions simultaneously. We then use the FWI with high-frequency seismic data to obtain the high-wavenumber components. Applications to the modified Marmousi and Overthrust models demonstrate that the JREI can invert a better starting velocity model for the FWI to achieve a high-resolution inversion result.

Gravity and magnetic inversion are important methods for comprehensive quantitative interpretation of data obtained in, e.g., mineral, oil and gas, and geothermal exploration. At present, the 3-D joint inversion technology of gravity and magnetic data is facing challenges from large-scale data exploration applications. In this letter, a new algorithm for 3-D joint inversion of gravity and magnetic data with high accuracy and low computational cost is presented. We use the geometric trellis method to perform fast forward calculations and then introduce the sparse constraint and adaptive sensitivity matrix into the model constraint terms. The inexact structural resemblance method is then used to add the cross-gradient constraint penalty term to the objective function. Finally, an algorithm (DS-TGN) combining data-space (DS) and truncated Gauss–Newton (TGN) methods is used to solve the joint inversion objective function. Numerical experiments with synthetic data show that the proposed algorithm can significantly reduce the computational cost and obtain high accuracy density and magnetization models with structural resemblance and sharp boundaries. We also apply the DS-TGN algorithm to data obtained in the area of Greater Khingan in northwestern Heilongjiang, China. The underground density and magnetization distribution results provide a high-resolution geological model for the detection of skarn-type deposits.

Building high-fidelity velocity models for salt structures is a valuable and difficult problem in seismic exploration. Acoustic-based full-waveform inversion (FWI) methods usually produce velocity artifacts around high-contrast interfaces due to the generation of converted waves. Therefore, elastic FWI (EFWI) should be used in salt model velocity building. Two problems that restrict EFWI are: lack of low-frequency seismic data and multiparameter coupling. For the first problem, envelope is a good choice because of its ability to reconstruct low-frequency components independent of the frequency range of seismic data. However, envelope is instantaneous energy flow and lacks polarity information, while the elastic waves are vectors. Thus, the direct use of envelope to reconstruct the low-frequency components of elastic waves causes serious artificial artifacts in envelope inversion. Therefore, we introduce signed demodulation and window average function to obtain the multiscale (MS) envelope with polarity, defined as the MS signed envelope to reconstruct low-frequency elastic data. The reconstructed low-frequency data are then used in EFWI, and an elastic MS signed direct envelope inversion algorithm is proposed. For the second problem, wave mode decomposition and hierarchical inversion strategies are integrated into the inversion to eliminate the multiparameter coupling effect. A salt layer model and BP model are used to verify the effectiveness of the algorithm. Finally, the deficiencies in the research of this article and further improvement plans are also discussed.

Considering the viscoelastic anisotropy and electrical depression characteristics of the complex geological media, we introduce the generalized standard linear solid (GSLS) model to describe the relaxation effect of the solid skeleton and the Cole-Cole model to describe the frequency dependence of electric conductivity. The seismoelectric model of the poro-viscoelastic anisotropic medium was constructed, and the corresponding wave and diffusion equations in the time domain were derived. We then analyze the characteristics of seismoelectric wavefields in viscoelastic transverse isotropic (TI) media with a homogenous model, a two-layer model and a layered-model with depression. Results show that the TI anisotropy, viscosity of fluid, tilt angle all have significant effects on the propagation of seismoelectric waves in the homogenous model. The strong attenuation of seismoelectric waves in the two-layer model shows the validity of the relaxed skeleton and frequency dependent conductivity used in our approach, which could also effectively capture the reflection and transmission phenomena in the seismic and associated EM fields in the layered model with depression.

Least-squares reverse time migration (LSRTM) attempts to produce a high-quality image for complicated subsurface structures. However, large amplitude discrepancies between the synthetic and observed seismic data are problematic for high-resolution imaging. Alternatively, correlative LSRTM (CLSRTM) misfit has been proposed to improve the imaging quality of complicated structures. However, the CLSRTM ignores the local characteristics of the 2-D seismic data. Thus, we developed a 2-D local correlative misfit for LSRTM (2-D-LCLSRTM) to improve the imaging resolution. In this case, a 2-D sliding window was used to obtain local-scale seismic data. A 2-D correlation method was then used to measure the similarity between the local-scale synthetic and observed data. Consequently, the 2-D-LCLSRTM misfit could reduce amplitude constraints and emphasize phase similarity, which has a potential for improving deep structure as it can boost weak seismic signals. To suppress the migration artifacts, we incorporated the sparsity promotion method with the 2-D-LCLSRTM misfit and used the fast iterative shrinkage-thresholding algorithm (FISTA) to solve it iteratively. In the numerical examples, a Marmousi model, a Salt model, and a marine field seismic dataset were used to test the effectiveness of the 2-D-LCLSRTM method. Compared with the commonly used RTM and sparsity promotion-based CLSRTM methods, the 2-D-LCLSRTM with sparsity promotion can better image deep reflectors and obtain high-resolution imaging results.

Surface monitoring of microseismic monitoring events is generally challenging because microseismic data have a low signal-to-noise ratio (SNR). Traditional event-detection methods struggle to detect weak microseismic events. A variance fractal dimension (VFD) method for automatic microseismic event detection via multitrace energy envelope stacking (MTEES) is introduced. In the first stage, we propose a processing microseismic data method based on the MTEES method. It increases the energy of weak microseismic data to avoid missed and false microseismic detection. Furthermore, it can greatly improve computational efficiency to satisfy real-time processing requirements. In the second stage, the VFD algorithm is applied to the data processed in the first stage to improve the feasibility and validity of microseismic event detection. A simulation test with perforation data shows the reliability of the new method in the automatic detection of microseismic events. In addition, we demonstrate that analogous results can be obtained when perforation data are not available by introducing a novel approach based on synthetic correction time. The new approach is particularly useful when perforation data are not recorded, representing a significant advantage over previous approaches. We describe the application of the novel method to a real microseismic data example from monitoring hydraulic fracture treatments in Shanxi Province, China, with and without perforation data. The new method yields improvement in microseismic event detection for microseismic monitoring. Therefore, we find a wide range of applications requiring analysis of microseismic data.

The exploration capacity of full waveform inversion (FWI) for deep targets is affected by the acquisition geometry, complexity of overlying strata and dip angle of the target, etc. It is of great importance to analyze the relationship between the dip angle of the target reflector and the incident/scattered angle of the wavefields near the target, so as to achieve the illumination distribution and improve the inversion quality accordingly. We propose an angle domain illumination compensated FWI strategy, which utilizes the local resolution function as preconditioning to the gradient of FWI in the local angle domain, in order to improve the inversion capability for deep targets. The local resolution function describes the inversion capacity for the local target in the target dip coordinate, which can be generated from the local illumination matrix that contains the illumination information for the target from different incident and scattering directions. The Marmousi model and the SEG/EAGE salt model are used to show the validity of this method. Results from the numerical experiments prove that the proposed method can effectively improve the inversion performance as well as increase the convergence of FWI.

Suppressing random noise in seismic data is a significant problem in seismic data processing. Often, there is serious aliasing between the effective signal and random noise, affecting the identification of weak signals, and even resulting in great difficulties in the suppression of conventional seismic signals. We propose an improved attention-guided convolutional neural network (ADNet) to eliminate seismic interference noise. After a sufficient amount of training, the network removes noise by transferring seismic data features learned from a synthetic dataset to tests with complex field data. Our workflow consists of four parts. First, in the model, we improve the feature enhancement module (FEM) and attention module (AM), increase the convergence speed, and enhance the expressive ability. Second, we use 2-D synthetic data to verify the ability of the model to suppress noise in seismic records. Third, we use 2-D real seismic data to further verify the denoising effect of the improved ADNet. Fourth, we convert the 3-D simulated seismic data and field data into 2-D data for processing and reorganize the 2-D denoising results into 3-D data. By comparing the noise suppression outcomes of several classic denoising methods, simulations and actual experiments show that the improved ADNet effectively maintains the signal amplitude, reduces the network depth, and better suppresses seismic noise. Hence, we believe that our model can be widely applied in the field of seismic data processing.

I develop inverse scattering methods for velocity reconstruction in the subsurface, based on multiple scattering theory, Gaussian beams and nonlinear Born approximation. This method is based on a new version of the distorted Born iterative inverse scattering method. It directly uses an explicit representation of the data sensitivity function in terms of Green functions, rather than the indirect optimization approach based on the adjoint state method. I propose three direct scattering methods for forward modeling, namely, Gaussian beam integral equation propagators, nonlinear Born approximation, and multiple scattering. First, I apply a sensitivity kernel incorporating the Gaussian beam summation-based integral equation method and compute the background medium Green's function in an inhomogeneous medium. Then I extend an approximate solution of the Lippmann-Schwinger equation, referred to as nonlinear Born approximation to inverse scattering problem. I apply the nonlinear Born approximation to forward modeling at each iteration. Furthermore, I combine the Gaussian beams with the multiple scattering theory for nonlinear scattering problems. I obtain the inversion results using the proposed approaches, which shows the capability for inverse scattering imaging. I have carried out several numerical experiments that involve reconstructing the resampled Marmousi and Society of Exploration Geophysicists (SEG)/European Association of Geoscientists and Engineers (EAGE) salt models from its smoothed version.

Uncertainty quantification related to nonlinear inverse scattering problems often involves the posterior covariance matrix that cannot be effectively estimated but still need to be considered as an important part of the nonlinear inverse scattering problem. The objective of this work is to show how to take advantage of a Bayesian framework to estimate the uncertainty of velocity reconstruction in the nonlinear inverse scattering imaging. The key ingredients are twofold. On the one hand, we rely on a Kalman Filter method, equipped with an optimization scheme, to solve the inverse problem. We use the distorted Born iterative method to formulate the sensitivity kernel. It directly uses an explicit representation of the data sensitivity function in terms of Green functions, rather than the indirect optimization approach based on the adjoint state method, in which the Green’s functions are based on integral equations. On the other hand, we use the direct envelope methods to provide the initial guess (large-scale smooth model) to overcome the nonlinearity in the inverse scattering problems. We first investigate the uncertainty of velocity imaging in high contrast media. Then we show by means of Lippmann-Schwinger-type equations that the uncertainty of multiparameter inverse scattering problems can be addressed. Numerical results dealing with the uncertainty of nonlinear inverse scattering problems in the case of both isotropic and anisotropic media highlight the important role played by uncertainty quantification.