Migration Imaging Research Articles

Seismic Image Dip Estimation by Multiscale Principal Component Analysis

Dip estimation of geological structures plays an important role in geophysical applications. Principal component analysis (PCA) is a common approach to estimating local dips by decomposing the local gradients of a seismic migration image and obtaining its principal eigenvector. However, PCA is difficult to obtain robust and high-resolution dip estimations for low signal-to-noise ratio (SNR) migration images, while multiscale schemes in digital image processing can achieve a better compromise between noise robustness and dip resolution. Therefore, we propose to adopt a multiscale PCA (MPCA) method coupled with a propagation-weight-based fusion mechanism for seismic dip estimation of low SNR migration image. MPCA consists of three steps: 1) constructing an image pyramid by repeating the low-pass filter from fine to coarse scales; 2) estimating the dip using the PCA method at each scale of the image pyramid; and 3) fusing the multiscale dip estimations using propagation weights from coarse to fine scales. We test the MPCA method on an omnidirectional dip pattern and three seismic migration images and compare with the conventional PCA and multiscale methods. The results demonstrate that MPCA yields robust and high-resolution dip estimations for low SNR seismic migration images.

Iterative Reweighted Least-Squares Gaussian Beam Migration and Velocity Inversion in the Image Domain Based on Point Spread Functions

Amplitude-preserving migration is very important for reservoir characterization, which can faithfully provide information on the strength of the reflectors. However, conventional migration algorithms do not compensate for variable illumination effects and can hardly obtain true amplitudes of medium parameter. Least squares migration (LSM) is an effective method to address this issue. Unfortunately, there is a key problem with LSM methods: most LSM methods only consider illumination compensation but not consider the accuracy of migration velocity model. The accuracy of the migration velocity model directly affects the quality of migrated images. Moreover, changes in velocity are more indicative of reservoir properties than reflectivity. Therefore, it is necessary to incorporate velocity estimation into migration imaging to realize joint inversions. Based on these facts, we present an iterative re-weighted LSM method by approximating the local Hessian using point spread functions. Then, we related the LSM results to the scattering potential, simultaneously achieving velocity update with illumination compensation. Based on the gradually changing characteristics of rock properties, we adopted a sparse derivative constraint rather than requiring the result to be sparse. Consequently, this processing caused the results to contain broader bandwidths, giving the image a more continuous and textured appearance. Next, we evaluated the proposed method using the Marmousi2 model. The results had higher resolution and a more reliable amplitude than the initial migration images. Hence, we efficaciously completed the velocity model update, with our method achieving encouraging results under both relatively accurate migration velocity and highly smoothed migration velocity model tests.

Well-Log Information-Assisted High-Resolution Waveform Inversion Based on Deep Learning

The high-resolution waveform inversion for seismic velocities is gaining increasing interest as we start to deal with complex structures. Although full waveform inversion (FWI) has been used for several years, obtaining high-resolution velocity models still presents many obstacles, such as the high computational cost and the limited bandwidth of the data. Thus, we propose a deep learning (DL)-based algorithm to build high-resolution velocity models using low-resolution velocity models, migration images, and well-log velocities as inputs. The well information, specifically, helps enhance the resolution with ground-truth information, especially around the well. These three inputs are fed to an improved neural network, a variant of U-Net, as three channels to predict the corresponding true velocity models, which serve as labels in the training. The incorporation of well velocities from several locations is crucial for improving the resolution of the output model. Numerical experiments on complex models demonstrate the robust performance of this network and the crucial role that well information plays, especially in generalizing the approach to models that differ from the trained ones and achieving superior performance compared with FWI.

Deep Velocity Generator: A Plug-In Network for FWI Enhancement

Known for its great potential for determining subsurface properties quantitatively, full-waveform inversion (FWI) is a hot topic in the field of exploration seismology. The success of FWI depends significantly on the accuracy of the starting model. Given that both the migration and velocity profiles originate from the same geological structure, the two should be morphologically consistent. Starting from the velocity‐reflector depth trade-off, we propose a deep learning approach with a new training paradigm for building a good starting model. A velocity model and the corresponding migration image are used to form 2-channel inputs, and the Generative Adversarial Network (GAN) is trained to minimize the difference between the output and the true velocity model. After the training, the velocity generator network becomes a plug-in component to enhance the FWI performance. The network can be well generalized to unseen data by training with only the synthetic data. We perform extensive experiments on our test dataset, the Marmousi model, the salt velocity model, and field data to demonstrate the effectiveness of our method. Besides, we briefly give an explanation of why our model produces such outputs in this manuscript, making the proposed method more controllable and credible.

Least-Squares Kirchhoff Depth Migration With Fast Point-Spread-Function Computation

Seismic migrations are generally formulated as the adjoint operators of linear forward modeling and often lead to images with degraded resolution, unbalanced illumination and migration artifacts, especially in surveys with geologic complexity and irregular acquisition geometry. Least-squares migration (LSM) is able to mitigate these problems and produce better resolved images that are suitable for subsequent AVO/AVA inversion. However, no matter what domain LSM is implemented in, the computational cost is still several times or even one order of magnitude more than that of traditional migration. In this paper, we present an efficient image-domain least-square Kirchhoff depth migration (LSKDM), in which the Hessian matrix is approximated by a grid of point-spread-functions (PSFs). Traditional PSF computing algorithm requires a non-negligible cost caused by a successive operation of modeling and migration, and has to satisfy a sampling restriction to avoid interference between nearby PSFs. We present in this paper that, by using the ray-based Green’s functions and the linear traveltime approximation, the PSFs can be constructed explicitly at a significantly reduced computational cost and is able to adapt flexible spatial sampling that is fine enough to detect small-scale illumination variation. With the constructed PSFs, we formulate an image-domain LSKDM to iteratively solve for the optimal reflectivities. Numerical tests on synthetic and field data examples demonstrate that the proposed LSKDM is highly efficient and is capable of producing images with enhanced spatial resolution and amplitude fidelity when compared with the Kirchhoff depth migration (KDM) image.

Inverse-Scattering Theory Guided U-Net Neural Networks for Internal Multiple Elimination

Deep neural networks can automatically fetch specific features from seismic data, which can be used in the process of multiple elimination. An extended single-sided autofocusing guided by inverse-scattering theory is introduced to remove internal multiples in a data-driven manner, which can be used as labels in deep neural networks. In this research, we have developed a novel workflow to explore the potential of neural networks in identifying the internal multiples with the guidance of inverse-scattering theory. In particular, we use the U-net with a self-attention block during the training process, which could extract features from seismic data effectively. The neural network is fed with training data pairs, consisting of the shot records with internal multiples, and the primary-only datasets as labels, which are generated by an extended single-sided autofocusing method. The testing pairs show that internal multiple elimination via the neural network takes the advantage of the extended single-sided autofocusing method and is cheaper when the neural network is well-trained. The numerical results demonstrate promising performances of the self-attention U-net method in terms of the noise resistance, internal multiples elimination, and the reverse time migration (RTM) images, in comparison with the extended single-sided autofocusing method.

From FWI to ultra-high-resolution imaging

In recent years, the development of time-lag full-waveform inversion (FWI) has enabled the use of the full wavefield (primary reflections, diving waves, and their multiples and ghosts) in the inversion process. With this advancement, it is possible to obtain a very detailed velocity model, ultimately reaching the point of deriving from the velocity a migration-like reflectivity image called the FWI image. When the FWI maximum frequency is increased, high-resolution velocity models are obtained, revealing superior reservoir information compared to conventional imaging results. Two case studies will be discussed in this paper. The first is in the Greater Castberg area where the 150 Hz FWI image greatly surpassed the Q Kirchhoff prestack depth migration image from the water-bottom level down to the reservoir (located at a depth of about 1.5 km). The second case study is over the Nordkapp Basin. The use of the full wavefield for the shallow ultra-high-resolution (UHR) FWI image (run at 200 Hz) revealed reverse faulting and pockmark details that were invisible with Kirchhoff prestack depth migration and reverse time migration. By using additional information present in multiples, ghosts, and diving waves, a spatial resolution down to 3 m was achieved. This made it possible to image very thin features without the need for a dedicated high-resolution acquisition design. The current UHR FWI image workflow provides velocity and reflectivity information in the near surface that is important in identifying optimal locations for various purposes such as well placement and wind-turbine installation.

A High-Efficiency Spectral Element Method Based on CFS-PML for GPR Numerical Simulation and Reverse Time Migration

Improving the accuracy and efficiency of the numerical simulation of ground penetrating radar (GPR) becomes a pressing need with the rapidly increased amount of inversion data and the growing demand for migration imaging quality. In this article, we present a numerical spectral element time-domain (SETD) simulation procedure for GPR forward modeling and further apply it to the reverse time migration (RTM) with complex geoelectric models. This approach takes into account the flexibility of the finite element methods and the high precision of the spectral methods. Meanwhile, in this procedure, the complex frequency shifted perfectly matched layer (CFS-PML) is loaded to effectively suppress the echo at the truncated boundary, and the per-element GPU parallel framework used can achieve up to 5.7788 times the efficiency compared with the CPU calculation. The experiments on SETD spatial convergence and CFS-PML optimal parameter selection showed that, under the same degree of freedom, the SETD offered substantially better accuracy compared with the traditional FETD. The experiments on RTM of different profiles with different orders of SETD via a complex geoelectric model verify the universality of the algorithm. The results indicate that the RTM imaging effect has been significantly improved with the increase of SETD order. It fully proves the great potential of efficient and high-precision SETD simulation algorithm in the RTM imaging direction and shows certain guiding significance for underground target structure exploration.

Simulation of Lunar Comprehensive Substructure With Fracture and Imaging of Later LPR Data From Chang’e-4 Mission

As one of the most important geophysical methods for detecting the lunar underground structure of the Moon, the lunar penetrating radar (LPR) is applied to the Chang’e-4 (CE-4) mission to explore the Von Kármán crater on the far side of the Moon. With the Yutu-2 rover continues to move towards the Zhinyu crater at the west, radar will likely detect the fractures created by the impact that formed the Zhinyu Crater. Therefore, the establishment of a comprehensive lunar subsurface structure model with fracture, random media and fractal terrain is crucial for the LPR data processing and the understanding of the lunar geological impact process. In addition, the method that can image the radar data with fractures well has great significance to the interpretation of the LPR data. In this paper, we establish a comprehensive lunar subsurface structure model firstly, the considering factors including fractures, random media, fractal terrain, and the radar response are calculated through forward modeling. Secondly, according to the high resolution and the sensitive to inhomogeneities of LPR data, we design a set of processing flow including plane-wave destruction, velocity analysis based on the focusing analysis method and migration imaging based on the velocity continuation. Finally, the results including simulation data and Antarctic fracture data are used to verify the effectiveness of imaging method.

Design of an active-source seismic detection system based on illumination analysis and migration imaging experiments: considering the Zhuxi mining area as an example

Design of an active-source seismic detection system based on illumination analysis and migration imaging experiments: considering the Zhuxi mining area as an example

Data reconstruction combining MWNI and CRS-based interpolation methods

Many seismic processes, such as migration, inversion, and quantitative interpretation, can be improved via multidimensional regularization and interpolation methods. Different algorithms based on signal processing principles have been proposed for multidimensional seismic data reconstruction. Similarly, the Common Reflection Surface (CRS) stack method, a technique based on ray-theory principles, can also be used for regularization and interpolation. CRS-based reconstruction requires determining the wavefront attributes of the CRS stacking operator directly from the data. These wavefront attributes are commonly searched through global optimization algorithms, where a coherence measure is maximized. The precision and reliability of the wavefront attributes (also called CRS attributes) depend on the optimization method used in their search and the quality of the input data. Onshore field data with missing traces and gaps due to missing shot records can introduce attribute errors and create artifacts that contaminate seismic stacks. To solve this problem, we propose applying the Minimum Weighted Norm Interpolation (MWNI) method for the reconstruction of the prestack data before estimating the CRS attributes. Then we use the wavefront attributes to CRS-based prestack data reconstruction and denoising. We validate the combination of MWNI and CRS-based interpolation methods with synthetic data and show that the CRS attributes extracted by the combined approach are very similar to the reference values. The proposed processing flow shows excellent results on an onshore vintage dataset from the Parnaíba Brazilian basin, where MWNI operates as a fold regularization. The reconstructed data obtained by combining MWNI and CRS show significant improvements compared to the reconstructed data using the two algorithms separately. Prestack time migration images show increased quality when the two methods are adopted in the processing flow.

Wavelet Entropy-Based Method for Migration Imaging of Hidden Microcracks by Using the Optimal Wave Velocity

Exploring the shape and direction of hidden cracks in a tunnel lining structure is one of the main objectives of ground penetrating radar (GPR) map interpretation. The most important factor that restricts the migration imaging of hidden cracks is the propagation velocity of electromagnetic waves. Determining the optimal electromagnetic wave velocity is the key to truthfully restoring the actual shape of hidden cracks. To study the GPR characteristic response signals of hidden cracks, forward simulation and model experiments of different cracks were performed. Subsequently, a method to determine the optimal electromagnetic wave velocity based on the wavelet entropy theory was proposed, and the frequency wavenumber domain migration (F-K) and Kirchhoff integral migration imaging method were combined. Horizontal, S-type, and inclined hidden fractures were examined by migration imaging. The results show that the radar characteristic response images of different cracks can be simulated forward by using the finite difference time domain method to write the fracture model instruction. Based on the wavelet entropy theory, the error range between the estimated value and true value was controlled within 4%. Taking the optimal electromagnetic wave velocity as the velocity parameter of the conventional migration method can make the migration more effective and suppress the interference of echo signals so that the diffraction wave converges, and the energy is more concentrated; thus, the real fracture morphology can be restored to the greatest extent. The research results can provide technical support for the fine detection of hidden quality defects in tunnel lining structures by GPR mapping.

Pre-stack elastic reverse time migration in tunnels based on cylindrical coordinates

Seismic forward-prospecting in tunnels is an important step to ensure excavation safety. Nowadays, most advanced imaging techniques in seismic exploration involve calculating the solution of elastic wave equation in a certain coordinate system. However, considering the cylindrical geometry of common tunnel body, Cartesian coordinate system seemingly has limited applicability in tunnel seismic forward-prospecting. To accurately simulate the seismic signal received in tunnels, previous imaging method using decoupled non-conversion elastic wave equation is extended from Cartesian coordinates to cylindrical coordinates. The proposed method preserves the general finite-difference time-domain (FDTD) scheme in Cartesian coordinates, except for a novel wavefield calculation strategy addressing the singularity issue inherited at the cylindrical axis. Moreover, the procedure of cylindrical elastic reverse time migration (CERTM) in tunnels is introduced based on the decoupled non-conversion elastic wavefield. Its imaging effect is further validated via numerical experiments on typical tunnel models. As indicated in the synthetic examples, both the PP- and SS-images could clearly show the geological structure in front of the tunnel face without obvious crosstalk artifacts. Migration imaging using PP-waves can present satisfactory results with higher resolution information supplemented by the SS-images. The potential of applying the proposed method in real-world cases is demonstrated in a water diversion tunnel. In the end, we share our insights regarding the singularity treatment and further improvement of the proposed method.

Ceramics in total disc replacements: A scoping review.

Ceramics are used in Total Disc Replacements (1) in articulating surfaces for their wear resistance and biocompatibility and (2) on endplates to promote osseointegration. They furthermore exhibit MRI and CT compatibility. These properties address main challenges associated with non-ceramic Total Disc Replacements i.e. wear, migration and postoperative imaging. While brittleness of ceramics caused fear of fracture in the past, improvements of ceramic materials were made and considerable clinical experience with ceramic Total Disc Replacements was gained. This review aims to assess the evidence on the use of ceramics in Total Disc Replacements and compare safety and effectiveness of ceramic Total Disc Replacements to spinal fusion and Total Disc Replacements in general. We conducted a scoping review on the use of ceramics in Total Disc Replacements using Scopus, Web of Science and PubMed. The review includes 36 clinical, ex vivo and nonhuman in vivo, tribological and mechanical studies and case reports. Ceramics are used in cervical Total Disc Replacements, with safety and efficacy confirmed in clinical studies, with up to 10 and 3.3years follow-up, for articulation and osseointegration applications, respectively. Clinical evidence shows that ceramic Total Disc Replacements (alike non-ceramic ones) restore segmental motion and result in non-inferior and possibly superior outcomes to spinal fusion. In vivo studies show osseointegration comparable to non-ceramic devices. Tribological studies suggest appropriate wear properties. We found no indications of systematic problems with the use of ceramics in Total Disc Replacements. Ceramics are suitable materials for Total Disc Replacements.

Non-artifact vector P- and S-wave separation for elastic reverse time migration

Elastic reverse time migration (RTM) uses the elastic wave equation to extrapolate multicomponent seismic data to the subsurface and separate the elastic wavefield into P- and S-waves. P- and S-wave separation is a necessary step in elastic RTM to avoid crosstalk between coupled wavefields. However, the current curl-divergence operator-based separation method has a polarity reversal problem in PS imaging, and vector separation methods often have separation artifacts at the interface, which affects the quality of the imaging stack. We propose a non-artifact P- and S-wave separation method based on the first-order velocity-strain equation. This equation is used for wavefield extrapolation and separation in the first-order staggered-grid finite-difference scheme, and the storage and calculation amounts are consistent with the classical first-order velocity-stress equation. The separation equation does not calculate the partial derivatives of the elastic parameters, and thus, there is no artifact in the separated P- and S-waves. During wavefield extrapolation, the dynamic characteristics of the reflected wave undergo some changes, but the transmitted wavefield is accurate; therefore, it does not affect the dynamic characteristics of the final migration imaging. Through numerical examples of 2D simple models, part SEAM model, BP model, and 3D 4-layer model, different wavefield separation methods and corresponding elastic RTM imaging results are analyzed. We found that the velocity-strain based elastic RTM can image subsurface structures well, without spike artifacts caused by separation artifacts, and without polarity reversal phenomenon of the PS imaging.

Research on noise suppression of imaging data by optimized least squares reverse time migration method

Least squares reverse time migration imaging is an imaging theory based on the accurate description of geological structures by seismic waves, which can greatly improve the detection accuracy. In the complex environment of deep region, the influence of noise on the imaging resolution of reverse time migration is particularly significant. In this paper, an optimized least squares reverse time migration imaging method with better amplitude fidelity and higher imaging accuracy is proposed. This method suppresses the noise interference by reducing the residual value between the inversion data and the actual data. The experimental results in the paper show that the optimized least squares reverse time migration can achieve high accuracy and high resolution imaging, especially in the deep region. This method is effective and feasible.

Shallow Ice‐Sheet Composite Structure Revealed by Seismic Imaging Near the West Antarctic Ice Sheet (WAIS) Divide Camp

AbstractSmall‐scale polar ice‐sheet composite structures can influence coarse‐grained climate models. For example, surface melting can hydro‐fracture the ice and may eventually lubricate the base. The drainage process will change the petrophysical properties of the subsurface and is predictable from high‐resolution seismic imaging. Ray‐based imaging methods, though limited by the approximations made, have mapped seismic velocities in 1D, but have yet to offer definitive information about the lateral heterogeneity. Here we use the wave‐equation‐based seismic imaging method to estimate shear‐wave velocities and to image an englacial reflector in 2D. We use 7‐day ambient‐noise data recorded by a linear array near the West Antarctic Ice Sheet (WAIS) Divide drilling site to perform the study. We obtain 2D vertical and horizontal shear‐wave velocity models and observe a lateral variation of the radial anisotropy along the acquisition line. The inverted velocity model is further used to estimate the thicknesses of firn‐air and firn layers. We also observe evident SH reflections and calculate the reflector image using robust zero‐offset imaging and a more precise reverse time migration imaging method. The imaged reflector is at about 1,700 m depth and is dipping to the southwest. We anticipate that a porosity change at that depth may cause this englacial seismic discontinuity near WAIS Divide. Our results demonstrate the feasibility of estimating the petrophysical properties of polar ice using seismic methods. We anticipate our work to be further used for understanding ice dynamics and thermodynamics, such as the stability of ice shelves, the retreat of the Antarctic ice sheet, and restoring the paleo‐sedimentary environment.

Enhancing one-way wave equation-based migration with deep learning

One advantage of one-way wave equation-based migration is its low computational cost. However, due to the limited wavefield propagation angle, it is difficult to use one-way wave equation-based migration for high-precision imaging of structures with large inclinations due to issues such as inaccurate amplitudes and migration image artifacts. In addition, when the model has large horizontal velocity differences, it is difficult for the one-way wave propagator to calculate an accurate wavefield phase. Reverse time migration (RTM) based on the two-way wave propagator has a high resolution and avoids the issues associated with one-way wave propagators; however, it has a high computational cost in practical applications. We develop a convolutional neural network (CNN) application mode that improves one migration method by learning from another one and design a CNN with a structure similar to U-net that combines the advantages of both migration methods. The CNN label is the RTM result, and the corresponding input is the result of one-way wave migration with a generalized screen propagator (GSP). The trained CNN model improves the amplitude in the one-way wave migration image and removes the errors caused by large lateral velocity perturbations. Moreover, by maintaining the high migration calculation efficiency, our CNN model allows for a high resolution, few artifacts, and accurate images of steep structures in the one-way wave migration result. With our method, the accuracy of the one-way wave migration result is close to that of the RTM result. The use of GSP-based migration in our CNN model rather than conventional RTM to generate prospecting images can considerably reduce the calculation costs.

Ultrasonic phased array phase shift migration imaging of irregular surface components using attenuation compensation and anti-aliasing technique

Extended non-stationary phase shift migration (ENPSM) is a promising imaging algorithm for ultrasonic phased array testing of components with irregular surfaces. In this paper, ENPSM is developed further by combining attenuation compensation (AC) with an anti-aliasing (AA) technique, in an approach called AC-AA-ENPSM, to address the negative effects of attenuation during ultrasonic wave propagation in metals and of jagged medium interface determined by traditional rectangular window. The attenuation term in AC-AA-ENPSM is measured by immersion testing with referenced samples. An anti-aliasing technique is used to smooth the spatial velocity distribution based on the weights of different velocities on a pixel grid. Experiments are performed on objects with irregular surfaces, varying attenuation characteristics and side-drilled hole (SDH) defects. A comparison of the results shows that the proposed AC-AA-ENPSM algorithm has the advantage of a higher signal-to-noise ratio (SNR) than traditional ENPSM. Moreover, the proposed algorithm has a computational efficiency at least 40 times higher than the total Focusing Method (TFM) algorithm, and the SNR difference of them is less than 2 dB.

Fast iterative method for seismic wave travel time calculation under undulating surface conditions

Seismic wave travel time is an important seismic attribute information and is widely used in various seismic forward and inversion methods, including seismic migration imaging, seismic tomography, seismic wave forward modeling and other core seismic data processing methods. The accuracy and efficiency of the travel time algorithm are important for the above methods. In the practical application of seismic exploration, it is often carried out under the condition of undulating surface, which has a significant impact on the travel time calculation. Therefore, it is of great significance to study an efficient and high-precision travel time calculation method that can adapt to undulating surface condition. In this paper, Fast iterative method (FIM) is modified to a topography travel time calculation method. The method employs an iterative method to solve the equation of function to obtain seismic wave travel time by maintaining a narrow band called the active list, and the algorithm can update all grid nodes in the active list at a time. We will verify the travel time computing power of the FIM method through several different velocity models.