Migration Velocity Model Research Articles

Passive Source Reverse Time Migration Based on the Spectral Element Method

AbstractIncreasing deployment of dense arrays has facilitated detailed structure imaging for tectonic investigation, hazard assessment and resource exploration. Strong velocity heterogeneity and topographic changes have to be considered during passive source imaging. However, it is quite challenging for ray‐based methods, such as Kirchhoff migration or the widely used teleseismic receiver function, to handle these problems. In this study, we propose a 3‐D passive source reverse time migration strategy based on the spectral element method. It is realized by decomposing the time reversal full elastic wavefield into amplitude‐preserved vector P and S wavefields by solving the corresponding weak‐form solutions, followed by a dot‐product imaging condition to get images for the subsurface structures. It enables us to use regional 3‐D migration velocity models and take topographic variations into account, helping us to locate reflectors at more accurate positions than traditional 1‐D model‐based methods, like teleseismic receiver functions. Two synthetic tests are used to demonstrate the advantages of the proposed method to handle topographic variations and complex velocity heterogeneities. Furthermore, applications to the Laramie array data using both teleseismic P and S waves enable us to identify several south‐dipping structures beneath the Laramie basin in southeast Wyoming, which are interpreted as the Cheyenne Belt suture zone and agree with, and improve upon previous geological interpretations.

Near-surface velocity inversion and modeling method based on surface waves in petroleum exploration: a case study from Qaidam Basin, China

Surface waves are widely used in the study of underground structures at various scales because of their dispersion characteristics in layered media. Whether in natural seismology or engineering seismology, surface wave analysis methods have matured and developed for their respective fields. However, in oil and gas exploration, many data processors still tend to consider surface waves as noise that needs to be removed. To make more people pay attention to the application of surface waves and widely utilize surface waves carrying the near surface information in oil and gas exploration, this paper takes the data processing of LH site in Qinghai, China as an example to apply surface wave analysis methods to oil and gas exploration. We first preprocess and perform dispersion imaging method on the seismic record in the LH site to obtain frequency-phase velocity spectrum with good resolution and signal-to-noise ratio. Then, utilizing clustering algorithms, it automatically identifies and picks dispersion curves. Finally, through a simultaneous inversion algorithm of velocity and thickness, it inverts the dispersion curves and obtain S-wave velocity profiles in the depth range of 0–200 m. The near surface is divided into four zones based on velocity ranges and depth ranges. Additionally, we apply the surface waves inversion results as constraints to first-arrival tomography and obtain objectively accurate P-wave velocity profiles and Poisson’s ratio profiles. The results indicate that by applying surface wave analysis methods, the near surface velocity information carried by surface waves can be extracted, providing near surface velocity models for static correction and migration. At the same time, compared with the surface wave application in engineering seismology, the scale of oil and gas exploration is larger, so that the data processing of surface waves is particularly important, otherwise it will affect the picking of the dispersion curve and inversion.

WISE: Full-waveform variational inference via subsurface extensions

We introduce a probabilistic technique for full-waveform inversion, using variational inference and conditional normalizing flows to quantify uncertainty in migration-velocity models and its impact on imaging. Our approach integrates generative artificial intelligence with physics-informed common-image gathers, reducing reliance on accurate initial velocity models. Considered case studies demonstrate its efficacy producing realizations of migration-velocity models conditioned by the data. These models are used to quantify amplitude and positioning effects during subsequent imaging.

Adaptive merging migration

Velocity errors and data noise are inevitable for seismic imaging of field data sets in current production; therefore, it is desirable to improve the seismic images as part of the migration process to mitigate the influence of such errors and noise. To address this, we develop a new method of adaptive merging migration (AMM). Our method can produce migrated sections of equal quality to conventional migration methods given a correct velocity model and noise-free data. In addition, it can ameliorate the seismic image quality when applied with erroneous migration velocity models or noisy seismic data. AMM uses an efficient recursive Radon transform to generate multiple p-component images, representing migrated sections associated with different local plane slopes. It then adaptively merges the subsections from those p-component images that are less distorted by velocity errors or noise into the whole image. Such merging is implemented by computing adaptive weights followed by a selective stacking. We use three synthetic velocity models and one field data set to evaluate the AMM performance on isolated Gaussian velocity errors, inaccurate smoothed velocities, velocity errors around high-contrast and short-wavelength interfaces, and noisy seismic data. Numerical tests conducted on synthetic and field data sets validate that AMM can effectively improve the seismic image quality in the presence of different types of velocity errors and data noise.

Integrating earthquake-based passive seismic methods in mineral exploration: Case study from the Gerolekas bauxite mining area, Greece

As the global need for aluminum constantly rises, bauxite is considered to be a critical mineral, and the mining industry is in search of new and effective exploration solutions. In this context, we design and implement a purely earthquake-based passive seismic survey at the Gerolekas bauxite mining site in Greece. It is a very difficult exploration setting, characterized by rough topography, limited accessibility, and a very complex geotectonic regime. We gather a passive seismic data set consisting of four months of continuous recordings (May to August 2018) from 129 stand-alone 3C seismological stations. We then analyze this data set and extract 848 microearthquakes that will serve as sources for the application of local earthquake tomography (LET) and transient-source seismic interferometry (TSI) by autocorrelation. We apply LET to estimate the 3D P- and S-wave velocity models of the subsurface below the study area and TSI by autocorrelation to retrieve the zero-offset virtual reflection responses below each of the recording stations. The velocity models provide a relatively coarse image of a previously completely unexplored part of the mining concession, whereas the higher-resolution virtual reflection imaging illuminates in detail the different interfaces. We also reprocess three lines of legacy active seismic data that were shot in 2003, using the LET P-wave velocity model for depth migration, and confirm the improvement of seismic imaging. Finally, we evaluate the obtained results using well data and jointly interpret them, extracting useful information on the expected target depths and indicating that earthquake-based passive seismic techniques can be an innovative and environmentally friendly option for mineral exploration.

Efficient prestack time migration velocity analysis: A correlation‐based global optimization approach

AbstractAn accurate migration velocity model is vital to seismic imaging. Conventional time‐domain migration velocity analysis techniques commonly obtain the velocity model by iteratively picking the velocity spectra of common reflection point gathers or scanning selection, which is usually laborious and computationally expensive. To solve these issues, we propose a more effective migration velocity analysis method for prestack time migration. This method is based on cross‐correlation stacked time shift functions of local migrated gathers and uses the very fast simulated annealing algorithm to semi‐automatically invert the migration velocity parameters. This new correlation‐based objective function can catch subtle bending features of the reflector in the local migrated gather. The proposed approach applies an efficient global optimization algorithm that does not depend on the initial parameter value and is easy to extend to the multiparameter complex case. Moreover, the strategy of using local migrated gathers can incorporate prior information in the inversion to avoid the effects of misidentifying reflection events and the interference of multiples. Furthermore, applying a dynamic parameter constraint strategy for the very fast simulated annealing algorithm improves the convergence speed. Both synthetic and real data examples demonstrate that the proposed migration velocity analysis method can efficiently build an accurate time migration velocity model, which can also provide a good initial velocity model for depth migration.

Stochastic velocity modeling for assessment of imaging uncertainty during seismic migration: Application to salt bodies

Variations in the migration velocity model directly affect the position of the imaged reflectors in the subsurface, leading to structural imaging uncertainties. These uncertainties are not explicitly addressed when trying to deterministically build an adequate velocity model. This paper presents a new stochastic geology-controlled velocity modeling method that handles the possible presence of a salt weld. This permits the generation of a large set of geologic scenarios and associated velocity models. Each model is used to remigrate the seismic data. Then, a statistical analysis of the resulting seismic images is performed to quantify the local variability of the seismic responses. The approach is applied to the imaging of salt diapirs in an iterative scheme (migrate, pick, and update). The results indicate that similar to stacking common-midpoint gathers, the statistical analysis preferentially preserves recurrent features from one image to another. In particular, this analysis permits the distinction between the connected and detached diapirs without prior knowledge about their connectivity, highlighting the potential of the method to resolve important aspects of the basin and reservoir architecture. More generally, it provides quantitative information on the parts of the seismic image most sensitive to migration velocity variations, which opens an interesting perspective to quantitative interpretation uncertainty assessment. Finally, the presented application also suggests that it is possible to significantly improve the quality of the generated seismic images by sampling many possible geologic scenarios.

Steeply dipping structure imaging with prismatic waves incorporating wavefield decomposition

AbstractPrismatic waves in seismic data can effectively delineate steeply dipping subsurface structures. However, the conventional migration of prismatic wave either requires the exact location of basement reflectors embedded in the accurate migration velocity model or the explicit isolation of prismatic wave from the recorded data to avoid crosstalk artefacts caused by mistaken imaging of primary reflections. Neither of the requirements is a trivial task for field data application with complicated subsurface structures. To image steeply dipping structures with an accurate but smooth enough migration velocity and without the need to extract prismatic waves from the recorded data, we analyse the prismatic imaging condition based on wavefield decomposition. In the up‐ and down‐going wavefield decomposition, the prismatic imaging condition can effectively remove the high‐wavenumber artefacts with incorrect polarity. However, the expected steeply dipping image is buried in low‐wavenumber noises. In contrast, the prismatic imaging condition based on left‐ and right‐going wavefield decomposition can accurately isolate the artefacts induced by the mistaken migration of primary waves from the steeply dipping interfaces in the image and is free of low‐wavenumber noises. Based on this analysis, we develop a reverse time migration workflow using the modified prismatic imaging condition to depict steeply dipping structures: First, we use the primary imaging condition based on the up‐and‐down‐going wavefield decomposition to obtain an image that can approximate the basement reflectors. Then, we use the modified prismatic imaging condition based on left‐ and right‐going wavefield decomposition to recover the steep interfaces. Finally, a combined subsurface image containing both basement reflectors and faults or steeply dipping structures is generated. We verify the feasibility and robustness of the modified prismatic imaging condition based reverse time migration method for delineating steeply dipping structures using several synthetic tests.

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.

Analysis of seismic inversion after depth migration for recovery of the low-frequency component of the model

Seismic inversion is used in practice as a tool for predicting reservoir properties. It allows one to extract a model with a high level of detail from seismic data, i.e. high-frequency component of the model. In this case, the input data are the time processing results, and the issues related to the low-frequency component of the model are not considered usually. In the presented work, a model-based seismic inversion algorithm is implemented. The input data for the inversion are the depth image results in true amplitudes and the depth migration velocity model. The possibilities of seismic inversion are numerically investigated to refine the low-frequency component of the model. Experiments were carried out using synthetic seismic data got for realistic Sigsbee model.

Seismic inversion of depth migration results

Seismic inversion is used in practice as a tool for predicting reservoir properties. It allows one to extract a model with a high level of detail from seismic data, i.e., high-frequency component of the model. In this case, the input data are the time processing results, and the issues related to the low-frequency component of the model are not considered usually. In the presented work, a model-based seismic inversion algorithm is implemented. The input data for the inversion are the depth image results in true amplitudes and the depth migration velocity model. The possibilities of seismic inversion are numerically investigated to refine the low-frequency component of the model. Experiments were carried out using synthetic seismic data got for realistic Sigsbee model.

A Polarization Migration Velocity Model Building Method for Geological Prediction Ahead of the Tunnel Face

It has been difficult to establish velocity models that use reflected seismic signals with advanced prediction ahead of the tunnel face. The accurate establishment of advanced velocity models face issues including artifacts in migration results and incorrect calculation of velocity. This study presents a polarization migration velocity model building method to solve these issues. First, the artifacts in migration interfaces were eliminated by the polarization characteristics of three-component reflected signals. Second, the optimum velocity ahead of the tunnel face was determined according to the energy stack characteristics of common interface points. Finally, the velocity model was established based on optimum velocity parameters and corresponding polarization migration interfaces using a three-dimensional and three-component numerical simulation conducted on faults with high dip and different inclinations. The results indicate that the velocity errors in the advanced velocity model were 1 and 2% for each of the two layers, and the position errors of the two interfaces were smaller than 3 and 2%. The experimental results of the Maanshan tunnel verified the effectiveness of the polarization migration velocity model building method.

Migration velocity analysis based on focusing diffractions generated from CRS wavefront attributes: Application to real land data

In land seismic data processing, the prestack time migration (PSTM) image remains the standard imaging output, but a reliable migrated image of the subsurface depends on the accuracy of the migration velocity model. We have adopted two new algorithms for time-domain migration velocity analysis based on wavefield attributes of the common-reflection-surface (CRS) stack method. These attributes, extracted from multicoverage data, were successfully applied to build the velocity model in the depth domain through tomographic inversion of the normal-incidence-point (NIP) wave. However, there is no practical and reliable method for determining an accurate and geologically consistent time-migration velocity model from these CRS attributes. We introduce an interactive method to determine the migration velocity model in the time domain based on the application of NIP wave attributes and the CRS stacking operator for diffractions, to generate synthetic diffractions on the reflection events of the zero-offset (ZO) CRS stacked section. In the ZO data with diffractions, the poststack time migration is applied with a set of constant velocities, and the migration velocities are then selected through a focusing analysis of the simulated diffractions. We also introduce an algorithm to automatically calculate the migration velocity model from the CRS attributes picked for the main reflection events in the ZO data. We determine the precision of our diffraction focusing velocity analysis and the automatic velocity calculation algorithms using two synthetic models. We also applied them to real 2D land data with low quality and low fold to estimate the time-domain migration velocity model. The velocity models obtained through our methods were validated by applying them in the Kirchhoff PSTM of real data, in which the velocity model from the diffraction focusing analysis provided significant improvements in the quality of the migrated image compared to the legacy image and to the migrated image obtained using the automatically calculated velocity model.

Building a full-waveform inversion starting model from wells with dynamic time warping and convolutional neural networks

Seismic full-waveform inversion (FWI) needs a feasible starting model because otherwise it might converge to a local minimum and the inversion result might suffer from detrimental artifacts. We have built a feasible starting model from wells by applying dynamic time warping (DTW) localized rewarp and convolutional neural network (CNN) methods alternatively. We use the DTW localized rewarp method to extrapolate the velocities at well locations to the nonwell locations in the model space. Rewarping is conducted based on the local structural coherence, which is extracted from a migration image of an initial infeasible model. The extraction uses the DTW method. The purpose of velocity extrapolation is to provide sufficient training samples to train a CNN, which maps local spatial features on the migration image into the velocity quantities of each layer. Furthermore, we design an interactive workflow to reject inaccurate network predictions and to improve CNN prediction accuracy by incorporating the Monte Carlo dropout method. We have determined that our method is robust against kinematic incorrectness in the migration velocity model, and it is capable of producing a feasible FWI starting model.

High-Resolution Velocity Model Building Based on Common-Source Migration Images and Convolutional Neural Networks

Building a reliable velocity model plays a vital role in seismic imaging and quantitative reservoir description. However, the current data-driven deep-learning-based velocity model building (VMB) approaches directly reconstruct the velocity model of the subsurface from prestack seismic recordings, which are very sensitive to the noise and amplitude mismatch in the data domain. In this letter, we propose a novel VMB approach based on common-source migration image gathers (CSMIGs) and convolutional neural networks (CNNs). The proposed CNN architecture uses the CSMIGs reconstructed by the reverse time migration approach and migration velocity model as the input data. It aims to capture the nonlinear relationship between the amplitude and phase information of CSMIGs and the optimal subsurface reflectivity model. Trained with realistic subsurface models, it can determine that the VMB approach is a computationally efficient solution for a high-resolution velocity reconstruction. In addition, the proposed approach has a better reconstruction performance, antinoise ability, and can be generalized much more easily than the data-driven VMB approach.

Elastic depth trends for siliciclastic sequences

Elastic depth trends are an integral component of reservoir characterization whether explicitly or implicitly constructed. Applications to seismic data include migration velocity model building, pore-pressure prediction, inversion, and amplitude-variation-with-offset (AVO) analysis. In practice, workflows often involve fitting well logs with smooth curves, without reference to known lithology-dependent templates that could provide realistic constraints. We have constructed porosity-depth trends for shales and sandstones with reference to reliable measurements that span mechanical compaction and chemical diagenesis domains. These porosity trends are then used as inputs to well-established rock-physics models to estimate the median and bounding trends for acoustic and shear impedance. The resulting modeling has shown an important sensitivity to the pore-pressure regime (notably in shales) and the onset of cementation (in sands), among other variables. AVO predictions made from these trends provide a useful reference frame for anomaly prediction with depth, which is critical in frontier exploration.

Fractured basement imaging using random-space-shift reverse time migration: A vertical seismic profile survey in the Bohai Bay Basin, China

Fractured basements are not only potential reservoirs for hydrocarbon resources, but they also provide significant storage space for carbon dioxide ([Formula: see text]) sequestration and radioactive waste disposal. However, fractured basements are challenging for seismic imaging methods due to the complexities in their fault and fracture networks, strong heterogeneity, highly variable structural dip, and strong impedance contrasts between the basement rocks and the surrounding sediments. We present a field case in which a walkaway vertical seismic profiling (VSP) survey was conducted at a fractured basement play located in the Bohai Bay Basin, China, to improve the resolution compared with a preexisting surface seismic profile. Using the advanced random-space-shift (RSS) reverse time migration (RTM), we obtain a high-resolution image with a clear delineation of the highly faulted dipping basement. From numerical and field examples, we find that the application of the RSS-RTM improves the final image by mitigating unavoidable errors in the migration velocity model, which would otherwise result in an unfocused image using the conventional RTM approach. In addition, we determine the importance of proper wavefield separation using 3C recordings, which is the key to ensuring the quality of the final image. With an optimized VSP imaging workflow, we provide an enhanced image for the fractured basement to support the geologic interpretations and development decisions.

Least-squares reverse time migration using convolutional neural networks

Least-squares reverse time migration (LSRTM) has the potential to reconstruct a high-resolution image of subsurface reflectivity. However, the current data-domain LSRTM approach, which iteratively updates the subsurface reflectivity by minimizing the data residuals, is a computationally expensive task. To alleviate this problem and improve imaging quality, we have developed an LSRTM approach using convolutional neural networks (CNNs), which is referred to as CNN-LSRTM. Specifically, the LSRTM problem can be implemented via a gradient-like iterative scheme, in which the updating component in each iteration is learned via a CNN model. To make the most of the observation data and migration velocity model at hand, we use the common-source reverse time migration (RTM) image, the stacked RTM image, and the migration velocity model rather than only the stacked RTM image as the input data of CNN. We have successfully trained the constructed CNN model on the training data sets with a total of 5000 randomly layered and fault models. Based on the well-trained CNN model, we have proved that our approach can efficiently recover the high-resolution reflection image for the layered, fault, and overthrust models. Through a marine field data experiment, we can determine the benefit of our constructed CNN model in terms of its computational efficiency. In addition, we analyze the influence of input data of the constructed CNN model on the reconstruction quality of the reflection image.

Sparsity‐promoting least‐squares reverse time migration via preconditioned Bregmanized operator splitting

ABSTRACTNowadays the least‐squares reverse time migration has become the most used migration method because of its accuracy in amplitude recovery and high‐resolution imaging, specifically its priority to image the beneath of structural domes such as salt domes. However, errors in the migration velocity model, inadequate physics of modelling/migration and too sparse data decrease the quality of the migrated image. Sparsity constraints help to mitigate the shortcomings of the least‐squares reverse time migration and stabilize the migration image, but the computational burden of sparse solvers is still a big challenge. In this paper, we propose a fast sparsity‐promoting least‐squares reverse time migration algorithm based on the Bregmanized operator splitting algorithm. In particular, we solve the least‐squares reverse time migration with ‐norm regularization to increase the image resolution while removing the artefacts which cannot be suppressed by the traditional least‐squares reverse time migration. Also we develop a preconditioned Bregmanized operator splitting algorithm where iteratively using of a preconditioner decreases the computational burden. The proposed method is applied to a few sets of synthetic data, and results are compared with reverse time migration or least‐squares reverse time migration to verify its superiority. Numerical tests demonstrate that the proposed preconditioned Bregmanized operator splitting algorithm converges to the desired migration image in a small number of iterations.

Angle domain common image gathers from reverse time migration by combining the Poynting vector with directional decomposition

ABSTRACTAngle domain common image gathers are crucial for seismic applications such as the migration velocity analysis and amplitude variation with angle analysis. Angle domain common image gathers are the gather of reflection coefficients when seismic waves incident at different angles on the same image point. The curvature of gathers can be used to update the migration velocity model. The amplitude variation of gathers characterizes different rock properties. Estimating the wavefield propagation direction is the most important step in the process of generating angle domain common image gathers. The wavefront propagation direction is perpendicular to the wavefront. Accurately estimating the wavefield propagation direction is crucial for calculating the reflection angle. Existing methods cannot provide the propagation direction with the high angular resolution when the subsurface structure is complex, meaning overlapped wave events exist. We propose a new method to overcome this problem. The proposed method separates overlapped waves with a directional filter bank after the up‐going and down‐going wavefield decomposition. Then, we apply the Poynting vector method to decomposed wavefield slices to determine the propagation directions of corresponding wavefield components. The key feature of the proposed method is that it does not require spatial local windows, and it is computationally efficient. Numerical tests demonstrate that our proposed method can accurately obtain the propagation direction of seismic waves in a wavefield including overlapped waves. The angle domain common image gathers generated using our proposed method have improved image quality with more accurate amplitudes and less noise compared with the angle domain common image gathers produced by the conventional Poynting vector method. It means our proposed method provides high‐quality prestack angle gathers for seismic attribute analysis.