Conventional Migration Methods Research Articles

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.

Improving image resolution using deabsorption prestack time migration with effective Q estimation: A back-propagation network approach

The conventional time migration method does not consider the attenuation caused by the viscoelasticity of the underground media during the imaging process. Therefore, the final imaging amplitude and phase include inaccuracies caused by attenuation. In this study, we develop a migration scheme to compensate for absorption and dispersion using an effective quality factor ( Q) estimation based on a back-propagation (BP) neural network. We use BP neural network technology to automatically estimate the effective Q value from stacked imaging data, thereby avoiding manual Q estimation using conventional methods. Our scheme can be incorporated into conventional seismic data-processing workflows. Furthermore, synthetic and field data sets are used to validate our scheme, which is used to acquire high-resolution images with low noise levels. In addition to developing a completely data-driven Q-value estimation strategy, this study demonstrates close integration of artificial intelligence, data mining, and conventional geophysics; our approach is appropriate for estimating the effective Q and has strong industrial application value and significance.

Steeply dipping structural target oriented viscoacoustic prismatic reverse time migration in frequency domain and its application

AbstractSteeply dipping structural imaging is a great challenge due to its poor illumination. Conventional migration methods are unable to produce an accurate image of complex steeply dipping structures. The prismatic wave can improve the illumination of steeply dipping structures and is often used to improve the imaging results of such structures. Traditional elastic wave theory assumes that seismic waves do not attenuate when propagating through subsurface media. However, during seismic wave propagation, the wave energy decays exponentially due to the absorption and attenuation of the ground layer. Subsurface attenuation leads to amplitude loss and phase distortion of seismic waves, resulting in blurring of migration amplitudes when this attenuation is not taken into account during imaging. To address this issue, a frequency‐domain Q‐compensated prismatic reverse time migration method is proposed, which derives Q‐compensated prismatic wavefield propagation operators. In the proposed frequency‐domain Q‐compensated prismatic reverse time migration, Q attenuation is fully compensated along three propagation paths and two propagation types of prismatic waves. The optimized four‐order mixed 25‐point difference format and LU decomposition method are used to solve the Q‐compensated prismatic wavefield propagation equations with high computational efficiency. Numerical and field data examples demonstrate that the proposed frequency‐domain Q‐compensated prismatic reverse time migration method can compensate for deep attenuation energy and improve the imaging resolution of steeply dipping structures.

Joint elastic reverse time migration of towed streamer and sparse ocean-bottom seismic node hybrid data

Compared to 1C towed-streamer (TS) seismic exploration, 4C ocean-bottom seismic (OBS) node exploration has great advantages in complex structure imaging, lithology, and fluid identification using elastic waves. However, sparse spatial sampling of OBS node surveys highlights the problems of the imaging acquisition footprint, poor phase continuity, and low signal-to-noise ratio (S/N) with conventional elastic reverse time migration (ERTM) methods. Therefore, a solution of joint ERTM (J-ERTM) is proposed by combining sparse OBS node data with dense TS data. In the J-ERTM of the hybrid data, a novel weighted boundary condition combined with acoustic-elastic coupling equations and a vector-based crosscorrelation imaging condition are presented to perform PP and PS imaging by receiver-side tensorial extrapolation of TS and OBS node hybrid data. A synthetic example demonstrates that our method can effectively process TS and OBS node hybrid data and improve elastic imaging problems caused by OBS node sparse acquisition. J-ERTM techniques also are applied to an active-source OBS data set from the South China Sea. To improve the imaging quality with limited data, some preprocessing procedures for field TS and OBS node hybrid data sets are necessary, such as denoising, OBS node relocation, OBS node orientation correction, OBS node calibration, and others. After preprocessing, the pressure component and velocity components have a more physical energy relationship, more consistent frequency range and wavelet, and a higher S/N. Finally, the preprocessed hybrid data can be used for J-ERTM. The imaging results demonstrate that our method works well with field data.

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.

A New Approach for Adaptive GPR Diffraction Focusing

Several researchers have utilized multipath summation to manage the common problem of scattered energy within GPR sections. Such energy results in degrading the lateral resolution and continuity of reflectors. If detailed velocity models are known, then it is fairly easy to focus the scattered energy by means of conventional migration methods. However, this is rarely the case in GPR sections, as the common-offset antenna array is mostly used, and therefore cannot provide velocity models. This gives an important advantage for the multipath summation method, which has proved to be successful in focusing such diffractions, without the need to build a detailed migration velocity field model. This multipath summation method is based on stacking (summation) of constant velocity migrated sections (weighted or not) over a predefined velocity range. The main drawback of this technique is the high computational cost and the need for user interference to select the appropriate stacking weights. We developed an improved implementation of the weighted multipath summation method that reduces both the computational cost, and the user interference in stacking weights selections. This data adaptive methodology can expedite the migration process, suppress the need for a detailed velocity model, and reduce the user subjectivity. Moreover, a data adaptive spectral scaling scheme was developed. This is applied on the output of the multipath summation process to reduce the expected blurriness in the resulting GPR sections.

MALMI: An Automated Earthquake Detection and Location Workflow Based on Machine Learning and Waveform Migration

Abstract Robust automatic event detection and location is central to real-time earthquake monitoring. With the increase of computing power and data availability, automated workflows that utilize machine learning (ML) techniques have become increasingly popular; however, ML-based classical workflows still face challenges when applied to the analysis of microseismic data. These seismic sequences are often characterized by short interevent times and/or low signal-to-noise ratio (SNR). Full waveform methods that do not rely on phase picking and association are suitable for processing such datasets, but are computationally costly and lack clear event identification criteria, which is not ideal for real-time processing. To leverage the advantages of both the methods, we propose a new workflow—MAchine Learning aided earthquake MIgration location (MALMI), which integrates ML and waveform migration to perform automated event detection and location. The new workflow uses a pretrained ML model to generate continuous phase probabilities that are then backprojected and stacked to locate seismic sources using migration. We applied the workflow to one month of continuous data collected in the Hengill geothermal area of Iceland to monitor induced earthquakes around two geothermal production sites. With a ML model (EQ-Transformer) pretrained using a global distribution of earthquakes, the proposed workflow automatically detects and locates 250 additional seismic events (accounting for 36% events in the obtained catalog) compared to a reference catalog generated using the SeisComP software. Most of the new events are microseismic events with a magnitude less than 0. Visual inspection of the waveforms of the newly detected events indicates that they are real seismic events of low SNR and are only reliably recorded by very few stations in the array. Further comparison with the conventional migration method based on short-term average over long-term average confirms that MALMI can produce much clearer stacked images with higher resolution and reliability, especially for events with low SNR. The workflow is freely available on GitHub, providing an automated tool for simultaneous event detection and location from continuous seismic data.

Reduced order model approach for imaging with waves

We introduce a novel, computationally inexpensive approach for imaging with an active array of sensors, which probe an unknown medium with a pulse and measure the resulting waves. The imaging function is based on the principle of time reversal in non-attenuating media and uses a data driven estimate of the ‘internal wave’ originating from the vicinity of the imaging point and propagating to the sensors through the unknown medium. We explain how this estimate can be obtained using a reduced order model (ROM) for the wave propagation. We analyze the imaging function, connect it to the time reversal process and describe how its resolution depends on the aperture of the array, the bandwidth of the probing pulse and the medium through which the waves propagate. We also show how the internal wave can be used for selective focusing of waves at points in the imaging region. This can be implemented experimentally and can be used for pixel scanning imaging. We assess the performance of the imaging methods with numerical simulations and compare them to the conventional reverse-time migration method and the ‘backprojection’ method introduced recently as an application of the same ROM.

VSP Imaging Using Free-Surface Multiples With Wavefield Decomposition: Synthetic and Field Data Examples

Vertical seismic profiling (VSP) is an effective technique to provide high-resolution seismic images of the reservoir area. However, the quality of the images is limited by the poor illumination of primary reflection wave. In conventional VSP imaging, only the upgoing primaries are used. Adding free-surface–related multiples into the imaging process can significantly improve the coverage of the illuminated area. Conventional migration methods using multiples need the complex process of multiple prediction. Data-to-data migration (DDM) is an effective imaging technique for multiples in which the recorded data is migrated directly. To improve the imaging quality of DDM in VSP imaging, we propose separating the wavefield into downgoing and upgoing components using Hilbert transform when reverse-time migration (RTM) is implemented in DDM, and the inverse-scattering imaging condition is further applied to the decomposed wavefields. The proposed method eliminates low-frequency noises and false images generated from the conventional cross-correlation imaging condition, and further enhance the illumination in the VSP imaging. Synthetic examples and application to a walkaway field data demonstrate that it can attenuate the noise and improve the imaging resolution effectively. By using DDM with inverse scattering imaging condition and wavefield decomposition based on Hilbert transform, VSP imaging using free-surface–related multiples becomes a practical complement for conventional VSP imaging.

Simulating and imaging wavefield of irregular topography based on spectral element method

For the high-precision seismic imaging of complex structures on irregular topographies, this study proposes a Chebyshev spectral-element reverse time migration (CSE-RTM) method coupled with the implicit Newmark time integral method. The approach is first applied to a numerical example to consider factors affecting numerical accuracy, such as the time step and the pattern of subdivision of the computational domain. The results show that the smaller the time step is, the higher the numerical accuracy is and that numerical accuracy improves when the grid nodes tend to be distributed uniformly in the computational domain. The spectral element method combines the advantages of the stronger boundary-related adaptability of the finite element method with the high accuracy and fast convergence of the spectral method. This method is applied to reverse time migration imaging, and tests of the model show that its accuracy of imaging is higher than that of the conventional finite-difference reverse time migration method in case of complex structures in areas with irregular topography. To improve the efficiency of parallel computing of the CSE-RTM, an iterative algorithm is proposed to solve the hierarchical equilibrium sparse linear equations (HELPs) for it based on the idea of the degrees of cohesion of freedom and local relaxation. This algorithm has the advantage that the efficiency of parallel computation does not decrease with an increasing number of processors while the number of iterations required for convergence is controlled. Owing to the fast convergence of the spectral method on a sparse grid coupled with the parallel computational scheme of HELPs, the efficiency of calculation of the CSE-RTM is significantly improved.

Fast Least-Squares Reverse Time Migration of OBN Down-Going Multiples

Nowadays the ocean bottom node (OBN) acquisition is widely used in oil and gas resource exploration and seismic monitoring. Conventional imaging algorithms of OBN data mainly focus on the processing of up-going primaries and down-going first-order multiples. Up-going multiples and higher-order down-going multiples are generally regarded as noise and should be eliminated or ignored in conventional migration methods. However, multiples carry abundant information about subsurface structures where primaries cannot achieve. To take full advantage of multiples, we propose a migration method using OBN down-going all-order multiples. And then the least-squares optimization algorithm is used to suppress crosstalks. Finally, a phase-encoding-based migration algorithm is developed to cut down the computational cost by blending several common receiver gathers together using random time delays and polarity reversals. Numerical experiments on the complex Marmousi model illustrate that the developed approach can enlarge the imaging area evidently, reduce the computational cost effectively, and enhance the image quality by suppressing crosstalks and improving the resolution.

Amplitude-Preserved Wave Equation: An Example to Image the Gas Hydrate System

Natural gas hydrate is an important energy source. Therefore, it is extremely important to provide a clear imaging profile to determine its distribution for energy exploration. In view of the problems existing in conventional migration methods, e.g., the limited imaging angles, we proposed to utilize an amplitude-preserved one-way wave equation migration based on matrix decomposition to deal with primary and multiple waves. With respect to seismic data gathered at the Chilean continental margin, a conventional processing flow to obtain seismic records with a high signal-to-noise ratio is introduced. Then, the imaging results of the conventional and amplitude-preserved one-way wave equation migration methods based on primary waves are compared, to demonstrate the necessity of implementing amplitude-preserving migration. Moreover, a simple two-layer model is imaged by using primary and multiple waves, which proves the superiority of multiple waves in imaging compared with primary waves and lays the foundation for further application. For the real data, the imaging sections of primary and multiple waves are compared. We found that multiple waves are able to provide a wider imaging illumination while primary waves fail to illuminate, especially for the imaging of bottom simulating reflections (BSRs), because multiple waves have a longer travelling path and carry more information. By imaging the actual seismic data, we can make a conclusion that the imaging result generated by multiple waves can be viewed as a supplementary for the imaging result of primary waves, and it has some guiding values for further hydrate and in general shallow gas exploration.

Inversion for Salt Flank Geometry Using Transmitted P- and S-Wave Travel Times

Accurate delineation of the salt flank is important for oil and gas exploration in areas with salt intrusion. We describe the application of deformable-layer tomography (DLT) to invert for the geometry of salt flank using travel times of transmitted P- and S-waves from surface sources to downhole receivers. The DLT allows us to take advantage of a common situation that the salt velocity is known and generally invariant, but we need to determine the variable geometry of salt flank. We demonstrate our new method using a physical model experiment mimicking the setup of a walkaway vertical-seismic-profiling (VSP) survey. We first use picked P-wave arrivals to invert for the salt flank geometry; the sinusoid shape of the salt flank is estimated fairly by the DLT due to the uneven P-wave raypath coverage. As an improvement, we further incorporate the S-wave arrivals in the DLT. The picking of S-wave arrivals is assisted with modeled S-wave arrivals based on the P-wave DLT model. The DLT solution using both P- and S-wave arrivals delineates the salt flank geometry more accurately than that using P-wave arrivals alone. The salt flank delineation using DLT can complement with the conventional salt proximity and migration methods. It could also serve as constraints or the initial model for the full-waveform inversion of salt geometry.

Elastic least-squares reverse-time migration with density variation for flaw imaging in heterogeneous structures

Ultrasonic techniques are able to accurately detect and characterize flaws in homogeneous structures. Elastic reverse time migration (ERTM) is a powerful tool to reconstruct high-resolution images of flaws. To achieve images with better quality, the solution can be obtained by iteratively finding an image generating the modeled data which can best match the measured data in a least-squares sense, i.e. least-squares migration (LSM). Combing ERTM and LSM, conventional elastic least-squares reverse time migration (ELSRTM) methods are based on the assumption of a constant density, which can lead to inaccurate amplitudes and parameter crosstalk artifacts in the reconstructed images. In this paper, an ultrasonic imaging technique based on the ELSRTM which considers density as well as longitudinal-(L-) and shear-wave (S-wave) velocity variations is explored for imaging flaws in heterogeneous structures. The ELSRTM with density variations can simultaneously reconstruct density and L- and S-wave velocity images, which can provide amplitude-preserving images and mitigate crosstalk artifacts. This method is applied to numerical as well as physical laboratory experiments and the results appear promising for flaw identification in heterogeneous structures.

High-resolution imaging: a wavelet stretch correction in de-absorption prestack time migration approach

ABSTRACT The wavelet stretch effects in pre-stack migration, which is similar to that of NMO stretch, distort the frequency spectrums and taper the high-frequency components at far offsets. The long-offset migrated results in conventional migration methods are muted, resulting in a loss of effective information about the subsurface investigation. Only short-offset data are used to obtain a high-resolution stacked image. However, long-offsets that correspond to high-incident-angle data are critical to velocity inversion and amplitude versus offset (AVO) analysis when estimating lithology and fluid product. Furthermore, attenuation of seismic waves due to the anelasticity of the subsurface medium will cause dissipation of seismic energy and loss of high frequencies, thus broadening the propagating wavelet and degrading the resolution of imaging. Both the wavelet stretch and the absorption will taper the high-frequency components. We developed a wavelet stretch correction and absorption compensation scheme during the migration process by shrinking the wavelet when applying the imaging condition, which matches the stretch effect caused by migration. The correction factor is derived based on the incidence angle associated with many factors. Moreover, the de-absorption pre-stack time migration (QPSTM) is implemented on the effective Q model that makes the Q modelling become easier. Both the application of synthetic and real data examples demonstrate that the proposed anti-stretch QPSTM can be particularly useful recovering the high-frequency components caused by anelasticity and wavelet stretch, and obtain higher-resolution images.

One-way propagators based on matrix multiplication in arbitrarily lateral varying media with GPU implementation

Conventional one-way migration methods have some shortcomings, such as their limitations in imaging large angles. A novel strategy is presented here to solve these issues. The Helmholtz operator of the two-way wave equation is a power of the square root operator of the one-way wave equation. Based on this connectivity, the computation of the square root of the Helmholtz operator based on matrix multiplication and the calculation of a one-way propagator with a Taylor series expansion, is studied. The one-way propagators used in this paper involve complete matrix multiplication, which is a distinct feature of this method and is suitable for parallel implementation. Analysing the values of matrix square roots generated by the classical eigenvalues decomposition method and an algorithm of computing matrix square roots with matrix multiplication, we prove that our algorithm can calculate a stable result, which lays a solid foundation for further study. The impulse response test proves the advantage of our proposed method over conventional one-way migration in describing the wavefield propagation of large angles in a medium with a strong lateral velocity variation. Moreover, we implement GPU and CPU versions of the algorithm and compared their efficiencies. The post-stack migration result of the Salt model and the pre-stack migration result of the Marmousi model further illustrate the superiority of our proposed migration method in imaging the complex and fine-scale structures compared to the conventional one-way migration method. This provides a promising practical application in seismic exploration.

Target‐oriented imaging using extended image volumes: a low‐rank factorization approach

ABSTRACTImaging in geological challenging environments has led to new developments, including the idea of generating reflection responses by means of interferometric redatuming at a given target datum in the subsurface, when the target datum lies beneath a complex overburden. One way to perform this redatuming is via conventional model‐based wave‐equation techniques. But those techniques can be computationally expensive for large‐scale seismic problems since the number of wave‐equation solves is equal to two times the number of sources involved during seismic data acquisition. Also conventional shot‐profile techniques require lots of memory to save full subsurface extended image volumes. Therefore, we can only form subsurface image volumes in either horizontal or vertical directions. To exploit the information hidden in full subsurface extended image volumes, we now present a randomized singular value decomposition‐based approach built upon the matrix probing scheme, which takes advantage of the algebraic structure of the extended imaging system. This low‐rank representation enables us to overcome both the computational cost associated with the number of wave‐equation solutions and memory usage due to explicit storage of full subsurface extended image volumes employed by conventional migration methods. Experimental results on complex geological models demonstrate the efficacy of the proposed methodology and allow practical reflection‐based extended imaging for large‐scale five‐dimensional seismic data.

Angle-stack reverse time migration in VTI media

Seismic anisotropy is widespread in real media, the conventional isotropic reverse time migration method will lead to problems such as inaccurate depth and unfocused energy in dealing with the anisotropic issues, and low frequency noise with strong amplitude in imaging results can not be completely removed by simple filtering method such as Laplace filter operator. To solve these problems, we have researched pseudo acoustic equation to simulate qP wave propagation in anisotropic VTI media and used the smoothed isotropy or elliptic anisotropy tapered circular region located in source points to suppress the qSV wave. On this basis, we also develop angle-stack reverse time migration method in VTI media, which stacks the imaging results in angle domain to suppress low-frequency noise and improve imaging accuracy. Model test and application example prove that this method can obtain accurate imaging results with balanced energy in steep dip structures, and has better effect in suppressing low-frequency imaging noise.

A practical implementation of subsalt Marchenko imaging with a Gulf of Mexico data set

Marchenko redatuming allows one to use surface seismic reflection data to generate the seismic response from sources at the surface to any point in the subsurface. Without requiring much information about the earth’s properties, the seismic response generated by Marchenko redatuming contains accurate estimates of not only the primaries but also the internal multiples. A target-oriented imaging method, referred to as Marchenko imaging, was implemented for imaging complex structures of the earth using the seismic response obtained through Marchenko redatuming. Taking account of the contribution of primaries and internal multiples, Marchenko imaging produces images that contain fewer artifacts than the images obtained using conventional imaging methods (e.g., reverse time migration) with the same input data. In this study, we applied Marchenko imaging to a field data set acquired at the Gulf of Mexico to produce an image of a subsalt area. We investigated two important and practical aspects of the Marchenko framework: (1) the missing near offsets in marine shot records and (2) the calibration of the reflection data. Finally, we suggested a workflow for processing the marine towed-streamer field data set acquired at the Gulf of Mexico, and we have developed a complete theoretical and practical framework to produce a target-oriented subsalt image using the Marchenko methods. The images obtained from Marchenko imaging are consistent and comparable, for the most part, with conventional migration methods. However, Marchenko imaging achieves improvements in the continuity of the geologic structures and in suppressing the artifacts that are caused by internal multiples.

Least-Square-Based Nonuniform Borehole SAR Imaging for Subsurface Sensing

This paper presents the least-square-based nonuniform borehole synthetic aperture radar (SAR) imaging method with cosine accuracy factor for subsurface sensing. Based on the Stolt migration, the frequency-wavenumber spectrum of nonuniform data is efficiently approximated in the least-square-sense for the target space generation. The nonuniform power exponent basis is interpolated into several uniform power exponent bases with cosine accuracy factors, and then a virtual uniform sample set with a larger scale is generated for frequency-wavenumber spectrum approximation and imaging process. The proposed method can give accurate subsurface image result with nonuniform data at a greatly reduced computational cost. The approximation error and computational cost of the proposed method are analyzed and compared with those of Gaussian nonuniform imaging method. The imaging capabilities of the proposed method are theoretically simulated and experimentally demonstrated for distributed targets. The results show that the normalized mean square error and normalized maximum error of the proposed method are at least 8.07 dB and 4.29 dB, respectively, lower than those of conventional Stolt migration method. The imaging properties of this proposed method are shown to be superior to the conventional Stolt migration method, Gaussian nonuniform imaging method and Kirchhoff migration method, which is suitable for large nonuniform SAR imaging applications.