Velocity Analysis Method Research Articles

TBM Advanced Geological Prediction via Ellipsoidal Positioning Velocity Analysis

Traditional seismic wave-based tunnel advanced geological forecasting techniques are primarily designed for drill and blast method construction tunnels. However, given the fast excavation speed and limited prediction space in tunnel boring machine (TBM) construction tunnels, traditional methods have significant technical limitations. This study analyzes the characteristics of different types of TBM construction tunnels and, considering the practical construction conditions, identifies an effective observation system and data acquisition method. To address the challenges in advanced forecasting for TBM construction tunnels, a method of ellipsoid positioning velocity analysis, which takes into account the constraints of three-component data directions, is proposed. Based on the characteristics of the advanced forecasting observation system, this method compares the maximum values on the spatial isochronous ellipsoidal surface to determine the average velocity of the geological layer rays, thereby enabling accurate inversion of the spatial distribution ahead. Utilizing numerical simulation, a model for the advanced detection of typical unfavorable geological formations is established by obtaining the wave field response characteristics of seismic waves in three-dimensional space, and the velocity structure of the model is retrieved through this velocity analysis method. In the engineering example, the fracture property, water content, and weathering degree of the surrounding rock are predicted accurately.

Ship-VNet: An Algorithm for Ship Velocity Analysis Based on Optical Remote Sensing Imagery Containing Kelvin Wakes

Extracting ship velocity vectors from optical remote sensing images is a very challenging task, and ship wakes are the only motion features of ships. However, because the sensor’s field of view is not sufficiently bright and the brightness is not uniform, the image contains noise, which makes it difficult to define and extract the wake of the ship. Velocity analysis of the extracted wake makes the whole process complicated and slow. Therefore, considering the above problems, this paper proposes Ship-VNet, an optical remote sensing image ship velocity analysis algorithm based on Kelvin wakes. In this model, the rotating target detection algorithm is used to detect the ship, and then, the classical relationship between the kinematic characteristics of the ship’s Kelvin wake and the velocity of the ship is studied and experimentally analyzed in the frequency domain. In addition, based on optical remote sensing images and corresponding real AIS data, a ship dataset with Kelvin wakes marked with heading velocity was constructed to verify the effectiveness of the proposed method. Compared with the ship velocity analysis method based on the frequency domain, which was also used in the previous research, the experiment demonstrates the superiority of the method in terms of analysis accuracy.

High-resolution permittivity estimation of ground penetrating radar data by migration with isolated hyperbolic diffractions and local focusing analyses

Ground penetrating radar (GPR) is important for detecting shallow subsurface structures, which has been successfully used on the Earth, Moon, and Mars. It is difficult to analyze the underground permittivity from GPR data because its observation system is almost zero-offset. Traditional velocity analysis methods can work well with separable diffractions but fail with strong-interfered diffractions. However, in most situations, especially for lunar or Martian exploration, the diffractions are highly interfered, or even buried in reflections. Here, we proposed a new method to estimate the underground permittivity and apply it to lunar penetrating radar data. First, we isolate a group of diffractions with a hyperbolic time window determined by a given velocity. Then, we perform migration using the given velocity and evaluate the focusing effects of migration results. Next, we find the most focused results after scanning a series of velocities and regard the corresponding velocity as the best estimation. Finally, we assemble all locally focused points and derive the best velocity model. Tests show that our method has high spatial resolution and can handle strong noises, thus can achieve velocity analyses with high accuracy, especially for complex materials. The permittivity of lunar regolith at Chang’E-4 landing area is estimated to be ∼4 within 12 m, ranging from 3.5 to 4.2 with a local perturbation of ∼2.3%, consistent with ∼3% obtained by numerical simulations using self-organization random models. This suggests that the lunar regolith at Chang’E-4 landing area is mature and can be well described by self-organization random models.

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.

Automatic velocity analysis using interpretable multimode neural networks

SUMMARY Seismic velocity analysis is the basis for seismic imaging and understanding complex subsurface geological structures. Although the performance of automatic velocity analysis methods based on Common Middle Point (CMP) data or Velocity Spectra (VS) is encouraging, particularly deep learning methods. However, most methods ignore the complementarity between CMP data and VS data, and only one of them is selected for velocity modelling. We propose a multimodal neural network (MMN) that combines the advantages of CMP data details representation and simplification of VS. MMN includes multilayer convolution structures and auto-encoder structures, which are used to extract time–space amplitude information from CMP gathers and energy groups features from VS data, respectively. This paper compared MMN with the CMP single-modal network (CSN) and the velocity spectra single-modal network (VSSN). Based on synthetic data, we investigated their differences in terms of continuity, accuracy, noise resistance and generalization. The MMN prediction results makes a trade-off between the overall continuity and local details. Visualization analysis of the intermediate feature maps explains the MMN velocity prediction mechanism, that is, the multi-angle representation and complementary fusion of velocity information. Finally, the performance of the proposed method is demonstrated using the braided river deposited field data example.

Ocean bottom seismometer data velocity analysis and Kirchhoff pre‐stack time migration

AbstractWith the increasing maturity of ocean bottom seismometer technology in gas hydrate exploration, more and more researchers apply ocean bottom seismometer exploration technology for offshore oil exploration. However, due to the special observation mode of ocean bottom seismometer, it is impracticable to process ocean bottom seismometer data using traditional data processing methods, such as velocity analysis and pre‐stack time migration. This manuscript proposed a new velocity analysis method for ocean bottom seismometer data, which obtains more accurate root‐mean‐square velocity than the existing method. Then we deduced the Kirchhoff pre‐stack time migration formula for ocean bottom seismometer data. Two models demonstrate the correctness of the velocity analysis and migration methods. Finally, the two methods were applied to the actual ocean bottom seismometer data, and the obtained migration profile is consistent well with the profile of towed streamer data in the nearby area.

The research and implementation of velocity analysis methods for reverse time migration angle-gather

The research and implementation of velocity analysis methods for reverse time migration angle-gather

An Automatic Velocity Analysis Method for Seismic Data-Containing Multiples

Normal moveout (NMO)-based velocity analysis can provide macro velocity models for prestack data processing and seismic attribute inversion. Datasets with an increasing size require conventional velocity analysis to be transformed to a more automatic mode. The sensitivity to multiple reflections limits the wide application of automatic velocity analysis. Thus, we propose an automatic velocity analysis method for seismic data-containing multiples to overcome the limit of multiple interference. The core idea of the proposed algorithm is to utilize a multi-attribute analysis system to transform the multiple attenuation problem to a multiple identification problem. To solve the identification problem, we introduce the local similarity to attribute the predicted multiples and build a quantitative attribute called multiple similarity. Considering robustness and accuracy, we select two supplementary attributes based on velocity and amplitude difference, i.e., velocity variation with depth and amplitude level. Then we utilize the technique for order preference by similarity to ideal solution (TOPSIS) to balance weights for different attributes in automatic velocity analysis. An RGB system is adopted for multi-attributes fusion in velocity spectra for visualization and quality control. Using both synthetic and field examples to evaluate the effectiveness of the proposed method for data-containing multiples, the results demonstrate the excellent performance in the accuracy of the extracted velocity model.

A robust migration velocity analysis method based on adaptive differential semblance optimization

Migration velocity analysis (MVA) is an efficient tool to reconstruct low wavenumber components of the model. Compared with data domain methods, the MVA methods are implemented in image domain by processing imaging results directly through minimizing moveouts and improving the coherence of the common image gathers (CIGs), such as angle domain common image gathers (ADCIGs) or subsurface offset domain image gathers (ODCIGs). As one of the wave-equation based migration velocity analysis (WEMVA) methods, differential semblance optimization (DSO) can automatically detect the moveout existed in the CIGs and is convenient to implement. However, referring to the CIGs contaminated by spurious imaging artefacts caused by uneven illumination and irregular observation geometry, the traditional DSO method may produce poor velocity update with oscillations, which can prevent rapid convergence to a correct velocity model. To deal with this issue and extend the solution space, we introduce an adaptive DSO (ADSO) method by replacing the traditional image matching with energy distribution matching. By reducing dependence on the imaging results and introducing a variable weighting function in the calculation of the adjoint sources, the amplitude of the inverted model can be evenly distributed and free of artefacts. Three numerical examples show that the ADSO method is robust with little artefacts presenting in the gradients. Combined with the improved gradient, the ADSO can lead to a stable update.

Feasibility of Estimating Parameters and Enhancing Reflections of Dipping Moho From Teleseismic P Wave Coda Autocorrelation

AbstractTeleseismic P wave coda autocorrelation can retrieve reflections from subsurface structural boundaries, which can be used in lithospheric imaging. However, dipping structures cause difficulty in extraction of reflections and errors in depth estimation. Based on a previously proposed velocity analysis method, we formulated a method for estimating the dipping parameters and correcting the effects of dipping structures. First, we derived an arrival time formula for the PP reflection of the dipping Moho, which includes the effects of the ray parameters, dipping structures, and azimuthal distribution of available events. Second, the formula was used for move out correction and velocity analysis. Finally, by scanning the peak of the Moho energy group in the velocity spectrum, which varies with the azimuth and dip angles, a method was proposed to estimate the dipping parameters of the Moho. We referred to the scanning map as a dipping spectrum. A field data test at station NE44 revealed the prospects of the practical applications of this method. The dip angle of the Moho below the station was 3°, and exhibited an azimuth angle of 25°NE. The stacked waveform of the Moho reflection was significantly enhanced. This method was successfully applied to more stations adjacent to station NE44. Our results provide new insight on the Moho in this area, which are important for imaging and interpretation in areas that contain a dipping Moho such as plate subduction zones.

3D Migration Depth Focus Velocity Analysis of Hand-Held Ground Penetrating Radar

Hand-held ground penetrating radar (GPR) systems have been widely applied to landmine detections during recent decades. The accuracy of an imaging result by migration for a hand-held GPR is strongly related to the accuracy of subsurface velocity distribution obtained from multi offset data. For shallow targets like landmines, the hyperbolas are usually not distinct in 2D slices and are masked by the surface reflections. In this article, we propose a 3D migration depth focus velocity analysis method for hand-held GPRs to estimate the background velocity of the subsurface. This method is performed based on the images generated by migrations. The objective function is defined as the proportion of the target on the depth slice containing the target. After migrating a GPR radargram with different velocities, the background velocity, which minimizes the objective function, can be determined by comparing the imaging results by migration using different velocities. To test the proposed method, we apply this procedure to experimental GPR data collected with an advanced landmine imaging system (ALIS) in the laboratory. Subsequently, the velocity of the background is obtained, 3D diffraction migration with the obtained velocity achieves subsurface imaging with high quality. The accurate position and depth of the target are obtained from the optimal migration image.

Extracting Reliable P‐Wave Reflections From Teleseismic P Wave Coda Autocorrelation

AbstractRecently, many studies have demonstrated the use of teleseismic P wave coda autocorrelation for imaging lithosphere structures. However, the reliability of the extracted reflections remains uncertain and a means of evaluation is lacking. In this study, we propose a velocity analysis method that conveniently solves this problem in place of a synthetic experiment. This method considers the average velocity used for the moveout correction as an unknown quantity and then uses a continuously varying average velocity for the moveout correction. Finally, this method obtains a stacked result that varies with the average velocity and the vertical two‐way travel time to produce a diagram. This method is similar to the velocity analysis method used in exploration seismology. In this diagram, reliable reflections correspond to focused energy clusters, while noise lacks this feature. Therefore, this method helps determine which reflections are reliable, while also finding the appropriate parameters for data processing. Synthetic data tests demonstrate the validity of this method as well as a test of field data for station BOSA in the Kaapvaal craton, South Africa, which illustrates the successful application of the method in the case of a sharp and flat Moho discontinuity. Finally, we applied the method to the NCISP‐6 dense array and observed obvious energy clusters, representing reflections from the Moho discontinuity in the results for most stations. The depth and shape of the Moho discontinuity determined by this test are consistent with receiver function results, which verify the robustness of this method in relatively complex applications.

Direct Velocity Inversion of Ground Penetrating Radar Data Using GPRNet

AbstractGround penetrating radar (GPR) is used to image the shallow subsurface as evident in earth and planetary exploration. Electromagnetic (EM) velocity (permittivity) models are inverted from GPR data for accurate migration. While conventional velocity analysis methods are designed for multioffset GPR data, to our knowledge, the velocity analysis for zero‐offset GPR has been underexplored. Inspired by recent deep learning seismic impedance inversion, we propose a deep learning guided technique, GPRNet, that is based on convolutional neural networks to directly learn the intrinsic relationship between GPR data and EM velocity. GPRNet takes in GPR data and outputs the corresponding EM velocity. We simulate numerous GPR data from a range of pseudo‐random velocity models and feed the datasets into GPRNet for training. Each training data set comprises of a pair of one‐dimensional GPR data and EM velocity. During training phase, the neural network's weights are updated iteratively until convergence. This process is analogous to full‐waveform inversion in which the best model is found by iterative optimization until simulated data matches observed data. We test GPRNet on synthetic testing datasets and the predicted velocity models are accurate. A case study is presented where this method is applied on a GPR data collected at the former Wurtsmith Air Force Base in Michigan. The inversion results agree with velocity models established by previous GPR inversion studies of the similar area. We expect the GPRNet open‐source software to be useful in imaging the subsurface for earth and planetary exploration.

Study on the Velocity Analysis Method of Low SNR Seismic Data

When the signal to noise ratio of seismic data is very low, velocity spectrum focusing will be poor., the velocity model obtained by conventional velocity analysis methods is not accurate enough, which results in inaccurate migration. For the low signal noise ratio (SNR) data, this paper proposes to use partial Common Reflection Surface (CRS) stack to build CRS gathers, making full use of all of the reflection information of the first Fresnel zone, and improves the signal to noise ratio of pre-stack gathers by increasing the number of folds. In consideration of the CRS parameters of the zero-offset rays emitted angle and normal wave front curvature radius are searched on zero offset profile, we use ellipse evolving stacking to improve the zero offset section quality, in order to improve the reliability of CRS parameters. After CRS gathers are obtained, we use principal component analysis (PCA) approach to do velocity analysis, which improves the noise immunity of velocity analysis. Models and actual data results demonstrate the effectiveness of this method.

Full-Waveform Inversion for Imaging Faulted Structures: A Case Study from the Japan Trench Forearc Slope

Full-waveform inversion (FWI) of limited-offset marine seismic data is a challenging task due to the lack of refracted energy and diving waves from the shallow sediments, which are fundamentally required to update the long-wavelength background velocity model in a tomographic fashion. When these events are absent, a reliable initial velocity model is necessary to ensure that the observed and simulated waveforms kinematically fit within an error of less than half a wavelength to protect the FWI iterative local optimization scheme from cycle skipping. We use a migration-based velocity analysis (MVA) method, including a combination of the layer-stripping approach and iterations of Kirchhoff prestack depth migration (KPSDM), to build an accurate initial velocity model for the FWI application on 2D seismic data with a maximum offset of 5.8 km. The data are acquired in the Japan Trench subduction zone, and we focus on the area where the shallow sediments overlying a highly reflective basement on top of the Cretaceous erosional unconformity are severely faulted and deformed. Despite the limited offsets available in the seismic data, our carefully designed workflow for data preconditioning, initial model building, and waveform inversion provides a velocity model that could improve the depth images down to almost 3.5 km. We present several quality control measures to assess the reliability of the resulting FWI model, including ray path illuminations, sensitivity kernels, reverse time migration (RTM) images, and KPSDM common image gathers. A direct comparison between the FWI and MVA velocity profiles reveals a sharp boundary at the Cretaceous basement interface, a feature that could not be observed in the MVA velocity model. The normal faults caused by the basal erosion of the upper plate in the study area reach the seafloor with evident subsidence of the shallow strata, implying that the faults are active.

Inspiration for Seismic Diffraction Modelling, Separation, and Velocity in Depth Imaging

Fractured imaging is an important target for oil and gas exploration, as these images are heterogeneous and have contain low-impedance contrast, which indicate the complexity in a geological structure. These small-scale discontinuities, such as fractures and faults, present themselves in seismic data in the form of diffracted waves. Generally, seismic data contain both reflected and diffracted events because of the physical phenomena in the subsurface and due to the recording system. Seismic diffractions are produced once the acoustic impedance contrast appears, including faults, fractures, channels, rough edges of structures, and karst sections. In this study, a double square root (DSR) equation is used for modeling of the diffraction hyperbola with different velocities and depths of point diffraction to elaborate the diffraction hyperbolic pattern. Further, we study the diffraction separation methods and the effects of the velocity analysis methods (semblance vs. hybrid travel time) for velocity model building for imaging. As a proof of concept, we apply our research work on a steep dipping fault model, which demonstrates the possibility of separating seismic diffractions using dip frequency filtering (DFF) in the frequency–wavenumber (F-K) domain. The imaging is performed using two different velocity models, namely the semblance and hybrid travel time (HTT) analysis methods. The HTT method provides the optimum results for imaging of complex structures and imaging below shadow zones.

Velocity and event‐slope analysis using a model‐based common‐diffraction‐surface stack operator

ABSTRACTOver the last two decades, scientists have introduced many ways to improve normal moveout velocity analysis by optimizing the resolution of the moveout velocity spectrum, a graph that displays a coherence value for every tested velocity traveltime pair. Almost all of these methods have failed to enhance resolution when faced with low‐fold common‐midpoint gathers, which might be caused by natural barriers or man‐made obstacles. Another problem is that many approaches are derived from very simple model assumptions that quickly break down for complex structures and do not provide enough model flexibility for an iterative and interactive velocity analysis. In this paper, we present a new velocity analysis method based on the model‐based common‐diffraction‐surface stack operator and apply it to two synthetic data sets, one with locally sparse common‐midpoint coverage and one with a laterally variable complex geological structure. We generate velocity spectra by calculating the semblance along spatial operators obtained for all possible emergence angles and an entire range of velocity models. Comparing the resolution of such velocity spectra with those obtained with the classical normal moveout velocity analysis shows, in both two analysed cases, that the stacking velocity can be estimated much more precisely. The reasons for this are that the event dip is handled independently from the velocity and that the semblance is obtained, for each zero‐offset sample, over a group of neighbouring common‐midpoint gathers instead of just one.

Method for obtaining high-resolution velocity spectrum based on weighted similarity

Seismic wave velocity is one of the most important processing parameters of seismic data, which also determines the accuracy of imaging. The conventional method of velocity analysis involves scanning through a series of equal intervals of velocity, producing the velocity spectrum by superposing energy or similarity coefficients. In this method, however, the sensitivity of the semblance spectrum to change of velocity is weak, so the resolution is poor. In this paper, to solve the above deficiencies of conventional velocity analysis, a method for obtaining a high-resolution velocity spectrum based on weighted similarity is proposed. By introducing two weighting functions, the resolution of the similarity spectrum in time and velocity is improved. Numerical examples and real seismic data indicate that the proposed method provides a velocity spectrum with higher resolution than conventional methods and can separate cross reflectors which are aliased in conventional semblance spectrums; at the same time, the method shows good noise-resistibility.

Wave‐Equation Migration Velocity Analysis Using Radon‐Domain Common‐Image Gathers

AbstractThe accuracy of the background velocity analysis is critical to image the subsurface structure in seismology. Wave‐equation migration velocity analysis using source‐domain common‐image gathers requires picking the relative moveouts based on semblance analysis. However, the picking process is complex and time‐consuming. To avoid picking and maintain efficiency in generating CIGs, an automatic wave‐equation migration velocity analysis method is proposed utilizing the focusing properties of the Radon‐domain common‐image gathers. Seismic events in the Radon‐domain common‐image gathers appear focused if an accurate migration velocity model is used; otherwise, the resultant events will be unfocused. The objective function is defined in the Radon domain to measure focus of the common‐image gathers automatically. The proposed migration velocity analysis method links the defocusing information to the migration velocity update under the assumption that model perturbations only result in slope shifts in the common‐image gathers. This method provides a reasonably accurate background velocity model for imaging and full‐waveform inversion without requiring an accurate initial velocity model. Tests on synthetic and field data demonstrate the effectiveness of the method. This method is tolerable to both inaccurate initial velocity models and the lack of low‐frequency data.

The effectiveness of a pseudo‐inverse extended Born operator to handle lateral heterogeneity for imaging and velocity analysis applications

ABSTRACTWave equation–based migration velocity analysis techniques aim to construct a kinematically accurate velocity model for imaging or as an initial model for full waveform inversion applications. The most popular wave equation–based migration velocity analysis method is differential semblance optimization, where the velocity model is iteratively updated by minimizing the unfocused energy in an extended image volume. However, differential semblance optimization suffers from artefacts, courtesy of the adjoint operator used in imaging, leading to poor convergence. Recent findings show that true amplitude imaging plays a significant role in enhancing the differential semblance optimization's gradient and reducing the artefacts. Here, we focus on a pseudo‐inverse operator to the horizontally extended Born as a true amplitude imaging operator. For laterally inhomogeneous models, the operator required a derivative with respect to a vertical shift. Extending the image vertically to evaluate such a derivative is costly and impractical. The inverse operator can be simplified in laterally homogeneous models. We derive an extension of the approach to apply the full inverse formula and evaluate the derivative efficiently. We simplified the implementation by applying the derivative to the imaging condition and utilize the relationship between the source and receiver wavefields and the vertical shift. Specifically, we verify the effectiveness of the approach using the Marmousi model and show that the term required for the lateral inhomogeneity treatment has a relatively small impact on the results for many cases. We then apply the operator in differential semblance optimization and invert for an accurate macro‐velocity model, which can serve as an initial velocity model for full waveform inversion.