Velocity Inversion Research Articles

Tectonic Implication of the 2022 MS 6.9 Earthquake in Menyuan, Qinghai, China: Analysis of Precise Earthquake Locations and InSAR.

Precise earthquake locations and InSAR (Interferometric Synthetic Aperture Radar) deformation observation are the major methods to understand the earthquake occurrence and disaster-causing process. This paper proposes a processing framework for analyzing strong earthquake mechanisms from one-dimensional velocity inversion to precise earthquake locations combined with InSAR deformation observation, and discusses earthquake-generating fault and dynamic mechanisms of tectonic deformation. We analyzed the Menyuan Ms 6.9 earthquake in 2022 and discuss the historical seismic activities and corresponding stress adjustment processes in the research region. To analyze and study the seismogenic structure and mechanism of the earthquake, we investigated the spatial and temporal distribution characteristics of the Menyuan earthquake sequence and analyzed the InSAR coseismic deformation field. We obtained the precise locations of the main shock and aftershocks and the coseismic InSAR deformation field of the main shock. It was confirmed that the Ms 6.9 earthquake was a shallow sinistral strike-slip earthquake, which led to the sequential activation of the Tuolaishan and Lenglongling faults. The main seismogenic fault of the mainshock was the northwestern end of the Lenglongling fault, and the earthquake rupture was segmented. It can be inferred that the earthquake was a stress-adjusted event triggered in the Qilian-Haiyuan tectonic belt caused by the northeasterly push of the Qinghai-Tibet Plateau. The risk of moderate to high earthquakes in the region remains high in the future, requiring enhanced seismic observations.

Design of a New Family of Kinematically Redundant Parallel Mechanisms With Two Rotations and One Translation

Abstract This article presents a new family of kinematically redundant parallel mechanisms (KR-PMs) with two rotations and one translation (2R1T) based on the Lie group method. Compared with the 2-UPR/RPU PM, these PMs have two main advantages, namely, larger reachable workspaces of the moving platforms and higher motion/force transmission performance, as verified by three typical cases. The inverse kinematics, velocities, singularities, and workspaces of the 2-UPR/PRPU PM are analyzed in detail. A new method for motion/force transmission performance evaluation of KR-PMs in accordance with the indices for nonredundant PMs is proposed. Moreover, the local and global motion/force transmission performances of the 2-UPR/PRPU PM are determined using the new method.

Seismic swarm intelligence inversion with sparse probability distribution of reflectivity

Seismic inversion, such as velocity and impedance, is an ill-posed problem. To solve this problem, swarm intelligence (SI) algorithms have been increasingly applied as the global optimization approach, such as differential evolution (DE) and particle swarm optimization (PSO). Based on the well logs, the sparse probability distribution (PD) of the reflectivity distribution is spatial stationarity. Therefore, we proposed a general SI scheme with constrained by a priori sparse distribution of the reflectivity, which helps to provide more accurate potential solutions for the seismic inversion. In the proposed scheme, as two key operations, the creating of probability density function library and probability transformation are inserted into standard SI algorithms. In particular, two targeted DE-PD and PSO-PD algorithms are implemented. Numerical example of Marmousi2 model and field example of gas hydrates show that the DE-PD and PSO-PD estimate better inversion solutions than the results of the original DE and PSO. In particular, the DE-PD is the best performer both in terms of mean error and fitness value of velocity and impendence inversion. Overall, the proposed SI with sparse distribution scheme is feasible and effective for seismic inversion.

Elastic Properties of Thermally Treated Diabase and Peridotite: Implications Toward the Elastic Properties of Oceanic Lithosphere

AbstractSupported by laboratory experimental results, the reduction in elastic thickness () and flexural rigidity () at trench outer rise, which enables bending, is attributed to damage. A series of laboratory experiments were performed on intact and thermally treated diabase and peridotite (400°C–800°C) under increasing confining pressures (3–200 MPa) at room temperature. Simultaneous porosity and elastic wave velocity (P and S) measurements were obtained under dry and fluid saturated conditions. The velocity and porosity inversions indicated a significant variation in crack properties (crack density, aspect ratio) and reduction in Young's modulus, of thermally treated samples in comparison to the intact samples. Hence the experimental results suggested weakening in elastic properties of a damaged oceanic lithosphere. Subsequently created lithospheric strength profiles showed a strength reduction in wet models in comparison to dry models. Under the assumption of different cooling models, the aging of the plate resulted in an increase in strength with deeper neutral zones. The outcome of the strength profiles was utilized to estimate and , which are also dependent on the elasticity of the lithosphere. The results showed a reduction in with increasing damage and suggest that the bending at the subduction zone is facilitated by weakened elastic properties of a damaged lithosphere. While most of the geophysical observations of were able to be constrained by the proposed damaged models, some shallow observations indicated enhanced damage triggered either by increased depth to brittle‐ductile transition or by large bending related faulting events.

Frequency-domain elastic full-waveform inversion based on Green functions

In this paper, the elastic full-waveform inversion in the frequency domain based on Green functions is investigated. The method allows to image media velocities of compressional wave and shear wave by using vertical and horizontal components. The forward problem is solved by the finite difference scheme with the perfectly matched layer in the frequency domain. The inversion is an optimization iterative process to minimize the residual between the synthetic data and the observed data on the surface. A gradient method is used to find the optimization direction, preconditioned with the diagonal part of the inverse Hessian matrix. The gradient is constructed based on Green functions, which serves to compute the gradient of the objective function with respect to model parameters effectively. The inversion is completed from low frequency to high frequency so that smaller wavelengths are progressively introduced in inversion. Numerical computations for the benchmark Overthrust model demonstrate the full-waveform inversion method in this paper can inverse media velocities of complex structures accurately.

Physics-driven self-supervised learning system for seismic velocity inversion

Seismic velocity inversion plays a vital role in various applied seismology processes. A series of deep learning methods have been developed that rely purely on manually provided labels for supervision; however, their performances depend heavily on using large training data sets with corresponding velocity models. Because no physical laws are used in the training phase, it is usually challenging to generalize trained neural networks to a new data domain. To mitigate these issues, we have embedded a seismic forward modeling step at the end of a network to remap the inversion result back to seismic data and thus train the neural network through self-supervised loss, i.e., the misfit between the network input and output. As a result, we eliminate the need for many labeled velocity models, and physical laws are introduced when back-propagating gradients through the seismic forward modeling step. We verify the effectiveness of our approach through comprehensive experiments on synthetic data sets, where self-supervised learning outperforms the fully supervised approach, which accesses much more labeled data. The superior performance is even more significant when compared with a new data domain that has velocity models with faults and more geologic layers. Finally, in case of unknown and more complex data types, we develop a network-constrained full-waveform inversion (FWI) method. This method refines the initial prediction of the network by iteratively optimizing network parameters other than the velocity model, as found with the conventional FWI method, and demonstrates clear advantages in terms of interface and velocity accuracy. With these measures (self-supervised learning and network-constrained FWI), our physics-driven self-supervised learning system successfully mitigates issues such as the dependence on large labeled data sets, the absence of physical laws, and the difficulty in adapting to new data domains.

Seismological reference earth model in South China (SREM-SC): Crust and uppermost mantle

The South China Block is located on the eastern margin of the Eurasian Plate and the western margin of the Pacific Plate. The South China Block is currently in a tectonically compressed environment, while the Tibetan Plateau is moving eastward and the Philippine Sea Plate is moving westward from geodetic observations. The South China Block is an ideal place to revisit tectonic history from the Archean to Cenozoic, where its information could be well preserved in the crust. In this study, we aim to build the crustal and uppermost mantle component of the Seismological Reference Earth Model in South China (SREM-SC) to provide a background velocity model for geological interpretations and fine-scale velocity inversion. The S-wave velocity model comes from combining models inverted by ambient noise tomography and surface wave tomography. The P-wave velocity model is obtained from converted S-wave velocity and joint inversion tomography. The density model is inferred from an empirical relationship with P-wave velocity. The Moho depth is obtained by a weighted averaging scheme of previously published receiver function results. The P-wave and S-wave velocity models have a grid interval of 0.5° in both latitude and longitude, and with a vertical sampling interval of 5 km down to the 60 km depth. This work provides the 3-D crust and uppermost mantle structures and a representative reference model beneath South China.

Forced rotation enhances cylinder flow-induced vibrations at subcritical Reynolds number

When a cylinder is mounted on an elastic support within a current, vortex-induced vibrations (VIV) may occur down to a Reynolds number (Re) close to $20$ , based on the body diameter ( $D$ ) and inflow velocity ( $U$ ), i.e. below the critical value of $47$ reported for the onset of flow unsteadiness when the body is fixed. The impact of a forced rotation of the elastically mounted cylinder on the system behaviour is explored numerically for $Re \leqslant 30$ , over wide ranges of values of the rotation rate (ratio between body surface velocity and $U$ , $\alpha \in [0,5]$ ) and reduced velocity (inverse of the oscillator natural frequency non-dimensionalized by $D$ and $U$ , $U^\star \in [2,30]$ ). The influence of the rotation is not monotonic, but the most prominent effect uncovered in this work is a substantial enhancement of the subcritical-Re, flow-induced vibrations beyond $\alpha =2$ . This enhancement is twofold. First, the rotation results in a considerable expansion of the vibration/flow unsteadiness region in the $({Re},U^\star )$ domain, down to $Re=4$ . Second, the elliptical orbits described by the rotating body are subjected to a major amplification, with a transition from VIV to responses whose magnitude tends to increase unboundedly with $U^\star$ , even though still synchronized with flow unsteadiness. The emergence of such galloping-like oscillations close to the onset of vibrations disrupts the scenario of gradual vibration growth with Re, as amplitudes larger than $10$ body diameters may be observed at $Re=10$ .

Automatic Horizon Picking Using Multiple Seismic Attributes and Markov Decision Process

Picking the reflection horizon is an important step in velocity inversion and seismic interpretation. Manual picking is time-consuming and no longer suitable for current large-scale seismic data processing. Automatic algorithms using different seismic attributes such as instantaneous phase or dip attributes have been proposed. However, the computed attributes are usually inaccurate near discontinuities. The waveforms in the horizontal direction often change dramatically, which makes it difficult to track a horizon using the similarity of attributes. In this paper, we propose a novel method for automatic horizon picking using multiple seismic attributes and the Markov decision process (MDP). For the design of the MDP model, the decision time and state are defined as the horizontal and vertical spatial position on a seismic image, respectively. The reward function is defined in multi-dimensional feature attribute space. Multiple attributes can highlight different aspects of a seismic image and therefore overcome the limitations of the single-attribute MDP through the cross-constraint of multiple attributes. The optimal decision is made by searching the largest state value function in the reward function space. By considering cumulative reward, the lateral continuity of a seismic image can be effectively considered, and the impacts of abnormal waveform changes or bad traces in local areas for automatic horizon picking can be effectively avoided. An effective implementation scheme is designed for picking multiple reflection horizons. The proposed method has been successfully tested on both synthetic and field data.

Physics-embedded inverse analysis with algorithmic differentiation for the earth’s subsurface

Inverse analysis has been utilized to understand unknown underground geological properties by matching the observational data with simulators. To overcome the underconstrained nature of inverse problems and achieve good performance, an approach is presented with embedded physics and a technique known as algorithmic differentiation. We use a physics-embedded generative model, which takes statistically simple parameters as input and outputs subsurface properties (e.g., permeability or P-wave velocity), that embeds physical knowledge of the subsurface properties into inverse analysis and improves its performance. We tested the application of this approach on four geologic problems: two heterogeneous hydraulic conductivity fields, a hydraulic fracture network, and a seismic inversion for P-wave velocity. This physics-embedded inverse analysis approach consistently characterizes these geological problems accurately. Furthermore, the excellent performance in matching the observational data demonstrates the reliability of the proposed method. Moreover, the application of algorithmic differentiation makes this an easy and fast approach to inverse analysis when dealing with complicated geological structures.

Robust Deep Learning Seismic Velocity Inversion Over Varying Acquisitions

Retrieving subsurface velocity information from recorded seismograms generally involves solving an inverse problem using various optimization methods. Deep learning has recently become an emerging alternative technique to provide solutions for such velocity inversion tasks. However, due to the lack of labeled data in field applications, supervised deep learning methods proposed for velocity model estimations often rely on training with synthetic modeled data. Applying these deep learning models to field data can be severely limited by differences between the configurations used in the synthetic training data and those for field survey. In this paper, we investigate the impacts of different acquisition geometries on deep learning velocity inversion and propose an acquisition-robust deep learning framework to improve model’s applicability by mitigating the impacts of different acquisition geometries. We train the proposed deep learning model with synthetic training data transformed to an alternative domain, e.g., wavenumber-time domain in this study. We demonstrate that deep learning models trained in this way are more robust and allow generalization over different acquisition configurations.

Phase shift migration based plane-wave imaging of cortical bone

Cortical bone, a highly attenuated, anisotropic, and multilayered biological medium with high acoustic impedance, presents significant challenges for high-frequency ultrasound to penetrate its complex structure and acquire high-quality images. The traditional method of using uniform sound velocity in ultrasonic dynamic focusing imaging is limited by emission energy and frame rate, which hinders the accurate and rapid reconstruction of multi-layer structures and clinical applications. In order to meet these challenges, this study proposes a novel method, called the phase shift migration-based plane-wave bone imaging via velocity inversion (PSM-PW-VI), that can accurately and quickly image the multi-layer structure of cortical bone. In the PSM-PW-VI method, two identical linear array probes are arranged in parallel on both sides of the cortical bone for data acquisition. First, the ultrasound velocity distribution in the imaging region is obtained by using ultrasound travel time inversion. Next, two images corresponding to the upper probe and lower probe are acquired in parallel in the frequency domain by employing a phase shift migration-based coherent plane-wave compounding method. Finally, the two images are merged to generate a complete ultrasound image of the cortical bone. Wave propagation in cortical bone is simulated by using the open source toolbox <i>k</i>-wave in MATLAB. <i>Ex-vivo</i> experiments are conducted on 2.5-mm-thick sawbones phantom and 2.45-mm-thick bovine bone plates to evaluate the feasibility of the proposed method, by using the Verasonics platform. Simulation, phantom (Sawbones), and <i>ex-vivo</i> experiments validate the effectiveness of the method. Notably, the average error of the thickness is less than 0.2 mm, and the relative error is less than 7% for both three-layer and five-layer cortical bone. The influence of the number of plane wave compounding angles on imaging quality is investigated, revealing that only 15 angles are sufficient to produce high-quality images. The influence of the velocity model on imaging accuracy is also examined since accurate sound velocity estimation is crucial for obtaining high-quality images of cortical bone. Finally, the performances of PSM-PW-VI and PSM-SA in imaging depth and efficiency are compared. The results demonstrate that the proposed PSM-PW-VI method offers significant improvements in temporal resolution, data storage and processing quantity, emission energy, and imaging depth. The experimental findings validate the effectiveness of the proposed method as an accurate and efficient ultrasound imaging tool for cortical bone, and its substantial role in promoting ultrasound bone imaging technology and clinical applications.

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.

AVO Inversion Based on Closed-Loop Multitask Conditional Wasserstein Generative Adversarial Network

Neural networks are commonly used for post-stack and pre-stack seismic inversion. With sufficient labelled data, the neural network-based seismic inversion results are more accurate than that use traditional seismic inversion methods. However, in the case of insufficient labeled data, the accuracy of neural networks-based seismic inversion results decreases and is even lower than those based on traditional inversion methods. In addition, the seismic inversion results based on neural networks generally suffer from lateral discontinuity. It further reduces the accuracy of the inversion results. To tackle these problems, we propose a pre-stack seismic amplitude variation with offset (AVO) inversion method based on Closed-Loop Multi-task conditional Wasserstein Generative Adversarial Network (CMcWGAN), which is a GAN-based AVO inversion method. CMcWGAN enables simultaneous and accurate inversion of P-wave velocity ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_p</i> ), S-wave velocity ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_s</i> ), and density ( ρ ). Moreover, it uses the low-frequency information of elastic parameters as a conditional input to alleviate the problem of lateral discontinuity in inversion results. Experimental results of simulated data show that the inversion results based on CMcWGAN have higher accuracy than those based on traditional AVO inversion methods. In addition, when the seismic angle gather is noisy, CMcWGAN has better robustness than the traditional methods. CMcWGAN can also obtain reasonable AVO inversion results in field seismic angle gather data.inversion results. Experimental results of simulated data show that the inversion results based on CMcWGAN have higher accuracy than those based on traditional AVO inversion method. In addition, when the seismic angle gather is noisy, CMcWGAN has better robustness than traditional method. CMcWGAN can also get reasonable AVO inversion results in field seismic angle gather data.

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

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

Inversion of Deep-Water Velocity Using the Munk Formula and the Seabed Reflection Traveltime: An Inversion Scheme That Takes the Complex Seabed Topography Into Account

The sound speed of seawater is not constant. It varies with season, day time, depth and range. This variation was not considered in marine seismic data processing and imaging before deep-water seismic acquisition became a routine activity. As a result, non-ignorable errors may be contained in the final migrated-image of deep-water seismic data. To eliminate such errors, we propose here a scheme for inverting the seawater velocity under the condition that the seabed has a complex topography. In this scheme, the seabed topography is represented by step grids constrained by measured water depth data, and the sound profile is assumed to be the one satisfying the Munk formula. Thus, only the parameters appearing in the Munk formula need to be inverted. This is quite different from the conventional inversion schemes that take the velocity as the inversion target. We use the conjugate gradient method to minimize the objective function given as the sum of squares of traveltime differences. Tests on synthetic common-shot data set show that our scheme presented here works as expected. Applications to real marine data set further validates the reasonability and feasibility of our scheme under complex seabed topography. To investigate the reliability of our inversion scheme, we migrate the real 2D data set using the Kirchhoff migration with the inverted Munk profile as the velocity model. The corresponding results show that the seawater velocity obtained by our inversion scheme improves the imaging quality of underwater structures.

Forward prediction for tunnel geology and classification of surrounding rock based on seismic wave velocity layered tomography

Excavation under complex geological conditions requires effective and accurate geological forward-prospecting to detect the unfavorable geological structure and estimate the classification of surrounding rock in front of the tunnel face. In this work, a forward-prediction method for tunnel geology and classification of surrounding rock is developed based on seismic wave velocity layered tomography. In particular, for the problem of strong multi-solution of wave velocity inversion caused by few ray paths in the narrow space of the tunnel, a layered inversion based on regularization is proposed. By reducing the inversion area of each iteration step and applying straight-line interface assumption, the convergence and accuracy of wave velocity inversion are effectively improved. Furthermore, a surrounding rock classification network based on autoencoder is constructed. The mapping relationship between wave velocity and classification of surrounding rock is established with density, Poisson's ratio and elastic modulus as links. Two numerical examples with geological conditions similar to that in the field tunnel and a field case study in an urban subway tunnel verify the potential of the proposed method for practical application.

A High-Resolution Velocity Inversion Method Based on Attention Convolutional Neural Network

A High-Resolution Velocity Inversion Method Based on Attention Convolutional Neural Network

Seismic vulnerability of the Campus II Universitas Syiah Kuala

Universitas Syiah Kuala (USK) is planning to develop a new campus (Campus II). The location of the study area is relatively close to the Seulimum fault which historically has generated a devastating earthquake. Therefore, a seismic vulnerability study is important to conduct in use for reference before the development. This study aims to determine the seismic vulnerability index in the Campus II development area of USK in Mesjid Raya District, Aceh Besar Regency using a Horizontal to Vertical Spectral Ratio (HVSR) method. Potential earthquake damage can be studied by analyzing the dominant frequency (f0), amplification (A0), seismic vulnerability (Kg), and Vs 30. The microtremor acquisition was carried out by using a Broadband seismometer with 100 sps at 37 measurement points with a distance of 500 meters for each station. We use the Geopsy software to analyze seismic data by applying Fourier transform and STA/LTA concept to Rayleigh waves and obtain the dominant frequency value. The 1D velocity inversion was derived by Dinver using the Neighborhood Algorithm (NA). The results of the analysis show that the dominant frequency value is higher around the STA17 point, while the amplification value and seismic vulnerability are highest in the north (at STA2 and STA3) and in the south (at STA28, STA29, and STA30). The higher amplification region is associated soft soil identified as alluvium (Qh). On the other hand, the 1D inversion results at a depth of 30 shows Vs values of 661.142 - 1106.31 m/s (at STA3 and STA17) which indicates the presence of bedrock, making it suitable for future development although more detail investigation is required.

3D velocity and anisotropy of the southeastern Tibetan plateau extracted by joint inversion of wave gradiometry, ambient noise, and receiver function

With the deformation pattern of the southeastern Tibetan Plateau remaining controversial, its anisotropic structure and the three-dimensional (3D) velocity model are powerful tools for understanding this issue. The surface wave dispersion curve (DC) and receiver function (RF) are sensitive to absolute velocity and elasticity contrasts, respectively. In this work, we propose a new method for the joint inversion of 3D shear velocity (Vs) and anisotropy using the datasets of RFs and DCs, which are obtained by combining the wave gradiometry method and ambient noise tomography. Because DCs and RFs are divided into back-azimuth bins, and both datasets in each bin are jointly inverted to obtain velocity in the corresponding direction, the method is called the azimuth-dependent joint inversion method. By applying this new method to the Temporary West Sichuan Array, we obtained a high-resolution 3D Vs model and anisotropic structure of the southeastern Tibetan Plateau. In our model, the vertically consistent fast propagation direction in the Songpan-Ganzi block (SGB) and the north Chuandian block (NCDB) suggest that the deformation in this region is largely coupled vertically. Our model provides anisotropic evidence of the Yangtze crystalline basement extending to the SGB. The low-velocity zone (LVZ) was much thicker and more prominent in the NCDB than in the SGD. Anisotropy in the middle lower crust of the NCDB does not show any sign of an LVZ flowing from the SGB or the Qiangtang block to the NCDB. These observations are contrary to those of the crust flow model, suggesting that the LVZ was generated locally. The mushroom-shaped high-velocity structure beneath the southern Chuandian block and boundary fault zone may be a relic of the Emeishan paleo-mantle plume.