Velocity Model Research Articles

Relocation of the 2018–2022 seismic sequences at the Central Gulf of Corinth: New evidence for north-dipping, low angle faulting

The Gulf of Corinth, Central Greece, is a highly active half-graben, characterized by seismicity which is more intense in its western part, while destructive earthquakes have also occurred towards its eastern end. We herein present an analysis of the seismicity in the Central Gulf of Corinth, for the period from June 2018 to December 2022. We applied the EQTransformer machine-learning model to enhance the initially available data, adding missing P- and S-wave arrival-times or improving existing ones. The events were initially located using a new local velocity model and then relocated using the double-difference method, including waveform cross-correlation data from local stations. The hypocenters, generally distributed at depths between 5 and 15 km, along with the focal mechanisms of significant earthquakes (1965 through 2022) and the geometry of mapped faults on the surface were co-examined to better understand their possible connection. It is shown that major outcropping north-dipping structures, such as the East Helike fault and its eastward offshore extension, match only with the southern bounds of seismicity. The Mw = 5.9, 1970 Antikira and Mw = 5.7, 1992 Galaxidi earthquakes cannot be associated with known mapped faults on the surface and likely occurred on low-angle, north-dipping planes. The variability in slip behavior of the low-angle detachment in the Gulf of Corinth, ranging from seismic slip to aseismic creep, probably accounts for the most part of the N-S extensional deformation. The spatial pattern of the 2018–2022 microseismicity delineates the edges of the rupture planes of major events that occurred during the instrumental era, including the Mw = 6.3, 1995 Aigion earthquake. The lack of aftershocks for significant earthquakes, including the Mw = 5.0, 8 October 2022 event, south of Desfina, is interpreted in terms of different pore pressure conditions, variations in fault-rock strength, and the preferred accumulation of high stress inside the upper crust.

Modelling the redshift-space cluster–galaxy correlation function on Mpc scales with emulation of the pairwise velocity distribution

ABSTRACT We present a method for modelling the cluster–galaxy correlation function in redshift space, down to $\sim$ Mpc scales. The method builds upon the so-called galaxy infall kinematics (GIK) model, a parametric model for the pairwise velocities of galaxies with respect to nearby galaxy clusters. We fit the parameters of the GIK model to a suite of simulations run with different cosmologies, and use Gaussian processes to emulate how the GIK parameters depend upon cosmology. This emulator can then be combined with knowledge of the real-space clustering of clusters and galaxies, to predict the cluster–galaxy correlation function in redshift space, $\xi _\mathrm{cg}^s$. Fitting this model to an observed $\xi _\mathrm{cg}^s$ enables the extraction of cosmological parameter constraints, and we present forecasts for a survey like that currently being done by the Dark Energy Spectroscopic Instrument (DESI). We also perform tests of the robustness of our constraints from fitting to mock data extracted from N-body simulations, finding that fitting to scales $\lesssim 3 \, h^{-1}\, \mathrm{Mpc}$ leads to a biased inference on cosmology, due to model mis-specification on these scales. Finally, we discuss what steps will need to be taken in order to apply our method to real data.

Implications of depth conversion on fault geometries and fault-risk assessment in the Smeaheia CO 2 storage site, northern North Sea

Assessments of fault geometries and fault-risk parameters are crucial when evaluating the integrity of a structurally controlled CO 2 storage site. To perform these assessments, seismic data, recorded in time, must be converted to depth. The velocity models used for this time-depth conversion influence the final depth image and, consequently, the geometry of the interpreted faults. Against this background, we create four velocity models for depth conversion, assess the impact on fault throw, dip, and thickness of the primary seal, and, subsequently, fault-risk assessment of the Vette Fault Zone in the Smeaheia CO 2 storage site. We find that fault throw and thickness of the primary seal are more influenced by depth conversion compared to fault dip. In contrast, the overall assessment of membrane seal presence and geomechanical integrity shows less sensitivity to the depth conversion process. Consequently, we suggest that a relatively robust fault-risk assessment can be made with a variety of velocity model designs and data input. Nevertheless, we find a mean difference of 2% shale gouge ratio, 4% slip tendency, and 9% dilation tendency for the Vette Fault Zone emphasizing the importance of accounting for the influence of depth conversion to optimize structural assessments in potential CO 2 storage sites.

High-resolution 3D ambient noise tomography around the Meishan-Chiayi active fault system of western Taiwan

The Meishan-Chiayi area of western Taiwan has a large probability of producing a major earthquake in the near future. Historically, one of the largest and most damaging of Taiwan’s earthquakes occurred there. It is, therefore, important to have a well-constrained upper crustal 3-D shear-wave velocity model that can be used to accurately determine ground motion predictions and fault geometry models used in seismic hazard and risk modelling. In this study, we carried out an ambient noise tomography experiment using 100 seismometers deployed with a ∼2 km spacing on a 20 by 20 km grid. The reliable periods of phase velocity from Rayleigh waves are 0.6 to 6.8 s, providing a well-resolved Vs structure from the surface to a depth of around 4 km. The velocity model displays a prominent, roughly northeast-striking change in Vs that follows the projected surface trace of the blind Chiayi thrust. The uplift of relatively higher Vs rocks in its hanging wall, together with a negative to positive change in dVs suggests that it dips gently eastward across the study area. A northward thickening of the lower Vs crust, together with a high negative dVs in the north of the study area is related to an increased thickness of foreland basin rocks across the Meishan fault. The Vs and dVs models provide reasonable evidence that the Meishan fault can be traced at a high angle from its surface rupture to the base of the model at 4 km depth. It cuts the Chiayi thrust.

Imaging method of surface defect using leaky Rayleigh wave with ultrasonic phased array

ABSTRACT Leaky Rayleigh wave testing has the advantages of non-contact, high sensitivity, and good beam directivity. However, current leaky Rayleigh wave testing mostly uses monolithic transducers and is seriously affected by acoustic wave attenuation and diffusion, resulting in poor image quality, limited detection range and low imaging efficiency. In this paper, a novel leaky Rayleigh wave testing method using an ultrasonic phased array is developed, and the total focusing method (TFM) is used to achieve high-quality imaging of defects. To improve imaging efficiency, the root-mean-square velocity (RMSV) model is utilised to rapidly acquire an approximate analytical solution for the time of flight of the acoustic wave. To obtain more accurate defect images further, the amplitude of the total focusing imaging signal is calibrated. The experimental results demonstrate that the proposed method achieves a 48% improvement in the imaging accuracy of a 1 mm hole, compared to the traditional Synthetic Aperture Focusing Technique (SAFT) imaging method. The proposed method achieves an 92% reduction in imaging time compared to the conventional TFM. The proposed method provides a new non-contact approach for high-accuracy and high-efficiency defect imaging using leaky Rayleigh wave.

High-Resolution Velocity and Seismic Anisotropy Structures of the 2021 Ms 6.0 Luxian Earthquake Zone in Sichuan Basin

Abstract The Ms 6.0 Luxian earthquake, which occurred in a shallow sedimentary cover on the southern margin of the Sichuan basin in China, stands as the most powerful earthquake ever recorded in this region. This study aims to integrate multiple seismological methods to comprehensively investigate the seismic nucleation environment. Using data from 91 densely distributed seismic stations within the Luxian earthquake zone, we constructed models for velocity, anisotropy, and interfaces. Our results suggest the presence of a detachment interface at depths of ∼4.0–5.0 km, which appears to function as a stress-decoupling layer. This is evidenced by a noticeable shift in the intensity and orientation of azimuthal anisotropy, transitioning from weak to strong, and altering its alignment from a northwest–southeast to a northeast–southwest orientation. The mainshock and aftershocks are predominantly clustered along the boundaries characterized by high- and low-velocity zones, as well as the boundary of VP/VS ratios beneath the detached layer. This suggests the likely existence of a pre-existing northwest–southeast-striking fault with a southwest dip, extending from the underlying basement to the overlying sedimentary cover. The radial anisotropy analysis reveals a predominance of negative values beneath the Huaying Mountain fault, whereas positive values are prominent in the Yujisi sedimentary syncline. This distinctive pattern implies tectonic movements related to fault activities within the fault zone. Based on our findings and previous research, we speculate that the Ms 6.0 Luxian earthquake may have been influenced by local stress fields and triggered by industrial activities.

Estimation of Water Velocity and Receiver Position Changes in Time-Lapse Seismic Using Machine Learning

Non-repeatability is one of the main obstacles in time-lapse seismic, as it significantly degrades the interpretation of reservoir-related signals. Correcting the data variations caused by non-repeatability (4D noise) is of paramount importance, which usually requires the estimation of the changing parameter. ln this paper, we propose a Machine Learning (ML) workflow for the quantitative estimation of two types of 4D noise: changes in the speed of sound in water and receiver lateral positions. A synthetic database, modeled from a velocity model estimated from the Brazilian pre-salt, and containing many time-lapse seismic surveys was generated for the supervised training of ML models. lnput samples consist of subsets of common-shot seismograms. We studied many combinations of ML regression algorithms and feature extraction techniques, for scenarios where data is contaminated, or not, with Gaussian random noise. The regression algorithms considered were Fully-Connected Neural Network, Extreme Gradient Boosting, and Bayesian Ridge. Four rectangular crops of the common-shot seismogram were tested as input features: full seismogram, first half of time samples, 11 smallest-offset traces, and the time samples focusing on the first arrivals region. The combination with the best trade-off between accuracy and model complexity is the Bayesian Ridge fed with the 11 smallest-offset traces, which estimated position and velocity time-lapse changes with median accuracy of 0.115 m and 0.017 m/s for the case with Gaussian noise. Besides the correction of repeatability-related variations, our results are useful in the 4D Full-Waveform lnversion which needs accurate parameters to produce good seismic images.

Reliability analysis of thermal barrier coatings under CMAS deposition and penetration

To address the issue of thermal barrier coating (TBC) failure and spalling caused by the deposition and infiltration of environmental particulate matter, a thermal-mechanical coupling model for EB-PVD TBC considering CMAS deposition failure was developed. Based on this model, the effects of CMAS deposition thickness and infiltration depth on TBC spalling failure were numerically studied. First, the deposition characteristics of CMAS were obtained using a critical velocity model. Second, the corresponding TBC failure criteria were established. Then, the effects of CMAS deposition thickness and infiltration depth on TBC spalling failure were specifically studied under different CMAS particle sizes and service times. The results show that the CMAS deposition thickness is the greatest at the leading edge and the pressure surface near the trailing edge of the blade. The CMAS infiltration depth is influenced by the temperature field, with higher temperatures leading to greater infiltration depth. With the simultaneous increase of service time and particle size, the average damage value of the coating surface (D¯) initially increases slightly to 0.2, then rapidly to 0.9, and finally slowly to 0.97, resulting in almost complete failure and spalling of the coating.

A novel car-following model for adaptive cruise control vehicles using enhanced intelligent driver model

ABSTRACT This paper proposes Enhanced Intelligent Driver Model for Adaptive Cruise Control (EIDM-ACC) vehicles, a novel car-following model that dynamically adjusts desired speed and considers acceleration inertia. The EIDM-ACC model is compared with two widely used models for simulating ACC vehicles – the ACC model developed by the PATH Project (PATH-ACC) at the University of California Transportation Institute and Continuous Asymmetric Optimal Velocity Relative Velocity (CAOVRV) model. Three models are calibrated and cross-validated using real vehicle trajectory data from the OpenACC dataset. Results show that the EIDM-ACC outperforms the other two models in small and large fluctuation stages. In addition, EIDM-ACC has better performance in capturing the instability and energy consumption of ACC vehicles, and also has advantages over the other two models in terms of safety.

SiameseFWI: A Deep Learning Network for Enhanced Full Waveform Inversion

AbstractThe performance of full‐wave inversion (FWI) depends highly on how we compare the simulated data to observed ones. The simplified assumptions used to generate the simulated data make such comparison even harder. To address this challenge, we introduce SiameseFWI, a novel approach to FWI that plays a critical role in the comparative analysis of simulated and observed seismic data. Employing a Siamese network, this methodology transforms the data into a shared latent space, enabling a robust and effective comparison of data representations. SiameseFWI leverages two identical Convolutional Neural Networks with shared weights trained in a self‐supervised framework, eliminating the necessity for labeled data. In each FWI iteration, the Siamese network and the velocity model are updated to minimize Euclidean distance loss between the latent representations of the data. Empirical evaluation conducted on the Marmousi2 and Overthrust models affirms the robust inversion performance of SiameseFWI compared to traditional FWI methodologies. Furthermore, its application to field data from Western Australia demonstrates its strength and efficacy in inversion. Notably, SiameseFWI exhibits robust inversion performance even in the presence of noise or when employing a linear initial model.

Insight into the Evolution of the Eastern Margin of the Wyoming Craton from Complex, Laterally Variable Shear Wave Splitting

Abstract Dense seismic arrays such as EarthScope’s Transportable Array (TA) have enabled high-resolution seismic observations that show the structure of cratonic lithosphere is more heterogeneous and complex than previously assumed. In this study, we pair TA data with data from the Bighorn Arch Seismic Experiment and the Crust and lithosphere Investigation of the Easternmost expression of the Laramide Orogeny (CIELO) to provide unprecedented detail on the seismic anisotropic structure of the eastern margin of the Wyoming Craton, where several orogens emerged from nominally strong cratonic lithosphere during the Laramide Orogeny. In this study, we use the splitting of teleseismic shear waves to characterize fabrics associated with deformation in the Earth’s crust and mantle. We constrain distinct anisotropic domains in the study area, and forward modeling shows that each of these domains can be explained by a single layer of anisotropy. Most significantly, we find a fast direction in the southern part of the Powder River Basin, which we refer to as the Thunder Basin Block (TBB), that deviates from absolute plate motion (APM). This change in splitting behavior coincides with changes in other modeled geophysical observations, such as active source P-wave velocity models, potential field modeling, and seismic attenuation analysis, which all show a significant change moving from the Bighorn Mountains to the TBB. We argue that these results correspond to structure predating the Laramide Orogeny, and most likely indicate a Neoarchean boundary preserved within the lithosphere.

Integration of Machine learning and equal differential time method for enhanced hypocenter localization in earthquake early warning systems: application to dense seismic arrays in Taiwan

The Earthquake Early Warning System (EEWS) acts as a vital instrument for reducing seismic risks in regions with high seismic vulnerability. A rapid and accurate hypocenter estimation is pivotal for the EEWS, providing the groundwork for more reliable magnitude and intensity assessments necessary for effective earthquake warnings. This study presents an algorithm that integrates machine-learning-based (near) real-time phase picking with an Equal Differential Time (EDT) rapid hypocenter location algorithm, applying it to a 3D velocity model. The phase-picking model, refined through data augmentation, enhances the precision of phase detection in continuous recordings and simultaneous multiple events while ensuring the swift detection of the P-phase, which is critical for early earthquake warnings. Our rapid earthquake location method calculates theoretical P arrivals from potential hypocenters, which are grid points in a 3D velocity model, to stations that are close to their grid points, with the arrivals being stored by the station. As P arrivals are detected, the differences in arrival times across stations are utilized in EDT for estimating hypocenters. Furthermore, our earthquake location algorithm is adept at localizing multiple seismic events, a capability that can diminish the risk of unreported cases in scenarios where events occur in close temporal and spatial succession in high seismicity regions. We applied the algorithm to real waveform recordings of recent earthquakes in Taiwan that satisfied the early warning criteria. The results suggest that our algorithm consistently yields more reliable hypocenter estimates compared to those from the currently operational EEWS in Taiwan. Moreover, our algorithm succeeded in locating an earthquake that the current EEWS overlooked due to its failure to recognize P arrivals. These results showcase the potential of our algorithm to provide more accurate hypocenter estimates and to locate earthquake events with complex seismic recordings.Graphical

Application of dynamic desired headway based adaptive backstepping sliding mode control design to mixed traffic system

To optimize the mixed car-following behavior of connected autonomous vehicles (CAVs) and human-driven vehicles (HDVs), an adaptive backstepping sliding mode control (ABSMC) strategy for longitudinal velocity and distance control model (namely, MCF-ABSMCM) is put forth. First, the variable desired headway model (VDHM) is designed based on the vehicle-to-vehicle and vehicle-to-infrastructure information. Also, an asymmetric stochastic car-following model (ASCM) is designed to realistically describe the stochastic factor as well as vehicle acceleration and braking behaviors in mixed car-following. Second, an adaptive cruise sliding mode control law with a backstepping approach is devised to precisely track the desired following distance based on the VDHM. After creating the basic diagram of the two-model traffic flow, the intelligent driver model (IDM) is used as a baseline to compare and analyze the traffic capacity. Lastly, the effectiveness of the MCF-ABSMCM approach is confirmed in terms of both the mixed traffic flow density wave evolution and the vehicle control strategy, and the MCF-ABSMCM is validated under various adversary inputs. The simulation results demonstrate that the mixed-flow queue can finish headway tracking and keep a safe distance when using the MCF-ABSMCM described. This control strategy has been validated in VISSIM to significantly improve the efficiency of traffic operations compared to IDM and ASCM.

Stability analysis of a stochastic port-Hamiltonian car-following model

Port-Hamiltonian systems are pertinent representations of many nonlinear physical systems. In this study, we formulate and analyse a general class of stochastic car-following models with a systematic port-Hamiltonian structure. The model class is a generalisation of classical car-following approaches, including the optimal velocity model of Bando et al (1995 Phys. Rev. E 51 1035), the full velocity difference model of Jiang et al (2001 Phys. Rev. E 64 017101), and recent stochastic following models based on the Ornstein–Uhlenbeck process. In contrast to traditional models where the interaction is totally asymmetric (i.e. depending only on the speed and distance to the predecessor), the port-Hamiltonian car-following model also depends on the distance to the follower. We determine the exact stability condition of the finite system with N vehicles and periodic boundaries. The stable system is ergodic with a unique Gaussian invariant measure. Other properties of the model are studied using numerical simulation. It turns out that the Hamiltonian component improves the flow stability and reduces the total energy in the system. Furthermore, it prevents the problematic formation of stop-and-go waves with oscillatory dynamics, even in the presence of stochastic perturbations.

Integrated geological and geophysical workflow for structural modelling: a case study from the contractional foothills zone of the Colombian Eastern Cordillera

In fold-and-thrust belts where there is a high degree of structural complexity, artificial geometrical distortions are often present on seismic reflection profiles. These need to be minimized during modelling. We document a workflow in which depth mapping, velocity model building, well calibration and cross-section balancing are integrated into the seismic interpretation process to generate trustworthy structural models in complex zones. The proposed methodology is exemplified by a case study from the foothills zone of the Colombian Eastern Cordillera. In addition, sequential kinematic restoration of the modelled structure allowed an evaluation to be made of hydrocarbon migration routes during the period between the Oligocene and the middle Miocene. Following the previously mentioned workflow, we document a failed exploratory case study where all elements of the petroleum system are present except the trap. In this context, the documented case is a typical velocity pull-up. From this and published case studies, we conclude that in Andean settings and probably most on land contractional-foothill settings, the use of seismic images only does not provide enough evidence for the presence of traps and additional surface geological signatures must be documented. The proposed workflow therefore appears to be a useful tool for evaluating the exploration risk in structurally complex fold-and-thrust belt settings.

A comparative study of heat absorption and chemical reaction on MHD flow with fractional derivatives

In this article, Caputo and Prabhakar fractional derivatives are used to analyze the influence of heat flux on fractionalized second grade flow. The fluid model is generalized by Fick’s and Fourier’s laws. Moreover, radiation and slip effects are also taken into account additionally. Fractional governing models are solved semi-analytically by using Caputo and Prabhakar fractional derivatives. The method of Laplace method is applied to solve the dimensional model for temperature, velocity, and concentration profiles. The results are contrasted visually. A variety of graphs are used to illustrate the impacts of several parameters, including the heat absorption Q, fractional parameter, magnetic parameter M, and chemical reaction R. It is evident from the figure that the velocity distribution is affected less by chemical and magnetic field, while the fluid velocity is affected more by diffusion-thermodynamics and mass Grashoff number. Furthermore, comparisons among classical and fractional fluid models are made to check the validity of the result. It is noted that the classical approach is less convenient as compared to the fractional approach.

Active and passive seismic data integration for delineating the near-surface velocity models at Ras Samadai area, Red Sea-Egypt

Active and passive seismic data integration for delineating the near-surface velocity models at Ras Samadai area, Red Sea-Egypt

Double-Difference Tomography with a Deep Learning–Based Phase Arrival Weighting Scheme and Its Application to the Anninghe–Xiaojiang Fault Zone

Abstract High-precision seismic phase arrivals are a prerequisite for building reliable velocity models with travel-time tomography. There has recently been a growing use of seismic phase arrival data obtained through deep learning techniques in travel-time tomography research. Nevertheless, a significant challenge that has emerged pertains to the assessment of the quality of these automatic arrivals. In this article, we used PhaseNet, a deep learning method, to automatically detect the arrival times of the P wave and S wave of 3086 seismic events recorded by dense seismic arrays, obtaining 87,553 high-quality arrivals. To evaluate the quality of the arrival times subsequently used for travel-time tomography inversion, we applied a weighting scheme that includes both detection probability value and signal-to-noise ratio. This new weighting scheme can effectively reduce the overall travel-time residual by 7%. The weighted data were then used in the double-difference tomography method to invert for the crustal velocity structure of the Anninghe–Xiaojiang fault zone. The resulting new model exhibits a lateral resolution of up to 0.25° and reveals velocity anomalies that exhibit a strong correlation with major geological features and block boundaries. Notably, the presence of low-VP and low-VS in the middle crust of the Ludian–Qiaojia seismic zone suggests the existence of hot and weak felsic rocks, as well as possible fluid presence beneath the seismogenic layer of this area. This study not only validates the practicality of using deep learning-based phase picking arrivals in travel-time tomography but also proposes a new weighting scheme to refine the tomographic velocity models.

Multi‐Scale Seismic Imaging of the Ridgecrest, CA, Region With Waveform Inversion of Regional and Dense Array Data

AbstractWe develop an inversion procedure for deriving multi‐scale velocity models with waveform inversions of earthquake and ambient noise data at multi‐frequency bands recorded by regional and dense sensor configurations. The method is applied for the area around the 2019 Ridgecrest earthquake rupture zones, utilizing data recorded by regional stations and dense 2D and 1D arrays with station spacings of ∼5 km and ∼100 m, respectively. Starting with regional Vp, Vs models and locations of Ridgecrest aftershocks, the velocity models and event locations are improved iteratively by inversions of waveforms recorded by regional stations and the 2D array, using a minimum spectral element size of ∼600 m. Waveforms from local events recorded by dense 1D arrays across the M7.1 rupture zone with frequencies of up to 10 Hz are used to resolve small‐scale features of the rupture zone and shallow crust with a local spectral element size of 80 m. The refined models provide self‐consistent descriptions of the rupture zone and the shallow crust embedded in the regional structures. The results reveal pronounced low Vs and high Vp/Vs in the M6.4 and M7.1 rupture zones coinciding with concentrations of seismicity, and also around the Garlock fault and in several local basins. We also observe clear velocity contrasts across the Garlock fault with polarity reversals along strike and with depth. The obtained multi‐scale velocity models can be used to improve derivations of earthquake source properties, simulations of dynamic ruptures and ground motions, and the understanding of fault and tectonic processes in the region.

Weakly anelliptical traveltime analysis: Ambiguity between subsurface and elasticity

The building of a subsurface and (anisotropic) velocity model from a single gather of reflection traveltime (kinematic) data is inherently ambiguous because the processing of such data can only determine a horizontal slowness component, not a vertical one. Based thereon, I derive a simple algorithm that generates an infinite series of combinations of the subsurface-velocity models, all of which will show nearly the same seismic kinematic response, as further demonstrated by simulating wave propagation through a model with different interface dips. This algorithm assumes, first, that all interface dips remain constant over the distance considered and, second, that an approximation of the elasticity model — that is, the linearization of a phase velocity — valid for weak anisotropy can be used. Furthermore, when applied to the classic and analytically solvable case of traveltime analysis for a stack of flat layers with weak transverse isotropy, the algorithm theoretically explains the combination of anisotropy parameters that govern the nonhyperbolic term of a traveltime series: the established [Formula: see text] and its new counterpart [Formula: see text] for a [Formula: see text] and [Formula: see text] wave, respectively.