Shear Wave Velocity Research Articles

Performance of Single Degree of Freedom (SDOF) Systems Subjected to The Near-Fault Earthquakes

This paper aims to assess the structures' seismic response with a system of Single-degree-of-freedom exposed to near-fault earthquakes, taking into account the effect of pulse period, shear wave velocity (VS30), and fault mechanism. Strong ground motion data were taken for different events in different places around the world and the prism software program is used for the analysis of seismic response for structures recognized as single-degree-of-freedom systems. Results show that the ground motion with a higher value of (VS30) provides an acceleration response higher than that of the lower value of (VS30). However, the findings revealed that the peak displacement requirements are observed in a nearby of the pulse period limits. In addition, it is noticed that there is an obvious increase in spectrum demand with longer pulse periods. Finally, results show at short vibration periods (T? 0.6sec) for both types (strike slip and dip slip) comparable results while an increment is observed in the results of the strike-slip for vibration periods more than (0.6sec) of the acceleration response spectrum. Nonetheless, for the velocity response spectrum dip-slip continuous in flocculating with a significant increase.

Influence of biocementation treatment extent on dynamic system performance: A centrifuge liquefaction study

Previous centrifuge experiments have shown Microbially Induced Calcite Precipitation (MICP) is a viable ground improvement technique to mitigate liquefaction. However, in these past experiments the entire sample in the model container was MICP treated. This study is an investigation of the effect of treatment extent on the system seismic performance by varying the depth of improvement, while maintaining the same cross-sectional area. Specifically, MICP treated zones (10 cm × 10 cm in model scale in plan view) were varied in depth to 100%, 75%, and 37% of the liquefiable sand layer depth. As a comparison, two additional models were constructed, an untreated and fully treated model. The treated models were initially prepared to a relative density of less than 40% and MICP treatment applied until a change of shear wave velocity (Vs) of 300 m/s was measured. All models were instrumented with accelerometers and pore pressure transducers to capture the dynamic responses during the same sequence of eight shaking events. Cone penetration resistance was measured to verify the uniformity of the untreated zones in the models and track the evolution of penetration resistances at three different times during the shaking sequence. Vs was measured after each shaking event to assess the cementation integrity of the MICP treated zones. Linear potentiometer and pre- and post-surface measurements were obtained to track seismic-induced settlements. Pore pressure generation and settlements were reduced in the MICP treated zones compared to untreated zones, showing the increased liquefaction resistance and system performance of MICP. Amplification of the strong ground motions in the models with treatment through the full liquefiable layer lead to significant degradation of the MICP. While a base isolation effect was observed in the models not treated to the full depth of the liquefiable layer, reducing seismic demands to the MICP treated zoned, and leading to minimal changes in cementation integrity. These findings suggest that there may be a benefit to not improving the entire liquefiable depth for maintaining cementation integrity of MICPtreated soils.

Detection of the low-velocity layer using a convolutional neural network on passive surface-wave data: An application in Hangzhou, China

Passive surface-wave methods using dense seismic arrays have gained growing attention in near-surface high-resolution imaging in urban environments. Deep learning (DL) in the extraction of dispersion curves and inversion can release a tremendous workload brought by dense seismic arrays. We presented a case study of imaging shear-wave velocity (Vs) structure and detecting low-velocity layer (LVL) in the Hangzhou urban area (eastern China). We used traffic-induced passive surface-wave data recorded by dense linear arrays. We extracted phase-velocity dispersion curves from noise recordings using seismic interferometry and multichannel analysis of surface waves. We adopted a convolutional neural network to estimate near-surface Vs models by inverting Rayleigh-wave fundamental-mode phase velocities. To improve the accuracy of the inversion, we utilized the sensitivities to weight the loss function. The average root mean square error from the weighted inversion is 46% lower than that from the unweighted DL inversion. The estimated pseudo-2D Vs profiles correspond to the velocities obtained from downhole seismic measurements. Compared with an investigation on the same survey area, our inversion results are more consistent with the Vs provided by downhole seismic measurements within 50–60 m where the LVL exists. The trained neural network successfully identified that the LVL is located at 50–60 m deep. To check the applicability of the trained neural network, we applied it to a nearby passive surface-wave survey and the inversion results agree with the existing investigation results. The two applications demonstrate the accuracy and efficiency of delineating near-surface Vs structures with the LVL from traffic-induced noise using the DL technique. The DL inversion has great potential for monitoring subsurface medium changes in urban areas.

Acoustic properties of hydrate-bearing sediments in permafrost from hydrate formation to shear processes

Under depressurization, the hydrate reservoirs undergo complex mechanical behaviors such as consolidation or shear. To deeply understand the evolution in the mechanical properties of soil, triaxial equipment with an ultrasonic system was used to detect the shear wave velocity in hydrate-bearing sediments. The effects of hydrate saturation, water, and stress state on shear-wave velocity are studied. The experimental results show that: The hydrate saturation significantly affects the wave velocity in the hydrate formation stage, while the water content has little influence on it. During the consolidation period, the notable increase in shear wave velocity indicates a decrease in soil void ratio, which means the soil has significant settlement deformation. With continuous shearing, the soil elastic modulus is damaged, and finally, the maximum damage can reach 35%. The conclusions can deepen understanding of the physical and mechanical properties of hydrate-bearing sediments in permafrost, and provide a reference for predicting the stratum stability during hydrate exploitation.

Small-strain shear modulus and liquefaction resistance of calcareous sand with non-plastic fines

Incorporation of non-plastic fines can dramatically affect the liquefaction resistance and stiffness of sands. In this study, the aim is to evaluate the influence of non-plastic fines on the liquefaction resistance and small-strain shear modulus of calcareous sand under cyclic loading. Forty-seven sets of undrained cyclic triaxial tests and companion bender element tests are conducted on reconstituted specimens. The cyclic behaviour of clean sand and silty sand with varying fines content is examined with respect to the global void ratio, relative density and granular skeleton void ratio. The findings demonstrate that the microscopic contacts between coarse and fine grains have a significant impact on the macroscopic behaviour of sand–fines mixtures. The experimental findings are evaluated using the equivalent granular skeleton void ratio, which has been recognised as a suitable parameter to describe the overall effect of fines. The findings on calcareous sand with fines are supplemented and compared with published data in accordance with the semiempirical simplified approach for liquefaction triggering based on shear wave velocity.

A Seismic Site Factor Adjustment Framework for Sloping Grounds and Its Application to Charleston, South Carolina

ABSTRACT Ground motions and seismic site factors for the design and analysis of infrastructure systems are typically computed based on one-dimensional (1-D) site response analysis, assuming the ground surface is horizontal and the earthquake waves propagate vertically near the ground surface. The 1-D results may not be applicable for sloping ground surfaces and layer interfaces, and therefore, the 1-D results must be adjusted for sloping grounds. In this study, a general framework for developing seismic site factors for sloping grounds and its application for Charleston, SC is presented. The two-dimensional (2-D) site response analysis results of sloping ground were compared with the 1-D horizontal ground results to develop slope adjustment factors. A total of 385 2-D nonlinear finite element simulations of sloping grounds were performed considering seven ground inclinations, 11 shear wave velocity profiles, and five ground motions representative of the area. The results show that the surface responses computed from sloping ground analyses significantly differ from those of horizontal ground analyses. By comparing the 1-D and 2-D results, a set of slope adjustment factors was developed and expressed as an easy-to-use chart. The slope adjustment factor at a project site can be obtained from the proposed chart using site-specific VS30 and ground inclination at a selected period.

Shear wave velocity prediction for fractured limestone reservoirs based on artificial neural network

AbstractShear wave velocity is an essential parameter in reservoir characterization and evaluation, fluid identification and prestack inversion. However, conventional data‐driven or model‐driven shear wave velocity prediction methods exhibit several limitations, such as lack of training data sets, poor model generalization and weak model robustness. In this study, a model‐ and data‐driven approach is presented to facilitate the solution of these problems. We develop a theoretical rock physics model for fractured limestone reservoirs and then use the model to generate synthetic data that incorporates geological and geophysical knowledge. The synthetic data with random noise is utilized as the training data set for the artificial neural network, and a well‐trained shear wave velocity prediction model, random noise shear wave velocity prediction neural network, is established by parameter tuning, which fits the synthetic data with noise well. The neural network is applied directly to the real field area. Compared with conventional shear wave prediction methods, such as empirical formulas and the improved Xu–White model, the prediction results show that the random noise shear wave velocity prediction neural network has better prediction performance and generalization. Furthermore, the prediction results demonstrate the efficacy of the proposed approach, and the approach has the potential to perform shear wave velocity prediction in real areas where training data sets are unavailable.

Maximizing hydropower station safety against earthquake through extreme learning machine-enabled shear waves velocity prediction

Hydropower stations are important infrastructures for generating clean energy. However, they are vulnerable to natural disasters such as earthquakes, which can cause severe damage and even lead to catastrophic failures. Therefore, it is essential to develop effective strategies for maximizing hydropower station safety against earthquakes. To evaluate the potential shear rate of surrounding rock layers, the shear wave velocity (Vs) parameter can be used as a useful tool. This parameter helps to determine the velocity at which shear waves travel through the rock layers, which can indicate their stability and susceptibility to earthquakes. This study will investigate the significance of the Vs parameter in evaluating the potential shear rate of rock layers surrounding hydropower stations and how it can be used to ensure their safety and efficiency in earthquake-prone regions. Furthermore, a novel approach is proposed in this research, which involves using extreme learning machine (ELM) technology to predict Vs and enhance the seismic safety of hydropower stations. The ELM model predicts the Vs of the soil layers around the hydropower station, a crucial factor in determining the structure’s seismic response. The predicted Vs is then used to assess seismic hazard and design appropriate safety measures. The ML-ELM model outperformed both the ELM and empirical models, with an RMSE of 0.0432 μs/ft and R2 of 0.9954, as well as fewer outlier data predictions. This approach shows promise for predicting Vs in similar environments, and future research could explore its effectiveness in other datasets and practical applications.

Three-dimensional path-effect model to simulate ground motion attenuation on the Korean Peninsula

In low-to-moderately active seismic regions such as the Korean Peninsula, it is difficult to analyze seismic hazards because only a limited number of strong ground motions have been recorded. To address this shortcoming in the data, ground motion simulation can be used to generate the strong ground motions associated with potentially large earthquakes, taking into account the attenuation that occurs as the distance from the source increases. To simulate this attenuation, the path effect, which is a component of the Fourier amplitude spectrum ( FAS), needs to be defined, along with the source and site effects. The path effect represents the propagation of seismic rays through a tectonic medium. In this study, a three-dimensional (3D) path effect model is developed for the Korean Peninsula, consisting of a 3D geometrical spreading factor, quality factor, and shear-wave velocity. The accuracy of the proposed model is verified by comparing the path-effect values obtained from the FAS of individual ground motions recorded on the Korean Peninsula and those calculated from the model. Examples that demonstrate the procedure to calculate the 3D path effect for the Korean Peninsula are also provided.

Prediction of local site influence on seismic vulnerability using machine learning: A study of the 6 February 2023 Türkiye earthquakes

This study uses machine learning to analyze local seismic features' influence on damage from the 6 February 2023 Türkiye Earthquakes.The input features include Vs30 (the average shear wave velocity to a depth of 30 m), f0 (the predominant frequency of the site), A0 (HVSR ratio for the site), and EBd (engineering bedrock depth), along with the target feature of damage status for 44 locations. Machine learning involves Random Forest (RF), K-nearest Neighbor (KNN), Logistic Regression (LR), Decision Trees (DT), Support Vector Machines (SVM), Stochastic Gradient Descent (SGD), and Multilayer Perceptron (MP) algorithms. Also, five-fold cross-validation is employed to acquire suitable hyperparameters, enhancing its efficacy in modeling small sample sets. RF emerged as the most effective in whole performance metrics, presenting recall scores for damage and no damage conditions respectively by a 94% and 92% ratio and achieving a damage status prediction accuracy of 93%. All remaining algorithms also exhibited remarkable performance, reaching a minimum accuracy of 89% by DT, and recall score for no damage condition with 80% by MP and damage condition with 88% by SVM and SGD. The outcomes definitively designate EBd as the most crucial parameter, attributing 52% importance to its role in building damage occurrence within the study area. In contrast, significance values were determined as 24%, 18%, and 6% for f0, Vs30 and A0 respectively. These findings underscore the importance of demonstrating that initial damage estimation in high seismic hazard zones can be effectively carried out using machine learning approaches through seismic-based local site parameters.

Three-dimensional shear wave velocity prediction by integrating post-stack seismic attributes and well logs: application on Asmari formation in Iran

Understanding the distribution of shear wave velocity (VS) in hydrocarbon reservoirs is a crucial concern in reservoir geophysics. This geophysical parameter is utilized for reservoir characterization, calculating elastic properties, assessing fractures, and evaluating reservoir quality. Unfortunately, not all wells have available VS data due to the expensive nature of its measurements. Hence, it is crucial to calculate this parameter using other relevant features. Therefore, over the past few decades, numerous techniques have been introduced to calculate the VS data using petrophysical logs in wells with limited information. Unfortunately, the majority of these methods have a drawback they only offer insight into the location of the wells and do not provide any details regarding the distribution of VS in the space between the wells. In this article, we employed three-dimensional post-stack seismic attributes and well-logging data integration to predict the distribution of VS in the Asmari formation in an Iranian oil field. To accomplish this objective, the model-based seismic inversion algorithm was utilized to convert the seismic section into the acoustic impedance (AI) section. Then, AI and seismic data were utilized in the cross-validation method to determine the relevant attributes for predicting the spatial distribution of VS throughout the entire reservoir area, using an artificial neural network. The proposed method was shown to provide 94% correlation and 109 m/s error between the actual and estimated VS. Also, the calculated VS section has a high correlation with the actual logs at the location of the wells.

Application of the BP neural network algorithm to the ultimate bearing capacity test of building structures

The ultimate bearing capacity test effect directly influences the safety performance of the design of building structures. To enhance the safety of building structures, applying the BP neural network algorithm to their ultimate bearing capacity test is studied to improve the test effect. The shear wave velocity of the building structure during stress is collected using the static probing technique. The input samples of the BP neural network are the building structure’s shear wave velocity and construction parameters. They are processed by dimensionality reduction through principal component analysis. The firework algorithm is used to optimize the weight of the BP neural network. An early termination training method is designed, and the optimal weight is combined to train the BP neural network. After training, the samples are input after dimensionality reduction, and the building structure’s ultimate bearing capacity test results are output. Experimental results show that this method can effectively collect the shear wave velocity of building structures and complete the dimensionality reduction of samples. Under different coaxial stresses, this method can effectively measure the ultimate bearing capacity, about 3800 kN. After parameter optimization, the test value of this method is very close to the target value; that is, the ultimate bearing capacity test precision is high.

Abnormal apparent supershear rupture with discontinuous jumping propagation during the 2023 Türkiye M7.8 earthquake

Earthquake ruptures along a single fault or along a connected system of faults are generally assumed to progress continuously. However, our analysis of the 2023 M7.8 Türkiye earthquake, using finite-fault joint source inversion, uncovered the occurrence of discontinuous rupture jumps. The main fault area adjacent to the splay fault where the earthquake started, and the deeper portion of the northeastern main fault segment exhibited triggered slip before the main rupture front arrived. Through seismic centroid analysis and finite-fault inversion, we estimated apparent rupture speeds within these slip patches reach approximately 6.0 km s-1, exceeding local S-wave velocity. The dynamic triggering mechanism induced the jumping rupture in these areas, resulting in an apparent rupture velocity surpassing the local shear wave velocity. These findings demonstrate the importance of dynamic triggering in adjacent fault systems during large earthquakes, influencing the extent and complexity of rupture propagation.

Unraveling Precambrian cratonic roots beneath South America: A contribution from surface wave tomography

We examine some aspects of the tectonic evolution of Precambrian cratonic roots beneath South America based on lithospheric distribution of shear-wave velocities. We derive our model by inverting 26,984 fundamental mode Rayleigh wave group velocity dispersion curves, at periods of 9–180 s. We first regionalize our measurements and then invert the result for a 3D S-wave velocity model extending to 200 km depth. Fast velocities beneath the Amazonian and São Francisco cratons, and beneath buried cratonic units in the Parnaíba and Paraná basins, are long wavelength features consistent with previous tomography studies. For the Amazonian craton at 150 km depth, we find an increase of velocities with province age, except for the Maroni-Itacaiúnas province, where we hypothesize that K'Mudku intraplate tectono-thermal events at the middle-late Mesoproterozoic and emplacement of a large igneous province following the breakup of Pangea could have altered at least partially its lithosphere. Our results are consistent with a São Francisco paleocontinent whose borders extend beyond the surface limits of the present São Francisco craton into the neighboring Araçuaí and Brasília belts. Based on slow shear-wave velocities in the upper mantle beneath the Borborema province, consistent with lithospheric thinning, we argue that a possible cratonic root of the São Francisco Paleocontinent beneath this province has likely been eroded away. This analysis is further corroborated by tectonic events that led to the alteration of the Borborema mantle, including hydration in the Paleoproterozoic, rifting in the early-middle Tonian, reworking during Neoproterozoic Brasiliano events, and lithospheric stretching during the breakup of Pangea. Finally, we also image a fast shear-wave velocity structure in the region of the Río de la Plata craton, consistent with magnetotelluric studies.

Lithological control of the active Mejillones fault from Triassic to Quaternary, northern Chile

Understanding the lithological control of crustal faults near cities is important for assessing the seismic hazard and characterising complex fault systems. We studied the Mejillones Fault (MF), located near one of the strategic ports in northern Chile. The MF is an active geological fault that extends 40 km in the N-S direction and is part of an extensive complex system of structures known as the Atacama Fault System (AFS). We built an S-wave tomography model using seismic ambient noise data that was recorded continuously in a dense network of stations from 2013 to 2015. The tomographic maps exhibited velocity anomalies that correlate with surface geology. We found lower velocities in the western part of the MF than in the eastern zone, with a shear wave velocity (Vs) of about 2.4 km/s, associated to fractured metamorphic rocks (Triassic) and marine sediments (Neogene-Quaternary). Higher velocities (Vs > 3.0 km/s) in the eastern part of the fault are related to igneous rocks from the Upper Jurassic. The difference in velocities between the western and eastern zones of the MF remained persistent in depth. The potential depth of the MF could be >5 km and delimits the boundary between 2 different geological zones. Intermediate Vs (∼2.8 km/s) at −23.25°, observed up to 4 km depth, can be associated with andesitic rocks deposited in an intra-arc basin (Upper Triassic) and accommodated by the MF over the last ∼200 Myr, evidencing the activity of this fault from Triassic to Quaternary.

A Critical Review of Cone Penetration Test-Based Correlations for Estimating Small-Strain Shear Modulus in North Sea Soils

The geotechnical characterisation of offshore wind farm sites requires measurement or estimation of the small-strain shear stiffness Gmax of the subsoil. This parameter can be derived from shear wave velocity Vs measurements if the bulk density of the soil is known. Since direct measurements of Vs are generally not available at all foundation locations in a wind farm, correlations with cone penetration test (CPT) results are often used to determine location-specific stiffness parameters for foundation design. Existing correlations have mostly been calibrated to onshore datasets which may not contain the same soil types and stress conditions found in the North Sea. The distinct geological history of the North Sea necessitates a critical review of these existing CPT-based correlations. They are evaluated against an extensive database of in situ Vs measurements in the southern North Sea. The importance of modelling the stress-dependent nature of Vs is highlighted, and a novel stress-dependent model for Vs from CPT data, which leads to an improved fit, is presented. As the small-strain stiffness is used as an input to foundation response calculations, the model uncertainty of the correlation can introduce significant uncertainty into the resulting foundation response. This transformation uncertainty is quantified for each of the correlations evaluated in this study and shows important variations.

Graph-learning approach to combine multiresolution seismic velocity models

SUMMARY The resolution of velocity models obtained by tomography varies due to multiple factors and variables, such as the inversion approach, ray coverage, data quality, etc. Combining velocity models with different resolutions can enable more accurate ground motion simulations. Toward this goal, we present a novel methodology to fuse multiresolution seismic velocity maps with probabilistic graphical models (PGMs). The PGMs provide segmentation results, corresponding to various velocity intervals, in seismic velocity models with different resolutions. Further, by considering physical information (such as ray path density), we introduce physics-informed probabilistic graphical models (PIPGMs). These models provide data-driven relations between subdomains with low (LR) and high (HR) resolutions. Transferring (segmented) distribution information from the HR regions enhances the details in the LR regions by solving a maximum likelihood problem with prior knowledge from HR models. When updating areas bordering HR and LR regions, a patch-scanning policy is adopted to consider local patterns and avoid sharp boundaries. To evaluate the efficacy of the proposed PGM fusion method, we tested the fusion approach on both a synthetic checkerboard model and a fault zone structure imaged from the 2019 Ridgecrest, CA, earthquake sequence. The Ridgecrest fault zone image consists of a shallow (top 1 km) high-resolution shear-wave velocity model obtained from ambient noise tomography, which is embedded into the coarser Statewide California Earthquake Center Community Velocity Model version S4.26-M01. The model efficacy is underscored by the deviation between observed and calculated traveltimes along the boundaries between HR and LR regions, 38 per cent less than obtained by conventional Gaussian interpolation. The proposed PGM fusion method can merge any gridded multiresolution velocity model, a valuable tool for computational seismology and ground motion estimation.

Analysis of shear wave velocity estimation using MASW on sloping grounds

Analysis of shear wave velocity estimation using MASW on sloping grounds

Shear-wave velocity prediction of tight reservoirs based on poroelasticity theory: A comparative study of deep neural network and rock physics model

The absence of shear-wave (S-wave) velocity in well log data limits the effective seismic characterization of lithology, petrophysical properties, and fluid distribution of tight reservoirs in the Chang 7 shale oil formations of Ordos Basin, west China. However, conventional rock physics models (RPMs), e.g., the Xu-White model, struggle to accurately obtain the key parameters such as pore aspect ratio in the process of S-wave velocity prediction. This work performs a comparative study of S-wave velocity prediction based on deep neural network (DNN) and RPM for such reservoirs. By employing the poroelasticity equations which are capable of describing elastic wave propagation in the complex reservoirs saturated with viscous fluid, the relation between elastic and petrophysical properties is established with a DNN-based method. This approach quantifies the connection between P-/S-wave velocities and rock properties. On the other hand, we reformulate the traditional Xu-White model by incorporating the decoupled Kuster-Toksöz (K-T) model and the poroelasticity equations with viscoelastic fluid in the modeling process. The variation of pore aspect ratio with depth is also considered to characterize the complex pore structures, which is appropriate for improving the accuracy of prediction. Log data from the three wells are considered for verification, of which results show that the S-wave velocity prediction methods help improve the final result. Aided by the DNN-based method, the prediction exhibits a better performance if the training data is sufficient. In particular, provided a small amount of measured S-wave data (even ten data points), the trained DNN model can also provide reasonable predictions.

The feasibility of point shear wave elastography (pSWE) in the assessment of pancreas stiffness in diabetic patients and healthy volunteers.

Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by the dysfunctional metabolism of carbohydrates, fats, and proteins caused by impaired insulin secretion and insulin resistance. This study investigated the feasibility of using point shear wave elastography (pSWE) of the pancreas by comparing the shear wave velocity (SWV) measurements of three anatomical areas in patients with T1DM and healthy volunteers. This study included 30 patients with T1DM (9 male, 21 female) and 23 healthy controls (11 men, 12 women). Two experienced certified operators performed the examinations and took the SWV measurements. The mean SWV of the entire pancreas parenchyma differed significantly between patients and controls (1.1 ± 0.29 and 0.74 ± 0.19 m/s, respectively; p ≤ 0.001). Moreover, the SWVs of the pancreatic segments were significantly different in patients and controls; the mean SWV values of the pancreas head, body, and tail (respectively) in patients vs. controls were 0.99 ± 0.36 vs. 0.76 ± 0.26 m/s (p = 0.012), 1.1 ± 0.52 vs. 0.74 ± 0.23 (p ≤ 0.001), and 1.0 ± 0.34 vs. 0.73 ± 0.28 (p ≤ 0.001). This study confirmed the feasibility of quantifying pancreas tissue stiffness with pSWE and revealed that patients with T1DM had higher pancreas tissue stiffness than controls. Further studies are required to determine the potential value of pSWE as a screening tool in patients with prediabetes.