S-wave Velocity Research Articles

Deformation of solid earth by surface pressure: equivalence between Ben-Menahem and Singh’s formula and Sorrells’ formula

SUMMARY Atmospheric pressure changes on Earth’s surface can deform the solid Earth. Sorrells derived analytical formulae for displacement in a homogeneous, elastic half-space, generated by a moving surface pressure source with speed $c$. Ben-Menahem and Singh derived formulae when an atmospheric P wave impinges on Earth’s surface. For a P wave with an incident angle close to the grazing angle, which essentially meant a slow apparent velocity $c_a$ in comparison to P- ($\alpha ^{\prime }$) and S-wave velocities ($\beta ^{\prime }$) in the Earth ($c_a \ll \beta ^{\prime } \lt \alpha ^{\prime }$), they showed that their formulae for solid-Earth deformations become identical with Sorrells’ formulae if $c_a$ is replaced by $c$. But this agreement was only for the asymptotic cases ($c_a \ll \beta ^{\prime }$). The first point of this paper is that the agreement of the two solutions extends to non-asymptotic cases, or when $c_a /\beta ^{\prime }$ is not small. The second point is that the angle of incidence in Ben-Menahem and Singh’s problem does not have to be the grazing angle. As long as the incident angle exceeds the critical angle of refraction from the P wave in the atmosphere to the S wave in the solid Earth, the formulae for Ben-Menahem and Singh’s solution become identical to Sorrell’s formulae. The third point is that this solution has two different domains depending on the speed $c$ (or $c_a$) on the surface. When $c/\beta ^{\prime }$ is small, deformations consist of the evanescent waves. When $c$ approaches Rayleigh-wave phase velocity, the driven oscillation in the solid Earth turns into a free oscillation due to resonance and dominates the wavefield. The non-asymptotic analytical solutions may be useful for the initial modelling of seismic deformations by fast-moving sources, such as those generated by shock waves from meteoroids and volcanic eruptions because the condition $c / \beta ^{\prime } \ll 1$ may be violated for such fast-moving sources.

Prism-shaped rock-physics template

Despite the emergence of statistical and intelligent approaches for quantitative seismic interpretation in recent years, rock-physics templates (RPTs) are still preferred because of their simplicity and easy implementation. RPTs have been introduced as a fundamental tool for lithology and fluid discrimination based on well-log and seismic data, which have already been proven in terms of accuracy and reliability. Considering the demand for comprehensive RPTs and improving their efficiency, I develop a novel 3D prism-shaped template for lithology and fluid discrimination called a prism-shaped RPT (P-RPT). For this, a theoretical methodology for designing conventional RPT is introduced with the aim of bounds and hybrid rock-physics models. Next, some novel triangular and rectangular ternary and binary templates are designed and connected to one another to build a prism for fluid discrimination and lithology identification. Two ternary charts are used for fluid and lithology, incorporating the P- and S-wave velocity ratios and lambda-mu parameters. Furthermore, binary charts, including acoustic impedance, Lamé parameters, and porosity, are designed and modified from the literature for fluid and lithology identification. Next, the obtained templates are successfully validated on blind data sets (ultrasonic, well logging, and seismic data) in different reservoirs with various lithologies and fluid types. The results indicate that the P-RPT could integrate the available RPTs into a 3D diagram and give a reliable framework for applications in seismic interpretation. User friendliness and generalizability are the most prominent advantages of this template for detecting fluid type and lithology based on well logs and seismic inversion, the results of which can be interpreted on a single chart rather than analyzing the data with different templates. The methodology and framework for implementing and generating templates are thoroughly explained in theory and practice to localize P-RPT or update the new RPT for another region.

Ultrasonic velocity anisotropy of Jurassic shales with different lithofacies

Abstract Given the growing importance of organic-rich shale as unconventional reservoirs, a thorough understanding of the elastic and anisotropic behavior of shales is of great concern. However, for lacustrine shales, the complex lithofacies assemblage with geological deposition makes it challenging. Four lithofacies (argillaceous, mixed, siliceous, and calcareous) are recognized for 40 lacustrine shale samples from Jurassic formation in Sichuan Basin on the basis of their mineral compositions. We perform ultrasonic velocity measurements on 40 pairs of shale plugs at varied confining pressures, attempting to uncover the controls on the anisotropic properties of different lithofacies. The experimental results reveal that the total porosity, clay, and organic matter would positively contribute to velocity anisotropy of Jurassic shales. Combined with microstructure and pressure-dependent velocity analysis, the preferred orientations of platy clay particles and lenticular kerogen, the development of clay pores along clay fabric, and the sub-parallel micro-cracks induced by hydrocarbon expulsion are treated to be the controlling mechanisms. We add the total porosity, clay content, and kerogen volume together, intending to distinguish the elastic and anisotropic properties of four lithofacies. Generally, argillaceous shales, the dominant lithofacies in Jurassic formation, could be characterized by the highest clay and total organic content (TOC), the lowest bedding-normal velocities, and the strongest velocity anisotropy. Finally, with the laboratory data, two rock-physics-driven exponential relationships are proposed to predict the P- and S-wave velocity anisotropy with the bedding-normal velocities.

Pre-stack seismic inversion based on model-constrained generative adversarial network

Pre-stack seismic inversion usually uses various traditional algorithms to estimate elastic parameters such as P-wave velocity, S-wave velocity, and density. It is hard to derive accurate elastic parameters due to their non-uniqueness and high dimensionality between elastic parameters and seismic data, the calculation of elastic parameters is inaccurate. Convolutional Neural Networks (CNNs) have high-dimensional feature space mapping capabilities, which are utilized to establish mapping relationships between seismic data and elasticity parameters. However, their effectiveness is greatly affected by label data, and at the same time, due to the lack of enough label data, resulting in a low degree of fitting between prediction results and real data. In addition, conventional seismic inversion methods based on CNNs lack physical model constraints, resulting in low accuracy and poor interpretability of prediction results. We propose a Cycle-consistent Generative Adversarial Network based on a geophysical mechanism (SeisInv-CycleGAN). Deterministic inversion results and labeled data are combined into hybrid geophysical data as a training set of SeisInv-CycleGAN with geophysical constraints. At the same time, the residual (seismic loss) between the seismic data synthesized by forward modeling and the actual data is used as part of the loss function. The SeisInv-CycleGAN does not require building an initial model, and it can achieve higher accuracy in prediction results with a small amount of labeled data.

Multi-task learning for seismic elastic parameter inversion with the lateral constraint of angle-gather difference

Pre-stack seismic inversion is an effective way to investigate the characteristics of hydrocarbon-bearing reservoirs. Multi-parameter application is the key to identifying reservoir lithology and fluid in pre-stack inversion. However, multi-parameter inversion may bring coupling effects on the parameters and destabilize the inversion. In addition, the lateral recognition accuracy of geological structures receives great attention. To address these challenges, a multi-task learning network considering the angle-gather difference is proposed in this work. The deep learning network is usually assumed as a black box and it is unclear what it can learn. However, the introduction of angle-gather difference can force the deep learning network to focus on the lateral differences, thus improving the lateral accuracy of the prediction profile. The proposed deep learning network includes input and output blocks. First, angle gathers and the angle-gather difference are fed into two separate input blocks with ResNet architecture and Unet architecture, respectively. Then, three elastic parameters, including P- and S-wave velocities and density, are simultaneously predicted based on the idea of multi-task learning by using three separate output blocks with the same convolutional network layers. Experimental and field data tests demonstrate the effectiveness of the proposed method in improving the prediction accuracy of seismic elastic parameters.

Use of spatial autocorrelation of microtremor using L- and triangle- arrays for estimations of S-wave velocity model and soil response at ITS Surabaya

Abstract Institut Teknologi Sepuluh Nopember (ITS) Surabaya is geologically vulnerable to earthquake hazards. Therefore, preparedness is necessary to address emergencies and risks caused by earthquake at ITS Surabaya. In this study, dispersion curve were obtained through the analysis of microtremor data using the Spatial Autocorrelation (SPAC) method. Microtremor data acquisition was performed at the PLN ITS Futsal Field using L- and triangle-arrays. We successfully got dispersion curves from both arrays. The shear wave velocity (Vs) profile and the best model of Vs profile for each array were obtained by inverting the dispersion curves. By using a two-layer model, the first layer produces identical velocities with a depth of 5.44 meters for an L-array and 5.02 meters for a triangle array. In the second layer, the resulting velocities for each array have quite a difference. The best model of Vs profile for both arrays were used to analyse 1D soil response with a linear approach in the frequency domain. Parameters including Peak Ground Acceleration (PGA), 5% damped Pseudo Spectral Acceleration (PSA), and Fourier Amplitude Ratio (FAR) were obtained from the linear approach analysis that could describe the ground’s response to seismic waves. Vs influences the soil response, the higher the difference in Vs values of a layer with the layer below it (half-space), the higher the increase in PGA, PSA, and FAR.

Effect of grain size on ductility and failure mechanism of fiber-reinforced coral sand cement-based composites

Fiber offers the potential for enhancing the resistance to deformation in engineering cement-based composites (ECC). However, replacing quartz sand in ECC with coral sand containing a large number of internal pores will make the interaction between aggregate, interface transition zone, and cementitious materials more complex. To investigate the influence of coral sand particle size and Polyvinyl alcohol (PVA) fiber content on the compression behaviour and internal microstructure of fiber-reinforced coral sand cement-based composites (FRCSCC), we conducted uniaxial compressive strength tests and real-time measurements of P-wave and S-wave velocities. We tested various mechanical parameters, such as density, elastic modulus, compressive strength, wave velocities, and toughness index. The findings revealed a notable trend in relation to the coral sand particle size. Specifically, as the particle size increased, the compressive strength of FRCSCC decreased by 26 % and the elastic modulus reduced by 11 %. During uniaxial compression, the calculated cumulative voltage energy (CVE) from P-wave and S-wave can be divided into three stages: elastic deformation, microcrack and macrocrack propagation, and main crack formation. Moreover, the increase of coral sand particle size from 0.075 mm to 1.00 mm resulted in the rise of residual stress from 8.7 % to 18.2 %, followed by the increase of toughness index from 2.5 to 3.2. Scanning electron microscope (SEM) images reveal that the main failure modes of PVA fiber in FRCSCC specimens during uniaxial compression are debonding pull-out and tearing. Larger particle sizes of coral sand are associated with uneven fiber dispersion, increased fiber fractures, and diminished ductility of the specimens. The results establish a theoretical foundation for offshore engineering construction.

Deep Structure of the Baikal Rift Zone and Central Mongolia

The upper mantle and the transition zone of the Baikal rift zone (BRZ) are studied. The observations are analyzed using P-wave receiver functions. It is found that in the BRZ central and northeastern part, the P410s converted seismic phase is preceded by a precursory wave with negative polarity which is formed in the low S-wave velocity layer at a depth of 350–410 km. A similar precursory wave with low S-wave velocity and negative polarity is formed at a depth of 600–660 km. The low-velocity layers are interpreted as resulting from the hydration of wadsleyite and ringwoodite during the subduction of the Pacific lithosphere. A similar study of the mantle in Central Mongolia found no expected signs of hydration. Modeling of the lithosphere–asthenosphere system in Central Mongolia by joint inversion of the body wave receiver functions and surface wave dispersion curves reveals a very thin lithospheric lid beneath Khangai and a thick layered asthenosphere to a depth of 200 km with a lithospheric inclusion between low-velocity layers.

Assessment of different agricultural soil compaction levels using shallow seismic geophysical methods

A well-developed soil structure is crucial for a fertile soil and rules its influence on the ecosystem. Slight variations in the way soil components are organized and connected can greatly impact the soil mechanical and hydraulic characteristics. However, these features are challenging to measure at a scale that is relevant for agricultural management. In this study, we assess the capability of two seismic geophysical methods, i.e. Seismic Refraction Tomography and Multichannel Analysis of Surface Waves, to monitor the effects of compaction on agricultural bare soil. The purpose is to highlight the different mechanical response caused both by soil plastic deformation and soil water distribution due to increasing compaction. Results demonstrate that both geophysical techniques provide sufficient information to capture the effects of soil compaction and distinguish its significance, while traditional direct measurements, being punctual, lack sufficient spatial coverage. P-wave velocities carry a strong imprint of soil compaction, provided by seismic refraction, incorporating the information given by the multiphase medium in its entirety. On the other hand, S-wave velocity derived from Surface Waves discriminates the effect of solid matrix structure. This work, moreover, aims to pave the way for seismic methods to spatially characterize compaction at field scale, illustrating its potential and suggesting possible developments.

Fluid identification in fractured media with genetic algorithm

The fluid identification of fracture media is susceptible to factors such as observation data, fluid sensitivity parameters, and inversion algorithms. However, appropriate nonlinear algorithms can be applied to reduce errors generated during the inversion process, thus helping to improve the reliability of fluid identification. In this paper, analysis and comparison are made to various reflection coefficient approximate formulas, of which the applicability and accuracy are demonstrated in fluid-saturated poroelastic media, and the objective function is constructed further from Russell's reflection coefficient formula. In this sense, the genetic algorithm (GA) can achieve the pre-stack inversion of seismic waves. Based on the inversion results such as P-, S-wave velocity, and density, the Russell fluid indicator, Poisson's ratio, and Poisson impedance are combined to identify the water-, gas-, and oil-bearing fracture. Finally, the applicability and reliability of the method are demonstrated through numerical calculations in some regions of Marmousi2 model and the field data. The results show that the seismic data can be preliminarily analyzed by the inversion method, followed by combining the Russell fluid indicator, Poisson's ratio, and Poisson impedance, effectively reducing the multiplicity of the inversion results and improving the reliability of fluid identification in fractured media.

A high-resolution seismic velocity model for East Asia using full-waveform tomography: Constraints on India-Asia collisional tectonics

East Asia's complex tectonic history makes it a unique place for plate-tectonic studies. While the crust and mantle structures beneath this region have been extensively investigated by receiver function and many different kinds of tomography studies, discrepancies among seismic velocity models have complicated our understanding of East Asia's tectonic evolution. Here, we use full-waveform adjoint tomography to study the seismic P- and S-wave velocity structures of the crust and mantle beneath East Asia. We use a large waveform dataset from 141 earthquakes recorded by 4640 broadband seismic stations. Our inversion optimizes the normalized correlation coefficient between synthetic and observed seismograms within individual time windows of different seismic phases, from P arrivals to the slowest surface waves. This approach allows us to fit regional multipathed waveforms and provides very well-resolved seismic P- and S-wave velocity structures from the crust to depths of about 800 km. Furthermore, we are able to obtain this resolution over most of East Asia allowing us to compare the amplitude and shapes of anomalies across the entire region. The observed structures in our new full-waveform tomography model (FWEA23) correlate well with tectonic units, revealing sharp contrasts across tectonic boundaries. Our model reveals narrow and strong linear fast anomalies associated with subduction zones in the western Pacific and Southeast Asia. Compared to these oceanic slabs, we observe different and more complicated mantle structures beneath the India-Asia continental collision zone. While our model spans a wide region in East Asia, we focus our interpretations on the seismic velocity anomalies beneath Tibet and surrounding regions, as our model provides significant implications for India-Asia continental collision tectonics. Our analysis suggests that cratonic Indian lithosphere collided with Eurasian plate around 25–20 Ma, subsequently underthrusting beneath Tibet horizontally with little deformation. This resulted in thickening, destabilization, and delamination of the rheologically weaker and less buoyant mantle lithosphere beneath north-central Tibet. Moreover, our model reveals isolated lithospheric fragments within the mantle transition zone and lower mantle beneath southern Tibet and northern India, suggesting that non-cratonic Greater India continental lithosphere has broken off and sunk into the deeper mantle. The lack of continuity among these lithospheric fragments suggests a convective destabilization process, distinct from typical oceanic plate subduction. Our results emphasize the important role of rheological properties of continental lithospheres in shaping the dynamics of continental collision.

Examination of shallow and deep S-wave velocity structures from microtremor array measurements and receiver function analysis at strong-motion stations in Kathmandu basin, Nepal

The Himalayan collision zone, where the Indian Plate subducts beneath the Eurasian Plate at a low angle, has caused many devastating earthquakes. The Kathmandu basin, situated in this region, is surrounded by mountains on all sides and is filled with distinct soft lake sediments with a highly undulating bedrock topography. The basin has been experiencing rapid urbanization, and the growing population in its major cities has increased the vulnerability to seismic risk during future earthquakes. Several strong-motion stations have recently been deployed in the Kathmandu basin. It is expected that the data captured by this strong-motion station array will further enhance our understanding of site amplification in sedimentary basins. Clear P-to-S converted waves have been observed in the strong-motion records. In this study, we investigate the medium boundary that generated these converted waves. First, we estimate the shallow velocity structures, which correspond to the topographic slopes or surface geology, beneath the strong-motion stations. We then apply a receiver function analysis to the strong-motion records. The receiver function indicates that the interface between the soft sediment and seismic bedrock serves as a boundary that generates converted waves. The obtained results can be used for tuning three-dimensional velocity structures.Graphical

3D VSP Imaging Using DAS Recording of P- and S-Waves in Vertical and Lateral Well Sections in West Texas.

A 3D vertical seismic profiling (VSP) survey was acquired using a distributed acoustic sensing (DAS) system in the Permian Basin, West Texas. In total, 682 shot points from a pair of vibroseis units were recorded using optical fibers installed in a 9000 ft (2743 m) vertical part and 5000 ft (1524 m) horizontal reach of a well. Transmitted and reflected P, S, and converted waves were evident in the DAS data. From first-break P and S arrivals, we found average P-wave velocities of approximately 14,000 ft/s (4570 m/s) and S-wave velocities of 8800 ft/s (3000 m/s) in the deep section. We modified the conventional geophone VSP processing workflow and produced P-P reflection and P-S volumes derived from the well's vertical section. The Wolfcamp formation can be seen in two 3D volumes (P-P and P-S) from the vertical section of the well. They cover an area of 3000 ft (914 m) in the north-south direction and 1500 ft (460 m) in the west-east direction. Time slices showed coherent reflections, especially at 1.7 s (~11,000 ft), which was interpreted as the bottom of the Wolfcamp formation. Vp/Vs values from 2300 ft (701 m) -8800 ft (2682 m) interval range were between 1.7 and 2.0. These first data provide baseline images to compare to follow-up surveys after hydraulic fracturing as well as potential usefulness in extracting elastic properties and providing further indications of fractured volumes.

Sensitivity analysis of S waves and their velocity measurement in slow formations from monopole acoustic logging while drilling

Monopole acoustic logging while drilling (LWD) enables the direct measurement of S-wave velocity in slow formations, which has been corroborated by recent theoretical and experimental studies. However, this measurement is hampered by the weakness of the S-wave signal and the lack of techniques to amplify it. To address this challenge, we analytically compute the monopole LWD wavefields, considering centralized and off-center tools in various slow formations. Modeling analysis reveals that four parameters primarily influence the excitation of the formation S wave: the formation S-wave velocity, the source-to-receiver distance, the radial distance from the receiver to the wellbore, and the source frequency. S-wave signals can be enhanced by judiciously optimizing these parameters during tool design. Furthermore, our research suggests that the S-wave velocity can be accurately extracted through the slowness-time correlation method only when formation S-wave velocities are in a suitable range. This is because an overly high S-wave velocity causes shear arrivals to interfere with the inner Stoneley mode, whereas an ultraslow formation S-wave velocity results in S-wave signals too faint to detect. For the LWD model with an off-center tool, simulations demonstrate that tool eccentricity, especially large eccentricity, can amplify the S wave and improve its measurement accuracy, provided that waveforms received in the direction of tool movement are used. In a very slow formation, we successfully extract the S-wave velocity from synthetic full wave data at that azimuth under conditions of large eccentricity, a task not achievable with a centralized instrument.

A preliminary investigation on the mechanical behaviour of a stiff Italian clay in the context of hydrogen storage

The large-scale use of renewable energy sources is closely linked to the ability to store excess energy generated during periods of overproduction for use when demand is at a peak. Storing green energy is therefore a key component in the move towards a carbon-neutral economy. Underground hydrogen storage in depleted oil and gas reservoirs may provide an efficient long-term solution. Cyclic injection and production of hydrogen alter the chemo-hydro-mechanical conditions of the reservoir and caprocks, and possible geomechanical consequences of such alterations must be preliminarily assessed for safe storage operations. This study aims at exploring the possible effects of cyclic mechanical loads, such as those that might be induced by hydrogen storage and production, on the mechanical behaviour of a clayey caprock. A series of triaxial tests, both monotonic and cyclic, were carried out on undisturbed samples of a stiff Italian clay cored from a caprock formation overlying a hydrocarbon reservoir. The results show that the material response is characterized by the distinctive stress-strain behaviour of stiff clays, with a rather high fragility, which was found to be highly dependent on the loading strain rate. During laboratory experiments conducted at frequencies larger than in situ ones, cyclic loading under stress control causes a gradual degradation of the material structure leading to the formation of a clear shear band followed by a reduction in shear strength. Eventually, failure occurs as the peak shear strength approaches the applied load. The progressive destructuration also implies a reduction in P- and S-wave propagation velocities and a significant change in the signal shape, which is therefore a promising parameter for monitoring the material degradation process.

Increasing P-Wave and S-Wave Velocity Resolution with FWI — a North Sea Shallow Water Case Study

Increasing P-Wave and S-Wave Velocity Resolution with FWI — a North Sea Shallow Water Case Study

Clay soil porosity estimation using seismic P- and S-wave velocities along Isfahan Metro Line 2

Clay soil porosity estimation using seismic P- and S-wave velocities along Isfahan Metro Line 2

The effects of precursory velocity changes on earthquake nucleation and stress evolution in dynamic earthquake cycle simulations

Seismic velocity changes in earthquake cycles have been observed over a wide range of timescales and may be a good indicator of the onset of future earthquakes. Understanding the effects of precursory velocity changes right before seismic and slow-slip events could potentially elucidate the onset and timing of fault failure. We use numerical models to simulate fully dynamic earthquake cycles in 2D strike-slip fault systems with antiplane geometry, surrounded by a narrow fault-parallel damage zone. By imposing S-wave velocity changes inside fault damage zones, we investigate the effects of these precursors on multiple stages of the seismic cycle, including nucleation, coseismic, postseismic, and interseismic stages. Our modeling results show a wide spectrum of fault slip behaviors including fast earthquakes, slow-slip events, and variable creep. One primary effect of the imposed velocity precursor is on the earthquake nucleation phase, and earlier onset of precursors causes earthquakes to nucleate sooner with a smaller nucleation size that is not predicted by theoretical equations. Furthermore, such precursors affect the nucleation of dynamic earthquakes and slow-slip events. Our results highlight the importance of short- and long-term monitoring of fault zone structures for better assessment of regional seismic hazard.

COMPREHENSIVE EVALUATION OF MACHINE LEARNING REGRESSION TECHNIQUES FOR SHEAR VELOCITY LOG PREDICTION (VS)

In addition to their numerous other applications, machine learning (ML) techniques can be employed to address regression problems. Linear and polynomial regression are well-established methods utilized widely due to the simplicity of their numerical implementation. But, when confronted with instances of complex problems, these approaches prove to be inefficient. This article examines the effectiveness of Support Vector Regression (SVR) and Bayesian regression in tackling the difficulties associated with regression in the reconstruction of well logs, with a specific focus on shear-wave logs. Regression analysis was employed to construct models that establish the relationship between target variable (features) and independent variables (features) to estimate the S-wave velocity. The findings of this study demonstrate the efficacy of various machine learning (ML) approaches when applied to regression analysis. The graphical analysis and statistical metrics utilized in this evaluation were implemented to quantify and assess qualitative aspects of the performance, respectively. The goal of this study is to establish the machine learning method that yields the most accurate estimation of the s-wave elastic physical property using the Norne field data set as a case study.

Fast and semi-automatic S-wave and P-wave velocity estimations from landstreamer data: a field case from the Middle East

Seismic surface and body wave analyses are powerful tools for the geotechnical characterization of sites. The use of landstreamers facilitates the acquisition of dense data sets over large areas. However, efficient processing workflows are needed to estimate 3D velocity models from these massive data sets. For surface wave analysis, the manual picking of dispersion curves (DCs) of large data sets is very time-consuming, whereas the accuracy can be biased by operator choices. We apply a semi-automatic workflow for the analysis, processing, and interpretation of a large-scale landstreamer data set acquired for engineering purposes in the Middle East. The workflow involves the application of a validated automatic DC picking algorithm, and the transformation of the DCs into S- and P-wave velocity models through the wavelength-depth technique. The method has a high level of automation, is data driven and does not require extensive data inversion. Another remarkable benefit is that the auto-picking is more than 1,000 times more efficient than standard manual picking and the estimated velocities are in good agreement with available geotechnical and geophysical information. We conclude that the semi-automatic approach may represent a fast and straightforward method suitable for both research and industrial projects, thus enhancing further collaborations and developments.