S-wave Velocity Research Articles

Left ventricular dysfunction in pulmonary arterial hypertension is attributed to underfilling rather than intrinsic myocardial disease: a CMR 2D phase contrast study

The pathophysiology underlying impaired LV function in PAH remains unclear, with some studies implicating intrinsic myocardial dysfunction and others pointing to LV underfilling. Evaluation of pulmonary vein area (PVA) and flow may offer novel, mechanistic insight by distinguishing elevated LV filling pressure common in myocardial dysfunction from LV underfilling. This study aimed to elucidate LV filling physiology in PAH by assessing PVA and flow using cardiac magnetic resonance (CMR) and compare pulmonary vein flow in PAH with HFrEF as a model representing elevated filling pressures, in addition to healthy controls. Patients with PAH or heart failure with reduced ejection fraction (HFrEF) referred for CMR were retrospectively reviewed, and healthy controls were included as reference. Pulmonary vein S, D and A-wave were compared between groups. Associations between pulmonary vein area (PVA) by CMR and echocardiographic indices of LV filling pressure were evaluated. Nineteen patients with PAH, 25 with HFrEF and 24 controls were included. Both PAH and HFrEF had lower ejection fraction and S-wave velocity than controls. PAH displayed smaller LV end-diastolic volumes than controls, while HFrEF demonstrated larger PVA and higher A-wave reversal. PVA was associated with mitral E/e′ ratio (r2 = 0.10; p = 0.03), e′ velocity (r2 = 0.23; p = 0.001) and left atrial volume (r2 = 0.07; p = 0.005). Among PAH, PVA was not associated with LV-GLS. A PVA cut-off of 2.3cm2 displayed 87% sensitivity and 72% specificity to differentiate HFrEF and PAH (AUC = 0.82). PAH displayed lower pulmonary vein S-wave velocity, smaller LV volume and reduced function compared with controls. Reduced LV function in PAH may be owing to underfilling rather than intrinsic myocardial disease. PVA demonstrates promise as a novel, non-invasive imaging marker to assess LV filling status.

Correlated crustal and mantle melting documents proto-Tibetan Plateau growth.

The mechanism that causes the rapid uplift and active magmatism of the Hoh-Xil Basin in the northern Tibetan Plateau and hence the outward growth of the proto-plateau is highly debated, more specifically, over the relationship between deep dynamics and surface uplift. Until recently the Hoh-Xil Basin remained uncovered by seismic networks due to inaccessibility. Here, based on linear seismic arrays across the Hoh-Xil Basin, we present a three-dimensional S-wave velocity (VS) model of the crust and uppermost mantle structure beneath the Tibetan Plateau from ambient noise tomography. This model exhibits a widespread partially molten crust in the northern Tibetan Plateau but only isolated pockets in the south manifested as low-VS anomalies in the middle crust. The spatial correlation of the widespread low-VS anomalies with strong uppermost mantle low-VS anomalies and young exposed magmatic rocks in the Hoh-Xil Basin suggests that the plateau grew through lithospheric mantle removal and its driven magmatism.

Prediction of tight sandstone reservoirs by multiwave joint prestack inversion technology: A case study of the Qiulin area in the Sichuan Basin

The Shaximiao (SXM) Formation of the Middle Triassic in the central Sichuan Basin is a river-lake sedimentary system with an average burial depth of 2800 m. The channel sand bodies of the SXM Formation in the Qiulin area are developed with large lateral variation and high- and medium-porosity sand. This leads to great differences in the seismic response and difficulty in predicting the sand bodies and reservoirs using P-wave data. We carry out well-log analysis for different types of sand bodies. The no. 8 sand body of high porosity can be effectively identified using P-wave impedance (PI). Whereas for the no. 7 sand body of relatively lower porosity (mainly due to the higher shale content in the no. 7 sand compared with the no. 8 sand), sandstone and mudstone can be distinguished using S-wave impedance (SI), and reservoirs can be recognized by the P- to S-wave velocity ratio ([Formula: see text]/[Formula: see text]). Based on the PP wave and the P-to-S converted wave (PS wave) (after matching) angle-stacked data, a prestack inversion of multicomponent data is performed. Multiwave joint inversion results (SI and [Formula: see text]/[Formula: see text]) have better accuracy and stronger stability than the PP-wave inversion in practical application. In addition, the PI of the joint inversion more clearly describes the distribution and boundaries of the channel sand and better matches the prediction and drilling data in sand 8. The SI of the joint inversion can identify subtle sand bodies (weak PI contrast) that are difficult to be detected by PP-wave inversion. The distribution of the reservoir predicted by the [Formula: see text]/[Formula: see text] of the joint inversion is clearer (the no. 7 sand body) and better than that by the PP-wave inversion. This study demonstrates the advantage of the multiwave joint inversion technology in tight sand identification.

Lithospheric structure of the Paraná, Chaco-Paraná, and Pantanal basins: Insights from ambient noise and earthquake-based surface wave tomography

To investigate the lithospheric seismic structure of southern South America beneath the Paraná, Chaco-Paraná, and Pantanal basins, we measure two distinct sets of dispersion curves of Rayleigh waves: the first corresponds to 25,986 phase velocity dispersion curves in the period range 5–50 s, extracted from cross-correlation of the ambient noise between pairs of stations and thus sensitive mainly to crustal structure; the second set is derived from group velocities extracted from earthquake recordings, resulting in 33,888 dispersion curves with periods ranging from 8 to 200 s, allowing us to probe deeper Earth structure. From these data sets, we derive two independent S-wave velocity models following a two-step inversion procedure, resulting in a crustal and a lithospheric mantle model with depths extending up to 50 and 200 km, respectively. Features imaged in our crustal model include low velocity anomalies in agreement with the surface position of the major sedimentary basins and high velocity anomalies within fold-thrust provinces and the basement of the Pantanal basin. In the uppermost mantle, we identify high velocities in the region of the Paraná basin, São Francisco and Amazonian cratons, and Luiz Alves block, and a low velocity feature beneath the Pantanal basin, which suggests a potentially thinner lithosphere under the basin. We also illuminate a strong velocity gradient boundary between the eastern border of the Pantanal basin with the Paraná basin, from crustal to lithospheric mantle depths, which seems to delineate the western and southern borders of the Paraná basin, coinciding with the Western Paraná Suture. Additionally, we construct a Moho depth proxy map for the study area, which has an overall good agreement with previous results, except for a thicker crust beneath the southern Chaco-Paraná basin. This area, together with a thicker crust counterpart in the Paraná basin, has a remarkable correlation with the position of a volcanic layer related to the Paraná Magmatic Province, which leads us to suggest that magmatic processes played an important role in the south Chaco-Paraná basin as well.

Exploring geothermal resources with the CSAMT and microtremor methods: a case study in Tangquan, Jiangsu Province, China

The development of geothermal energy has received extensive attention because of global energy scarcity and environmental pollution. The Tangquan area is in the north of the Yangtze fold belt, and the lack of magmatic rock development has resulted in a low-temperature type of geothermal reservoir. There are sporadic hot springs and cold springs exposed in the study area, but the uncertain deep geological structure, unknown geothermal reservoir formation mechanism, and lack of detailed exploration data seriously restrict the exploration and development of geothermal resources in the area. Filling underground faults with water can significantly reduce electrical resistivity, while traps filled with warm water can cause a decrease in S-wave velocity. Thus, a new integrated geophysical method, including the controlled source audio-frequency magnetotelluric (CSAMT) and microtremor methods, is applied for geothermal exploration in the region. The combination of CSAMT and microtremor methods can determine thermal-controlled and water-conducting structures more effectively and locate geothermal storage more accurately. The source, channel, storage, and cover of regional geothermal reservoir formation are analyzed using geophysical and geological data. That is, the regional NE- and NW-trending faults are explained as thermal-controlled and water-conducted structures, respectively. The deep Sinian Dengying Formation supplies a heat- and water-bearing space. The tectonic intersection area, especially the tensional fault zone, is found to be closely related to the existence of geothermal reservoirs, which is well verified by later drilling results. Finally, a geothermal reservoir model is established to comprehensively understand the distribution of geothermal energy in the region.

Bayesian linearized inversion for petrophysical and pore-connectivity parameters with seismic elastic data of carbonate reservoirs

Abstract Carbonate reservoirs are important targets for promoting the oil and gas reserve exploration and production in China. However, such reservoirs usually contain the developed complex pore structures, which heavily affect the precision in seismic prediction of petrophysical parameters. As one of the most important parameters to characterize reservoir rock, pore-related parameters can not only describe the pore structure, but also be used to evaluate the oil/gas bearing capabilities of potential reservoirs. The conventional rock-physics models (e.g. Gassmann's model) are formulated assuming fully-connected pores, which is unable to accurately capture the geometrical complexity in real rocks. In order to characterize the influences of multiple pores on the elastic properties, this work presents a rock-physics modelling method for carbonates, wherein the percentage composition of connected pores is equivalently quantified as the pore-connectivity factor. The method treats the pore-connectivity factor as an objective variable to characterize the spatial variations of pore structure. Specifically, the method combines the differential equivalent medium theory and Gassmann's model, and derives a linearized forward operator to quantitatively link porosity, water saturation, and pore-connectivity factor to seismic elastic parameters. According to the Bayesian linear inverse theory, the simultaneous estimation of petrophysical and pore-connectivity parameters is achieved. To characterize the statistical variations with multiple lithofacies, the Gaussian mixture model is employed to quantify the prior distribution of the objective variables. The posterior distribution of the objective variables is analytically expressed with the linearized forward operator. Numerical experiments show that the accuracy of the proposed method in predicting elastic parameters is improved. Compared with the conventional Xu-White model and the varying pore aspect ratio method, the accuracy of predicted P-wave velocity increases by 10.29% and 1.33%, respectively, and the predicted S-wave velocity increases by 6.44% and 0.03%, in terms of correlation coefficient. The application to the field data validates the effectiveness of the method, wherein the porosity and water saturation results help indicating the spatial distribution of potential reservoirs.

Anisotropy in shale and its impact on AVO modelling and prospect de-risk: a case study of a Central North Sea field

The inherent anisotropy in shale is an attribute associated with its clay minerals platelets alignment and layering and very common with transversely isotropic (TI) medium. This implies that velocity is fastest along the horizontal axis and slowest along the vertical axis, which could affect velocity signature from sonic tools measurements in deviated wells. This may lead to wrong amplitude variation with offset (AVO) signature, inaccurate depth conversions, seismic-to-well tie difficulty etc. Nineteen (19) wells in Forties field andthirteen (13) in Huntington field were investigated and interpreted for the presence of anisotropy effect within the caprock over the reservoir Forties sands of the Sele (shales) Formation. In order to analyse the Thomsen weak anisotropy parameters (ϵ, γ and δ) per field, wells with closer Sele depth range were considered. The well inclinations ( θ ), average compressional (α) and shear (β) velocities per well were utilised for forward and inverse modelling of Thomsen anisotropy equations. The estimated anisotropy (VTI) parameters and the P-wave ( α o ) and S-wave velocities (βo ) at zero inclinations in Sele Formation for the studied wells are: α o = 2448 ± 15.9 m/s, β o = 1003 ± 34.8 m/s, ϵ = 0.15 ± 0.02, γ = 0.33 ± 0.10, δ = 0.030 ± 0.015 in the Forties field Sele, while α o = 2775 ± 23.6 m/s, β o = 1306 ± 47.9 m/s, ϵ = 0.22 ± 0.02, γ = 0.43 ± 0.14, δ = 0.022 ± 0.018 in the Huntington field. AVO responses were compared, assuming both isotropic and anisotropic P–P reflectivity models. Result shows an increase in AVO gradient for all anisotropic case in both fields. AVO signatures were either completely or averagely masked in both fields using the isotropic assumptions. There is also an increase in AVO gradient for all anisotropic cases in both fields. Velocity decreases of about 1.2% and 3.5% were observed in the Sele Formation for the Forties and Huntington fields respectively for anisotropy. The corrected two-way traveltime (TWT) should in principle allow for a better seismic-to-well tie estimation. Similarly, using the faster deviated velocity (i.e. uncorrected velocity) in any depth conversion would result in under estimation of the actual depth across the field.

Estimation of site-specific amplification function from quarter-wavelength velocity profile and its application to obtain local amplification maps

Quarter-wavelength (QWL) velocity refers to the S-wave velocity at a depth equal to one-fourth of the wavelength of the seismic waves. It provides valuable information about the characteristics of the subsurface material properties affecting seismic waves propagation. The Swiss Seismological Service (SED) network, with over 200 stations across different lithologies, offers a rich dataset to implement correlation between site properties and site amplification factors. The current study is based on a subset of 113 selected SED seismic stations for which shear-wave velocity (Vs) profiles from geophysical measurements are available. We first meticulously analyzed and adjusted them to accurately determine the bedrock depth of the sites they are located on. Using empirical spectral modelling (ESM), we computed amplification functions from recorded earthquakes, referenced to the Swiss standard rock profile with a VS30 of 1100 m/s. Then we performed a bivariate analysis between empirical amplification functions, QWL velocity and QWL velocity contrast. The resulting coefficients are used to predict elastic amplification at frequencies between 0.5 and 10 Hz, with predictions generally being within 20–28% of observed values. We also developed an empirical equation relating VS30 (time-averaged Vs to 30 m depth) and κ0 (high-frequency attenuation parameter), to incorporate the anelastic term in the amplification. Applying our method to a 3D geophysical model of Visp, a high seismic hazard zone in Switzerland, we found it effective in predicting 1D site amplification, but noted caution in areas susceptible of 2D/3D resonance effects. A calibration function based on observed vs. predicted amplification and the frequencies of estimation normalized by the expected 2D resonance frequency improved predictions for Visp. Our study demonstrates that QWL velocity profiles offer a straightforward approach for characterizing site effects, which can be used in seismic hazard assessment to evaluate the potential amplification of ground motions.

3D shear wave velocity and azimuthal anisotropy structure in the shallow crust of Binchuan Basin in Yunnan, Southwest China, from ambient noise tomography

The Binchuan Basin in northwest Yunnan, southwest China, is a rift basin developed at the intersection of the Red River Fault and Chenghai Fault, where historical earthquakes have occurred. Understanding the fine velocity structure of the shallow crust in this region can help improve earthquake location accuracy and our understanding of the relationship between fault zone structures and fault slip behaviors. Using the continuous waveform data recorded by 381 dense array stations in 2017, we obtained 7 915 Rayleigh-wave phase velocity dispersion curves in the period band of 0.2–6 ​s from ambient noise cross-correlation functions after rigorous data processing and quality control. We determined 3D isotropic and azimuthally anisotropic shear wave velocity models at depths above 6 ​km in the shallow crust based on the direct surface wave azimuthal anisotropic tomography method. The isotropic model reveals a strong correspondence between the S-wave velocity structure at depths of 0–1 ​km and the regional topography and lithology. The Binchuan depocenter, Zhoucheng depocenter, Xiangyun Basin, and Xihai Rift Basin are primarily composed of Quaternary deposits, which show low-velocity anomalies, while the regions with the Paleozoic shale, limestone, and basalt exhibit high-velocity anomalies. The nearly N–S orientation of fast directions from azimuthal anisotropy models are mainly controlled by the active Binchuan Fault with N–S strike as well as the NNW-oriented primary compressive stress.

Near-surface velocity inversion and modeling method based on surface waves in petroleum exploration: a case study from Qaidam Basin, China

Surface waves are widely used in the study of underground structures at various scales because of their dispersion characteristics in layered media. Whether in natural seismology or engineering seismology, surface wave analysis methods have matured and developed for their respective fields. However, in oil and gas exploration, many data processors still tend to consider surface waves as noise that needs to be removed. To make more people pay attention to the application of surface waves and widely utilize surface waves carrying the near surface information in oil and gas exploration, this paper takes the data processing of LH site in Qinghai, China as an example to apply surface wave analysis methods to oil and gas exploration. We first preprocess and perform dispersion imaging method on the seismic record in the LH site to obtain frequency-phase velocity spectrum with good resolution and signal-to-noise ratio. Then, utilizing clustering algorithms, it automatically identifies and picks dispersion curves. Finally, through a simultaneous inversion algorithm of velocity and thickness, it inverts the dispersion curves and obtain S-wave velocity profiles in the depth range of 0–200 m. The near surface is divided into four zones based on velocity ranges and depth ranges. Additionally, we apply the surface waves inversion results as constraints to first-arrival tomography and obtain objectively accurate P-wave velocity profiles and Poisson’s ratio profiles. The results indicate that by applying surface wave analysis methods, the near surface velocity information carried by surface waves can be extracted, providing near surface velocity models for static correction and migration. At the same time, compared with the surface wave application in engineering seismology, the scale of oil and gas exploration is larger, so that the data processing of surface waves is particularly important, otherwise it will affect the picking of the dispersion curve and inversion.

Experimental study on shield tunnel seepage control via microbially induced calcite precipitation

This study investigated the potential use of microbially induced calcite precipitation (MICP) to prevent seepage in shield tunnels with the aim of decarbonizing tunnel engineering. An apparatus was developed to conduct scale model tests to evaluate the effectiveness of using MICP for shield tunnel seepage control. To understand the MICP process and its induced change in seepage flow rate, a series of 1-g physical model tests were conducted using the designed apparatus to investigate the effect of injection methods, grouting pressure, and calcium carbonate (CaCO3) content produced as well as its distribution on the reduction of seepage flow rate for thephysical tunnel model with different backfills behind its linings. The variation law of the pore pressure near grouting hole of the tunnel segment was also revealed. Results indicated that when the amount of CaCO3 precipitation in sand-grout mixtures was 10.53% and 10.12%, water seepage flow rate for thephysical tunnel modelwith Fujian- and coarse-sand-grout backfill respectively reduced by 94.3% and 73.8% of their respective initial values, and S-wave velocity increased by 89.6% and 84.9% for Fujian- and coarse-sand-grout mixture, respectively. The grouting pressure needed to be controlled within a certain range to prevent the unstable CaCO3 precipitates from being washed away. The testing results also showed that the one-phase injection method was more effective in controlling seepage water into a shield tunnel. Based on the findings of the scale model tests, some vital considerations and suggestions were presented on the use of MICP approaches for shield tunnel seepage control.

Use of decision tree ensembles for crustal structure imaging from receiver functions

SUMMARY Mode conversion of P waves at the boundary between Earth's crust and upper mantle, when analysed using receiver functions (RFs), allows characterization of Earth structure where seismic station density is high and earthquake sources are favourably distributed. We applied two ensemble decision tree algorithms—Random Forest (RanFor) and eXtreme Gradient Boost (XGBoost)—to synthetic and real RF data to assess these machine learning techniques' potential for crustal imaging when available data are sparse. The synthetic RFs, entailing both sharp increases in seismic velocity across the Moho and gradational Moho structures, calculated with and without added random noise, correspond to idealized crustal structures: a dipping Moho, Moho offset by crustal-scale faults, anti- and synform Moho structures and combinations of these. The RanFor/XGBoost algorithm recovers input structures well regardless of event-station distributions. Useful crustal and upper mantle seismic velocities can also be determined using RanFor and XGBoost, making it possible to image crustal thickness and P- and S-wave velocities simultaneously from RFs alone. We applied the trained RanFor/XGBoost to RFs determined from real seismic data recorded in the contiguous United States, producing a map of the Moho and P- and S-wave velocities of the lowermost crust and uppermost mantle. Use of XGBoost, which evaluates residuals between input RFs and ground-truth to update the decision tree using the gradient of a penalty function, improves the crustal thickness estimates.

Geotectonic architecture beneath Northern Vietnam revealed by local earthquake tomography combining seismic data from multiple networks

Extrusion tectonics driven by the collision of Indian-Eurasian continents and the relative motion among microcontinents (e.g. South China, Simao-Indochina blocks) has led to multi-phase and complex geological activity and structures in Northern Vietnam. While extensively studied, its detailed crustal architecture in association with multiple large-scale shear zones and plate boundaries remains unclear and is key to resolving long-lasting tectonic debates. This study constructs the first 3-D regional S-wave velocity (Vs) model with consistent P-wave velocity (Vp) and Vp/Vs ratio models by jointly using the crustal and head waves (e.g. Pg/Sg and Pn/Sn phases) from an augmented earthquake catalog combining the Vietnam National Earthquake Catalog (VNCAT) and the data from multiple adjacent seismic networks. The quality of phase picking and earthquake location incorporating seismic data from various sources are carefully examined and collected for the tomographic inversion. We use a stepwise joint inversion scheme to subsequently constrain the earthquake location, 1-D and 3-D velocity models. The output models suggest that the Song Ma suture represents a better candidate for the Indochina-South China boundary rather than the Red River Shear Zone. The latter seems to act as a crustal structure while the former and the Dien Bien Phu Fault are likely lithospheric ones. Several other geologic features such as the Tu Le volcanic basin, the Song Hong basin, and the Day Nui Con Voi metamorphic complex are also clearly depicted and discussed in the model. This new set of the Vp, Vs, and Vp/Vs models could further contribute to better assessment to seismic hazards in the northern Vietnam.

Ambient noise imaging for municipal solid waste landfill structure detection based on the common-midpoint two-station analysis with distributed acoustic sensing

SUMMARY Distributed acoustic sensing (DAS) enables high-density sampling of seismic wavefields at low cost compared to conventional geophones. This capability facilitates structural detection of a municipal solid waste (MSW) landfill, which is important for protecting the surrounding ecosystem. However, processing the vast amount of data from DAS array for ambient noise imaging can be computationally intensive. To address this, we employed the common-midpoint two-station (CMP-TS) analysis to enhance the efficiency of ambient noise imaging in the MSW landfill. CMP-TS analysis involves selecting pairs of traces at equal distances on both sides with the subarray midpoint as symmetry, which reduces the number of DAS array recordings for cross-correlation calculations. After positioning the DAS arrays linearly on top of the MSW landfill to automatically collect ambient noise, we used the CMP-TS analysis in the cross-correlation calculations to speed up the measurement of dispersion. The S-wave velocity structure of the study region was obtained quickly by inverting the extracted dispersion curves using the gradient optimization method. Ambient noise imaging based on CMP-TS analysis with DAS was applied to a test of an area-type MSW landfill. The resulting S-wave velocity section revealed a discontinuous low-velocity zone, validated by the high-density resistivity method. This low-velocity zone was interpreted as containing leachate from waste decomposition, and its discontinuity may be caused by excessive differences in the waste residues settling rates under compaction. Employing CMP-TS analysis in ambient noise data collected by DAS offers more cost-effective monitoring and a reliable basis for environmental pollution prevention and control.

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.

Near-surface characterization using shear-wave resonances: A case study from offshore Svalbard, Norway

Shear-wave (S-wave) resonances are typically observed when the surficial marine sediments over a rock substrate have relatively low S-wave velocities. We observe these phenomena using ocean-bottom fiber-optic distributed acoustic sensing (DAS) in two subsea fiber-optic telecommunication cables in Svalbard, Norway. Strong seismic energy from sufficiently large earthquakes is required to trigger and enhance the multiple-order modes of S-wave resonances. Here, we use the interpreted S-wave resonance frequencies of the first two modes to determine the thickness and the S-wave velocity of the near-surface low-velocity layer (LVL) beneath the seafloor. In addition, we use existing active P-wave seismic reflection data to determine the LVL thickness and to help build a more accurate S-wave velocity model from the S-wave resonance frequencies. The estimated S-wave velocity varies laterally within the LVL formation. Here, we find that the sediments or deposits with high S-wave velocity presented in the estimated LVL model agree with the distribution of some glacigenic sediments and landforms deposited in the survey area. Therefore, S-wave resonances measured by ocean-bottom DAS can be used to characterize the corresponding near-surface LVLs.

Application of multi-component seismic data in identifying dolomite reservoirs in the Sichuan Basin

Abstract A comprehensive drilling of wells has been conducted in the Permian Qixia Formation in the central Sichuan Basin, revealing a significant number of dolomite reservoirs. High- and medium-porosity dolomite reservoirs are the main gas-producing reservoirs in the Qixia Formation. Seismic PP-wave data show a “bright spot” for high-porosity dolomite reservoir formations but weak responses for medium-porosity dolomite reservoir formations, which is attributed to the inability of P waves to distinguish between medium-porosity reservoirs and limestone. However, medium-porosity dolomite and limestone have different S-wave velocities. Therefore, in this study, the identification of different-porosity dolomite reservoirs using multi-component seismic data was investigated. A comprehensive analysis of the elastic waves by forward modeling shows that the PS-wave amplitude is more sensitive to medium-porosity dolomite than the PP-wave amplitude. Therefore, medium-porosity dolomite reservoirs can be predicted using the amplitude attributes of the PS-wave, and high-porosity dolomite reservoirs can be characterized using the PP-wave. Meanwhile, the elastic parameter λρ (the product of Lame constant λ and density ρ), which is highly correlated with the dolomite content, can be used as an indicator of dolomite formations. Furthermore, compared to the results of PP-wave inversion, the elastic parameters derived from the joint inversion of PP- and PS-waves exhibited a better correspondence with the well-logging results. The comprehensive use of the seismic amplitude responses of PP- and PS-waves and multi-component seismic joint inversion can effectively predict high- and medium-porosity dolomite reservoirs. The predicted results can support the exploration and development of the Qixia Formation.

Deep-learning viscoelastic seismic inversion for mapping subsea permafrost

Marine seismic surveys can be used to map ice-bearing subsea permafrost on a large scale. However, current seismic processing technologies have limited capacity to image permafrost distribution at depth, mainly due to the low sensitivity of primary reflections and refractions to the velocity inversion found at the base of permafrost. Guided waves and multiples are more sensitive to the velocity variations below the top of permafrost, but they remain challenging to use in physics-based inversion approaches. A deep-learning-based seismic inversion has the potential to improve seismic imaging below the top of permafrost by automatically extracting information from all wave modes. We develop a multi-input neural network (NN) to estimate seismic velocities from marine seismic data. The network is trained on synthetic data generated from the representative distributions of the seismic properties of subsea permafrost. We find that our network can image large velocity contrasts and reversals in depth, typical of subsea permafrost. We use our network to estimate the P- and S-wave velocity and Q-factor models from a seismic line in the Beaufort Sea. The NN indicates highly discontinuous subsea permafrost with variable thickness in the area. Our work shows that deep-learning-based seismic inversion could become a cost-effective technology to map the distribution of subsea permafrost on a large scale and, more generally, high-velocity geologic layers located in shallow waters.

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.