Shear Wave Velocity Estimation Research Articles

Estimating shear wave velocity and site characterization of western Riyadh City, Saudi Arabia based on multichannel analysis of surface waves

Estimating shear wave velocity and site characterization of western Riyadh City, Saudi Arabia based on multichannel analysis of surface waves

The Estimation of Shear Wave Velocity for Shallow Underground Structures in the Central Himalaya Region of Nepal

A subsurface investigation was conducted to assess the suitability of a site for potential tunnel construction, focusing on the determination of shear wave velocities (Vs) in subsurface materials. This study employed three distinct methods to analyze Vs in weathered soft rock: drilling mechanism, multichannel analysis of surface waves (MASW), and microtremor array measurement (MAM). Through the utilization of MASW and MAM, empirical relationships were established, enabling the determination of Vs based solely on soil type and depth, offering a practical alternative to the limitations of SPT N-Value, particularly when exceeding 50 blows. The comparison of Vs values obtained from these methods revealed a close alignment between empirical techniques and MASW/MAM, which proved to be cost-effective and an efficient alternative to drilling for comprehensive underground structure assessments. The reliability of MASW was further underscored through its comparison with existing empirical methods. Moreover, the empirical approach demonstrated its efficacy in predicting velocities in weathered soft rock within the Central Himalayan region of Nepal, thus enhancing the feasibility study of underground structures. Lastly, this study proposed a Vs-Depth correlation specifically tailored for highly weathered meta-sandstone bedrock resulting in clay and sandy soils.

Examination of rapid adjustment system based on screen score obtained using continuous shear wave elastography.

Continuous shear wave elastography (C-SWE) can be expected to be applied to portable muscle elasticity diagnosis. To establish diagnostic technology, it will be necessary to improve measurement techniques and quantitative measurement accuracy. In this study, we investigated two screen scores: the quality index (Q-index), which determines whether the intensity of a power Doppler image is appropriate, and the shear wave propagation direction index (SWDI), which determines the uniformity of shear wave propagation. First, we performed numerical simulations with white noise and found that the coefficient of variation of shear wave velocity estimation was less than 5% when the normalized Q-index was greater than 0.27. Furthermore, regarding the SWDI, we clarified the relationship between the standard deviation in shear wave propagation direction and the SWDI. Next, the relationship between the Q-index and coefficient of variation of estimated shear wave velocity was evaluated through experiments using a tissue-mimicking phantom. The results showed that there was a negative correlation between the Q-index and the coefficient of variation, and the fluctuation of the propagation velocity could be inferred from the Q-index. Finally, we showed the results of applying the screen scores to muscle relaxation monitoring and confirmed its usefulness in clinical applications. By applying the screen scores, we showed improved stability in speed estimation in C-SWE, and demonstrated the possibility of clinical applicability.

Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran.

Shear wave transit time is a crucial parameter in petroleum engineering and geomechanical modeling with significant implications for reservoir performance and rock behavior prediction. Without accurate shear wave velocity information, geomechanical models are unable to fully characterize reservoir rock behavior, impacting operations such as hydraulic fracturing, production planning, and well stimulation. While traditional direct measurement methods are accurate but resource-intensive, indirect methods utilizing seismic and petrophysical data, as well as artificial intelligence algorithms, offer viable alternatives for shear wave velocity estimation. Machine learning algorithms have been proposed to predict shear wave velocity. However, until now, a comprehensive comparison has not been made on the common methods of machine learning that had an acceptable performance in previous researches. This research focuses on the prediction of shear wave transit time using prevalent machine learning techniques, along with a comparative analysis of these methods. To predict this parameter, various input features have been employed: compressional wave transit time, density, porosity, depth, Caliper log, and Gamma-ray log. Among the employed methods, the random forest approach demonstrated the most favorable performance, yielding R-squared and RMSE values of 0.9495 and 9.4567, respectively. Furthermore, the artificial neural network, LSBoost, Bayesian, multivariate regression, and support vector machine techniques achieved R-squared values of 0.878, 0.8583, 0.8471, 0.847 and 0.7975, RMSE values of 22.4068, 27.8158, 28.0138, 28.0240 and 37.5822, respectively. Estimation analysis confirmed the statistical reliability of the Random Forest model. The formulated strategies offer a promising framework applicable to shear wave velocity estimation in carbonate reservoirs.

Novel techniques for in situ estimation of shear-wave velocity and damping ratio through MASW testing part II: a Monte Carlo algorithm for the joint inversion of phase velocity and phase attenuation

SUMMARY This paper deals with in situ characterization of the small-strain shear-wave velocity VS and damping ratio DS from an advanced interpretation of Multi-channel Analysis of Surface Waves (MASW) surveys. A new approach based on extracting Rayleigh wave data using the CFDBFa method has been discussed in the companion paper. This paper focuses on mapping the experimental Rayleigh wave phase velocity and attenuation into profiles of VS and DS versus depth, which is achieved through a joint inversion procedure. The joint inversion of phase velocity and attenuation data utilizes a newly developed Monte Carlo global search algorithm, which implements a smart sampling procedure. This scheme exploits the scaling properties of the solution of the Rayleigh eigenvalue problem to modify the trial earth models and improve the matching with the experimental data. Thus, a reliable result can be achieved with a limited number of trial ground models. The proposed algorithm is applied to the inversion of synthetic data and of experimental data collected at the Garner Valley Downhole Array site, as described in the companion paper. In general, inverted soil models exhibit well-defined VS profiles, whereas DS profiles are affected by larger uncertainties. Greater uncertainty in the inverted DS profiles is a direct result of higher variability in the experimental attenuation data, the limited wavelength range at which reliable values of attenuation parameters can be retrieved, and the sensitivity of attenuation data to both DS and VS. Nonetheless, the resulting inverted earth models agree well with alternative in situ estimates and geological data. The results stress the feasibility of retrieving both stiffness and attenuation parameters from active-source MASW testing and the effectiveness of extracting in situ damping ratio estimates from surface wave data.

Novel techniques for in situ estimation of shear-wave velocity and damping ratio through MASW testing – I: a beamforming procedure for extracting Rayleigh-wave phase velocity and phase attenuation

SUMMARY A robust, in situ estimate of shear-wave velocity VS and the small-strain damping ratio DS (or equivalently, the quality factor QS) is crucial for the design of buildings and geotechnical systems subjected to vibrations or earthquake ground shaking. A promising technique for simultaneously obtaining both VS and DS relies on the Multichannel Analysis of Surface Waves (MASW) method. MASW can be used to extract the Rayleigh wave phase velocity and phase attenuation data from active-source seismic traces recorded along linear arrays. Then, these data can be inverted to obtain VS and DS profiles. This paper introduces two novel methodologies for extracting the phase velocity and attenuation data. These new approaches are based on an extension of the beamforming technique which can be combined with a modal filter to isolate different Rayleigh propagation modes. Thus, the techniques return reliable phase velocity and attenuation estimates even in the presence of a multimode wavefield, which is typical of complex stratigraphic conditions. The reliability and effectiveness of the proposed approaches are assessed on a suite of synthetic wavefields and on experimental data collected at the Garner Valley Downhole Array and Mirandola sites. The results reveal that, under proper modelling of wavefield conditions, accurate estimates of Rayleigh wave phase velocity and attenuation can be extracted from active-source MASW wavefields over a broad frequency range. Eventually, the estimation of soil mechanical parameters also requires a robust inversion procedure to map the experimental Rayleigh wave parameters into soil models describing VS and DS with depth. The simultaneous inversion of phase velocity and attenuation data is discussed in detail in the companion paper.

Comparing Artificial Intelligence Algorithms with Empirical Correlations in Shear Wave Velocity Prediction

Accurate estimation of shear wave velocity (Vs) is crucial for modeling hydrocarbon reservoirs. The Vs values can be directly measured using the Dipole Shear Sonic Imager data; however, it is very expensive and requires specific technical considerations. To address this issue, researchers have developed different methods for Vs prediction in underground rocks and soils. In this study, the well logging data of a wellbore in the Iranian Aboozar limestone oilfield were used for Vs estimation. The Vs values were estimated using five available empirical correlations, linear regression technique, and two machine learning algorithms including multivariate linear regression and gene expression programming. Those values were compared with the real Vs data. Furthermore, three statistical indices including correlation coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the effectiveness of the applied techniques. The mathematical correlation obtained by the GEP algorithm delivered the most accurate Vs values with R2 = 0.972, RMSE = 0.000290, and MAE = 0.000208. Compared to the available empirical correlations, the obtained correlation from the GEP approach uses multiple parameters to estimate the Vs, thereby leading to more precise predictions. The new correlation can be used to estimate the Vs values in the Aboozar oilfield and other geologically similar reservoirs.

A novel directional-oriented method for predicting shear wave velocity through empirical rock physics relationship using geostatistics analysis

This study attempts to design a novel direction–oriented approach for estimating shear wave velocity (VS) through geostatistical methods (GM) using density employing geophysical log data. The research area involves three hydrocarbon wells drilled in carbonate reservoirs that are comprised of oil and water. Firstly, VS was estimated using the four selected empirical rock physics relationships (ERR) in well A (target well), and then all results were evaluated by ten statistical benchmarks. All results show that the best ERR is Greenberg and Castagna, with R2 = 0.8104 and Correlation = 0.90, while Gardner's equation obtained the poorest results with R2 = 0.6766 and correlation = 0.82. Next, Gardner's method was improved through GM by employing Ordinary Kriging (OKr) in two directions in well A, and then Cross-Validation and Jack-knife methods (JKm and CVm, respectively) were used to assess OKr's performance and efficiency. Initially, CVm and JKm were employed to estimate Vs using the available density and its relationship with shear wave velocity, where the performance of CVm was better with R2 = 0.8865 and correlation = 0.94. In this step, some points from the original VS were used to train the data. Finally, Vs was estimated through JKm and using the relationship between the shear wave velocity of two wells near the target well, including wells B and C; however, in this step, the original shear wave velocity of the target well was completely ignored. Reading the results, JKm could show excellent performance with R2 = 0.8503 and Corr = 0.922. In contrast to previous studies that used only Correlation and R-squared (R2), this study further provides accurate results by employing a wide range of statistical benchmarks to investigate all results. In contrast to traditional empirical rock physics relationships, the developed direction-oriented technique demonstrated improved predicted accuracy and robustness in the investigated carbonate field. This work demonstrates that GM can effectively estimate Vs and has a significant potential to enhance VS estimation using density.

Statistical and machine learning hybridization for predicting shear wave velocity in tight sand reservoirs: A case study

Shear wave velocity plays an important role in describing the reservoir's petrophysical and geomechanical characteristics. It is also important to understand the mechanical behavior of the reservoir since it is closely related to rock strength and stiffness. However, it is rarely logged due to the high acquisition cost. Geoscientists have developed a variety of methods for predicting shear wave velocities, including linear regression and neural networks. However, none of these statistical approaches have been combined with unsupervised learning to evaluate the model's efficacy. Most of the multivariate correlations reported in the literature for shear wave prediction in the tight sand reservoirs did not account for the multicollinearity effect of the VP-VS correlation, which may result in overfitting of the model. Considering these factors, we utilized wireline logs to model multivariate linear and non-linear regression using k-means squares-based electrofacies (EF). We further converted them into EF-constrained supervised learning models. A multicollinearity effect was assessed in linear regression models to observe the need for ridge regression. The results indicate that EF-constrained statistical models and adaptive neuro-fuzzy inference system (ANFIS) models enhanced accuracy in predicting shear velocity. However, the multivariate non-linear regression analysis predicted the shear velocity more accurately than the multivariate linear regression analysis. The absence of data points within the EF1 has an adverse effect on the accuracy of all statistical and ANFIS models. The results of this study will help to understand the impact of lithological EF on estimating shear wave velocity using multivariate linear regression, multivariate non-linear regression, and ANFIS.

Quasi-site-specific prediction of shear wave velocity from CPTu

A rapid and reasonable estimation of the local site shear wave velocity (Vs) profile is indispensable for site assessment. For soil deposits, researchers have developed numerous empirical correlations between Vs and piezocone penetration test (CPTu) based on a global/regional (generic) database. However, these transformations are usually biased for local sites because the generic database contains all available data without regard to site-specific differences, and the transformation uncertainty is usually non-negligible due to the complicated geology factors and measurement errors. Moreover, it is challenging to construct a site-specific correlation due to the sparse in-situ measurements. To this end, this study develops a quasi-site-specific CPTu-Vs transformation model that algorithmically combines both the generic database and site-specific measurements to provide reliable Vs prediction with uncertainty description. A generic database containing 3116 cases of 6 parameters is collected from different countries, including corrected cone tip resistance, sleeve frictional resistance, depth, soil behavior index, pore pressure ratio, and Vs. As the significant impact of soil type on CPTu-Vs correlation, independent Johnson transformation (JS) for different soil types is first adopted to normalize the generic database as a whole, ensuring the constructed model is applicable to any soil type site. Three state-of-art quasi-site-specific models are investigated for CPTu-Vs transformation: probabilistic multiple regression (PMR), the hybridization method (HYD), and the hierarchical Bayesian model (HBM). Empirical correlations and the recently-developed data-driven model (XGBoost) are also considered to illustrate the novelty of the quasi-site-specific model. The applications on three sites with different soil types show that considering soil type in JS improves model accuracy and reduces the transformation uncertainty. The HBM with multiple basic CPTu parameters performs best regarding accuracy, uncertainty, and robustness.

Shear-wave velocity estimation based on rock physics modelling of a limestone gas reservoir in the Pannonian Basin

In the Pannonian Basin, especially in Croatia, there are a small number of wells for which acquired shear-wave (S-wave) velocities have been obtained. Often, quantitative interpretation relies only on compressional-wave (P-wave) velocity data, and S-wave velocity must be modelled. S-wave velocity estimation in combination with other petrophysical data is essential for detailed reservoir characterization. P- and S-wave velocities enable seismic modelling of different saturation states in a reservoir. This paper demonstrates a workflow for S-wave velocity estimation where S-wave velocity data are absent in the gas field and neighbouring fields with the same lithology, based on the Kuster–Toksöz and Xu–Payne models applied to the Pannonian Basin limestone reservoir. The results are calibrated with the P-wave velocity obtained from borehole data and the vertical seismic profile (VSP) S-wave interval velocity. Although rock physics models are idealized analogues of real rocks, a very good correlation was obtained between the modelled and measured P-wave velocity, as well as between the modelled S-wave velocity and the VSP interval velocity. The study also illustrates the problem of defining the pore aspect ratio in zones of increasing shale content. Due to the limited research on the limestones of the Pannonian Basin, these results enable a better understanding of the seismic parameters of the Pannonian Basin limestones. The results indicate that the proposed workflow gives an adequate estimation of S-wave velocities.

Trans-dimensional inversion of multimode seismic surface wave data from a trenched distributed acoustic sensing survey

SUMMARYSeismic data acquired from surface-deployed distributed acoustic sensing (DAS) fibre are broad band and typically dense spatially sampled. Corresponding to these features, compared with geophone data, the low-frequency components in DAS data show higher signal-to-noise ratio and multimode dispersion curves by broad-band DAS data exhibit a higher resolution, which increases the investigation depth of near-surface structures and enhances identification and picking of dispersion curves, respectively. Therefore, DAS data are ideal for the estimates of reliable and highly resolved near-surface velocity profiles. As surface-wave dispersion inversion (SWD) is a natural scheme for near-surface investigation, in this study we have formulated a DAS-SWD inversion in which multiple SWD modes are extracted from the DAS data, and are used as input to a trans-dimensional (TD) inversion procedure, in which the number of subsurface layers is treated as an unknown. Vibroseis data with a minimum frequency of 1 Hz were sensed along a horizontal surface trench as part of a baseline seismic survey carried out by the University of Calgary at the Containment and Monitoring Institute Field Research Station in Newell County, Alberta, Canada. These surface DAS data readily permit the picking of multimode dispersion curves, which are observed to enhance velocity profile resolution in both shallow and deep regions of the near-surface simultaneously, with the TD algorithm adapting the model to reflect this improved resolution. To avoid collecting abnormal model samples with thin-interleaved high- and low-velocity layers based on the known geological information of the field site, we employed constraints that preclude the structures that have velocity drops over 100 m s−1 along depth. Data errors are estimated via a non-parametric iterative process in terms of covariance matrices that include off-diagonal elements. Synthetic examples show that SWD with higher-order modes provides additional constraints on the structure and accurate noise estimation. Inversion of the field data resulted in high-resolution estimates of shear wave velocity as a function of depth throughout the top 120 m of the subsurface. The inferred structure is consistent with existing estimates of the regional lithology but resolves additional layers between 1- and 50-m depth.

Robust estimation of shear wave velocity in a carbonate oil reservoir from conventional well logging data using machine learning algorithms

In recent decades, various applications of shear wave velocity have been reported in oil and gas projects. These applications include the determination of lithology, estimation of geomechanical parameters, reservoir fluid detection, etc. The shear wave velocity is usually measured or recorded by DSI (Dipole Shear Sonic imager) instruments. This log is not usually available in all wells of a hydrocarbon field because of the high cost of running this log compared to other conventional logs. However, it is tried to estimate the shear wave velocity from other related logs.The literature contains many empirical rock physics models used to estimate shear wave velocity. Most of them were developed for sandstone lithologies and didn't applicable in carbonate reservoir because of existing some vertical and horizontally complex fracture networks in the carbonate reservoirs that cause a quite different behavior for the shear wave velocity.The main objective of this research is to concentrate on the effect of lithology in the estimation of the target log for a carbonate reservoir. At the earliest step, preprocessing is performed and 455 data points of several conventional well logs, i.g., acoustic, density, neutron porosity, resistivity, gamma-ray, calcite volume, dolomite volume, and water saturation logs have been applied to generate the synthetic shear wave velocity log in a carbonate oil reservoir.First, original data are divided into training, validation, and testing subsets. Different data-driven predictive models were built: Multiple Linear Regression (MLR), Ensemble Learning Method (ELM), and three architectures of Artificial Neural Networks (ANN). The performance of all techniques was compared with each other. Finally, the feed-forward neural network shows the highest accuracy with R-values of 0.99 and 0.96 for the training and testing datasets. Moreover, the network design and tuning parameters of the model are adjusted using the grid search optimization technique to obtain the optimal design of the artificial neural network for estimating the shear wave velocity log. Hence, a deep artificial neural network, i.e., [8–11–15-1] network with bayesian regularization training algorithm and hyperbolic tangent sigmoid transfer function, is proposed to predict the target response in other wells.

Regional-scale site characterization mapping in high impedance environments using soil fundamental resonance (f0): New England, USA

In this work, we develop regional-scale site characterization maps of New England, a glaciated region in the Eastern United States, using site fundamental frequency (f0). Due to the strong impedance contrast that creates strongly resonant site response behavior in New England, f0 is the preferred site response proxy for the region and best characterizes spatial variability in soil amplification. We first develop a database of 1577 f0 values collected from the literature (1313) and picked from HVSR curves collected during an additional field campaign (487). Using the surficial geologic units from the conterminous US surficial geology map, we compute distributions of f0 for each of the surficial geologic units mapped in New England. We find that the thick glaciofluvial ice-contact sediments and thick proglacial sediments of Cape Cod and Long Island are characterized by f0 distributions with the lowest medians (1.06 and 1.03 Hz respectively) and narrowest interquartile ranges (0.23 and 0.28 Hz respectively) in New England, which we interpret as being the thickest sediments in the region. The f0 distribution of the thin, proglacial sediments, mostly fine-grained in the Boston Basin, the coast of Lake Champlain and the Maine coast has a relatively low median (2.70 Hz), however it has high variability (3.65 Hz interquartile range) since the sediment thickness varies widely in this geologic unit. The f0 distribution of the thin alluvial sediments of the Connecticut River Valley also has a low f0 median (1.83 Hz) however, it has less variability than the proglacial sediments, mostly fine-grained thin (1.59 Hz). We present maps of the median and interquartile range of the f0 distributions and their corresponding Vs30 distribution approximations. Vs30 distributions are developed for each of the surficial geologic units by assuming a layer-over-halfspace model for site response and establishing estimates of sediment average shear-wave velocity (Vsavg) for each of the units based on a limited number of shear wave velocity profiles from the region. We compare our results against two existing site characterization maps for the region, Wald and Allen (2007) and Becker et al. (2011) and find that our updated maps result in median Vs30 values that tend to be higher than Becker et al. (2011) except in geologies with consistently deep profiles, and lower than Wald and Allen (2007) median values, with the exception of coastal zone sediments.

Shear wave velocity estimation in the Bengal Basin, Bangladesh by HVSR analysis: implications for engineering bedrock depth

The S-wave velocity (VS) in the Bengal Basin, Bangladesh has not been resolved from the ground surface to an intermediate depth in a regional context despite its importance for seismic hazard and risk evaluation. For this reason, we estimated VS profiles beneath 19 seismic stations in Bangladesh to a depth approximately 2800 m by employing full horizontal to vertical spectral ratio (HVSR) curve inversion under the diffuse field theory for the noise wavefield. The seismic stations are concentrated in three tectonic zones within the basin: the Surma basin (SB, Zone 1), Bengal Foredeep (BF, Zone 2), and Chittagong Tripura Fold Belt (CTFB, Zone 3). Full HVSR analysis (from 0.2 to 10 Hz) allowed us to obtain deep profiles with combined insights from shallow geotechnical boreholes and deep P-wave velocity (VP) information from active seismic surveys. From the resultant VS profiles, engineering bedrock (VS > 760 m/s) depths were also identified throughout the study area for the first time. The VS profiles within the Holocene to Miocene sedimentary sequences showed rapid variations from location to location. This is due to the highly variable near-surface geology caused by the dense and complex river network and tectonic deformation in Bangladesh. Except for three stations, the engineering bedrock depth exceeded 30 m at all stations. These results indicate the existence of deep soft soil in the study area, where VS30 based site characterization is inappropriate. Furthermore, seismic site response was estimated at a station (DHAK) by simulating a subduction zone earthquake. The resulting response spectrum (RS) exhibited ground motion amplification over a longer period, suggesting that multistory buildings at the site may be at risk if subjected to large earthquakes. The outcomes of this study can serve as useful guidelines for seismic risk reduction planning in Bangladesh.

Seismic microzonation based on HVSR inversion results for shear wave (Vs30) mapping and soil vulnerability in West Sulawesi and South Sulawesi Regions

The Horizontal to Vertical Spectral Ratio (HVSR) is a seismic analysis method used to obtain information about subsurface geological characteristics. This method is based on the spectral ratio analysis between the horizontal and vertical components of earthquakes or other vibration sources. HVSR has proven to be an effective tool in evaluating rock layers, layer thickness, rock hardness, and geological hazard potential. In the HVSR method, seismic data are measured using three-component seismometers, the N-S (North-South), E-W (East-West), and vertical components. In this study, the HVSR method is used for Vs30 inversion to estimate shear wave velocity at a depth of 30m. The southern and western regions of Sulawesi have various geological conditions and high seismicity levels. The results of this inversion can be used for mapping soil vulnerability indices. The obtained soil vulnerability index values indicate high vulnerability near the Palu-Koro fault, with A0 9.27 at F0 3.2 Hz. This method can also estimate shear wave velocity values at a depth of 30 m. Vs30 values range from 200 m/s to 500 m/s at various measurement points, indicating variability. According to the SNI 1726:2012 and NEHRP classifications, the research area has soil conditions ranging from medium to soft.

Two-stage broad learning inversion framework for shear-wave velocity estimation

Shear-wave (S-wave) velocity is considered an essential parameter for the study of the earth, and Rayleigh wave inversion has been widely accepted and used to determine it. Given high-quality measured dispersion curves, the inversion performance depends on the applied optimization algorithm inside the inversion process. We propose a novel inversion framework to promote efficient and accurate inversion, i.e., a two-stage broad learning inversion framework (TS-BL). The proposed TS-BL not only inherits the powerful mapping capability and simple configured structure of broad learning (BL) network but also makes two significant improvements to better acclimatize itself to Rayleigh wave inversion. First, TS-BL adopts a two-stage inversion strategy to perform optimizing two times. It does not yield the same search space in the two inversion stages. In the first stage, because the inversion aims to find an approximation rather than the accurate value of model parameters, the difficulty in constructing the mapping model is reduced by sacrificing accuracy. Then, an effective BL network can be established using smaller sample sizes. In the second stage, the search space becomes much narrower, commencing with the approximation results obtained in the prior stage. This helps the final BL network to easily and quickly model the actual relationship between measured dispersion curves and unknown model parameters. After that, the forward modeling of measurements rather than the validation data set is exploited for tuning the network’s hyperparameters. The physical model is superior to the validation data set for selecting a suitable network complexity to adapt to the measured dispersion curves because the latter only describes an overall relationship. As a result, accurate S-wave velocities can be efficiently acquired by using the proposed TS-BL with a low cost of training samples. The efficiency and reliability of TS-BL have been demonstrated in numerical and field data examples.

Experience of differential diagnosis of metastases in peripheral lymph nodes in the progression of cervical cancer and endometrial cancer by determining the shear wave velocity

The purpose of this study is to evaluate the possibilities of using the technique of determining the shear wave velocity in the differential diagnosis of pathologically altered lymph nodes in assessing the therapeutic effect of cervical cancer and endometrial cancer. The results of 20 patients with hyperplastic lymph nodes were analyzed, of which 8 patients were diagnosed with a cytologically confirmed metastatic lesion based on a set of ultrasound signs. When using the shear wave velocity estimation technique in these nodes, a high velocity was determined in all observations, the average value of which was about 2.9 m/sec, while in hyperplastic nodes of a benign nature, this velocity averaged about 1.3 m/sec.

Estimation of shear sonic logs in the heterogeneous and fractured Lower Cretaceous of the Danish North Sea using supervised learning

AbstractShear wave velocity information is valuable in many aspects of seismic exploration and characterization of reservoirs. However, shear wave logs are not always available in the interval of interest due to cost and time‐saving purposes. In this study, we present a tailored supervised learning approach to estimate shear wave velocity from well‐log measurements in the Lower Cretaceous succession of the Valdemar and Boje fields in the Danish North Sea. Our objective is to investigate the performance of four supervised learning regression models (linear, random forest, support vector and multi‐layer perceptron). A limited well‐log data set from six wells is used for training and testing the supervised learning models. A set of well data containing normalized gamma ray, compressional wave velocity, neutron porosity and medium resistivity logs gave reasonable shear wave velocity estimates in the test wells with root‐mean‐square error scores within the range of other published studies. Based on limited input data and complex geology, the multi‐layer perceptron was the most successful model in predicting the reservoir sections of the test wells. However, all models lacked stability in the overburden zones. Lastly, re‐training the multi‐layer perceptron on the six wells to predict missing shear wave velocity in a nearby well showed promising results for further reservoir characterization. The obtained results can yield useful input into, for example, seismic pre‐stack inversion, amplitude versus offset analysis and rock physics analysis.

Comparison of dynamic and static properties of sandstone and estimation of shear wave velocity and Poisson’s ratio

Comparison of dynamic and static properties of sandstone and estimation of shear wave velocity and Poisson’s ratio