Determine Shear Wave Velocity Research Articles

Prediction of shear wave velocity in three sedimentary rocks in East Baghdad oilfield using multiple regression analysis

Shear wave is a crucial parameter for assessing the wellbore stability, the stress response, and rock deformation. It is essential for constructing the mechanical earth model (MEM) for many applications related to reservoir geomechanics including wellbore stability, sand production, hydraulic fracturing, and fault reactivation. However, shear sonic data is often omitted during the well-logging measurements for cost and saving purposes. To overcome this challenge, recent research has been focused on determining shear wave velocity through the use of core plugs, empirical correlations, artificial intelligence techniques, and multiple regression to quantify and evaluate the mechanical properties of subsurface formations without performing direct measurements at the wellbore. The greatest difference between this study and the literature is to predict the shear wave velocities for three sedimentary rocks based on conventional well logs. This study has been conducted on datasets of two wells drilled in the East Baghdad oilfield, for which there is a lack of shear wave data. Two formations (Tanuma and Zubair formations) within the production section of this field were conducted to develop new models for determining the shear wave velocity using multiple regression analysis. These two formations primarily consist of three lithologies: limestone, sandstone, and shale. Before the model development, data analysis on the selected data was applied to figure the most influential parameter(s) in determining the shear wave velocity. The results of the developed models are then compared with the previous models in the literature. The results showed that the multiple regression analysis technique is a powerful technique in determining shear wave velocity with high-performance capacity. The correlation coefficient ( ) and the root mean square error (RMSE) were 0.84 and 0.092 for limestone, 0.84 and 0.0972 for sandstone, and 0.86 and 0.0796 for shale respectively. Furthermore, the performance of the developed models is well matched to the actual shear wave data rather than the Castagna correlations. The findings of this study are effective in determining shear wave velocity for future applications related to reservoir geomechanics without needing costly well-log or core measurements.

Application of active MASW study on irregular and undulated sloping terrain: a case study

ABSTRACT Multichannel Analysis of Surface Waves (MASW) is a geophysical technique used for deciphering subsurface profiling by the determination of shear wave velocity. It is increasingly utilized in sloping terrain; however, its accuracy is limited by the presence of undulating or irregular topography. The study analysed the impact of undulating topography on sloping terrain using models of depressed and elevated topography. The surface waves lose more energy as depth, width, and steepness of the undulated topography increased. The study revealed that the elevated topography had a greater influence than depressed topography. The undulating topography in close proximity to the source resulted in a decrease in resolution dispersion, however the terrain located further away did not have a substantial impact. The study aims to identify suitable configurations that must be taken into account while identifying the subsurface characterization of a sloping terrain using the MASW.

Variability in receivers responses of MASW test on undulated grounds: A numerical perspective

This article presents methodological and signal processing cautions associated with utilizing the multichannel analysis of surface waves (MASW) test for determining shear wave velocity (Vs) profiles on undulated ground surfaces. The past studies highlight that the conventional MASW test, which is suitable for flat ground surfaces, may not directly apply to undulated grounds with features like heave, crest, etc. Previous studies on MASW test lack an understanding of the influence of source and receiver positioning on various undulations. Three scenarios of ground undulations were created in GiD software and analyzed in the OpenSEES software framework. There were 12 receivers used for these ground undulations based on the Fourier amplitude spectrum attenuation. The receivers' responses (horizontal and vertical components of wavefields) from these ground undulations were analyzed through signal processing using cross-correlation, time-lag, and wavelet coherence. Notably, the undulated topography significantly impacted the vertical components of the wavefield. The phase angle variations from wavelet coherence could be used to determine the minimum frequency of the dispersion curve for these ground undulations. Significant variations in the phase velocity were observed at lower frequencies in the presence of ground undulations, while on a flat surface, they remained unaffected. The effect of under−/over-estimation of Vs (in the range of 50–100 m/s) was successfully demonstrated in each respective undulation with the methodological and processing cautions needed in the MASW analysis.

Shear Wave Velocity Determination of a Complex Field Site Using Improved Nondestructive SASW Testing.

The nondestructive spectral analysis of surface waves (SASW) technique determines the shear wave velocities along the wide wavelength range using Rayleigh-type surface waves that propagate along pairs of receivers on the surface. The typical configuration of source-receivers consists of a vertical source and three vertical receivers arranged in a linear array. While this approach allows for effective site characterization, laterally variable sites are often challenging to characterize. In addition, in a traditional SASW test configuration system, where sources are placed in one direction, the data are collected more on one side, which can cause an imbalance in the interpretation of the data. Data interpretation issues can be resolved by moving the source to opposite ends of the original array and relocating receivers to perform a second complete set of tests. Consequently, two different Vs profiles can be provided with only a small amount of additional time at sites where lateral variability exists. Furthermore, the testing procedure can be modified to enhance the site characterization during data collection. The advantages of performing SASW testing in both directions are discussed using a real case study.

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.

LIQUEFACTION-INDUCED DAMAGE IN THE CITIES OF ISKENDERUN AND GOLBASI AFTER THE 2023 TURKEY EARTHQUAKE

<p>On February 6th 2023 two major earthquakes struck southeast Turkey, M7.7 Kahramanmaras and M7.6 Elbistan, respectively. Unfortunately, due to the impact of these catastrophic events more than 50 000 casualties and 35 000 collapsed buildings have been reported since then. The aim of the study is to demonstrate preliminary site response analysis and assessment of re-liquefaction potential of sites which have been affected by the earthquakes – especially the cities of Iskenderun and Golbasi. Both site-specific areas have clear evidences of liquefaction and lateral spreading events which imply the focus of the presented paper. A series of geophysical MASW and microtremor tests have been performed in order to determine shear wave velocities up to depth of 30 m as well as the fundamental natural frequency of the soil deposits. </p> <p>Moreover, samples have been collected from sand and silt ejecta in order to evaluate some basic physical properties – grain-size curves, specific gravity and plasticity parameters. On the basis of the obtained data seismic classification of the investigated sites according to current design codes has been made and in-depth distance to relatively stiff layer has been assumed. For the sake of evaluating risk of re-liquefaction the widely-used simplified stress-based approach to triggering assessment has been adopted considering some rules of the thumb (e.g., sieve analysis and plasticity properties evaluation). Lastly, post-liquefaction reconsolidation settlement and lateral displacement have been determined in terms of future earthquakes</p>

Use of machine learning in determining Gmax from bender element tests

The use of bender element is one of the most popular methods of determining shear wave velocity, and hence elastic shear modulus due to its relatively straightforward experimental set-up. While several analysis methods have been proposed, manual interpretation using the first arrival continues to be favoured owing to its simplicity. This paper presents a novel automated program for determining the shear wave velocity and associated maximum shear modulus. The proposed new method involves the use of Convolutional Neural Networks (CNNs) to predict the most probable shear wave velocity using a range of input frequencies as the inputs. Estimates made by the trained CNN are compared to values determined using more traditional interpretation methods (first-arrival, cross-correlation and frequency domain). The program is able to autonomously determining the shear modulus in the three principal orientations (Gvh, Ghv, and Ghh) at a range of stress levels. The shear modulus determined using the range of techniques showed great agreement. Statistical analysis of the determined shear modulus regression of over 0.99 between interpretations made using first arrival and that estimated using the new CNN approach.

A new machine learning approach for estimating shear wave velocity profile using borelog data

The shear wave velocity is among the key parameters that are responsible for damage caused during an earthquake. Determining shear wave velocity is a costly and time-consuming direct geophysical method. In the present paper, a machine learning model has been developed to obtain the subsurface shear wave velocity profile without using direct methods. The bore log information and the subsurface shear wave velocity profile available at various stations of Japan’s Kyoshin network (K-NET) have been utilized for training this machine learning model. The parametric correlation study indicates that simple parameters like rock/soil type, the thickness of the layer in the model, and standard penetration test (SPT-N) value directly correlate with the medium’s shear wave velocity. A stacked ensemble machine learning model named VelProfES (an acronym for Velocity Profiler using Ensemble machine learning models) has been developed in this paper and has been utilized for effective prediction of the shear wave velocity profile using basic information from borelog data. The dataset used in the training and testing of the machine learning model consists of borelog and shear wave velocity information from 1101 stations. Of 1101 stations, 657, 283, and 71 stations have been utilized for training, testing, and validating the machine learning model. Training, testing, and validation of the developed machine learning model consist of parameters from 12351, 5279, and 1388 velocity layers. The problem of data imbalance based on soil type has been addressed using an additional 10510 layers of synthetic borelog data generated from conditional generative adversarial networks (CTGANs). A feature and model ablation study was conducted for the VelProfES model to provide substantiation for the model and feature selection choices. The predicted shear wave velocity profiles were compared at specific stations, focusing on average velocities at 5, 10, 15, and 20 depths. Further, the predicted values have been compared with the empirical relation of Sil and Haloi (2017) and a trained polynomial model. The machine learning model demonstrates close alignment between predicted and actual values across a broad spectrum of velocities, a contrast not observed in the empirical relation and polynomial model. The results show that the machine learning models and augmented data generated using CTGANs can efficiently minimize the error between actual and predicted subsurface shear wave velocity values.

Appraisal of rock dynamic, physical, and mechanical properties and forecasting shear wave velocity using machine learning and statistical methods

Direct determination of shear wave velocity requires time, cost, and high accuracy due to the complexity of the rock texture. In current research statistical and intelligent approaches have been used to predict the shear wave velocity of rock samples. Also, a new correlation between dynamic and static rock properties was established and the shear wave velocity was estimated based on index tests using Gaussian process regression, multivariate linear regression, feedforward back-propagation artificial neural network, and K-nearest neighbor methods. In total, 120 data related to limestone and sandstone samples of the main projects were used for modeling. Water absorption, compressional wave velocity, and density were used as inputs. The outcomes revealed that the PW/SW ratio is equal to 1.69. Various statistics were used to check the method results. The statistical results showed that it is possible to forecast Ed, and SW with high accuracy. Also, the precision of the GPR was higher than the FBP-ANN, statistical analysis, and KNN. Estimation of SW by GPR showed R of 0.992, and RMSE of 0.06, respectively. These four methods were able to estimate the SW with a mean variation percentage of +0.19%. It's important to consider that these models are best suited for predicting SW when the predictor indicators fall within the same range as this study.

Shear-wave velocity determination by combining data from passive and active source field investigations in Kumamoto city, Japan

We present the 1D subsoil structure and local site effects at KUMA strong ground motion station in Kumamoto City, Japan. We analyze data from a field campaign conducted in the framework of the Blind Prediction BP1 test of the 6th IASPEI/IAEE International Symposium: Effects of Surface Geology on Seismic Motion. In parallel with other participants of the BP1 test, we process data from passive and active source measurements aiming to determine the shear-wave velocity, Vs, structure and the site response at KUMA station. Passive measurements are associated to five microtremor arrays. In each array, seven seismometers have been deployed in a common-center triangle shape, recording microtremors simultaneously for 1 to 2 h. The vertical component of microtremors was analyzed using the spatial autocorrelation (SPAC) method. Cross-correlation coefficients were computed for all station pairs available for each array. By fitting the average SPAC’s coefficients to the first-kind zero-order Bessel function, J0, and assuming that microtremors primarily comprise fundamental mode Rayleigh waves, phase velocity dispersion curves were determined. Phase velocity values for frequencies > 15 Hz were obtained from data of a close-by active source geophone profile. We integrated the data with those of the passive measurements and obtained an experimental phase velocity dispersion curve. The resulting curve shows low velocity variation, from 150 to 200 m/s, in the surface layers, whereas significant dispersion appears in frequencies below 2.5 Hz. By inverting this curve, we achieved to determine the 1D shear-wave velocity structure at KUMA station. Site response characteristics were determined by applying the Horizontal-to-Vertical-Spectral-Ratios method. Significantly amplified peaks in the frequency range between 0.3 to 1.5 Hz dominate HVSR spectral ratios. These peaks correspond to resonant frequencies of soils and originate from different impedance contrasts within the substratum of the site.Graphical

Field and laboratory measurements of shear wave velocity in unsaturated soils

Shear wave velocity measurements in soil are important for assigning site soil profiles to seismic site classes and for calculating other dynamic soil properties. The seismic cone penetration test (SCPT) is commonly used to determine shear wave velocity profiles in the field. In the laboratory, bender elements provide an easy means to measure shear wave velocity of soil specimens from discrete depths. In this study, shear wave velocity measurements obtained from unsaturated soil profiles in the field and on field samples in the laboratory under similar stress conditions were analyzed and compared. Additionally, the influence of seasonal changes on the behavior of shear wave velocity was investigated. Comparisons of shear wave velocities from the field and laboratory were favorable for similar moisture and stress conditions. However, results showed that as the degree of saturation increases and suction decreases the shear wave velocity can decrease significantly.

Prediction of Sonic Shear Wave Using Artificial Neural Network

Shear wave time is an important parameter participating in the calculation of rock mechanics properties. Many evaluation processes including wellbore stability analysis and sand production prediction are based on rock mechanics properties so these processes are directly related to the estimation of shear wave time. Several empirical correlations have been developed to predict shear wave time using regression analysis and artificial neural network techniques for the estimation of relationships between a dependent variable and one or more independent variables for certain conditions of the reservoir. However, they are not appropriate for reservoirs with different conditions as well as all effective parameters are not considered in previous relationships. In this study, the artificial neural network is adopted for predicting shear wave time using datasets consisting of 1922 data points for a certain directional oil well from Iraqi Fauqi oil field wells. Two sets of input parameters are tried: the first trial includes the readings of seven logs (Gamma-ray, caliper, compressional sonic wave, density, neutron, deep resistivity, true vertical depth), while the second trial includes the azimuth and the inclination angles in addition to the above seven readings. The optimum structure for both datasets is obtained using 12 neurons in a single hidden layer (ANN-7-12-1 and ANN-9-12-1). The statistical results reveal that an improvement is achieved when the well azimuth and inclination are included in the ANN model. A mathematical model with high performance using an artificial neural network has been developed. The mean square error and the determination coefficient for the developed model were 14.22 and 0.952 for ANN-7-12-1, while they were 9.62 and 0.966 for ANN-9-12-1, respectively. This study presents a simple mathematical model for further determination of shear wave velocities using ANN techniques which can be then integrated with the existent petroleum software programs.

Distributed acoustic sensing for active offshore shear wave profiling

The long-term sustainability of the offshore wind industry requires the development of appropriate investigative methods to enable less conservative and more cost-effective geotechnical engineering design. Here we describe the novel use of distributed acoustic sensing (DAS) as part of an integrated approach for the geophysical and geotechnical assessment of the shallow subsurface for offshore construction. DAS was used to acquire active Scholte-wave seismic data at several locations in the vicinity of a planned windfarm development near Dundalk Bay, Irish Sea. Complimentary additional datasets include high-resolution sparker seismic reflection, cone penetration test (CPT) data and gravity coring. In terms of fibre optic cable selection, a CST armoured cable provided a reasonable compromise between performance and reliability in the offshore environment. Also, when used as a seismic source, a gravity corer enabled the fundamental mode Scholte-wave to be better resolved than an airgun, and may be more suitable in environmentally sensitive areas. Overall, the DAS approach was found to be effective at rapidly determining shear wave velocity profiles in areas of differing geological context, with metre scale spatial sampling, over multi-kilometre scale distances. The application of this approach has the potential to considerably reduce design uncertainty and ultimately reduce levelised costs of offshore wind power generation.

Seismic site characterization with shear wave (SH) reflection and refraction methods

Reflection and critically refracted seismic methods use traveltime measurements of body waves propagating between a source and a series of receivers on the ground surface to calculate subsurface velocities. Body wave energy is refracted or reflected at boundaries where there is a change in seismic impedance, defined as the product of material density and seismic velocity. This article provides practical guidance on the use of horizontally propagating shear wave (SH-wave) refraction and reflection methods to determine shear wave velocity as a function of depth for near-surface seismic site characterizations. Method principles and the current state of engineering practice are reviewed, along with discussions of limitations and uncertainty assessments. Typical data collection procedures are described using basic survey equipment, along with information on more advanced applications and emerging technologies. Eight case studies provide examples of the techniques in real-world seismic site characterizations performed in a variety of geological settings.

Seismic Surface Waves and Borehole Methods to Determine Shear Wave Velocity: A Review of Measurement Practice by Contractors in the UK

Seismic Surface Waves and Borehole Methods to Determine Shear Wave Velocity: A Review of Measurement Practice by Contractors in the UK

Constructing shear velocity models from surface wave dispersion curves using deep learning

Surface wave tomography has been widely used to determine shear wave velocities by inverting surface wave dispersion curves. Conventional least-squares inversions strongly depend on an initial model and Monte Carlo inversion algorithms are usually time-consuming. In this study, we apply a deep neural network (DNN) to surface wave dispersion curves to investigate whether the initial model can be relaxed and whether reliable shear velocity models can be constructed. By applying our method to synthetic and field data, our results show that: (1) by constructing a well-trained DNN model from the global continental CRUST1.0 data, the DNN approach is effective and efficient to determine shear velocity structures using Rayleigh wave dispersion curves; (2) using the well-trained DNN model, no prior model is required, relaxing the requirement of an initial model; (3) the well-trained DNN model can be used to construct pseudo 3D seismic models across different continental areas.

New Approach to Concurrent VS and VP Measurements Using Bender Elements

Abstract Bender elements (BEs) have become a routine geotechnical laboratory tool for seismic wave velocity measurements. Since the 1980s, this testing technique has gained popularity, currently being available in many geotechnical laboratories worldwide in a variety of apparatuses. The advantage of simultaneously measuring small- and large-strain soil stiffnesses in each device and the easiness and low-cost implementation of BE are the main reasons for their common application. Although there is already a standardized procedure for BE testing (ASTM D8295-19, Standard Test Method for Determination of Shear Wave Velocity and Initial Shear Modulus in Soil Specimens Using Bender Elements), the use of high-frequency pulses for the simultaneous measurement of compressional (VP) and shear (VS) wave velocities is not considered. In contrast, the use of high-excitation frequencies is usually discouraged, as they tend to induce spurious participation of high-vibration modes in the BE response. However, this work shows that the use of higher vibration modes can be advantageous to evaluate P-wave velocities from standard BE testing. Thus, this paper presents a new approach for the concurrent measurements of VP and VS using a typical installation of BE. The new approach is first demonstrated by experimental measurements and subsequently validated using high-frequency laser vibrometer measurements of the actual BE deformation (nanometer scale) under different excitation frequencies. The laser vibrometer measurements show the displacements of the transmitter BE as a function of the input frequency in not only the horizontal but also in the vertical directions, demonstrating the generation of P-waves when higher vibration modes are excited. The measured VP values are shown to be in good agreement with the predicted values using Biot’s equations. Thus, the proposed methodology addresses the current knowledge gap in the use of BE for concurrent P-wave and S-wave velocity measurements. The generated wavelengths are large enough to travel through the soil skeleton instead of the pore water only.

Modified refracted ray path method for determination of shear wave velocity profiles using seismic cone

The commonly used data reduction methods to determine shear wave velocity (Vs) require the depth interval of the Vs profile to be the same as the in-situ testing interval. The Vs values derived in this way are the mean values within the depth intervals. However, the in-situ multilayer interfaces do not always coincide with the predetermined testing depths, and the materials may change dramatically across layers and thus the above interpretation methods may not always produce representative soil properties. In this study, a modified refracted ray path method for more flexible Vs calculation is proposed. The method takes advantage of the routine cone penetration testing data obtained in SCPT tests and applies a combined approach for the soil layer identification using both the corrected cone tip resistance (qt) and the Soil Behavior Type Index (Ic) as indicators. Vs is then calculated using the Snell's law based on the original shear wave travel times and the newly identified layer profiles. The proposed method can avoid the error caused by crossing the multilayer boundaries and provide a more accurate prediction of soil type and the corresponding stiffness properties. The data show that by using the proposed method, the accuracy of the estimated Vs can be improved by up to 77% for a soil layer with interface not located at testing depths, as compared to Vs obtained from the conventional methods.

Determine Shear wave velocities and Elasto-Dynamic properties for Nassiriya Refinery project in Thi qar Governorate- Nassiriya District- South of Iraq

Four seismic refraction profiles for both compressional (P) and shear (S) waves had been surveyed within Nassiriya District/Thi Qar Governorate, South of Iraq, by the use of two impacts; normal and reverse shootings. This technique was done in order to delineate depths and thicknesses of the layers (soils) also elastic modulus were calculated for Knowledge a nature of the area soil to build engineering facilities. The calculations demonstrate that there are three shallow subsurface layers were found. The average of velocities and thicknesses of the first layer are equal to 645.95, 271.6 m/sec and 4.75 m, and for the second one 1130.55, 506 m/sec and 4.5m. and for the third one are 1666.6, 714 m/sec and 12.for each P and S-waves respectively. Moreover, Site engineering information including geotechnical properties were also measured and analyzed to enhance the main targets of this study, where the mean dynamic elastic constants were ranged between {κ=(597.92-4376.06) Mpa, μ=(138.31-1063.43) Mpa, E=(385.23-2951.24) Mpa and, σ=(0.39-0.38)}.

Monitoring the strength properties of electrokinetically treated soil by bender elements to determine the treatment period

Bender elements have been used as a non-destructive soil investigation technique by many researchers and have proven to be effective in predicting the shear strength of various soils. In this paper, electrokinetic treatment tests were performed with embedded bender elements to monitor the increase in the shear strength of a soft sandy clay during the treatment. The bender element system, designed and assembled for this study, was integrated into the electrokinetic treatment process in order to quell a common uncertainty associated with this form of soil improvement technique, namely: when is the treatment completed? The cross-correlation and first-peak arrival times were used to measure the shear wave velocity of a clayey soil under the treatment of electrokinetics using bender elements. To determine shear wave velocity before and during treatment, a variety of shear wave tests were performed every hour of treatment using frequencies ranging from 100 Hz to 2500 Hz via the use of bender element. The results show that monitoring the soil improvement during the treatment by bender elements can shorten the treatment time by 43% and reduce the energy consumption, which is a major expenditure in an electrokinetic treatment process, by 33% while consistently improving the shear strength and the load capacity by approximately 200% and 300%, respectively.