The field measurement of shear wave velocity (Vs) is essential for geotechnical design practices, as it directly provides the initial tangent shear modulus at very small strain levels (γ_s < 10^-6) in geo-materials. The small strain shear stiffness is a fundamental soil property for assessing dynamic loading responses, ground vibrations, and static deformation problems related to shallow and deep foundations. In addition, the Vs is one of the critical elements in evaluating seismic ground hazards such as site amplification and liquefaction potential. Various field and laboratory geotechnical site investigation programs in Kazakhstan have been conducted to understand basic soil behavior. However, in-situ geophysical seismic surveys such as surface reflection and refraction tests and down-hole and cross-hole tests were generally not included in the site investigation program in Kazakhstan, and a few limited seismic surveys have been carried out for specific projects. In most prior construction projects, the small strain shear stiffness was assessed by limited data using general empirical correlations from other in-situ measurements or selective laboratory testing programs that may result in significant uncertainties. In this study, in-situ dynamic soil characteristics of loam soils using active MASW (multi-channel analysis of surface waves) testing are evaluated to obtain comprehensive insights for geotechnical boundary value problems. The resulting Vs profiles are in good agreement with a-priory known geotechnical information (e.g., borehole logs) of sites. Thus, to minimize potential uncertainties of dynamic soil properties estimation via in-situ tests, MASW methods are suggested for construction works in Kazakhstan.

A deep learning model has been developed to associate microtremor H/V spectra with soil profiles. This model has the capability to predict soil profiles based on microtremor H/V spectra at any given location. In constructing the model, the first step involves converting acceleration H/V spectra into color spectra (color images), which are then classified into observation sites using deep learning techniques. The dataset utilized in this study comprises microseismic motions with accelerations of 50 gal or less, in total 13,150 waveforms obtained from 87 K-NET stations. The learning model for these color images is tested and demonstrates the ability to classify observation sites with an accuracy of approximately 80%. Additionally, the model is employed to identify the color images of 10 locations within the Kansai University campus, where the soil profiles are known, and compare these profiles with K-NET sites exhibiting similar spectra.

Significant uncertainties in the characteristics of natural geomaterials limit the applicability of theoretical predictive models for real-life problems in geotechnical engineering. Over the past few decades, various geophysical techniques, based on the characteristics of seismic wave propagation in heterogenous geomaterials, have been used to reduce the epistemic part of these uncertainties. These techniques generally rely on sparse field seismic measurements on the ground surface, or within a borehole, to retrieve information on the subsurface layering and material properties. One of the major shortcomings of these approaches is their selectiveness in using only part of the recorded data (for example, using first-arrival times only). The full-waveform inversion (FWI) technique, on the other hand, utilizes the entire content of the seismic record to extract subsurface properties. This method, however, has not been attractive to the geoengineering community due to its high computational cost and involved formulation, both of which render FWI an abstract technique rather than a practical approach. In this study, we propose to use Physics-Informed Neural Networks (PINNs) to alleviate these two limitations and develop a robust, yet not complicated, inversion technique for geotechnical applications. Acting as a bridge between traditional physical models and data-driven neural networks, PINNs infuse the underlying physics into neural networks by adding the governing equations to the loss function. The resultant algorithm can train the model with fewer data points and better predict the response beyond the range of the training data set. PINNs can also be used to solve seismic inversion problems by defining unknown P- and S-wave velocities as trainable parameters. To demonstrate the effectiveness of the proposed approach, we apply it to the problem of 2D geotechnical subsurface characterization. We consolidate the governing geometric and material parameters into a set of normalized parameters, such as dimensionless frequency and normalized thickness. Then, we generate synthetic data using a Finite Volume forward solver of the Navier-Cauchy equation and use our FWI-PINNs approach to retrieve unknown normalized parameters. Lastly, we use actual seismic records at the Treasure Island Downhole Array site to investigate the performance of FWI-PINNs under realistic conditions. With the fast-growing advancements in GPU-based machine learning algorithms and their public availability and simplicity, we believe the proposed inversion method can turn into a fast, robust, and practical site characterization tool in the geotechnical engineering community.

As part of the SHARP-storage project, a new ground motion model (GMM) for the North Sea is being developed. The GMM will be defined for a reference rock condition, and amplification factors to estimate shaking at the soil surface will be derived based on a database of one-dimensional (1D) site response analyses. This paper describes the selection and development of site profiles that will be used in the site response analyses. The tectonic and geological development of the North Sea is complex. The sediments encountered in the North Sea have been deposited in dynamically changing environments ranging from arctic to temperate and reworked by the movement of ice sheets during at least three different glacial periods. As a result, a wide range of soil types and properties are found. To capture this variability, we first developed nine base case profiles representative of different locations and soil conditions encountered in the North Sea. We then modified the nine base case profiles to explore the effects of elastic site period and profile depth. The profiles are based on geological, geophysical, and geotechnical data collected from projects conducted in the North Sea. The GMM and accompanying amplification functions will increase the accuracy of seismic hazard evaluations for critical infrastructure in the North Sea, such as offshore wind farms and future carbon storage sites.

One-dimensional seismic ground response analysis is typically performed to assess the nonlinear dynamic behavior of soils in the design of marine land reclamations in high seismicity regions. These analyses require a selection of input ground mo tions, for which seismic design codes generally recommend having seismic-hazard-consistent earthquake causal rupture parameters, such as rupture mechanism, magnitude, and source-to-site distance. This study investigated the impact of including earthquake causal rupture parameters in input ground motion selection criteria on the results of one-dimensional seismic ground response analysis of marine land reclamation profiles. Multiple one-dimensional seismic ground response analyses were performed to cover a range of marine land reclamation soil conditions and response spectral amplitudes. Candidate ground motion catalogues were developed considering hazard-consistent secondary intensity measures, with and without bounds placed on earthquake causal rupture parameters. Sets of ground motions were selected from these catalogues based on their fit with the respective target response spectrum. Input-tosurface amplification factors were calculated for the various input ground motions and marine land reclamation profiles. The results of the analyses were compared to assess the differences in amplification factors calculated with input motions selected using the different approaches for considering earthquake causal rupture parameters

Turkey witnessed thousands of aftershocks following the Turkey-Syria Earthquake (EQ) of 2023. Concerning the same, the Hatay province of southern Turkey also experienced an aftershock of 6.4 (M_w) with epicenter near Samandag District. As per United States Geological Survey (USGS), Samandag has been exposed to numerous minor to strong earthquakes since 1981. Performing a ground response analysis on the aforementioned geographic area would help in understanding the behavior of in-situ soil, in response to the ground motions recorded, as attempted in this work. For the purpose 67 EQ records (recorded between December, 2013 and April, 2023) from No. 3140 recording station are used in Horizontal-to-Vertical Spectral Ratio (HVSR) method to determine average HVSR curve and subsequently the predominant frequency (f_peak) for the station. Using this value of f_peak and regionally available correlations, value of average 30m shear wave velocity (V_s30) of the recording station, is determined. Obtained V_s30 is found comparable with existing studies for the above recording station. Further, the approximate depth of bedrock (D_b) for the region is estimated using regional correlations between f_peak and D_b. Till D_b, the soil types are approximated using Latin Hypercube Sampling (LHS) and other engineering properties of soil are estimated using appropriate empirical equations. Using the predetermined value of D_b, soil properties and surface ground motions recorded at 3140, bedrock motions are estimated based on deconvolution. Considering estimated bedrock motion and surface recorded motion, amplification factor for each of the 67 ground motion records are determined and variation in amplification factor with PHA is obtained for the region. Such understanding will be helpful in arriving at surface seismic hazard for a target/ probable bedrock seismic hazard value for the region, which can further be used in EQ resistant design and quantification of EQ induced damages for the region.