Near-surface Characterization Research Articles

Electrical resistivity tomography inversion combining deep variational autoencoder and stochastic adaptive sampling

Geophysical methods, such as electrical resistivity tomography (ERT), can be used to imaging the near-surface electrical resistivity as field measurements depend on the subsurface porosity, water saturation and fluid salinity. ERT has been widely applied to investigate mineral and groundwater resources, and in archaeological, environmental, and engineering studies. The prediction of subsurface electrical conductivity from ERT data requires solving a geophysical inverse problem. For near-surface characterization studies, this is often accomplished with deterministic inverse methods. These methods linearize the problem around an initial solution, and their smoothness depends on an imposed a priori spatial regularization term. Depending on this parameterization, these methodologies might struggle to capture the natural variability of the subsurface. Moreover, deterministic solutions have limited capabilities for uncertainty assessment. On the contrary, stochastic inverse methods can assess uncertainties by predicting multiple model realizations that fit similarly the recorded ERT data. However, they are often more computationally expensive than deterministic solutions. Deep learning algorithms based on deep generative models have been used to re-parametrize model and data spaces into low-dimensional domains and efficiently solve geophysical inverse problems. However, within this context, uncertainty assessment is challenging. We propose a deep convolutional variational autoencoder (VAE) coupled with stochastic adaptive optimization to perform stochastic ERT inversion. Geostatistical simulations of electrical resistivity are used as training data set of the VAE. After training, the VAE generates electrical resistivity models that reproduce the statistics and spatial continuity patterns of the training data set. Then, the VAE latent space is iteratively perturbed and updated with adaptive stochastic sampling based on the misfit between observed and predicted ERT data. The proposed methodology is illustrated in two-dimensional synthetic and real data sets to illustrate the ability of the proposed method to predict reliable electrical resistivity models while generating multiple possible scenarios for uncertainty assessment.

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.

Integrated Geophysical Models for Interpretations of Heterogeneous Subsurface Environments

Near-surface characterization can be vulnerable to misinterpretation due to limited resolution and methodological limitations. Validation from different parameters is necessary to substantiate the results and reduce errors during interpretations. This research aims to develop an alternative integrated method of a 2-D cross-plot model to enhance subsurface interpretations based on the model's criteria, resulting in better geological interpretation. Geophysical methods such as electrical resistivity and seismic refraction are utilized in a test model and a case study area to observe the capabilities of the integrated method approach. The goal of this study was to cluster two or more different parameters into a single model for a direct presentation of the subsurface model. This includes the characterization of subsurface properties, data integration using cross-plot analysis, and the development of a 2-D cross-plot model. This method visually represents the relationship between two or more attributes, allowing for the identification of anomalies. In the test model, the lithology consists of sandy silt derived from granitic residual soil, whereas in the case study model, it was dominated by weathered granitic residual soil with the presence of saturated zones. The 2-D cross-plot model provides a comprehensive interpretation where Quadrant, Q1 shows the vulnerable zones for sliding mass bodies. The plane weakness was successfully identified based on the cross-plot model as the weathered zone, and subsurface features were determined. The estimated volume of mass movement was successfully calculated for the case study area based on the determination of the sliding plane. The integrated method of cross-plot analysis along with the development of 2-D cross-plot models proves to be an informative approach for subsurface characterization and enhances subsurface imaging.

Converted wave tomography based on inverse level set and adjoint formulation

SUMMARY Shear wave velocity (Vs) is a fundamental property of elastic media whose estimation from PS converted waves is challenging and requires modelling the boundary where P to S conversion occurs. This paper presents a PS tomography where seismic wave conversion/reflection points correspond to reflectors modelled with the level-set function set to zero [ϕ(x, z) = 0]. The proposed method aims for stable Vs inversion in a seismic acquisition setting using multicomponent receivers. Synthetic models simulating true Vs, Vp and the location of the geological reflector are used in the study. The inversion starts by locating a flat reflector, ϕ(x, z) = 0, which defines the zone Ω1 between the surface and the reflector, where the initial Vs and Vp fields are also set. To calculate the traveltimes of incident PT (P wave that propagates in Ω1 from source to the reflector), converted PS and reflected PP waves, for both observed and modelled data (forward problem), the methodology proposed by Rawlinson and Sambridge is adopted. This method uses the arrival times of the P waves, Tpt, from the seismic source at each reflector point as secondary sources generating the times Tps and Tpp. These times are calculated as a solution to the eikonal equation by using the Fast Marching method. The PS and PP residual times are minimized by updating Vs, Vp and ϕ(x, z) = 0 through adjoint variables designed from a formulation using Lagrange Multipliers in a variational context. The performance of the algorithm is evaluated for models with synclinal, sinusoidal and monoclinal reflector geometries using numerical tests considering the inversion of: (1) ϕ, given the true values of Vs and Vp; (2) ϕ and Vs, given the true value of Vp; (3) ϕ and Vp, given the true value of Vs and (4) the three parameters ϕ, Vs and Vp, simultaneously. Good results are obtained by inverting Vs and ϕ, given the true value of Vp. The simultaneous inversion of the three parameters exhibits promising results, despite the illumination problems caused by the different distribution of the PS, PP and PT time gradients due to the geometry of the reflectors and the acquisition setting (sources–receivers in the same plane). The proposed tomography estimates Vs and reflector positions which could help in statics corrections and improve the lithological characterization of near surface.

Near-Surface Geophysical Characterization of a Marble Deposit to Promote a Sustainable Small-Scale Mining

Small-scale mining (SSM) is responsible for almost all the production of non-metallic minerals in the world and represents around 80% of the mining in Brazil. The lack of direct geological information increases the level of uncertainty associated with the exploratory process, compromises mine planning, limits mineral extraction, and contributes to maximizing environmental issues. In this research, near-surface geophysical methods, including Electrical Resistivity, Capacitive Resistivity, Ground Penetrating Radar (GPR), and Transient Electromagnetic (TEM), were applied to characterize a marble deposit in an SSM located in the Campos do Jordão region, São Paulo state, southeast Brazil. The geophysical methods used provide indirect information about the subsurface geology based on the contrast in electrical and electromagnetic properties. Resistivity results show the efficiency of locating marble deposits, as well as fracture zones. GPR profiles allowed for the investigation of the structural heterogeneities in the subsurface. Geophysical data and lithological information from drill holes were integrated into Micromine software and guided the development of a geological model and a conceptual pit model. The information inferred from the pit modeling allowed us to analyze the potential of the deposit and should be used to assist in developing sustainable mining planning. The results of this work demonstrate that the investment in geophysical research can support the modernization of an SSM and contribute to more sustainable and productive mining.

Emerging technologies and advanced analyses for non-invasive near-surface site characterization

The in-situ small-strain shear modulus of soil and rock materials is a parameter of paramount importance in geotechnical modeling. It can be derived from non-invasive geophysical surveys, which provide the possibility of testing the subsurface in its natural and undisturbed condition by inferring the velocity of propagation of shear waves. In addition, for soil dynamics and earthquake engineering applications, the small-strain damping ratio plays a relevant role, yet its estimation is still challenging, lacking consolidated approaches for its in-situ evaluation. Recent advancements in instrumentation, such as distributed acoustic sensing (DAS), combined with advanced analysis methodologies for the interpretation of seismic wave propagation (e.g., machine learning and full waveform inversion), open new frontiers in site characterization. This paper presents and compares some advanced applications of measuring 1D and 2D variations in shear wave velocity and attenuation in-situ with reference to a specific case history.

Groundwater Potential Evaluation and Aquifer Characterization using Direct Current Resistivity Method in Wasinmi South-West Nigeria

The effectiveness in near-surface characterization makes geoelectrical resistivity techniques viable for groundwater exploration, geotechnical studies and environmental investigations. Geo-electric soundings were carried out in 25 different locations in Wasinmi, South-West Nigeria to acquire aquifer properties required for groundwater production of the study area using Direct Current Resistivity (DCRE) of Vertical Electrical Sounding (VES) utilizing AGI Super-sting Earth Resistivity meter with schlumberger configuration; current electrode spacing (AB) ranging from 1 to maximum of 100 m and the potential electrodes (MN) were consequently changed from 0.25 m to 5 m respectively. The simulated results of the 25 VES points reveal the presence of 3 to 4 geo-electric layers. The top layer comprises clay, sand, siltstone intercalations. Layers underneath the top soil are the Lithified Laterite, Clayey Sand, Clay/Shale, Sandy Clay and Limestone/Sandstone, Saturated, Sandstone Aquifer. The area is also characterized with relatively high depth to sandstone aquifer which varies from 20 m to 69.1 m. This can be attributed to the thick water saturated Shale/Clay and Clayey Sand in the area. However, at the extreme of North-Western part of the study area, depths to aquifer are far lower, with VES location VESWAS15 having a thickness of about 14.4 m. Aquifer thickness in the study area varied from 14.4m to 63.5m. VESWAS19 has the highest aquifer resistivity value (2473 Ωm) in the study area, with depth to aquifer of 69.1 m. The transmissivity varies between 0.17695×/s in VESWAS4 to 8.97654×/s in VESWAS13. This study further shows that aquifer resistivity values vary from 284 Ωm to 2473 Ωm with a range of 2189 with the resistivity values suggesting lithology in the suite of Sandy Clay, Sandstone and Karst Limestone found to be widespread in the study area with mean aquifer thickness value of 32.25 m and a variance of 124.63 m...

Surface distributed acoustic sensing for high-resolution near-surface characterization: Results from a 3D onshore field experiment

Distributed acoustic sensing technology enables high-density seismic acquisition at a fraction of the cost. When deployed on the surface, surface distributed acoustic sensing (S-DAS) acquisition provides a cost-effective solution for dense high-resolution near-surface characterization through the analysis and inversion of surface waves. This is made possible by the relatively low cost of the fiber and the dense spatial sampling of the realized seismic data. The S-DAS data were collected during the acquisition of a 3D land large-scale field test and processed with a focus on recent advancements in the use of surface wave analysis and inversion. We compare and validate the result from the S-DAS recording with colocated multicomponent (3C) geophones and a conventional high-density surface seismic nodal acquisition. The comparison to 3C geophones demonstrated that for applications such as surface-wave inversion, S-DAS can outperform conventional geophones and shows consistency between electrical resistivity tomography and surface seismic inversion from S-DAS. In addition, continuous passive recording of environmental noise also offers a convenient alternative to active shooting allowing for surface wave inversion from reconstructed virtual shots.

Directional sources and distributed acoustic sensing deployments for near-surface characterization

Active near-surface surveys using distributed acoustic sensing (DAS) have been practically limited to 2D inline acquisitions followed by Rayleigh-wave dispersion analysis. In this study, we analyze different acquisition setups and the subsurface properties that can be estimated from them. First, we find a surface survey dedicated to Love-wave acquisition. We use a horizontal source and common-receiver sorting of the DAS data for a pure Love-wave velocity dispersion analysis yielding an S-wave velocity profile. Common-receiver sorting has the added benefit of practically eliminating the effect of the gauge length. Next, downhole DAS is used in a vertical seismic profile-type survey to recover a 1D P-wave velocity through checkshot analysis. Finally, we find that the horizontal portion of a fiber deployed in a deviated borehole records high-frequency data from surface sources. We use first arrivals for diving-wave tomography, yielding a 2D P-wave velocity model above and below the fiber. We validate the inversion results of all acquisitions through a comparison with geophone-derived velocity models. We conclude that source orientation plays a crucial role and should be considered during survey planning in conjunction with DAS directivity. As anticipated, fiber coupling can strongly influence the signal-to-noise ratio and needs to be adequately planned. In vertical boreholes, infilling offers a viable solution for deployment in existing wells. For deviated boreholes, installation inside the casing yields suboptimal coupling, but more adequate deployment protocols may yield high-frequency data useful for velocity model building and imaging. From a practical point of view, the acquisitions that we describe are difficult to justify due to their operational costs and limitations. Nonetheless, they highlight the potential of DAS in going beyond traditional analysis.

Frequency-domain electromagnetic induction for the prediction of electrical conductivity and magnetic susceptibility using geostatistical inversion and randomized tensor decomposition

High-resolution characterization of near-surface systems is crucial for a variety of subsurface applications. Frequency-domain electromagnetic (FDEM) induction has been widely used in near-surface characterization compared with other geophysical methods due to its flexibility in acquisition and its ability to survey large areas with high resolution but with relatively low costs. FDEM measurements are sensitive to subsurface electrical conductivity (EC) and magnetic susceptibility (MS). However, the prediction of these properties requires solving a geophysical inverse problem. We combine ensemble smoother with multiple data assimilation (ES-MDA) and model reparameterization via randomized tensor decomposition (RTD) to simultaneously predict EC and MS from measured FDEM data. ES-MDA is an iterative data assimilation method that can be applied to nonlinear forward operators and provides multiple posterior realizations conditioned on the geophysical measurements to evaluate the model uncertainty. However, its application is usually computationally prohibitive for large-scale 3D problems. To overcome this limitation, we reduce the model parameters using RTD and then perform the inversion in the low-dimensional model space. The method is applied to synthetic and noisy real data sets. In the synthetic application example, the predicted posterior realizations illustrate the ability of our method to recover the true models of EC and MS accurately. The real case application comprises FDEM data acquired over arable land characterized by quaternary siliciclastic deposits with geoarchaeological features. We assess the performance of the inversion method at a borehole location not used to constrain the inversion. The inverted models do capture the available log data, illustrating the applicability of the inversion method to noisy real data.

Relating Near-Surface Ti Electrode Structure with Electrochemical Nitrate Reduction Performance Via Synchrotron X-Ray Characterization

The global nitrogen cycle has been severely skewed since the widespread adoption of the Haber-Bosch process to produce ammonia (NH3). Currently, removal of reactive nitrogen (inorganic forms besides N2) from the environment lags behind its production and emission to the environment. Nitrate is one of the most prevalent waterborne nitrogen pollutants and, in its excess, threatens the health of ecosystems. Enabling a sustainable food-energy-water nexus requires feeding a growing population while minimizing environmental impacts. Therefore, selective electrochemical nitrate reduction (NO3RR) to NH3 can couple water purification and NH3 production, helping offset the energy- and carbon-intensive Haber Bosch process. NH3 recovery from emissions could contribute over 20 million tons N per year by 2050 (~ 10% of projected N demand).Noble and transition metal catalysts including single metals (e.g., Pt, Rh, Ru, Ir, Pd, Cu, Ag, Au)[1,2] and alloys (e.g., CuNi)[3] have been studied for NO3RR. Notably, under acidic conditions, polycrystalline titanium has been demonstrated to display robust and efficient NO3RR.[4] Ti is corrosion-resistant, a poor hydrogen evolution catalyst, and a readily available, abundant metal. However, under acidic and reducing conditions, Ti forms a water-stable hydride (TiHx, 0<x≤2).[5] It remains unclear how the degree of surface hydride formation – which alters the physical and electronic properties of the electrode surface – impacts NO3RR performance. Thus, rationally implementing Ti-catalyzed NO3RR requires improved understanding of how near-surface Ti-hydride forms and influences NO3RR activity and selectivity.In this work, we show that near-surface Ti-hydride formation is a function of the duration and magnitude of applied NO3RR potential. A combination of ex situ grazing-incidence X-ray diffraction (GIXRD – probes long-range, crystalline order) and X-ray absorption spectroscopy (XAS – probes short-range, atom-specific probe into local coordination environments) enabled quantitative near-surface characterization of Ti-hydride electrodes. Through electrochemical testing and density functional theory calculations, we investigated the role near-surface Ti-hydride may play in electrochemical nitrate conversion. Our results preliminary suggest that near-surface hydride content plays a relatively minor role in steering NO3RR performance compared to applied potential and electrolyte effects. In addition, we are performing ongoing in situ GIXRD and XAS measurements to interrogate the transient nature and interplay of near-surface hydride formation and NO3RR. Put in context, our results help prioritize how Ti-catalyzed NO3RR processes can be optimized for electrified ammonia production and wastewater remediation.[1] G. E. Dima, A. C. A. de Vooys, M. T. M. Koper, Journal of Electroanalytical Chemistry 2003, 554–555, 15–23.[2] G. E. Dima, G. L. Beltramo, M. T. M. Koper, Electrochimica Acta 2005, 50, 4318–4326.[3] Y. Wang, A. Xu, Z. Wang, L. Huang, J. Li, F. Li, J. Wicks, M. Luo, D.-H. Nam, C.-S. Tan, Y. Ding, J. Wu, Y. Lum, C.-T. Dinh, D. Sinton, G. Zheng, E. H. Sargent, J. Am. Chem. Soc. 2020, 142, 5702–5708.[4] J. M. McEnaney, S. J. Blair, A. C. Nielander, J. A. Schwalbe, D. M. Koshy, M. Cargnello, T. F. Jaramillo, ACS Sustainable Chem. Eng. 2020, 8, 2672–2681.[5] Y. Liu, Z. H. Ren, J. Liu, R. F. Schaller, E. Asselin, J. Electrochem. Soc. 2019, 166, C3096–C3105.

Near-surface characterization using distributed acoustic sensing in an urban area: Granada, Spain

SUMMARY The Granada Basin in southeast Spain is an area of moderate seismicity. Yet, it hosts some of the highest seismic hazards in the Iberian Peninsula due to the presence of shallow soft sediments amplifying local ground motion. In urban areas, seismic measurements often suffer from sparse instrumentation. An enticing alternative to conventional seismometers is the distributed acoustic sensing (DAS) technology that can convert fibre-optic telecommunication cables into dense arrays of seismic sensors. In this study, we perform a shallow structure analysis using the ambient seismic field interferometry method. We conduct a DAS array field test in the city of Granada on the 26 and 27 August 2020, using a telecommunication fibre. In addition to the existing limitations of using DAS with unknown fibre-ground coupling conditions, the complex geometry of the fibre and limited data recording duration further challenge the extraction of surface-wave information from the ambient seismic field in such an urban environment. Therefore, we develop a processing scheme that incorporates a frequency–wavenumber (f−k) filter to enhance the quality of the virtual shot gathers and related multimode dispersion images. We are able to use this data set to generate several shear-wave velocity (VS) profiles for different sections of the cable. The shallow VS structure shows a good agreement with different geological conditions of soil deposits. This study demonstrates that DAS could provide insights into soil characterization and seismic microzonation in urban areas. In addition, the results contribute to a better understanding of local site response to ground motion.

Efficient 1.5D full waveform inversion in the Laplace-Fourier domain

Accurate near-surface characterization and velocity model building are important for a number of geotechnical and geophysical applications. 3D full waveform inversion (FWI) can be used to generate a detailed velocity model, but must be provided with a good initial velocity model. A method for producing such an initial velocity model is explored in this paper. Assuming that the near-subsurface can be locally approximated by an effective flat-layered medium, a specialized form of acoustic FWI is proposed. The inversion is based on regularized least-squares in the Laplace-Fourier domain as a measure to mitigate the cycle-skipping problem. Forward modeling is carried out by solving a number of independent finite-difference problems in parallel, for a set of horizontal wavenumbers. The set is determined adaptively, using a pair of algorithms discussed in detail. A numerical implementation of the inverse Hankel transform, used to synthesize data in the frequency-offset domain, is also described. The forward modeling scheme is validated against analytic solutions of the Helmholtz equation, showing good agreement between the two. FWI is tested by inverting a synthetic dataset consisting of flat layers with embedded velocity anomalies. Important features are recovered after inversion at a small number of complex frequencies, providing a detailed initial model for successive 3D FWI.

Near-surface geophysical characterization of gully erosion hazard-prone area in Calabar, southern Nigeria

Near-surface geophysical characterization of gully erosion hazard-prone area in Calabar, southern Nigeria

Inversion of Rayleigh-wave ellipticity for shallow strong-velocity-contrast structures

Rayleigh-wave ellipticity polarization, or horizontal-to-vertical ([Formula: see text]) spectral ratio, is particularly sensitive to shallow earth model parameters (e.g., S-wave velocity). We develop a novel Rayleigh-wave [Formula: see text] inversion method to delineate near-surface strong-velocity-contrast structures. In the new inversion method, the S-wave velocity model is perturbed until the synthetic Rayleigh-wave [Formula: see text] curve is fitted to the observed one based on the crosscorrelation misfit function. This criterion neglects the absolute amplitude of the observed and synthetic [Formula: see text] curves, which can eliminate the amplitude influence caused by inconsistent coupling of the horizontal and vertical geophones. Peaks and/or troughs in the [Formula: see text] curve are inverted with no need of picking. Numerical simulations indicate that the proposed [Formula: see text] inversion succeeds for models consisting of unconsolidated sediments overlying stiff bedrock with large velocity contrasts. We also find that the active Rayleigh-wave ellipticity estimation and inversion can be carried out for reconstructing pseudo-2D laterally varying models. Application to a real-world example suggests that a 2D model above 6 m in depth with large velocity contrasts is successfully reconstructed from Rayleigh-wave ellipticity data. This method provides an effective and efficient alternative for near-surface characterization.

Near-surface characterization and delineation of 2-D shear wave velocity profile from geologically complex landscape of Kangra Valley, NW Himalaya, India

Near-surface characterization and delineation of 2-D shear wave velocity profile from geologically complex landscape of Kangra Valley, NW Himalaya, India

Near-Surface Soil Moisture Characterization in Mississippi’s Highway Slopes Using Machine Learning Methods and UAV-Captured Infrared and Optical Images

Near-surface soil moisture content variation is a major factor in the frequent shallow slope failures observed on Mississippi’s highway slopes built on expansive clay. Soil moisture content variation is monitored generally through borehole sensors in highway embankments and slopes. This point monitoring method lacks spatial resolution, and the sensors are susceptible to premature failure due to wear and tear. In contrast, Unmanned/Uncrewed Aerial Vehicles (UAVs) have higher spatial and temporal resolutions that enable more efficient monitoring of site conditions, including soil moisture variation. The current study focused on developing two methods to predict soil moisture content (θ) using UAV-captured optical and thermal combined with machine learning and statistical modeling. The first method used Red, Green, and Blue (RGB) color values from UAV-captured optical images to predict θ. Support Vector Machine for Regression (SVR), Extreme Gradient Boosting (XGB), and Multiple Linear Regression (MLR) models were trained and evaluated for predicting θ from RGB values. The XGB model and MLR model outperformed the SVR model in predicting soil moisture content from RGB values. The R2 values for the XGB and MLR models were &gt;0.9 for predicting soil moisture when compared to SVR (R2 = 0.25). The Root Mean Square Error (RMSE) for XGB, SVR, and MLR were 0.009, 0.025, and 0.01, respectively, for the test dataset, affirming that XGB was the best-performing model among the three models evaluated, followed by MLR and SVR. The better-performing XGB and MLR models were further validated by predicting soil moisture using unseen input data, and they provided good prediction results. The second method used Diurnal Land Surface Temperature variation (ΔLST) from UAV-captured Thermal Infrared (TIR) images to predict θ. TIR images of vegetation-covered areas and bare ground areas of the highway embankment side slopes were processed to extract ΔLST amplitudes. The underlying relationship between soil surface thermal inertia and moisture content variation was utilized to develop a predictive model. The resulting single-parameter power curve fit model accurately predicted soil moisture from ΔLST, especially in vegetation-covered areas. The power curve fit model was further validated on previously unseen TIR, and it predicted θ with an accuracy of RMSE = 0.0273, indicating good prediction performance. The study was conducted on a field scale and not in a controlled environment, which aids in the generalizability of the developed predictive models.

Waveform Inversion of Shallow Seismic data with Randomly Selected Sources

Applying waveform inversion with randomly selected sources (RS) increases the convergence rate of the optimization process within full-waveform inversion (FWI) workflows and reduces overall computational time. FWI has been shown to be a valuable addition to the existing geophysical methods for near-surface characterization. Accurate 3D modeling of the (visco) elastic wavefield allows to diminish assumptions about wave propagation and include surface- and body-wave based arrivals within the inversion workflow. This approach could result in reliable high-resolution subsurface models, but generally it comes at a high computational cost for each individual source modeling. Commonly, multiple sources are involved in the inversion process, which proportionally increases the resulting computations for a time-domain FWI, but seismic data with dense source/receiver coverage usually contain redundant information. This is especially true for seismic near-surface applications, where the number of recorded sources per wavelength of interest are normally excessive. The inversion performance was increased by randomly selecting a subset of sources at each FWI iteration. The method's effectiveness is obvious on a FWI near-surface void detection application. Synthetic 2D experiments for fixed and rolling spreads showed comparable results with fewer calculations. The best performance was achieved when a single random source was used for each inversion iteration. The effectiveness of the method was also evident on a shallow 3D field dataset collected in the Sonora Desert in western Arizona, where data were acquired over a 10-m deep void with known location, orientation, and dimensions.

Near-Surface 2D Imaging via FWI of DAS Data: An Examination on the Impacts of FWI Starting Model

Full waveform inversion (FWI) and distributed acoustic sensing (DAS) are powerful tools with potential to improve how seismic site characterization is performed. FWI is able to provide true 2D or 3D images of the subsurface by inverting stress wave recordings collected over a wide variety of scales. DAS can be used to efficiently collect high-resolution stress wave recordings from long and complex fiber optic arrays and is well-suited for large-scale site characterization projects. Due to the relative novelty of combining FWI and DAS, there is presently little published literature regarding the application of FWI to DAS data for near-surface (depths &lt; 30 m) site characterization. We perform 2D FWI on DAS data collected at a well-characterized site using four different, site-specific 1D and 2D starting models. We discuss the unique benefits and challenges associated with inverting DAS data compared to traditional geophone data. We examine the impacts of using the various starting models on the final 2D subsurface images. We demonstrate that while the inversions performed using all four starting models are able to fit the major features of the DAS waveforms with similar misfit values, the final subsurface images can be quite different from one another at depths greater than about 10 m. As such, the best representation(s) of the subsurface are evaluated based on: (1) their agreement with borehole lithology logs that were not used in the development of the starting models, and (2) consistency at shallow depths between the final inverted images derived from multiple starting models. Our results demonstrate that FWI applied to DAS data has significant potential as a tool for near-surface site characterization while also emphasizing the significant impact that starting model selection can have on FWI results.

Extraction of Rayleigh, Love, and Virtual Refraction Waves From 3C High-Speed-Train-Induced Vibrations for Near-Surface Characterization

Extraction of Rayleigh, Love, and Virtual Refraction Waves From 3C High-Speed-Train-Induced Vibrations for Near-Surface Characterization