Elastic Inversion Research Articles

Autoencoded Elastic Wave-Equation Traveltime Inversion: Toward Reliable Near-Surface Tomogram

Due to unexpected environmental variations and poor consistencies in land data acquisition, complex near-surface seismograms are usually polluted unreasonably with a low signal-to-noise ratio (SNR). These complicated circumstances bring more challenges in identifying accurate first arrivals for the following wave-equation traveltime inversion (WT). Recently, the autoencoder (AE) is a typical unsupervised learning network whose basic principle is to compress the input seismic data for their intrinsic features in the latent space with an encoder and, thereafter, to decipher these features for seismic profiles as the output with a decoder. This process is fully automatic with high stability and is not very sensitive to data quality. In this paper, we propose an elastic WT inversion algorithm based on the AE method (AEWT) to invert the P-velocity model. Compared with the standard WT, the AEWT method automatically extracts the intrinsic features of the refractions with AE as reference data for the misfit functionals. Feature images in the latent space show similar but intensified sensitivity to the traveltimes with respect to velocity perturbations. We present one synthetic and two field data tests for comparing the proposed AEWT and the standard WT tomograms to investigate the locations of a buried fault and the depth of a buried sinkhole. All these experiments demonstrate that the proposed elastic AEWT method can reduce errors caused by low SNR and obtain a more reliable and stable P-velocity tomogram.

Electrical–Elastic Joint Inversion Method for Fracture Characterization in Anisotropic Media

Electrical–Elastic Joint Inversion Method for Fracture Characterization in Anisotropic Media

Study on the Application of Elastic Wave CT Technique to Detect the Effect of Post-Grouting of Pile Foundation

The post-grouting technology can effectively improve the bearing capacity and reliability of foundation piles, and it is of great academic and economic significance to evaluate the effect of post-grouting. However, in general, the evaluation of the effect of post-grouting slurry on foundation piles is still relatively single, and most researchers only test the improvement of the bearing characteristics of post-grouting piles, but there are few research results on the distribution state of slurry consolidation and the range of the reinforced soil. In this paper, elastic wave CT was used to detect the bridge piles of Taizhou Bay Du Xiaipu Bridge, and elastic wave CT technology was applied to the detection of the post-grouting effect of foundation piles experimentally. The spatial distribution characteristics of the underground grouting reinforcement area were revealed from the perspective of visualization, and the influencing factors of the elastic wave inversion results were analyzed. The conclusions obtained from elastic wave CT detection were similar to those of electromagnetic wave CT, and the laws are basically consistent with the current engineering experience, reflecting the applicability of using elastic wave CT to detect the effect of post-grouting of pile foundation.

Simultaneous Sparse Deconvolution

Increasing seismic resolution has been long pursued by the geophysical community, serving, among other applications, for more detailed interpretation of seismic clinoforms, better seismic inversion and quantitative interpretation. Sparse deconvolution methods play central role in this pursuit. However, although sparse methods have performed well for single seismic stack or acoustic inversion tasks, their application for multi-stack seismic volumes and consequent use in elastic inversion is still a challenging and ongoing research topic. The challenge is to obtain reflectivity volumes with high correlation between them. In this study, we present a new method to perform simultaneous sparse deconvolution (SSD) in a group of seismic volumes associated with different reflection angles. The proposed algorithm enforces co-localization of the spikes on the estimated reflectivity traces and additionally allows user control of the sparsity via hyperparameters. The method is validated in both synthetic and real datasets proving its co-localization capability and resulting in higher correlations between the reflectivity volumes, when compared to independent sparse deconvolution (ISD) of the seismic stacks. The resulting reflectivity volumes are, henceforth, better suited for downstream tasks such as high resolution amplitude versus angle (AVA) analysis, or input for high resolution elastic inversions.

Elastic impedance inversion incorporating fusion initial model and kernel Fisher discriminant analysis approach

Seismic inversion is a significant technique for estimating petroleum reservoir parameters. The low frequency component of the initial model represents the geological background information, which plays an important role in the seismic inversion. It is challenging to precisely depict the actual geological model in seismic inversion because of the inherent velocity-depth ambiguity. Therefore, the initial model which is closer to genuine geological backdrop is essential. We propose a workflow which estimates a fusion initial model based on data fusion algorithms. It is well known that seismic facies analysis can provide more low-frequency information about the geological background. For example, the boundaries of sedimentary bodies can be represented by seismic facies classification data. We utilize a combination of the seismic facies classification data and well curves interpolation initial models to accurately invert the special geological body with the support of a feature-level fusion algorithm. Then, a practical pre-stack seismic inversion method is implemented, and a field data example further demonstrates its applicability and steadiness in seismic inversion.

Early arrival waveform inversion using data uncertainties and matching filters with application to near-surface seismic refraction data

We develop an early arrival waveform inversion (EAWI) technique for high-resolution near-surface velocity estimation by iteratively updating the P-wave velocity model to minimize the difference between the observed and calculated seismic refraction data. Traditional EAWI uses a least-squares penalty function and an acoustic forward-modeling engine. Conventional least-squares error is sensitive to data with low signal-to-noise ratio (S/N) and iterations of EAWI stop at a local-minimum data misfit or at the preassigned maximum number of iterations. These stopping criteria can result in overfitting the data. In addition, fitting the elastic field data with an acoustic modeling engine can introduce artifacts in velocity estimation, especially in land data with significant elastic effects. To overcome these challenges, we develop a robust EAWI (REAWI) method by (1) incorporating the data uncertainties into the penalty function and (2) mitigating the elastic effects using a matching filter workflow. The data uncertainties are estimated from waveform reciprocal errors. When full-waveform reciprocity is not available, trace interpolation is applied. The proposed method prevents closely fitting data with low S/N, avoids overall overfitting by stopping the iterations when a normalized chi-square ([Formula: see text]) waveform misfit of one is achieved, and is less affected by elastic effects. Numerical examples and application to near-surface refraction data at a groundwater contamination site suggest that the final REAWI models are more accurate than the corresponding EAWI models, at the same level of misfit. This is the first known application of a matching filter workflow to real land data. The final REAWI models satisfy an appropriate misfit between the real data and predicted elastic P-wave data, making this approach in this respect equivalent to elastic waveform inversion. We also develop a method to analyze model constraint by examining the energy of the wavefield Fréchet derivative thereby avoiding the influence of the data residuals in traditional Fréchet kernels.

Image-domain DAS 3D VSP elastic transmission tomography

SUMMARY Full-wavefield elastic imaging of active-source seismic data acquired by downhole receivers commonly offers higher-resolution subsurface images in the vicinity of a borehole compared to conventional surface seismic data sets, which can lack higher-frequency wavefield components due to longer travel paths and increased attenuation. An increasingly used approach for downhole acquisition is vertical seismic profiling (VSP), which has become more attractive when coupled with distributed acoustic sensing (DAS) using optical fibres installed in wells. The main difficulty for generating high-quality images with full-wavefield imaging tools for DAS VSP data, though, is the need for an accurate velocity model. To build plausible velocity models using active-source DAS VSP data, we adopt a 3-D image-domain elastic transmission tomography technique, originally developed for surface-recorded passive (microseismic) data, by exchanging the source and receiver positions (i.e. reciprocity) to mimic a passive-seismic surface monitoring scenario. The inversion approach exploits various images for each source constructed through time-reverse imaging (TRI) of downgoing P- and S-wave first-arrival waveforms. The TRI process uses the kinetic term of the (extended) PS energy imaging condition that exhibits sufficient sensitivity to velocity model errors. The method automatically updates the P- and S-wave velocity models to optimize image focusing via adjoint-state inversion. We illustrate the efficacy of the adopted elastic inversion technique using an active-source DAS 3-D VSP field data set acquired in the North Slope of Alaska. The numerical experiments demonstrate that the inverted elastic velocity models can be further used in full-wavefield acoustic/elastic imaging algorithms to obtain accurate subsurface images.

Elastic moduli parameterization and inversion considering nanopores effects in shale

Nanopores are widely developed in organic kerogen in shale. Due to the large surface-bulk ratio, nanopores cause significant surface effects that affect the overall elastic properties of shale. It is essential to consider the effects of nanopores when applying the prestack seismic inversion technique for shale reservoir prediction. Based on classical elastic theory, the technique of amplitude variation with offset (AVO) can usually extract velocity of the longitudinal wave and shear wave, elastic modulus, and density in shale reservoir from prestack seismic amplitudes but often ignores the surface effects of nanopores. For this reason, we have derived a new AVO parameterization combining the nanoporoelasticity theory and AVO technique. The newly derived parameterization contains four parameters (shear modulus of matrix, nanopores-related parameter, shear modulus of saturated rock, and density) which can be used to estimate the nanopore properties of shale. The model test and real seismic data examples verify the feasibility and suitability of the proposed method by applying the Bayesian inversion technique with smooth background constraint.

Elastic direct envelope inversion based on wave mode decomposition for multi-parameter reconstruction of strong-scattering media

The parameter reconstruction of strong-scattering media is a challenge for conventional full waveform inversion (FWI). Direct envelope inversion (DEI) is an effective method for large-scale and strong-scattering structures imaging without the need of low-frequency seismic data. However, the current DEI methods are all based on the acoustic approximation. Whereas, in real cases, seismic records are the combined effects of the subsurface multi-parameters. Therefore, the study of DEI in elastic media is necessary for the accurate inversion of strong-scattering structures, such as salt domes. In this paper, we propose an elastic direct envelope inversion (EDEI) method based on wave mode decomposition. We define the objective function of EDEI using multi-component seismic data and derive its gradient formulation. To reduce the coupling effects of multi-parameters, we introduce the wave mode decomposition method into the gradient calculation of EDEI. The update of Vp is primarily the contributions of decomposed P-waves. Two approaches on Vs gradient calculation are proposed, i.e. using the petrophysical relation and wave mode decomposition method. Finally, we test the proposed method on a layered salt model and the SEG/EAGE salt model. The results show that the proposed EDEI method can reconstruct reliable large-scale Vp and Vs models of strong-scattering salt structures. The successive elastic FWI can obtain high-precision inversion results of the strong-scattering salt model. The proposed method also has a good anti-noise performance in the moderate noise level.

Elastic impedance inversion for stress indicator in weakly orthorhombic media

Hydraulic fracturing is a crucial technology for improving permeability and production of shale reservoirs. The precise estimation of the stress distribution has a significant guidance for optimizing the placement of hydraulic fracturing. Assuming that the shale gas reservoir is a weakly anisotropic medium with orthorhombic symmetry, a new stress indicator parameterized by rock mechanical parameters and fracture parameters is firstly presented to predict the differential horizontal stress ratio in shale gas reservoirs. Then, we derive a novel simplified P-to-P reflection coefficient and a logarithmic normalized elastic impedance (EI) as a function of Young's modulus, Poisson's ratio, Thomsen's WA parameter δb and normal excess compliance ZN. Next, we adopt azimuthal EI inversion in a Bayesian framework to estimate rock mechanics parameters and fracture parameters directly on a fractured shale gas field seismic data. Finally, the stress indicator is determined by utilizing four inverted parameters. Synthetic examples demonstrate that the proposed approach produces stable parameter estimates even with moderate noise, verifying the feasibility and effectiveness of the method. Test on a field data set illustrates that the inversion results can be reasonably estimated, and the stress indicator derived by the inversion accords with the geomechanics result. Compared with the previous method, the new stress indicator has a higher capability to describe stress characteristics in the shale reservoir. We conclude that this stress evaluation procedure can provide reliable guidance for well location deployment and hydraulic fracturing reformation.

Influence of shear velocity on elastic full-waveform inversion: Gulf of Mexico case study using multicomponent ocean-bottom node data

Elastic full-waveform inversion (FWI) is superior to acoustic FWI due to its ability to simulate complex mode conversions in fast-varying elastic media. Using elastic FWI may become important when building velocity models in areas of large complex salt bodies that we typically see in the Gulf of Mexico. Accounting for elastic effects in FWI can reduce the artifacts that are caused by using an acoustic approximation. We often rely on petrophysics relations to define an initial shear velocity model, which also could be challenging. We demonstrate how to take advantage of the reflected converted waves from the top of the salt interface recorded in horizontal geophones to improve the estimation of the background shear velocity. Then, we illustrate the impact of the initial background shear velocity in the elastic inversion of the long-offset low-frequency hydrophone data. The workflow is based on a combination of wave-equation traveltime inversion, to cope with the cycle skipping, and FWI, to obtain higher-resolution results.

Probabilistic inversion of seismic data for reservoir petrophysical characterization: Review and examples

The physics that describes the seismic response of an interval of saturated porous rocks with known petrophysical properties is relatively well understood and includes rock physics, petrophysics, and wave propagation models. The main goal of seismic reservoir characterization is to predict the rock and fluid properties given a set of seismic measurements by combining geophysical models and mathematical methods. This modeling challenge is generally formulated as an inverse problem. The most common geophysical inverse problem is the seismic (or elastic) inversion, i.e., the estimation of elastic properties, such as seismic velocities or impedances, from seismic amplitudes and traveltimes. The estimation of petrophysical properties, such as porosity, lithology, and fluid saturations, also can be formulated as an inverse problem and is generally referred to as rock-physics (or petrophysical) inversion. Several deterministic and probabilistic methods can be applied to solve seismic inversion problems. Deterministic algorithms predict a single solution, which is a “best” estimate or the most likely value of the model variables of interest. In probabilistic algorithms, on the other hand, the solution is the probability distribution of the model variables of interest, which can be expressed as a conditional probability density function or a set of model realizations conditioned on the data. The probabilistic approach provides a quantification of the uncertainty of the solution in addition to the most likely model. Our goal is to define the terminology, present an overview of probabilistic seismic and rock-physics inversion methods for the estimation of petrophysical properties, demonstrate the fundamental concepts with illustrative examples, and discuss the recent research developments.

Seismic diagenetic facies prediction of tight sandstone in the offshore sparse well area: An example from the Xihu Depression of the East China Sea Basin

The purpose of this study is to forecast the spatial distribution of diagenetic facies (DF) of tight sandstone in the offshore sparse wells area. Analytical test data, rock physics experiment data and prestack seismic data were used to study the spatial distribution of seismic diagenetic facies (SEISDF) of tight sandstone of the second (H4b) submember of the fourth (H4) member of the Huagang (E3h) Formation in the N gas-field, Xihu Depression, East China Sea Basin. Based on diagenesis and diagenetic minerals, core-based DF of the H4b sandstone were divided into chlorite-coated facies (CHF), calcite-cemented facies (CAF), dissolution facies (DIF), tightly compacted facies (TIF), and quartz-cemented facies (QUF). Various seismic elastic parameters were obtained by prestack elastic parameter inversion method, and sensitive seismic elastic parameters were selected according to the seismic identification principle of DF. In order to predict the spatial distribution of DF, four DF and sensitive seismic elastic parameters were integrated to establish a SEISDF prediction model based on supervised learning, and obtain a diagenetic facies comprehensive index (DFCI) that can distinguish four DF. Then, the 90° phase conversion technique of seismic lithology was used to realize the consistency of the DFCI between isochronous stratigraphic interfaces with core-based DF. The stratal slicing technique of seismic geomorphology was used to make stratal slices of the DFCI between isochronous stratigraphic interfaces. Representative stratal slices were singled out, and the spatial distribution of the SEISDF was interpreted. This study shows that the H4b1 sublayer primarily develops the TIF, while the H4b2 and H4b3 sublayers chiefly develop the QUF, CHF, and DIF. Compared with the H4b2 and H4b3 sublayers, the areas of the QUF and DIF in the H4b4, H4b5, and H4b6 sublayers slightly increase. The QUF are mainly distributed in the subaqueous distributary channel and a small number of sheet sand and channel bar depositional facies in the form of continuous sheets. The CHF are primarily developed in lenticular shape in the channel bar and a small amount of sheet sand and subaqueous distributary channel depositional facies. The most favorable DIF are chiefly developed in the subaqueous distributary channel, channel bar, and sheet sand depositional facies near the fault, which are distributed in strips. This study proposes a very valuable method for the SEISDF prediction of tight sandstone, which can provide a reference for the study of DF of similar strata in other sedimentary basins.

3D elastic multisource full waveform inversion on distributed GPU clusters

Full-waveform inversion (FWI) serves as a useful tool to quantitatively investigate the properties of the media. One of the greatest challenges is its enormous computational cost, especially for the three-dimensional (3D) case. The multisource encoding strategy is a crucial method to speed up FWI. However, current 3D elastic FWI algorithms with the simultaneous sources, such as random time series and random polarity methods, may be subjected to the crosstalk noise, which severely degrades inversion quality. We present a novel 3D elastic crosstalk-free multisource full-waveform inversion method (MFWI). The encoded sources with a series of single-frequency components are excited to perform the seismic wavefield simulation. Extracting the wavefield corresponding to each single-frequency harmonic source by phase-sensitive detection (PSD) algorithm, the multisource wavefield can be decomposed. Therefore, summing the gradients with respect to the model parameters of all decomposed single frequencies can effectively avoid the crosstalk in our MFWI algorithm. Furthermore, to increase the practicability and universality of the algorithm, we adopt the graphic processing unit (GPU) based on the domain decomposition to improve the MFWI algorithm. The multi-scale strategy with a flexible encoding number in each scale helps to enhance the robustness of our algorithm. The numerical examples demonstrate that our method can achieve domain decomposition simulation and obtain 3D crosstalk-free multisource elastic wave inversion results.

The 2011 Cordón Caulle eruption triggered by slip on the Liquiñe-Ofqui fault system

Determining the mechanisms that promote large silicic eruptions is one of the biggest challenges in volcanic hazard assessment. The 2011-2012 Cordón-Caulle eruption in Chile was one of the largest silicic eruptions of the 21st century and was characterized by a rapid change from explosive to effusive behavior. This eruption was preceded by inflation from 2007 to 2009, followed by two years of barely any ground deformation. Despite intensive monitoring by geodetic and seismological data, its trigger remains undetermined. Here, we benefit from SAR imagery over the Puyehue Cordón-Caulle Volcanic Complex acquired by ALOS-1, ENVISAT and SENTINEL-1 data, to analyze the temporal and spatial behavior of ground displacements before, during and after the eruption. First, we find that a similar prolate spheroidal source explains the ground deformation for the pre-eruptive and post-eruptive periods. Then, we use 3D numerical elasto-plastic models to assess the failure conditions resulting from the pre-eruptive magma injection. Our results show that such a magma injection was too small to trigger the eruption. Therefore we explore other eruption triggers. Analytical elastic inversion models show that the ground displacements observed during the explosive phase may have been produced by slip motion along a NNW-striking dextral-strike slip, double-branch fault of the north-trending Liquiñe-Ofqui Fault System (LOFS), or along a single southern branch fault of the LOFS and collapse of the caldera. When investigating the elasto-plastic deformation pattern resulting from dextral slip along this branch-fault system, we obtain a sub-vertical dilatational plastic zone that connects the reservoir wall to the surface in a location that coincides with that of the 2011 eruption. Hence, we propose that this LOFS branch-fault eventually destabilized (perhaps weakened by the 2007-2009 episode of magma injection), and then slipped in a way that opened channels for fluid migration from the magma reservoir up to the surface.

Multi-parameter pre-stack seismic inversion based on deep learning with sparse reflection coefficient constraints

In the field of seismic inversion, Convolutional Neural Network (CNN) has been extensively applied for their powerful capability of feature extraction and nonlinear fitting. However, the insufficient amount of labeled seismic inversion dataset impedes the application of CNN in seismic elastic inversion. Besides, the lack of effective geophysical constraints in conventional CNN will make the network prone to over-fitting, leading to unstable inversion results. In this paper, a workflow is developed for generating sufficient and diverse datasets for pre-stack seismic inversion with limited log and seismic data. The Sequential Gaussian Co-Simulation algorithm is used to simulate the changes in the reservoir space under the constraints of the low-frequency model. At the same time, the Elastic Distortion algorithm is used to simulate the complex geological structures. This can increase the diversity of the strata longitudinal combination by enriching the combination mode of stratigraphic parameters. Besides, the combination of a U-net and three fully connected networks (UCNN) is proposed to predict the elastic parameters from seismic data. In UCNN, the sparse reflection coefficient is used as a constraint to improve the accuracy of the network. The performance of this method was evaluated by synthetic and field data examples. The results show not only the effectiveness of the proposed method but also demonstrate its outperformance over the conventional deep learning method. The R2 scores of density, Vp and Vs are 0.94, 0.98, 0.98.

Elastic wave full-waveform inversion in the time domain by the trust region method

In this paper, we investigate the elastic wave full-waveform inversion (FWI) by the trust region method. FWI is an optimization problem of minimizing the misfit between the observed data and simulated data. Usually the gradient-based search methods such as the steepest descent method or quasi-Newton's methods are applied to update the model parameters iteratively. And the L-BFGS method with approximated Hessian information is preferred for its high computational efficiency and accuracy. At each iteration, a line search method computes a search direction and then finds a suitable step length or how far to move along the direction. The trust region method is an effective alternative to solve nonlinear optimization problems. In this paper, we investigate the performance of the trust region method in elastic time-domain FWI. In the trust region method, a trial step length called the trust-region radius is forced within a certain neighborhood of the current iterate point and a trust-region subproblem requires to solve. In order to solve the trust-region subproblem, the dogleg method and the two dimensional subspace method are adopted in this paper. In the trust region method, as long as the trust-region radius is well updated, the model updating can be performed for every iteration with the fixed trial step and there is no more extra computation for forward problem like the line search method. Numerical computations for the benchmark Marmousi model are given. The inversion results and comparisons show that the trust region method is very efficient and behaves better than the line search method such as the well-known L-BFGS method.

Elastic properties and litho‐fluid facies estimation from pre‐stack seismic data through bidirectional long short‐term memory networks

ABSTRACTOne of the main goals of pre‐stack seismic inversion is the estimation of elastic properties (i.e. P‐, S‐ wave velocities and density) and litho‐fluid classes in the investigated area. To this end, many inversion strategies have been proposed, but the most popular is based on a two‐step inversion approach: First, elastic properties are inferred from pre‐stack data, and then a classification algorithm is used to convert the outcomes of the first stage into litho‐fluid facies. In this work, we propose an alternative approach based on recurrent neural networks. We train two bidirectional long short‐term memory networks to predict the inverse mappings from pre‐stack seismic data to elastic properties, and litho‐fluid classes. In the elastic inversion, we also use a Monte Carlo simulation approach to properly propagate onto the model space both the uncertainties related to noise contamination in the data and to the modelling error introduced by the network approximation. One crucial aspect of any machine‐learning inversion strategy is the definition of an appropriate training set. In this case, the models forming the training and validation examples are drawn according to a previously defined elastic and facies prior models derived from actual well log recordings. In particular, we assume a Gaussian‐mixture elastic prior, and we also take into account the uncertainties affecting the estimation of the transition probabilities of facies. We invert each seismic gather independently, and in this context, the generation of the training set, and the learning process can be accomplished with a very limited computational effort on a common notebook. Once trained, the networks estimate the elastic properties, the litho‐fluid facies and the related uncertainties from the pre‐stack data in near real‐time. Synthetic and field data inversions are used to validate the proposed method. The network predictions are also benchmarked against the outcomes of a more standard two‐step approach that combines a linear elastic inversion and a subsequent point‐wise Bayesian classification. Our results demonstrate that the implemented algorithm guarantees more accurate elastic property estimations and facies predictions than the standard inversion strategy. In particular, the predictions provided by the long short‐term memory network are less affected by erroneous assumptions on the noise statistics and prior model and by errors in the estimated source wavelet.

Elastic impedance inversion method based on Quantum Annealing MH algorithm

Seismic inversion is one of the important methods to realize reservoir prediction in oil and gas field exploration. The seismic stochastic inversion based on Metropolis-Hastings algorithm makes full use of well logging data and improves the vertical resolution of inversion results. However, conventional Metropolis-Hastings algorithm has slow convergence speed and poor stability. In this paper, an elastic impedance inversion method based on optimized Metropolis-Hastings algorithm is established by combining quantum annealing algorithm with Metropolis-Hastings algorithm. The feasibility of the method is verified by model calculation and actual data test.

A reality check on full-wave inversion applied to land seismic data for near-surface modeling

We evaluate the performance of traveltime tomography and full-wave inversion (FWI) for near-surface modeling using the data from a shallow seismic field experiment. Eight boreholes up to 20-m depth have been drilled along the seismic line traverse to verify the accuracy of the P-wave velocity-depth model estimated by seismic inversion. The velocity-depth model of the soil column estimated by traveltime tomography is in good agreement with the borehole data. We used the traveltime tomography model as an initial model and performed FWI. Full-wave acoustic and elastic inversions, however, have failed to converge to a velocity-depth model that desirably should be a high-resolution version of the model estimated by traveltime tomography. Moreover, there are significant discrepancies between the estimated models and the borehole data. It is understandable why full-wave acoustic inversion would fail — land seismic data inherently are elastic wavefields. The question is: Why does full-wave elastic inversion also fail? The strategy to prevent full-wave elastic inversion of vertical-component geophone data trapped in a local minimum that results in a physically implausible near-surface model may be cascaded inversion. Specifically, we perform traveltime tomography to estimate a P-wave velocity-depth model for the near-surface and Rayleigh-wave inversion to estimate an S-wave velocity-depth model for the near-surface, then use the resulting pairs of models as the initial models for the subsequent full-wave elastic inversion. Nonetheless, as demonstrated by the field data example here, the elastic-wave inversion yields a near-surface solution that still is not in agreement with the borehole data. Here, we investigate the limitations of FWI applied to land seismic data for near-surface modeling.