Full Waveform Inversion Scheme Research Articles

Assessing Time-Lapse Full-Waveform Inversion Strategies in a Brazilian Pre-Salt Setting

We conducted a comparative study of time-lapse full-waveform inversion (time-lapse FWI) strategies, considering a typical deep-water Brazilian pre-salt setting. This study employed a realistic P-wave model, ocean bottom nodes (OBN) acquisition, noisy data, and a Gaussian anomaly to represent time-lapse model changes. We evaluated the four most commonly used time-lapse FWI schemes. In the first, known as parallel time-lapse FWI, two independent FWI processes are performed from the same initial model, utilizing baseline and monitor datasets. In the second strategy, namely sequential time-lapse FWI, the retrieved baseline model serves as the starting model for inverting monitor data. In both cases, the time-lapse model is derived by subtracting the retrieved baseline model from the retrieved monitor model. The remaining two methods were double-difference and central-difference time-lapse FWI. Our findings demonstrate that all these schemes can detect model variations of 3%. Remarkably, the central-difference time-lapse FWI method demonstrated superior accuracy in producing time-lapse models and, as such, presents itself as a promising strategy for implementation in time-lapse studies within Brazilian pre-salt regions.

Robust joint adaptive multiparameter waveform inversion with attenuation compensation in viscoacoustic media

Full-waveform inversion (FWI) has been proven as an effective method to estimate subsurface parameters by iteratively reducing the data residual between the predictions and the observations. Nevertheless, FWI is greatly dependent on the initial model, and a poor initial model will lead to an incorrect solution. Furthermore, owing to the anelasticity of the earth, seismic waves will attenuate during propagation, which results in an attenuated gradient and makes the convergence rate of FWI even worse in viscoacoustic medium. Commonly, multiparameter (e.g. velocity and Q) waveform inversion can be used to mitigate these problems. Benefiting from the theory of Q-compensated wavefield propagation, we develop a Q-compensated joint multiparameter waveform inversion method to weaken the nonlinearity of the FWI objective function, which enables it to cope with challenges related with attenuation-induced gradient energy loss and cycle skipping simultaneously. We refer to our Q-compensated joint multiparameter FWI scheme as QJMFWI. The main contributions of QJMFWI are as follows: (1) given the difficulty associated with the estimation of velocity and Q simultaneously in viscoacoustic media, QJMFWI provides a straightforward waveform inversion method for velocity and Q model construction, by which we can obtain velocity and Q information with improved accuracy and resolution; and (2) compared with conventional FWI methods, QJMFWI relaxes the requirement for a good initial velocity and Q model, which can avoid trapping into local minima. Numerical and field data examples demonstrate that QJMFWI is an effective method to invert for accurate subsurface parameters in viscoacoustic media.

Self-supervised, active learning seismic full-waveform inversion

A novel recursive, self-supervised machine-learning (ML) inversion scheme is developed. It is applied for fast and accurate full-waveform inversion of land seismic data. ML generalization is enhanced by using virtual super gathers (VSGs) of field data for training. These are obtained from midpoint-offset sorting and stacking after applying surface-consistent corrections from the decomposition of the transmitted wavefield. The procedure implements reinforcement learning concepts by adopting an inversion agent to interact with the environment and explore the model space under a data misfit optimization policy. The generated parameter distributions and related forward responses are used as new training samples for supervised learning. The active learning (AL) paradigm is further embedded in the procedure, for which queries on data diversity and uncertainty are used to generate fully informative reduced sets for training. The procedure is recursive. At each cycle, the physics-based inversion is coupled to the ML predictions via penalty terms that promote a long-term data misfit reduction. The resulting self-supervised, AL, physics-driven deep-learning inversion generalizes well with field data. The method is applied to perform full-waveform inversion (FWI) of a complex land seismic data set characterized by transcurrent faulting and related structures. High signal-to-noise VSGs are inverted with a 1.5D Laplace-Fourier FWI scheme. The AL inversion procedure uses a small fraction of data for training while achieving sharper velocity reconstructions and a lower data misfit when compared with previous results. AL FWI is highly generalizable and effective for land seismic velocity model building and for other inversion scenarios.

3D frequency-domain acoustic full-waveform inversion using diffraction-angle filtering

The success of seismic imaging techniques such as full-waveform inversion (FWI) depends on using a good starting velocity model or a background velocity model, which can be effectively built by long-wavelength components in the early stages of FWI. This study proposes an optimal strategy for performing 3D frequency-domain FWI with diffraction-angle filtering (DAF). This approach can perform optimized single-frequency FWI with lower computational costs and build a long-wavelength velocity structure by separating low-wavenumber components with DAF. Prior to applying this strategy, we confirm the performance of 3D frequency-domain FWI with DAF in the radiation pattern analysis. DAF effectively separates the diffraction energy of partial derivative wavefields according to the diffraction angle with a negligible increase in computational costs. We then apply our 3D FWI strategy with DAF on synthetic data generated from the 3D SEG/EAGE overthrust model. The wavenumber components are effectively separated, and a long-wavelength velocity model is estimated with DAF compared to conventional FWI. Finally, we apply our strategy to 3D ocean-bottom seismic data from the North Sea. As a result, we observe that 3D FWI with the DAF method successfully reconstructs a long-wavelength velocity structure on the real data using only a single-frequency component.

A progressive data assimilation strategy in full-waveform adjoint tomography

Elastic full-waveform inversion (FWI) is powerful in resolving fine-scale structures in complex models, but suffering from strong nonlinearity and slow convergence rate, especially in the presence of cycle-skipping issues when the initial model is not well constrained. We present a progressive data assimilation (PDA) strategy to mitigate the cycle-skipping issue and boost the convergence rate. Specifically, we split each seismogram into several time windows with a constant length after the first arrival. At each iteration, the PDA strategy automatically screens the windows that are not cycle skipped for inversion according to the criteria of similarity measured between the observed and predicted waveform within a given window. Through the iterations, the model improves and more data will automatically be assimilated into the inversion, forming a positive feedback of model update, and speed up the convergence rate. To provide additional constraints and also stabilise the inversion at the beginning, we include low-frequency surface waves and examine their contribution to the inversion. We validate the effectiveness of the PDA strategy in FWI through numerical experiments using the 1D linear gradient velocity model and the Foothill model. Compared with the conventional FWI strategy, the proposed PDA strategy in an elastic FWI can effectively mitigate the cycle-skipping problem and significantly accelerate the convergence rate. We further apply this method to the field data, and it improves the shallow part of the model with only a fraction of the entire dataset, demonstrating the great potential for practical usage.

Stepsize sharing in time-lapse full-waveform inversion

Full-waveform inversion (FWI) methods can produce high-resolution images of the physical properties of the subsurface. FWI has become a powerful tool for time-lapse or 4D seismic inversion, with applications in the monitoring of reservoir changes with injection and production, and potentially long-term storage of carbon. Current time-lapse FWI strategies include the parallel strategy (PRS), the sequential strategy, the double-difference strategy (DDS), the common-model strategy (CMS), and the central-difference strategy (CDS). PRS time-lapse inversion is affected by convergence differences between the baseline and monitoring inversions, as well as nonrepeatable noise and nonrepeatable acquisition geometries between surveys. The other strategies are largely efforts to fix the sensitivities of PRS, but robust solutions are still sought. We hypothesize that several problems in time-lapse FWI arise from the independence of step lengths during updating. This is supported by synthetic data tests, which indicate that stepsize sharing reduces artifacts caused by the variability in PRS convergence. Two strategies, which we refer to as stepsize-sharing PRS (SSPRS) and stepsize-sharing CMS, are then designed to address these remaining issues. In this paper, we have tested our methods in five scenarios, including noise-free data, nonrepeated noises, nonrepeatable source positions, biased starting models, and a combination of the latter three. The comparisons between the SSPRS and other strategies indicate that the SSPRS can adapt to all tested scenarios well. Especially, except for the DDS which is extremely sensitive to the nonrepeatable source positions, only the SSPRS can provide meaningful results in the latter two scenarios when compared with others. Furthermore, given that SSPRS through its sharing incurs half of the time cost of seeking stepsizes compared with the PRS and DDS, the total computational cost of SSPRS is less than half of that of the CMS and CDS.

Joint towed streamer and ocean-bottom-seismometer data multi-parameter full waveform inversion in acoustic-elastic coupled media

Short-offset towed streamer data, and sparse ocean-bottom seismometer (OBS) data are not conducive to applying multi-parameter full waveform inversion (FWI) in production. It is challenging to reconstruct deep velocity in the former, and the latter suffers from severe acquisition footprints. We developed a joint acoustic-elastic coupled full waveform inversion (J-AEFWI) method, in which towed streamer data and ocean-bottom seismometer data were used jointly to build P-wave and S-wave velocity models. A new joint objective function was established using the least-squares theory, and the joint acoustic-elastic coupled full waveform inversion method on the acoustic-elastic coupled equation was derived. The method can inject the residuals of the towed streamer data and the ocean-bottom seismometer data in time backward propagating to update P-wave and S-wave velocity models. The synthetic experimental results show that joint acoustic-elastic coupled full waveform inversion obtains more accurate results than when using these two types of data alone. Compared to using the towed streamer or ocean-bottom seismometer data alone, the joint acoustic-elastic coupled full waveform inversion method leads to better illumination of the deep background velocities and suppression of acquisition footprints. The results of joint acoustic-elastic coupled full waveform inversion were slightly better than those of the cascaded full waveform inversion strategy. To further demonstrate the benefit of the proposed method, we applied it to the field data, and better results are obtained as expected.

Joint FWI of Active Source Data and Passive Virtual Source Data Reconstructed Using an Improved Multidimensional Deconvolution

Traditional full waveform inversion (FWI) highly depends on sufficient low-frequency data or a good initial model. Passive seismic data contain rich low-frequency components, and passive seismic FWI using virtual source data by seismic interferometry (SI) is a promising method. However, the distribution of passive sources in the subsurface is always inhomogeneous, which will lead to artifacts in the reconstruction results by SI using cross-correlation (CC). SI by multidimensional deconvolution (MDD) can counteract the source inhomogeneity but requires the separation of the reference wavefields, which is difficult to achieve for noise source data. To mitigate this problem, we propose an improved SI method by linear Radon transform based multidimensional deconvolution (LRTMDD). The interferometric point-spread function can be extracted more accurately and efficiently in the linear Radon domain, thus improving the reconstruction results. The passive virtual source full waveform inversion (PVSFWI) based on LRTMDD is further proposed, which can effectively use the low-frequency information in the virtual source data to invert the macroscopic velocity structures even in the case of inhomogeneous source distributions, and without the need to estimate the virtual source wavelets. A joint multi-source FWI strategy is proposed to solve the problem of missing low-frequency data suffered by active source FWI. Numerical experiments on the Marmousi model and the SEG/EAGE overthrust model show that the proposed methods can fully combine the respective advantages of multisource seismic data to stably achieve high-accuracy velocity models in the case of inhomogeneous passive source distributions and the lack of low-frequency data in active seismic data.

Hierarchical transfer learning for deep learning velocity model building

Deep learning is a promising approach to velocity model building because it has the potential of processing large seismic surveys with minimal resources. By leveraging large quantities of model-gather pairs, neural networks (NNs) can automatically map data to the model space, directly providing a solution to the inverse problem. Such mapping requires big data, which proves prohibitive for 2D and 3D surveys of realistic size. We have developed a transfer learning (TL) strategy. A network is first trained on a smaller subproblem, which then becomes the starting solution to a larger, more difficult data set, akin to the hierarchical multiscale strategy for full-waveform inversion. We perform TL by having subobjectives that escalate in complexity and by first training an NN at estimating horizontally layered velocity models and then proceeding to train an augmented network at estimating 2D dipping layered models. TL improves convergence and allows using a lesser quantity of 2D models for training. For synthetic tests, the structural similarity index measure of 2D interval velocity models in the time domain is [Formula: see text] and the root-mean-square (rms) error is [Formula: see text] m/s. We benchmark our algorithm on the Marmousi2 model and observe that our method can apply to velocity models with continuous deformed layers with dips up to 35°. We benchmark our algorithm on 2D marine field data and produce an rms velocity model that leads to coherent stacking and a time interval velocity model that reproduces salient features of the stacked section. TL expedites and regularizes training and data-driven techniques may be applied to field data with minimal preprocessing even though we lack real target velocity models.

Application of a global–local full‐waveform inversion of Rayleigh wave to estimate the near‐surface shear wave velocity model

AbstractWe present a global–local full‐waveform inversion (FWI) of surface waves to estimate a high‐resolution shear wave velocity model of the near‐surface at the site of Grenoble (France). The seismic data we use have been acquired in the framework of the InterPACIFIC project. A first attempt is made by employing the multichannel analysis of surface waves (MASW) to invert the fundamental modes of Rayleigh waves. This inversion is solved through a global optimization driven by the neighbourhood algorithm. However, the limiting 1D assumption, together with the severely ill‐posedness of the MASW inversion motivate us to exploit all the information content of the recorded Rayleigh waves (i.e., travel‐times and amplitudes) by implementing a global–local FWI approach. We first perform a global FWI in which a genetic algorithm (GA) is used to minimize the L2 norm difference between observed and simulated seismic waveforms up to 30 Hz. This inversion employs a two‐grid scheme in which the subsurface is described by a fine grid input to the forward modelling, whereas the unknowns are defined on a coarse grid. A bilinear interpolation brings the coarse‐grid model into the fine grid. To accelerate the convergence, two strategies are considered; offset marching and frequency marching. Then, the long‐wavelength velocity profile provided by the GA inversion is used as the starting point for a local FWI for further refinement of the predicted model. In this case, we employ a frequency‐marching strategy up to a maximum frequency of 60 Hz. Both the global and local approaches use a finite difference code for the forward computation. Despite the limited a priori information infused into the inversion framework, the presented global–local FWI scheme provides reliable results as demonstrated by the good match between recorded and predicted seismograms and by the agreement of the estimated velocity with both nearby borehole data and stratigraphic information on the study site. Our global–local strategy also achieves better data fitting and improved matching with the available borehole data with respect to the velocity profile estimated when the local FWI is started from the MASW predictions.

Feasibility of seismic time-lapse monitoring of CO2 with rock physics parametrized full waveform inversion

SUMMARY Carbon capture and storage is an important technology for greenhouse gas mitigation. Monitoring of CO2 storage should, in addition to locating the plume, provide quantitative information on CO2 saturation. We propose a full waveform inversion (FWI) algorithm for the prediction of the spatial distribution of CO2 saturation from time-lapse seismic data. The methodology is based on the application of a rock-physics parametrized FWI scheme that allows for direct updating of reservoir properties. We derive porosity and lithology parameters from baseline data and use them as input to predict CO2 saturation from monitor data. The method is tested on synthetic time-lapse data generated for the Johansen formation model. Practical issues associated with field data applications, such as acquisition limitations, construction of the initial model, noise and uncertainty in the rock physics model, are taken into account in the simulation. The results demonstrate the robustness of our approach for reconstructing baseline and monitor models. We also illustrate the potential of the approach as compared to conventional two-step inversion algorithms, in which an elastic FWI prediction of velocities and density is followed by rock physics inversion.

A Cyclic Multi-Stage Implementation of the Full-Waveform Inversion for the Identification of Anomalies in Dams

For the safe and efficient operation of dams, frequent monitoring and maintenance are required. These are usually expensive, time consuming, and cumbersome. To alleviate these issues, we propose applying a wave-based scheme for the location and quantification of damages in dams. To obtain high-resolution “interpretable” images of the damaged regions, we drew inspiration from non-linear full-multigrid methods for inverse problems and applied a new cyclic multi-stage full-waveform inversion (FWI) scheme. Our approach is less susceptible to the stability issues faced by the standard FWI scheme when dealing with ill-posed problems. In this paper, we first selected an optimal acquisition setup and then applied synthetic data to demonstrate the capability of our approach in identifying a series of anomalies in dams by a mixture of reflection and transmission tomography. The results had sufficient robustness, showing the prospects of application in the field of non-destructive testing of dams.

The connection of velocity and impedance sensitivity kernels with scattering-angle filtering and its application in full waveform inversion

Multi-scale strategies such as starting from the low-frequency and early-arrival part of recorded data are commonly used in full waveform inversion (FWI) to maneuver complex nonlinearity. An alternative way is to apply appropriate filtering and conditioning to the misfit gradient in the model domain. In acoustic constant-density media, we prove that velocity and impedance sensitivity kernels are equivalent to applying a high-pass and a low-pass scattering-angle filter to a conventional single-parameter velocity (CSV) kernel. The high-pass scattering-angle filter allows the velocity kernel to include low-wavenumber updates (tomography component). In contrast, the low-pass scattering-angle filter helps the impedance kernel to yield high-wavenumber updates (migration component). The velocity model can be updated using a hybrid gradient of two components combined with appropriate weights. This FWI scheme is able to overcome the potential nonlinearity and partially mitigate the cycle-skipping problem. Numerical examples for the SEG/EAGE overthrust model and the Marmousi model demonstrate that the hybrid gradient facilitates FWI to converge faster to the true model even in cases when conventional CSV-based FWI fails.

A consistent multiparameter Bayesian full waveform inversion scheme for imaging heterogeneous isotropic elastic media

SUMMARY The inversion of complete seismic waveforms offers new perspectives to better constrain the elastic properties of Earth’s interior. However, models of density and seismic velocities obtained from full waveform inversions are generally characterized by very different and uneven spatial resolutions. Because the 3-D structure of the Earth represents small deviations from average reference Earth models, the absolute values of density, VP and VS in the Earth are strongly correlated. Here, we exploit this strong correlation between model parameters as a priori information introduced into a new full waveform inversion algorithm, by considering a non-diagonal 3-D model covariance matrix in which the spatial correlations of elastic properties are described with an exponential covariance function. The inverse of such a model covariance matrix is easy to compute, and we thus have all the ingredients to construct a consistent Bayesian full waveform inversion scheme. We show that taking into account the correlations between density and seismic velocities can lead to dramatic improvements on the reconstructed models of density, seismic velocities and VP/VS ratio. This new imaging approach opens new perspectives for refining tomographic images of density and seismic velocities in the lithosphere and upper mantle on a regional scale by full waveform inversion of teleseismic body waves.

Suppressing position approximation imaging artifacts in full waveform modeling and inversion of marine seismic data

Full-waveform inversion (FWI) is an advanced seismic imaging method for determining high resolution velocity models of different scales, such as in subseafloor geo-hazard monitoring and risk analysis. Among all two-way wave-equation based modeling methods, a mixed-grid finite-difference modeling (FDM) in frequency domain has the advantage of low numerical dispersion errors for mesh size up to 1/4 wave-length. However, irregular source and receiver positions situated away from the FDM nodes introduce position approximation error (PAE) as large as half mesh size. When a source or receiver is situated in strong heterogeneity or near a free surface, the PAE induces imaging artifacts in high frequencies. We propose an imaging artifact suppression method for multi-scale FDM and FWI in marine seismic survey. The delta function of the source or receiver position is approximated by a discrete Kaiser-windowed sinc function in unbounded medium. The free-surface effects to sources and receivers are compensated with the mirror source concept. The final coefficients for delta function approximation and wavefield interpolation are spatially confined tensor products. The results using ocean bottom cable and vertical cable survey layouts in synthetic models validate the accuracy and feasibility of this new FDM and FWI strategy. The FWI tests shows that this strategy has reduced small scale artifacts at high frequencies for survey layouts of good and poor illuminations. The extension of our method to 3-D visco-acoustic model or reverse time migration is straightforward.

Target-oriented high-resolution elastic full-waveform inversion with an elastic redatuming method

Characterizing the elastic properties in deep-buried reservoirs beneath complex overburden structures remains challenging for seismic inversion. Elastic full-waveform inversion (FWI) is capable of quantitatively estimating the subsurface elastic properties with a reasonably high resolution. However, elastic FWI using high frequencies is computationally expensive because fine discretization is required to stabilize the wavefield simulation. In addition, it is challenging for elastic FWI to obtain the high-resolution inversion results of the deep targets due to complex overburden structures and limited energy illumination of the target of interest. To overcome these limitations, we propose a target-oriented high-resolution elastic FWI scheme by using the estimated elastic data for a virtual survey deployed only above a zone of interest. Specifically, we have developed an elastic redatuming approach to retrieve virtual elastic data by solving an iterative least-squares data-fitting problem. In addition to the elastic multicomponent data recorded at the acquisition surface, an estimate of the overburden model is required. A synthetic data example indicates that an overburden model estimated by elastic FWI with a low-frequency band enables our elastic redatuming approach to reconstruct the virtual elastic data representing the seismic reflection response from the target zone. We then perform regularized elastic FWI by using the redatumed elastic data to estimate the elastic properties in the target zone with reduced computational cost. In the numerical examples, the Marmousi2 synthetic data and 2D Volve field data are used to demonstrate the feasibility and practicality of the proposed method.

Regularization tunnelling for full waveform inversion

SUMMARY Prior knowledge can be a powerful source of information in seismic inversion, leading to substantially more accurate models than those derived from seismic data alone. Prior knowledge about clustering of rock physics properties, supplied by appropriate rock physics models, or nearby well-logs, may be especially informative. However, incorporating general forms of this type of information into local optimization methods, such as those used in full waveform inversion (FWI), is problematic, since it will often call for model updates involving a departure from the basin of a local minimum. Here, we propose an optimization strategy for FWI, in which global regularization information is partially accounted for, and in which a model can, under the right circumstances, ‘tunnel’ between basins, to honour prior information. The process is based on standard FWI updates and begins with an initial model in which every model grid cell is in one of a range of pre-defined clusters. At each iteration, a standard update is computed, but it is then followed by a second update, in which each grid cell tunnels to a new cluster if the move is warranted by the update history of the model (its ‘momentum’) and the model regularization penalty (its ‘potential’). To limit the ability of the tunnelling procedure from introducing spatial variations on scales smaller than those contained in the seismic data, these conditions are considered in combination with a small mode filter. We implement the procedure by building on an existing 2-D viscoelastic FWI algorithm, limited such that only the P-wave velocity and density are updated, and evaluate it by subjecting it to a variety of basic tests involving simulated data. We conclude that the basic approach, and this specific implementation, using a simple initial model, provides a combination of well-resolved structures and grid cells occupying appropriate clusters that cannot be produced by standard means.

Joint Physics and Data Driven Full-Waveform Inversion for Underground Dielectric Targets Imaging

To better reconstruct underground targets based on ground-penetrating radar (GPR) data, this paper proposes a joint physics and data driven full-waveform inversion (PDD-FWI) scheme. This scheme combines a physics-based non-iterative approach and a data-driven deep neural network (DNN) to reconstruct target location, shape and permittivity accurately. Firstly, the normalized range migration algorithm (RMA) is introduced to extract the target contour and location information, which not only improves the robustness of the proposed scheme, but also ensures adaptability to different GPR equipment. Then, the GPR dielectric target reconstruction network (GPRDtrNet) is developed based on the improved U-net structure, including reducing network layers and adding multi-scale additive spatial attention gates and skip-connection structures. Compared with previous DNN-based reconstruction methods, GPRDtrNet has the advantages of small data requirement, high accuracy, strong generalization and noise tolerance. Finally, the simulated and real dataset containing kinds of targets is constructed to train and test GPRDtrNet. The results show that the proposed method can reconstruct underground dielectric targets accurately with high robustness and noise tolerance.

Angle Domain Illumination Compensated Full Waveform Inversion

The exploration capacity of full waveform inversion (FWI) for deep targets is affected by the acquisition geometry, complexity of overlying strata and dip angle of the target, etc. It is of great importance to analyze the relationship between the dip angle of the target reflector and the incident/scattered angle of the wavefields near the target, so as to achieve the illumination distribution and improve the inversion quality accordingly. We propose an angle domain illumination compensated FWI strategy, which utilizes the local resolution function as preconditioning to the gradient of FWI in the local angle domain, in order to improve the inversion capability for deep targets. The local resolution function describes the inversion capacity for the local target in the target dip coordinate, which can be generated from the local illumination matrix that contains the illumination information for the target from different incident and scattering directions. The Marmousi model and the SEG/EAGE salt model are used to show the validity of this method. Results from the numerical experiments prove that the proposed method can effectively improve the inversion performance as well as increase the convergence of FWI.

Numerical Verification of Full Waveform Inversion for the Chang’E-5 Lunar Regolith Penetrating Array Radar

The lunar regolith penetrating array radar (LRPR) carried by Chang’E-5 (CE-5) has explored the subsurface regolith structure of the moon and guided the drill sampling procedures. To evaluate LRPR performance in subsurface imaging and physical-parameter estimation, we apply multiscale full-waveform inversion (FWI) with total variation (TV) regularization to the synthetic CE-5 LRPR data. The time-domain multiscale inversion strategy gives a low-frequency update for the deep region and increases the frequency range for updating the shallow area. The TV regularization reduces the image noise and improves the inversion accuracy of local structures. To mimic the actual scenario, we use an LRPR source wavelet obtained from the LRPR instrument prototype for our FWI test. The LRPR source wavelets include the effect of the antenna radiation patterns and signal scattering from the metallic lander. We test the proposed FWI scheme on two heterogeneous models of the lunar regolith and demonstrate that the scheme effectively reduces the signal scattering from the metallic lander and provides a reliable way to image the lunar regolith structures. These images can be used to estimate the regolith physical parameters.