Full-waveform Inversion Results Research Articles

Enabling Uncertainty Quantification in a standard Full Waveform Inversion method using Normalizing Flows

In order to maximize the utility of seismic imaging and inversion results, we need to compute not only a final image but also quantify the uncertainty in that image. While the most thorough approach to quantify the uncertainty is to use a method such as Markov chain Monte Carlo (MCMC), which systematically samples the entire posterior distribution, this is often inefficient and not all applications require a full representation of the posterior. We use normalizing flows (NF), a machine learning technique to perform uncertainty quantification (UQ) in full waveform inversion (FWI), specifically for time-lapse data. As with any machine learning algorithm, the NF learns only the mapping from the part of the prior spanned by the training data to the distribution of final models spanned by the training data. Here we make use of this property to perform UQ efficiently by learning a mapping from the prior to the distribution that really characterizes the model perturbations within a specific range. Our approach involves using a range of starting models, paired with final models from a standard FWI as training data. While this does not capture the full posterior of the FWI problem, it enables us to quantify the uncertainties associated with updating from an initial to a final model. Since our target is to perform UQ for time-lapse imaging, we use a local wave-equation solver that allows us to solve the wave equation in a small subset of our entire model, thereby keeping computational costs low. Numerical examples demonstrate that incorporating the training step for NF provides a distribution of model perturbations, which is dependent on a designated prior, to quantify the uncertainty of FWI results.

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 < 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.

Low-Frequency Prediction Based on Multiscale and Cross-Scale Deep Networks in Full-Waveform Inversion

The well-known cycle-skipping problem in full-waveform inversion (FWI) can make the iterative solution fall into local minima and produce an undesired inverted result when reliable low-frequency components in seismic data and a good initial model are not available. The recovery of low-frequency data can effectively solve the cycle-skipping problem. However, hardware limitations have made it difficult to obtain reliable low-frequency components in seismic data. Thus, we adopt a multi-scale and cross-scale convolutional neural network (MCCNN) to build the nonlinear mapping between high-frequency and low-frequency data from synthetic training data sets. The major benefit of MCCNN is that it can fully use the multi-scale and cross-scale information in the high-frequency data to predict the low-frequency data. Several numerical experiments show the effectiveness and benefits of the low-frequency recovery of MCCNN. On the one hand, introducing the in-stage multi-scale and across-stage cross-scale information can accelerate the convergence rate in the training process and improve the low-frequency prediction accuracy. On the other hand, MCCNN has good generalization abilities in predicting the low-frequency data from the Marmousi and Overthrust models, different model sizes, and wavelet types and frequencies. The acoustic FWI results show that the predicted low-frequency data can effectively prevent the inversion from falling into a local minimum and help FWI obtain an accurate velocity model even if we start from a poor initial model.

Cross-correlation Full Waveform Inversion for Sound Speed Reconstruction in Ultrasound Computed Tomography.

Ultrasound computed tomography (USCT) is considered to have great potential for breast cancer screening. Compared with the ray based methods, the reconstructed image using full waveform inversion (FWI) methods have higher spatial resolution. However, the results of FWI is difficult to converge to the real value when cycle skipping occurs. In this paper, a cross-correlation full waveform inversion(CC-FWI) is proposed for USCT image reconstruction. In the first stage, the ajoint source is adjusted as the residual of predicted signal and time-shifted measured signal to avoid cycle skipping. In the remaining stage, the FWI with source encoding is employed to accelerate convergence. The simulations are conducted to demonstrate the validity of the proposed algorithm. The root mean squared error (RMSE) of the proposed algorithm is much smaller than that of conventional FWI. The results suggest that CC-FWI is effective in avoiding cycle skipping. Clinical Relevance- Clinical relevance- New imaging modalities of high resolution, safety to examines for early-stage breast cancer imaging are urgently needed for researching and development. Ultra-sound computed tomography (USCT) is supposed to meet the above requirements and it can be potentially deployed in breast scanning.

Characterizing near-surface structures at the Ostia archaeological site based on instantaneous-phase coherency inversion

Traditional least-squares full-waveform inversion (FWI) suffers from severe local minima problems in the case of the presence of strongly dispersive surface waves. In addition, recorded wavefields are often characterized by amplitude errors due to varying source coupling and incorrect 3D-to-2D geometric-spreading correction. Thus, least-squares FWI is considered less suitable for near-surface applications. We introduce an amplitude-unbiased coherency measure as a misfit function that can be incorporated into FWI. Such coherency has been used previously in phase-weighted stacking to enhance weak but coherent signals. The benefit of this amplitude-unbiased misfit function is that it can extract information uniformly for all seismic signals (surface waves, reflections, and scattered waves). Using the adjoint-state method, we show how to calculate the gradient of this new misfit function. We validate the robustness of the new approach using checkerboard tests and synthetic data contaminated by random noise. We then apply the new FWI approach to a field data set acquired at an archaeological site located in Ostia, Italy. The goal of this survey is to map the unexcavated archaeological remains with high resolution. We identify a known tumulus in the FWI results. The instantaneous-phase coherency FWI results also establish that the shallow subsurface under the survey lines is quite heterogeneous. The instantaneous-phase coherency FWI of near-surface data can be a promising tool to image shallow small-scale objects buried under shallow soil covers, as found at archaeological sites.

Full waveform inversion of the scattered wavefield: resolution analysis

The paper studies the influence of the scattered component of the wavefield on the full waveform inversion results. For different scenarios, the contribution of scattered waves was investigated. Utilizing the one step of the quasi-Newton FWI method, we demonstrate that scattered wavefields bring helpful information in the context of the solution of the inverse dynamical problem.

Using convolutional neural networks to develop starting models for near-surface 2-D full waveform inversion

SUMMARY Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamentally change near-surface (<30 m) site characterization by enabling the recovery of high-resolution (metre-scale) 2-D/3-D maps of subsurface elastic material properties. Yet, FWI results are quite sensitive to their starting model due to their dependence on local-search optimization techniques and inversion non-uniqueness. Starting model dependence is particularly problematic for near-surface FWI due to the complexity of the recorded seismic wavefield (e.g. dominant surface waves intermixed with body waves) and the potential for significant spatial variability over short distances. In response, convolutional neural networks (CNNs) are investigated as a potential tool for developing starting models for near-surface 2-D elastic FWI. Specifically, 100 000 subsurface models were generated to be representative of a classic near-surface geophysics problem; namely, imaging a two-layer, undulating, soil-over-bedrock interface. A CNN has been developed from these synthetic models that is capable of transforming an experimental wavefield acquired using a seismic source located at the centre of a linear array of 24 closely spaced surface sensors directly into a robust starting model for FWI. The CNN approach was able to produce 2-D starting models with seismic image misfits that were significantly less than the misfits from other common starting model approaches, and in many cases even less than the misfits obtained by FWI with inferior starting models. The ability of the CNN to generalize outside its two-layered training set was assessed using a more complex, three-layered, soil-over-bedrock formation. While the predictive ability of the CNN was slightly reduced for this more complex case, it was still able to achieve seismic image and waveform misfits that were comparable to other commonly used starting models, despite not being trained on any three-layered models. As such, CNNs show great potential as tools for rapidly developing robust, site-specific starting models for near-surface elastic FWI.

Target-oriented inversion using the patched Green’s function method

We have designed a target-oriented methodology to perform full-waveform inversion (FWI) using a frequency-domain wave propagator based on the so-called patched Green’s function (PGF) technique. Originally developed in condensed matter physics to describe electronic waves in materials, the PGF technique is easily adaptable to the case of wave propagation in spatially variable media in general. By dividing the entire computational domain into two sections, namely the target area and the outside target area, we calculate the Green’s functions related to each section separately. The calculations related to the section outside the target are performed only once at the beginning of the inversion, whereas the calculations in the target area are performed repeatedly for each iteration of the inversion process. With the Green’s functions of the separate areas, we calculate the combined Green’s function of the two systems patched together through application of a recursive Dyson equation. The calculation is exact; that is, it is the same as the Green’s function calculated by directly solving the Helmholtz equation. By performing 2D and time-lapse experiments on the Marmousi and a Brazilian presalt velocity models, we determined that the target-oriented PGF reduces the computational time of the inversion without compromising accuracy. In fact, when compared with conventional FWI results, the PGF-based calculations are identical but done in a reduced time according to the target size.

Prehistoric chamber tombs or geological pitfall? A multimethod case study from Ancient Aigeira with a focus on seismic full‐waveform inversion

AbstractWe show an extensive multimethod geophysical study of focusing on some enigmatic subsurface structures found at Ancient Aigeira (N Peloponnes, Greece) that could be interpreted either as prehistoric chamber tombs or complex weathering patterns of the local marl–conglomerate rock sequences. It turns out that the nonseismic methods do not allow to distinguish between an archaeological and a geological origin of the observed patterns with certainty. In contrast, we demonstrate how shear‐wave seismics and full‐waveform inversion (FWI) can be used in archaeological prospection for distinguishing between these alternative essentially different interpretational models that are not distinguishable through nonseismic prospection data. The example site Aigeira is strategically well located on a hill on the Northern Peloponnese overlooking the Corinthian Gulf and has been inhabited with occupational gaps since Middle Neolithic times until the 12th to early 14th century ce. Magnetics, ground‐penetrating radar (GPR) and electrical resistivity tomography (ERT) reveal a honeycomb‐shaped anomaly pattern that could have been interpreted as a system of prehistoric chamber tombs. The time‐domain SH‐FWI strategy based on a sequential inversion of low‐pass and band‐pass filtered data results in subsurface models for shear‐wave velocity and density that accurately fits the complicated seismic data set. A highly heterogeneous subsurface is revealed that is characterized by linear cracks on a decimetre scale. The seismic FWI results are compared in detail with GPR, ERT and among each other. It turns out that the FWI result is consistent with each of these other geophysical methods but provides a more comprehensive subsurface characterization that it is supported by corings in addition. With the help of the seismic survey, we can reject the interpretation hypothesis of a prehistoric cemetery with chamber tombs and confirm that the enigmatic geophysical patterns represent a geological weathering structure that could be addressed as a reincised fan delta draped by reddish palaeosols.

Nonrepeatability effects on time-lapse 4D seismic full-waveform inversion for ocean-bottom node data

Full-waveform inversion (FWI) can be applied to time-lapse (4D) seismic data for subsurface reservoir monitoring. However, nonrepeatability (NR) issues can contaminate the data and cause artifacts in the estimation of 4D rock and fluid property changes. Therefore, evaluating and studying the NR effects on the 4D data and FWI results can help, for instance, discriminate inversion artifacts from true changes and guide seismic survey design and processing workflows. Using realistic reservoir models, data, and field measurements of NR, we find the effects of NR source-receiver position and seawater velocity changes on the data and the 4D FWI results. We show that ignoring these NR effects in the inversion can cause strong artifacts in the estimated velocity change models and thus should be addressed before or during inversion. The NR source-receiver positioning issue can be successfully addressed by 4D FWI, whereas the NR water velocity issue requires measurements or estimations of water velocities. Furthermore, we compare the accuracy and robustness of the parallel, double-difference, and central-difference 4D FWI methods to realistic NR ocean-bottom node data in a quantitative way. Parallel 4D FWI fails to capture geomechanical changes and also overestimates the aquifer layer changes with NR data. Double-difference 4D FWI is capable of recovering the geomechanical changes, but it is also sensitive to NR noises, generating more artifacts in the overburden. By averaging the forward and reverse bootstrap 4D estimates, central-difference 4D FWI is more robust to NR noises and also produces the most accurate 4D estimates.

Challenges in shallow target reconstruction by 3D elastic full-waveform inversion — Which initial model?

Elastic full-waveform inversion (FWI) is a powerful tool for high-resolution subsurface multiparameter characterization. However, 3D FWI applied to land data for near-surface applications is particularly challenging because the seismograms are dominated by highly energetic, dispersive, and complex-scattered surface waves (SWs). In these conditions, a successful deterministic FWI scheme requires an accurate initial model. Our study, primarily focused on field data analysis for 3D applications, aims at enhancing the resolution in the imaging of complex shallow targets, by integrating devoted SW analysis techniques with a 3D spectral-element-based elastic FWI. From dispersion curves, extracted from seismic data recorded over a sharp-interface shallow target, we build different initial S-wave ([Formula: see text]) and P-wave ([Formula: see text]) velocity models (laterally homogeneous and laterally variable), using a specific data transform. Starting from these models, we carry out 3D FWI tests on synthetic and field data, using a relatively straightforward inversion scheme. The field data processing before FWI consists of band-pass filtering and muting of noisy traces. During FWI, a weighting function is applied to the far-offset traces. We test 2D and 3D acquisition layouts, with different positions of the sources and variable offsets. The 3D FWI workflow enriches the overall content of the initial models, allowing a reliable reconstruction of the shallow target, especially when using laterally variable initial models. Moreover, a 3D acquisition layout guarantees a better reconstruction of the target’s shape and lateral extension. In addition, the integration of model-oriented (preliminary monoparametric FWI) and data-oriented (time windowing) strategies into the main optimization scheme has produced further improvement of the FWI results.

Improvement of ground-penetrating radar full-waveform inversion images using cone penetration test data

Detailed characterization of aquifers is critical and challenging due to the existence of heterogeneous small-scale high-contrast layers. For an improved characterization of subsurface hydrologic characteristics, crosshole ground-penetrating radar (GPR) and cone penetration test (CPT) measurements are performed. In comparison to the CPT approach, which can only provide 1D high-resolution data along vertical profiles, crosshole GPR enables measuring 2D cross sections between two boreholes. In general, a standard inversion method for GPR data is the ray-based approach, which considers only a small amount of information and can therefore only provide limited resolution. In the past few decades, full-waveform inversion (FWI) of crosshole GPR data in the time domain has matured, and it provides inversion results with higher resolution by exploiting the full-recorded waveform information. However, FWI results are limited due to complex underground structures and the nonlinear nature of the method. A new approach that uses CPT data in the inversion process is applied to enhance the resolution of the final relative permittivity FWI results by updating the effective source wavelet. The updated effective source wavelet possesses a priori CPT information and a larger bandwidth. Using the same starting models, a synthetic model comparison between the conventional and updated FWI results demonstrates that the updated FWI method provides reliable and more consistent structures. To test the method, five experimental GPR cross section results are analyzed with the standard FWI and the new proposed updated approach. The synthetic and experimental results indicate the potential of improving the reconstruction of subsurface aquifer structures by combining conventional 2D FWI results and 1D CPT data.

Refraction waves full waveform inversion of deep reflection seismic profiles in the central part of Lhasa Terrane

The deep reflection seismic method is an effective technique for detecting the fine structures of the lithosphere and solving deep geological problems. At present, the methods for obtaining the underground velocity from land deep reflection seismic profile mainly rely on the travel-time information based velocity analysis and tomography, which theoretically limit the resolution of the imaging results. To achieve the goal of using deep reflection seismic profile for crustal-scale high-precision velocity imaging, we must finally overcome the problem of full waveform inversion (FWI) using full wavefield data. In this paper, we successfully conduct FWI using the early-arrival wavefields (mainly primary and multiple refraction wavefields) in the deep reflection seismic profile, which can be seen as the first important step towards the goal of crustal-scale high-precision velocity imaging. We propose a robust refraction waves FWI workflow with global normalized cross-correlation, multiple refraction matching, reverse time source estimation and wavenumber domain gradient filtering as its core algorithm. The application of the proposed FWI method to the refraction waves of the deep reflection seismic profiles in the central part of Lhasa Terrane shows that the refraction FWI images have a higher resolution to underground structures both in horizontal and vertical directions than the first-arrival tomography images. The lateral velocity change can be well-matched with the typical geological characteristics of the work area. The FWI result can better describe the lateral boundaries of the strata in different eras. At a depth of less than 3 km from the surface, the refraction FWI can correct the interface morphology of the tomography results and recover some small-scale velocity structures and high-velocity anomalies.

3D aquifer characterization of the Hermalle-sous-Argenteau test site using crosshole ground-penetrating radar amplitude analysis and full-waveform inversion

To improve the understanding of flow and transport processes in the critical zone, high-resolution and accurate estimation of the small-scale heterogeneity is essential. Preferential flow paths related to high-porosity layers and clay lenses in gravel aquifers greatly affect flow and transport processes in the subsurface, and their high electrical contrast to their surrounding matrix and limited extent can act as low-velocity electromagnetic waveguides. In the past decade, time-domain full-waveform inversion (FWI) of crosshole ground-penetrating radar (GPR) data has shown to provide 2D decimeter-scale resolution images of relative permittivity and electrical conductivity of the subsurface, which can be related to porosity and soil texture. Most studies using crosshole GPR FWI resolved high-porosity zones that were identified by an amplitude analysis approach. But clay lenses or zones with higher electrical conductivity that act as low-velocity waveguides are hard to distinguish in the measured data and amplitude analysis because of the absence of characteristic wave-propagation features. We have investigated a set of nine crosshole GPR data sets from a test site in Hermalle-sous-Argenteau near the Meuse River in Belgium to characterize the aquifer within a decimeter-scale resolution and to improve the understanding of a previously performed heat tracer experiment. Thereby, we extend the amplitude analysis to identify two different types of low-velocity waveguides either caused by an increased porosity or a higher electrical conductivity (and higher porosity). Combining the GPR amplitude analysis for low-velocity waveguide zones with the standard FWI results provided information on waveguide zones, which modified the starting models and further improved the FWI results. Moreover, an updated effective source wavelet is estimated based on the updated permittivity starting models. In comparison with the traditional FWI results, the updated FWI results present smaller gradient of the medium properties and smaller root-mean-squared error values in the final inversion results. The nine crosshole sections are used to generate a 3D image of the aquifer and allowed a detailed analysis of the porosity distribution along the different sections. Consistent structures of the permittivity and electrical conductivity show the robustness of the updated FWI results. The aquifer structures obtained by the FWI results agree with those results of the heat tracer experiment.

A Frequency-Domain Quasi-Newton-Based Biparameter Synchronous Imaging Scheme for Ground-Penetrating Radar With Applications in Full Waveform Inversion

Full waveform inversion (FWI) of ground-penetrating radar (GPR) data is becoming a promising technique to facilitate the interpretation of surface-GPR data and the mapping of the subsurface. However, more general FWIs still require a sufficient amount of RAM memory, and it is difficult to produce an accurate and representative reconstruction result due to a large amount of the Hessian matrix calculations and singular value decomposition (SVD). In this article, we developed a novel full-waveform approach of the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm for surface-GPR data that is based on a quasi-Newton framework and the total variation (TV) regularization. The proposed approach uses an L-BFGS algorithm and combines a scale-transformation regularization technique to mitigate the ill-posed problem of inversion, which can impose a biparameter preinformation constraint to ensure the stability of inversion, and adaptive regularization weights are applied to improve the convergence efficiency of inversion. To demonstrate the novelty and effectiveness of the proposed scheme, we tested our FWI algorithm using synthetic data and in-site field data. In the testing, we focus on analyzing the influence of different aspects of the FWI results, including different scale factors, regularization weights, inversion strategies, acquisition configurations, initial models, and the noisy data set. In particular, the FWI experiment is performed to demonstrate the applicability of the proposed algorithm. The results show that the proposed algorithm can effectively reconstruct the biparameter near the subsurface with high accuracy, which makes our approach very attractive for attribute analysis applications and makes the surface-GPR FWI commercially viable.

Seismic inversion by Newtonian machine learning

We present a wave-equation inversion method that inverts skeletonized seismic data for the subsurface velocity model. The skeletonized representation of the seismic traces consists of the low-rank latent-space variables predicted by a well-trained autoencoder neural network. The input to the autoencoder consists of seismic traces, and the implicit function theorem is used to determine the Fréchet derivative, i.e., the perturbation of the skeletonized data with respect to the velocity perturbation. The gradient is computed by migrating the shifted observed traces weighted by the skeletonized data residual, and the final velocity model is the one that best predicts the observed latent-space parameters. We denote this as inversion by Newtonian machine learning (NML) because it inverts for the model parameters by combining the forward and backward modeling of Newtonian wave propagation with the dimensional reduction capability of machine learning. Empirical results suggest that inversion by NML can sometimes mitigate the cycle-skipping problem of conventional full-waveform inversion (FWI). Numerical tests with synthetic and field data demonstrate the success of NML inversion in recovering a low-wavenumber approximation to the subsurface velocity model. The advantage of this method over other skeletonized data methods is that no manual picking of important features is required because the skeletal data are automatically selected by the autoencoder. The disadvantage is that the inverted velocity model has less resolution compared with the FWI result, but it can serve as a good initial model for FWI. Our most significant contribution is that we provide a general framework for using wave-equation inversion to invert skeletal data generated by any type of neural network. In other words, we have combined the deterministic modeling of Newtonian physics and the pattern matching capabilities of machine learning to invert seismic data by NML.

Characterization of silty to fine‐sandy sediments with SH waves: full waveform inversion in comparison with other geophysical methods

ABSTRACTWe apply seismic full waveform inversion to SH‐ and Love‐wave data for investigating the near‐surface lithology at an archaeological site. We evaluate the resolution of the applied full waveform inversion algorithm through ground truthing in the form of an excavation and sediment core studies. Thereby, we investigate the benefits of full waveform inversion in comparison with other established methods of near‐surface prospecting in terms of resolution capabilities and interpretation security. The study is performed in a presumed harbour area of the ancient Thracian city of Ainos. The exemplary target is the source of a linear magnetic anomaly oriented perpendicular to the coast, which was found in a previous magnetic gradiometry survey, suggesting a mole. The SH‐wave full waveform inversion recovered a subsurface SH‐wave velocity model with submeter resolution showing lateral and vertical velocity variation between 40 and 150 m/s. To tame the non‐linearity of the full waveform inversion, a sequential inversion of frequency bands has to be combined with time‐windowing in order to separate the Love wave from the reflected SH wavefield. We compare the full waveform inversion results with multichannel analysis of surface waves, standard seismic reflection imaging, electrical resistivity tomography and electromagnetic induction. It turns out that the respective depth sections are correlated to a certain degree with the full waveform inversion results. However, the structural resolution of the other geophysical methods is significantly lower than for the full waveform inversion. An exception is the reflection seismic imaging, which shows the same resolution as full waveform inversion but can only be interpreted together with the full waveform inversion–based velocity model. An archaeological excavation as well as coring data allows ground truthing and a direct understanding of the geophysical structures. The results show that the target was a sort of near‐surface trench of about 3–4 m width and 0.8 m to 1.0 m depth, filled with silty sediment, which differs from the layered surrounding in colour and composition. The ground truthing revealed that only SH‐wave full waveform inversion and seismic reflection imaging could image the trench and sediment structure with satisfying lateral and depth resolution. We emphasize that the velocity distribution from SH‐wave full waveform inversion agrees closely with the excavated subsurface structures, and that the discovered changes in seismic velocity correlate with changes in the sand content in the respective sediment facies sequences. The study demonstrated that SH‐wave full waveform inversion is capable to image structural and lithological changes in the near subsurface at scales as low as 0.5 m, thus providing the high resolution needed for archaeological and geoarchaeological prospection.

Gas hydrate quantification in Walker Ridge block 313, Gulf of Mexico, from full-waveform inversion of ocean-bottom seismic data

The Gulf of Mexico (GOM) Joint Industry Project Leg 2 logging-while-drilling data in Walker Ridge lease block 313 (WR313) in the GOM detected gas hydrate in coarse- and fine-grained sediments at sites WR313-G and WR313-H. The coarse-grained units are thin ([Formula: see text]) and highly saturated, whereas the fine-grained unit is thick (approximately 200 m) with low saturation and fracture-filling gas hydrate. Unlike its coarse-grained counterpart, the seismic character of the fine-grained unit does not clearly indicate the presence of gas hydrate, which would likely have remained undiscovered in the absence of drilling. In this paper, through frequency-domain acoustic full-waveform inversion (FWI) of ocean-bottom seismometer data along a 2D multichannel seismic transect near sites WR313-G and WR313-H, we detect and quantify gas hydrate in the fine-grained unit. Key results are as follows: First, the base of the gas hydrate stability zone, which is not obvious in the reflection profile, can be discerned in the FWI results. Second, the gas hydrate in the fine-grained unit is mainly confined to the area between two sets of opposite-dipping normal faults implying that the fault architecture may be partially responsible for this gas hydrate accumulation and distribution.

Estimation of velocity and borehole receiver location via full waveform inversion of vertical seismic profile data

ABSTRACTBecause a vertical seismic profile (VSP) survey includes the installation of geophones into a borehole, the results of data processing are influenced by the deviation error of the borehole trajectory. In particular, when full waveform inversion (FWI) is applied to VSP data, receiver location errors distort the results of velocity inversion. Under this circumstance, we attempt to estimate the exact locations of receivers and resolve the distortion of the inverted velocity model by adding receiver locations to the inversion parameters. This makes the FWI process a multi-parametric inverse problem. In this case, it is desirable to invert each parameter group alternatively. Because receiver coordinates depend on borehole trajectory, the inverse problem is regularised by adding a term measuring the curvature of the borehole trajectory gradient to obtain a physically meaningful solution. In addition, we propose a grid search method to improve the estimation of receiver coordinates. In the case of VSP data, the inversion of receiver coordinates using the grid search method is more efficient and robust than using a local descent method. Through numerical examples, we verify that the proposed inversion algorithm can improve FWI results and mitigate velocity distortion. We also demonstrate that the convergence of FWI can be improved by applying the grid search method to the estimation of receiver coordinates.

Full waveform inversion based on a local traveltime correction and zero-mean cross-correlation-based misfit function

Full waveform inversion (FWI) suffers from the cycle skipping problem, because the observed data usually lack low-frequency components or due to errors in the wavelet estimation. In addition, the strong low-frequency non-zero-mean noise can have a large impact on FWI results. Thus, we propose a local waveform traveltime correction scheme to solve the situations when the observed data lack low-frequency components or when the estimation for the wavelet is incorrect. We use a sliding time window, which is used to decrease the traveltime differences between the calculated and observed data to increase the cross-correlation between them. Besides, we propose a zero-mean normalized cross-correlation misfit function to reduce the interference of the low-frequency non-zero-mean noise. Therefore, we propose new approaches to improve FWI results whether the observed data lack low-frequency components or the observed data are contaminated by the non-zero-mean low-frequency noise. Numerical examples on Marmousi model show the feasibility of a FWI based on the zero-mean normalized cross-correlation misfit function and a FWI based on the local traveltime correction method.