Good Starting Model Research Articles

Use of three-dimensional crossgradient joint inversion of marine controlled-source electromagnetic (CSEM) and seismic refraction data for overburden geohazards mapping.

Offshore energy-transition activities typically require the construction of new subsurface facilities where overburden heterogeneity and deformation pattern may not be fully understood. Anisotropic marine controlled-source electromagnetic (CSEM) and seismic refraction imaging can be adapted to geohazards assessment of the overburden to mitigate safety and costs risks in such areas. This is investigated using an area in deepwater NW Borneo as the type-example. For this proof-of-concept study, a well-established Multiphysics joint inversion code was extended to handle marine seismic refraction data from conventional towed streamers. Realistic field-scale synthetic models with anomalous resistivities and velocities in the shallow section (∼50-200 m below mudline) were built and their CSEM and seismic refraction responses were first inverted separately and then jointly in 3D using the crossgradient method. The joint inversion recovered the anomalous bodies better than the separate inversions. The velocity model from the joint inversion showed improvement from the separate inversion but required a good starting model. As a further test of the methodology, first-break travel times were picked from legacy 3D seismic data and inverted with the available sparse CSEM data. For model verification, resistivity and velocity profiles from separate and joint inversion models were extracted at a well location in the survey area and compared with actual well logs. The joint inversion models matched the well logs better than the separate inversion models. Moreover, known geohazards (complex fault-sets and lateral-inflows) from independent geomatic study were imaged in the joint inversion models. Also, the resistivity anisotropy ratio tracked anomalous zones that correspond to zones of recent complex faulting and potential lateral inflows in the overburden mass transport systems. Thus, joint inversion of CSEM and seismic refraction data has potential to improve the mapping of overburden hazards offshore, with the associated resistivity anisotropy serving as a proxy for active ground deformation.

On accounting for the effects of crust and uppermost mantle structure in global scale full-waveform inversion

Summary Fundamental mode surface wave data have often been used to construct global shear velocity models of the upper mantle under the so-called “path average approximation”, an efficient approach from the computational point of view. With the advent of full-waveform inversion and numerical wavefield computations, such as afforded by the spectral element method, accounting for the effects of the crust accurately becomes challenging. Here, we assess the merits of accounting for crustal and uppermost mantle effects on surface and body waveforms using fundamental mode dispersion data and a smooth representation of the shallow structure. For this we take as reference a model obtained by full waveform inversion and wavefield computations using the spectral element method, model SEMUCB-WM1 (French and Romanowicz, 2014) and compare the waveform fits of synthetics to different parts of three component observed teleseismic records, in the period band 32-300 s for body waves and 40-300 s for surface waves and their overtones for three different models. The latter are: a dispersion-only based model (model Disp_20s_iter5), and two models modified from SEMUCB-WM1 by successively replacing the top 200 km (model Merged _200km) and top 80 km (model Merged _80km), respectively, by a model constrained solely by fundamental mode surface wave dispersion data between periods of 20 and 150 s. The crustal part of these 3 models (resp. SEMUCB-WM1) is constrained from dispersion data in the period range 20-60 s (resp. 25-60 s), using the concept of homogenization (e.g., Backus 1962, Capdeville & Marigo 2007) which is tailored to simplify complex geological features, enhancing the computational efficiency of our seismic modeling. We evaluate the fits to observed waveforms provided by these 3 models compared to those of SEMUCB-WM1 by computing three component synthetics using the spectral element method for 5 globally distributed events recorded at 200+ stations, using several measures of misfit. While fits to waveforms for model 3 are similar to those for SEMUCB-WM1, the other two models provide increasingly poorer fits as the distance travelled by the corresponding seismic wave increases and/or as it samples deeper in the mantle. In particular, models 1 and 2 are biased towards fast shear velocities, on average. Our results suggest that, given a comparable frequency band, models constructed using fundamental mode surface wave data alone and the path average approximation, fail to provide acceptable fits to the corresponding waveforms. However, the shallow part of such a 3D radially anisotropic model can be a good starting model for further full waveform inversion using numerical wavefield computations. Moreover, the shallow part of such a model, including its smooth crustal model, and down to a maximum depth that depends on the frequency band considered, can be fixed in FWI iterations for deeper structure. This can save significant computational time when higher resolution is sought in the deeper mantle. In the future, additional constraints for the construction of the homogenized model of the crust can be implemented from independent short period studies, either globally or regionally.

The bad and the good of trends in model building and refinement for sparse-data regions: pernicious forms of overfitting versus good new tools and predictions.

Model building and refinement, and the validation of their correctness, are very effective and reliable at local resolutions better than about 2.5 Å for both crystallography and cryo-EM. However, at local resolutions worse than 2.5 Å both the procedures and their validation break down and do not ensure reliably correct models. This is because in the broad density at lower resolution, critical features such as protein backbone carbonyl O atoms are not just less accurate but are not seen at all, and so peptide orientations are frequently wrongly fitted by 90-180°. This puts both backbone and side chains into the wrong local energy minimum, and they are then worsened rather than improved by further refinement into a valid but incorrect rotamer or Ramachandran region. On the positive side, new tools are being developed to locate this type of pernicious error in PDB depositions, such as CaBLAM, EMRinger, Pperp diagnosis of ribose puckers, and peptide flips in PDB-REDO, while interactive modeling in Coot or ISOLDE can help to fix many of them. Another positive trend is that artificial intelligence predictions such as those made by AlphaFold2 contribute additional evidence from large multiple sequence alignments, and in high-confidence parts they provide quite good starting models for loops, termini or whole domains with otherwise ambiguous density.

One‐Dimensionl Inversion of Magnetotelluric Field Data by SIS and Occam's Inversion Technique: A Case Study from the Indian Himalaya

AbstractElectromagnetic method is one of the most effective geophysical methods in earth crust and mantle structure studies. Magnetotelluric (MT) data are used to obtain resistivity image of earth's surface on the depth scale of few hundred of meters to several tens of km. One‐dimensional (1D) MT inversion is carried out to estimate electrical resistivity variation of earth's surface with depth. In the present research paper one‐dimensional inversion of MT sounding curves is carried out on nine sites in foothill of Himalaya near Roorkee to Hardwar region. A non‐iterative and minimum norm estimator based algorithm Straight forward Inversion Scheme (SIS) is used to obtain continuous smoothest resistivity depth model. Another iterative and minimum norm estimator based algorithm Occam's inversion scheme is also used to obtain smoothest one‐dimensional inversion model. The data is inverted for both TE‐ and TM‐mode of polarization. The inverted data shows one‐dimensional behavior of subsurface resistivity up to depth about 0–15 km with resistivity variation ≈10–100 ohm‐m in both modes of polarization and after that the subsurface structure is two‐dimensional in both modes. 1D inversion of data provides a good starting model to reduce the computational cost of two‐dimensional inversion.

Wasserstein Distance-Based Full-Waveform Inversion With a Regularizer Powered by Learned Gradient

Full-waveform inversion (FWI) is a powerful technique for building high-quality subsurface geological structures. It is known to suffer from local minima problems when a good starting model is lost. To obtain a desirable solution, regularization constraints are needed to impose suitable priors, in particular for salt models. Recent studies have allowed cooperating the denoising operator as a specific prior with optimization algorithms to effectively solve various image reconstruction tasks. Inspired by the promising performance of regularization by denoising (RED), we propose a flexible unsupervised learning FWI framework, in which the regularizer is an extensive variant of RED that powered by learned gradient for addressing salt models inversion. To further mitigate local minima issues, the Wasserstein distance induced by optimal transport theory with a new preprocessing transformation is applied as a measure in data domain. Integrating the physical constraints featured by the wave equation and the model priors featured by the deep convolutional neural network, our method is able to produce measurement consistent and high-resolution results. We experimentally compare the proposed method with traditional total variation regularized FWI and RED regularized FWI on the well-known geological models. The numerical results demonstrate the effectiveness of our method for handling the inversion task of high-contrast media in the presence of a free-surface case. Moreover, our framework is adaptive to restrict the solutions by leveraging the regularizer with learned gradient, whose training does not need any geological images, thus paving the way to develop FWI algorithms with state-of-the-art deep learning techniques in interdisciplinary research.

Elastic Reflection Waveform Inversion With a Nonlinear Born Scattering Operator for Multiparameter Reconstruction

Elastic full waveform inversion (FWI) is one of the most popular tools in recovering the subsurface P- and S-velocity models with high resolution. However, it requires either a good starting model or low-frequency data to avoid the cycle-skipping issue. Elastic reflection waveform inversion (ERWI) uses the migration/demigration process to compute the low-wavenumber gradient. One drawback of conventional ERWI is that it mainly relies on the first-order Born scattering and only utilizes primary reflection wave paths, providing limited wavenumber illumination in the update. In this letter, a nonlinear Born scattering operator is incorporated with ERWI for multiparameter reconstruction to mitigate the drawback of the conventional linear Born approximation. Finally, we use numerical examples to show that the nonlinear Born scattering operator allows us to take advantage of the multiples and enlarge the wavenumber illumination in ERWI results.

Deep Velocity Generator: A Plug-In Network for FWI Enhancement

Known for its great potential for determining subsurface properties quantitatively, full-waveform inversion (FWI) is a hot topic in the field of exploration seismology. The success of FWI depends significantly on the accuracy of the starting model. Given that both the migration and velocity profiles originate from the same geological structure, the two should be morphologically consistent. Starting from the velocity‐reflector depth trade-off, we propose a deep learning approach with a new training paradigm for building a good starting model. A velocity model and the corresponding migration image are used to form 2-channel inputs, and the Generative Adversarial Network (GAN) is trained to minimize the difference between the output and the true velocity model. After the training, the velocity generator network becomes a plug-in component to enhance the FWI performance. The network can be well generalized to unseen data by training with only the synthetic data. We perform extensive experiments on our test dataset, the Marmousi model, the salt velocity model, and field data to demonstrate the effectiveness of our method. Besides, we briefly give an explanation of why our model produces such outputs in this manuscript, making the proposed method more controllable and credible.

Inversion using adaptive physics‐based neural network: Application to magnetotelluric inversion

ABSTRACTA new trend to solve geophysical problems aims to combine the advantages of deterministic inversion with neural network inversion. The neural networks applied to geophysical inversion have had limited success due to the need for extensive training of datasets and the lack of generalizability to out‐of‐sample scenarios. Deterministic regularized inversion often requires a good starting model to avoid possible local minima in highly nonlinear problems. We have developed a physics‐based neural network procedure that combines the advantages of deterministic and neural network inversions in a coupled inversion scheme. The new inversion algorithm is formulated as a constrained minimization problem with a composite objective function composed of data misfit, neural network training function and a coupling model objective function that links the two inversion schemes through a reference model. We investigate two strategies to update the reference model in the coupling model objective function using either a fully trained network or an adaptively trained network that keeps evolving and learning during the inversion. First, we propose a procedure to generate the training dataset required for the fully trained network to improve the network generalization. Second, we extend the analysis of the adaptively trained network inversion without preparing a training dataset, so it is suitable for most exploration scenarios. Our approach enables the network to learn only the relevant model distribution. Predictions from the network are used to constrain the deterministic inversion using a new predicted reference model. We demonstrate the convergence of our procedure in recovering high‐resolution resistivity models when applied to synthetic magnetotellurics data. We compare the recovered resistivity model from a deterministic inversion, a neural network inversion and physics‐based neural network inversions using fully and adaptively trained networks, respectively. The results indicate that the adaptive physics‐based neural network is superior as it does not require preparing a training dataset but can still recover reliable resistivity models.

Deep learning inversion with supervision: A rapid and cascaded imaging technique

Machine learning has been demonstrated to be extremely promising in solving inverse problems, but deep learning algorithms require enormous training samples to obtain reliable results. In this article, we propose a new solution, the deep learning inversion with supervision (DLIS) and applied it for corrosion mapping in guided wave tomography. The inversion results show that when dealing with multiple defects of complex shape on a plate-like structure, DLIS methods can reduce the scale of training set effectively compared with other deep learning algorithms in experiment because a good starting model is provided and the nonlinearity between the global minimum and observed wave field is greatly reduced. In terms of reconstruction accuracy using experimental data, the thickness maps produced by DLIS are reliable with high accuracy. With few modifications, this method can be conveniently extended to 3D cases. These results imply that DLIS is one of the promising methods to be applied in fields with similar physics like non-destructive evaluation (NDE), biomedical imaging and geophysical prospecting.

Fractal-Constrained Crosshole/Borehole-to-Surface Full-Waveform Inversion for Hydrogeological Applications Using Ground-Penetrating Radar

Full-waveform inversion (FWI) is considered one of the most promising interpretation tools for hydrogeological applications using ground-penetrating radar. However, FWI has had limited practical uptake for several reasons: large computational requirements, an inability to reconstruct loss mechanisms of soil, and the need for a good initial starting model. We aim to address these issues via a novel FWI subject to a fractally correlated distribution of water. Initially, the dispersive properties of the soil are expressed as a function of the water fraction using a semiempirical model. This approach means that the permittivity, conductivity, and relaxation mechanisms are all correlated, and therefore, sensitivity problems between the permittivity and loss mechanisms no longer affect the performance of FWI. Subsequently, the distribution of the water fraction is constrained to follow a fractal geometry. Fractal-correlated noise is then compressed using a principal component analysis (PCA) in order to further reduce the number of the system’s unknowns and accelerate FWI. PCA reduces the volume and dimensions of the optimization space, and thus, initialization is no longer necessary. Finally, a novel measurement configuration is suggested that uses superposition with all the individual measurements in order to reduce the number of forward models that need to be executed for every iteration of FWI. These enhancements substantially reduce the computational requirements of FWI and therefore eliminate the need for high-performance computers and time-consuming algorithms. The proposed scheme has been successfully tested with several numerical case-studies, which indicates the potential of this approach to become a commercially appealing interpretation tool for hydrogeology.

Correlative Full-Intensity Waveform Inversion

Full-waveform inversion (FWI) is considered an effective technique for building high-resolution velocity models by fitting observed seismology waveforms. When the observed waveforms lack low frequencies or when the starting model is dissimilar to the true model, FWI usually suffers from cycle-skipping problems. To mitigate this difficulty, we propose a new correlative objective function that matches the phase differences between the seismic-waveform intensities to provide a good starting model. The waveform intensity separates the frequency band of the original data into an ultralow-frequency part and a higher frequency part, even when the original data lacking in low-frequency information. As the low-frequency part of the intensity is less prone to the cycle-skipping problem, it can be used to build an initial model for FWI. Furthermore, the source wavelets, in practice, are estimated from the observed data, which may be inaccurate in amplitude, phase shift, and time delay. To mitigate the inaccurate-source problem, a Wiener filter is constructed to build a source-independent objective function. Applications to the Marmousi model and Chevron blind-test model demonstrate that the proposed method converges to an acceptable initial model for conventional FWI. It is highly efficient and insensitive to inaccurate source wavelets, including inaccurate amplitude, phase shift, and time delays.

Bayesian transdimensional seismic full-waveform inversion with a dipping layer parameterization

Seismic full-waveform inversion (FWI) has become a popular tool for estimating subsurface models using the amplitude and phase of seismograms. Unlike the conventional gradient-based approach, Bayesian inference using Markov chain Monte Carlo (MCMC) sampling can remove dependence on starting models and can quantify uncertainty. We have developed a Bayesian transdimensional (trans-d) MCMC seismic FWI method for estimating dipping-layer velocity models, in which the number of layers is unknown. A time-domain staggered-grid finite-difference wave equation solver is used for forward modeling. The FWI and MCMC methods are known to be computationally expensive. Two strategies are used to get practical computational performance. A layer-stripping strategy is used to accelerate sampler convergence, and a parsimonious dipping layer parameterization is used so that the MCMC algorithm can search broadly with fewer iterations. The parameters for each layer are velocity, thickness, and lower interface dip angle. We find that this parameterization has sufficient flexibility to invert for narrow 2D velocity models using small offset data. Model stitching is then used to bring several such inversions together to create larger 2D models. In turn, these can be used as starting models for gradient-based adjoint FWI to image complicated geologic settings. Two synthetic 2D numerical examples, including the Marmousi model, are considered, using seismic data dominated by reflections. Creation of good starting models traditionally requires significant human effort. We determine how much of that effort can be substituted with computation.

Full-waveform inversion of salt models using shape optimization and simulated annealing

A good starting model is imperative in full-waveform inversion (FWI) because it solves a least-squares inversion problem using a local gradient-based optimization method. A suboptimal starting model can result in cycle skipping leading to poor convergence and incorrect estimation of subsurface properties. This problem is especially crucial for salt models because the strong velocity contrasts create substantial time shifts in the modeled seismogram. Incorrect estimation of salt bodies leads to velocity inaccuracies in the sediments because the least-squares gradient aims to reduce traveltime differences without considering the sharp velocity jump between sediments and salt. We have developed a technique to estimate velocity models containing salt bodies using a combination of global and local optimization techniques. To stabilize the global optimization algorithm and keep it computationally tractable, we reduce the number of model parameters by using sparse parameterization formulations. The sparse formulation represents sediments using a set of interfaces and velocities across them, whereas a set of ellipses represents the salt body. We use very fast simulated annealing (VFSA) to minimize the misfit between the observed and synthetic data and estimate an optimal model in the sparsely parameterized space. The VFSA inverted model is then used as a starting model in FWI in which the sediments and salt body are updated in the least-squares sense. We partition model updates into sediment and salt updates in which the sediments are updated like conventional FWI, whereas the shape of the salt is updated by taking the zero crossing of an evolving level set surface. Our algorithm is tested on two 2D synthetic salt models, namely, the Sigsbee 2A model and a modified SEG Advanced Modeling Program (SEAM) Phase I model while fixing the top of the salt. We determine the efficiency of the VFSA inversion and imaging improvements from the level set FWI approach and evaluate a few sources of uncertainty in the estimation of salt shapes.

Reflection waveform inversion with variable density

Full waveform inversion (FWI) is considered as an effective technique for high-resolution velocity model building. It requires a good starting model to avoid the cycle-skipping problem when the low-frequency data are unavailable. To provide a background staring model for FWI, reflection waveform inversion (RWI) is successfully applied to emphasize on low-wavenumber velocity model updating. Given a proper reflectivity model, the low-wavenumber gradient of RWI is obtained by cross-correlating the background wavefield and the reflection wavefield. While explicitly generating the reflection wavefield from the reflectivity model is time-consuming. Therefore, in order to mitigate the time-consuming problem, we have proposed an efficient RWI approach by alternatively updating the density model and the low-wavenumber velocity model. This approach doesn't require explicitly generating the reflection wavefield. We use synthetic and field data to demonstrate that, the proposed method could effectively provide a good starting model for FWI.

Adaptive waveform inversion: Practice

Adaptive waveform inversion (AWI) reformulates the misfit function used to perform full-waveform inversion (FWI), so that it no longer contains local minima related to cycle skipping. It does this by finding a model that drives the ratio of the predicted and observed data sets to unity rather than driving the difference between these two data sets to zero as is the case for conventional FWI. We apply AWI to a 3D field data set acquired over a pervasive gas cloud in the North Sea, comparing its performance with that of conventional FWI in a variety of circumstances. When starting inversion from 3 Hz, and using a good starting model obtained from reflection tomography, FWI and AWI generate similar models although the FWI result contains edge artifacts that are not produced by AWI. However, when the starting frequency is increased to approximately 6 Hz, or when the starting model is less accurate, FWI fails to recover a good model whereas AWI continues to converge. When both of these conditions apply, FWI fails comprehensively, leading to a model that is significantly worse than the starting model, whereas the AWI result remains largely unaffected. We applied Kirchhoff depth migration to the fully-processed data using the FWI result obtained following reflection tomography, and using the AWI result obtained from a simple one-dimensional starting model. We use the resulting migrated volumes, together with measures of residual moveout throughout the volume, to show that the AWI result from a simple starting model is at least as good as the FWI result obtained following tomography. We conclude that AWI is robust in the presence of cycle skipping on this 3D field data set, and can proceed successfully from a less-accurate starting model, and from a higher starting frequency, in circumstances in which FWI fails completely.

Projection methods and applications for seismic nonlinear inverse problems with multiple constraints

Nonlinear inverse problems are often hampered by local minima because of missing low frequencies and far offsets in the data, lack of access to good starting models, noise, and modeling errors. A well-known approach to counter these deficiencies is to include prior information on the unknown model, which regularizes the inverse problem. Although conventional regularization methods have resulted in enormous progress in ill-posed (geophysical) inverse problems, challenges remain when the prior information consists of multiple pieces. To handle this situation, we have developed an optimization framework that allows us to add multiple pieces of prior information in the form of constraints. The proposed framework is more suitable for full-waveform inversion (FWI) because it offers assurances that multiple constraints are imposed uniquely at each iteration, irrespective of the order in which they are invoked. To project onto the intersection of multiple sets uniquely, we use Dykstra’s algorithm that does not rely on trade-off parameters. In that sense, our approach differs substantially from approaches, such as Tikhonov/penalty regularization and gradient filtering. None of these offer assurances, which makes them less suitable to FWI, where unrealistic intermediate results effectively derail the inversion. By working with intersections of sets, we avoid trade-off parameters and keep objective calculations separate from projections that are often much faster to compute than objectives/gradients in 3D. These features allow for easy integration into existing code bases. Working with constraints also allows for heuristics, where we built up the complexity of the model by a gradual relaxation of the constraints. This strategy helps to avoid convergence to local minima that represent unrealistic models. Using multiple constraints, we obtain better FWI results compared with a quadratic penalty method, whereas all definitions of the constraints are in terms of physical units and follow from the prior knowledge directly.

Automatic traveltime inversion via sparse decomposition of seismic data

We have developed an automatic traveltime inversion (ATI) method to estimate the macrovelocity model from reflection seismic data. First, we extract the kinematic information (i.e., source/receiver ray parameters, traveltime, and source/receiver coordinates) of locally coherent events using a sparse-decomposition method. And then we evaluate a new strategy to calculate the reflection traveltime residual based on a ray-intersection criterion, eliminating the influence of seismic amplitude to the estimation of the traveltime residual. The velocity model can be updated iteratively by minimizing the traveltime residual functional with a gradient-based method. To obtain a smooth gradient free of artifacts, we first estimate the high-wavenumber components of the functional gradient with a total variation (TV) regularization method and then subtract it from the full gradient. Because the reflection traveltime residual calculation and velocity update are fully automated procedures, the proposed traveltime inversion method is referred to as ATI. We determine with 2D synthetic and field examples that ATI does not need a good starting model. Furthermore, it requires neither low-frequency seismic data nor long-offset acquisition. Nevertheless, the proposed traveltime residual calculation strategy is only valid for the 2D case, which limits its 3D applicability. We explore a possible solution for 3D extension.

Nonlinear inversion of resistivity sounding data for 1-D earth models using the Neighbourhood Algorithm

To reduce ambiguity related to nonlinearities in the resistivity model-data relationships, an efficient direct-search scheme employing the Neighbourhood Algorithm (NA) was implemented to solve the 1-D resistivity problem. In addition to finding a range of best-fit models which are more likely to be global minimums, this method investigates the entire multi-dimensional model space and provides additional information about the posterior model covariance matrix, marginal probability density function and an ensemble of acceptable models. This provides new insights into how well the model parameters are constrained and make assessing trade-offs between them possible, thus avoiding some common interpretation pitfalls. The efficacy of the newly developed program is tested by inverting both synthetic (noisy and noise-free) data and field data from other authors employing different inversion methods so as to provide a good base for comparative performance. In all cases, the inverted model parameters were in good agreement with the true and recovered model parameters from other methods and remarkably correlate with the available borehole litho-log and known geology for the field dataset. The NA method has proven to be useful whilst a good starting model is not available and the reduced number of unknowns in the 1-D resistivity inverse problem makes it an attractive alternative to the linearized methods. Hence, it is concluded that the newly developed program offers an excellent complementary tool for the global inversion of the layered resistivity structure.

Skeletonized wave-equation inversion in vertical symmetry axis media without too much math

We have developed a tutorial for skeletonized inversion of pseudo-acoustic anisotropic vertical symmetry axis (VTI) data. We first invert for the anisotropic models using wave-equation traveltime inversion. Here, the skeletonized data are the traveltimes of transmitted and/or reflected arrivals that lead to simpler misfit functions and more robust convergence compared with full-waveform inversion. This provides a good starting model for waveform inversion. The effectiveness of this procedure is illustrated with synthetic data examples and a marine data set recorded in the Gulf of Mexico.

3D-NuS: A Web Server for Automated Modeling and Visualization of Non-Canonical 3-Dimensional Nucleic Acid Structures

The inherent conformational flexibility of nucleic acids facilitates the formation of a range of conformations such as duplex, triplex, quadruplex, etc. that play crucial roles in biological processes. Elucidation of the influence of non-canonical base pair mismatches on DNA/RNA structures at different sequence contexts to understand the mismatch repair, misregulation of alternative splicing mechanisms and the sequence-dependent effect of RNA–DNA hybrid in relevance to antisense strategy demand their three-dimensional structural information. Furthermore, structural insights about nucleic acid triplexes, which are generally not tractable to structure determination by X-ray crystallography or NMR techniques, are essential to establish their biological function(s). A web server, namely 3D-NuS (http://iith.ac.in/3dnus/), has been developed to generate energy-minimized models of 80 different types of triplexes, 64 types of G-quadruplexes, left-handed Z-DNA/RNA duplexes, and RNA–DNA hybrid duplex along with inter- and intramolecular DNA or RNA duplexes comprising a variety of mismatches and their chimeric forms for any user-defined sequence and length. It also generates an ensemble of conformations corresponding to the modeled structure. These structures may serve as good starting models for docking proteins and small molecules with nucleic acids, NMR structure determination, cryo-electron microscope modeling, DNA/RNA nanotechnology applications and molecular dynamics simulation studies.