D.J. Verschuur
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Seismic wave propagation in the Earth's subsurface is influenced by anelastic attenuation, which causes energy loss and waveform distortion, degrading image resolution. This effect, quantified by the quality factor (Qf), is particularly pronounced in settings such as carbon capture and storage and near-surface studies, where fluids, gases or unconsolidated sediments are present. Conventional Qf estimation methods – such as spectral ratio and centroid frequency shift – often rely on simplifying assumptions, have limitations in heterogeneous media and produce smeared Qf results. We address these limitations by integrating attenuation compensation and Qf estimation directly into the full wavefield migration framework. Our method embeds Qf into a one-way forward modelling operator and applies full-waveform matching on residual data to estimate attenuation, compensating for it during migration. Implemented in the image domain within a wave-equation tomography framework, it links model and Qf perturbations for robust, localized estimation. Tests on synthetic and field data confirm that the approach accurately recovers both reflectivity and attenuation models, improving resolution and producing more geologically consistent images. Compared with conventional spectral-based Qf estimation methods, the proposed full-waveform matching framework jointly estimates reflectivity and attenuation during migration while maintaining control over internal multiples.
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Seismic wave propagation in the Earth's subsurface is influenced by anelastic attenuation, which causes energy loss and waveform distortion, degrading image resolution. This effect, quantified by the quality factor (Qf), is particularly pronounced in settings such as carbon capture and storage and near-surface studies, where fluids, gases or unconsolidated sediments are present. Conventional Qf estimation methods – such as spectral ratio and centroid frequency shift – often rely on simplifying assumptions, have limitations in heterogeneous media and produce smeared Qf results. We address these limitations by integrating attenuation compensation and Qf estimation directly into the full wavefield migration framework. Our method embeds Qf into a one-way forward modelling operator and applies full-waveform matching on residual data to estimate attenuation, compensating for it during migration. Implemented in the image domain within a wave-equation tomography framework, it links model and Qf perturbations for robust, localized estimation. Tests on synthetic and field data confirm that the approach accurately recovers both reflectivity and attenuation models, improving resolution and producing more geologically consistent images. Compared with conventional spectral-based Qf estimation methods, the proposed full-waveform matching framework jointly estimates reflectivity and attenuation during migration while maintaining control over internal multiples.
Full wavefield-based joint reflection and attenuation estimation
first land-data results
Seismic wave attenuation significantly degrades seismic data, reducing bandwidth, resolution, and amplitude reliability, and complicating imaging and inversion. Conventional spectral-based Q-estimation methods often provide laterally smeared results and are decoupled from imaging, limiting their effectiveness in complex land environments. This study investigates the applicability of Q-compensated Full Wavefield Migration (QFWM), an attenuation-aware extension of Full Wavefield Migration, in which visco-acoustic wave propagation is incorporated directly into the modeling and inversion process to jointly estimate reflectivity and the quality factor Q through full-waveform matching. Application to land seismic data demonstrates that QFWM produces clearer and more continuous reflectors, improved focusing at depth, and partial recovery of high-frequency content compared with conventional acoustic FWM, indicating effective compensation for intrinsic attenuation. The inverted Q model exhibits geologically plausible spatial variability, with low-Q zones correlating with areas of enhanced image improvement. These results highlight the potential of QFWM as a practical tool for attenuation-aware imaging and subsurface characterization in land seismic applications, particularly in strongly attenuating environments such as near-surface and geothermal settings.
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Seismic wave attenuation significantly degrades seismic data, reducing bandwidth, resolution, and amplitude reliability, and complicating imaging and inversion. Conventional spectral-based Q-estimation methods often provide laterally smeared results and are decoupled from imaging, limiting their effectiveness in complex land environments. This study investigates the applicability of Q-compensated Full Wavefield Migration (QFWM), an attenuation-aware extension of Full Wavefield Migration, in which visco-acoustic wave propagation is incorporated directly into the modeling and inversion process to jointly estimate reflectivity and the quality factor Q through full-waveform matching. Application to land seismic data demonstrates that QFWM produces clearer and more continuous reflectors, improved focusing at depth, and partial recovery of high-frequency content compared with conventional acoustic FWM, indicating effective compensation for intrinsic attenuation. The inverted Q model exhibits geologically plausible spatial variability, with low-Q zones correlating with areas of enhanced image improvement. These results highlight the potential of QFWM as a practical tool for attenuation-aware imaging and subsurface characterization in land seismic applications, particularly in strongly attenuating environments such as near-surface and geothermal settings.
This research presents a hybrid AI–HPC acceleration framework designed to overcome the computational barriers of 2-D seismic imaging based on Full Wavefield Modeling. By exploiting the decoupling of frequency components, the proposed method reduces the computational burden of physics-based simulations through a sparse frequency sampling strategy. The omitted frequency slices are accurately reconstructed using an Attention U-Net, which employs attention mechanisms to filter noise and preserve structural details during the interpolation process. To further optimize runtime efficiency, the performance-hotspot wavefield propagation and scattering kernels are optimized via Numba-based Just-In-Time compilation. This implementation strategy eliminates the overhead of the Python interpreter, enabling native machine-code execution with SIMD vectorization and multi-core parallelism. Numerical experiments conducted on synthetic models with varying geological complexity confirm that this framework delivers substantial reductions in computation time while maintaining high wavefield accuracy, stable inversion convergence, and similar resolution in reflectivity imaging.
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This research presents a hybrid AI–HPC acceleration framework designed to overcome the computational barriers of 2-D seismic imaging based on Full Wavefield Modeling. By exploiting the decoupling of frequency components, the proposed method reduces the computational burden of physics-based simulations through a sparse frequency sampling strategy. The omitted frequency slices are accurately reconstructed using an Attention U-Net, which employs attention mechanisms to filter noise and preserve structural details during the interpolation process. To further optimize runtime efficiency, the performance-hotspot wavefield propagation and scattering kernels are optimized via Numba-based Just-In-Time compilation. This implementation strategy eliminates the overhead of the Python interpreter, enabling native machine-code execution with SIMD vectorization and multi-core parallelism. Numerical experiments conducted on synthetic models with varying geological complexity confirm that this framework delivers substantial reductions in computation time while maintaining high wavefield accuracy, stable inversion convergence, and similar resolution in reflectivity imaging.
Because dispersion of surface waves is mainly sensitive to stiffness and fundamental-mode damping inversion is depth-limited, attenuation at depth is typically poorly resolved in surface-wave inversion. To overcome this issue, the current study develops a determinant-based multi-mode surface-wave inversion workflow to estimate the material damping ratio in horizontally layered viscoelastic media. Based on frequency-dependent phase velocity and phase damping ratio, synthetic data are constructed in the form of complex wavenumbers. The misfit is defined as the mean absolute value of the row-normalized dispersion-equation determinant evaluated at the observed frequency-wavenumber pairs. A genetic algorithm inverts for layer-wise damping ratio (assuming equal P- and S-wave damping) by minimizing this misfit across all frequencies. In a 4-layer synthetic example (5–50 Hz), multi-mode inversion turns out to yield more stable damping profiles and lower errors than fundamental-mode-only inversion. This improvement is explained by sensitivity tests on a two-layer model, which show that especially under high impedance contrast, higher modes retain sensitivity to damping of deeper layers while the fundamental mode loses sensitivity. Future work will address robustness to velocity uncertainty and explore waveform-based misfits motivated by the observed amplitude sensitivity.
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Because dispersion of surface waves is mainly sensitive to stiffness and fundamental-mode damping inversion is depth-limited, attenuation at depth is typically poorly resolved in surface-wave inversion. To overcome this issue, the current study develops a determinant-based multi-mode surface-wave inversion workflow to estimate the material damping ratio in horizontally layered viscoelastic media. Based on frequency-dependent phase velocity and phase damping ratio, synthetic data are constructed in the form of complex wavenumbers. The misfit is defined as the mean absolute value of the row-normalized dispersion-equation determinant evaluated at the observed frequency-wavenumber pairs. A genetic algorithm inverts for layer-wise damping ratio (assuming equal P- and S-wave damping) by minimizing this misfit across all frequencies. In a 4-layer synthetic example (5–50 Hz), multi-mode inversion turns out to yield more stable damping profiles and lower errors than fundamental-mode-only inversion. This improvement is explained by sensitivity tests on a two-layer model, which show that especially under high impedance contrast, higher modes retain sensitivity to damping of deeper layers while the fundamental mode loses sensitivity. Future work will address robustness to velocity uncertainty and explore waveform-based misfits motivated by the observed amplitude sensitivity.
This work explores how changes in scattering strength in multiply-scattering media can be quantified using synthetic wavefields computed with Foldy's method for isotropic point scatterers. By averaging waves across random realizations, the effective complex wavenumber, which is linked to the scattering strength through the effective attenuation and phase velocity, can be estimated. We estimate the attenuation coefficient from average recordings at different offsets. Simulations show that the attenuation coefficient can be reliably calculated for a wide frequency band around the central frequency of the source wavelet, but that accuracy declines at low and high frequencies due to model and source spectrum limitations. The method currently applies only to 2D isotropic point scatterers with constant scattering amplitude and assumes the scatterer number density to be known, but the extension to estimating relative changes in scattering strength for models with varying scattering amplitudes but equal scatterer number density is straightforward.
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This work explores how changes in scattering strength in multiply-scattering media can be quantified using synthetic wavefields computed with Foldy's method for isotropic point scatterers. By averaging waves across random realizations, the effective complex wavenumber, which is linked to the scattering strength through the effective attenuation and phase velocity, can be estimated. We estimate the attenuation coefficient from average recordings at different offsets. Simulations show that the attenuation coefficient can be reliably calculated for a wide frequency band around the central frequency of the source wavelet, but that accuracy declines at low and high frequencies due to model and source spectrum limitations. The method currently applies only to 2D isotropic point scatterers with constant scattering amplitude and assumes the scatterer number density to be known, but the extension to estimating relative changes in scattering strength for models with varying scattering amplitudes but equal scatterer number density is straightforward.
Deep learning approaches for seismic and Distributed Acoustic Sensing (DAS) data denoising often exhibit limited generalization under complex noise profiles and domain shifts. To address this, we propose a computationally efficient framework that repurposes general-purpose Vision Foundation Models (VFMs) for geophysical tasks using Parameter-Efficient Fine-Tuning (PEFT). Specifically, our architecture employs a pre-trained DINOv3 encoder adapted via Low-Rank Adaptation (LoRA), which adapts latent features with minimal memory overhead while mitigating catastrophic forgetting. Moreover, to improve performance on unseen data, we introduce a kurtosis-guided unsupervised Test-Time Adaptation (TTA) strategy that updates only LoRA parameters during inference. This allows the model to self-calibrate to site-specific noise characteristics by identifying information-rich regions based on kurtosis and performs self-training without access to ground truth. Experiments on exploration field seismograms and DAS-VSP data from the Utah FORGE site show that our framework matches or exceeds the performance of domain-specific models and other VFMs baselines. The evaluation on unseen cross-site data from Groß Schönebeck geothermal site further demonstrates the robust generalization capabilities of the framework, highlighting the potential of adapting pre-trained VFMs for data-intensive challenges in exploration seismology.
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Deep learning approaches for seismic and Distributed Acoustic Sensing (DAS) data denoising often exhibit limited generalization under complex noise profiles and domain shifts. To address this, we propose a computationally efficient framework that repurposes general-purpose Vision Foundation Models (VFMs) for geophysical tasks using Parameter-Efficient Fine-Tuning (PEFT). Specifically, our architecture employs a pre-trained DINOv3 encoder adapted via Low-Rank Adaptation (LoRA), which adapts latent features with minimal memory overhead while mitigating catastrophic forgetting. Moreover, to improve performance on unseen data, we introduce a kurtosis-guided unsupervised Test-Time Adaptation (TTA) strategy that updates only LoRA parameters during inference. This allows the model to self-calibrate to site-specific noise characteristics by identifying information-rich regions based on kurtosis and performs self-training without access to ground truth. Experiments on exploration field seismograms and DAS-VSP data from the Utah FORGE site show that our framework matches or exceeds the performance of domain-specific models and other VFMs baselines. The evaluation on unseen cross-site data from Groß Schönebeck geothermal site further demonstrates the robust generalization capabilities of the framework, highlighting the potential of adapting pre-trained VFMs for data-intensive challenges in exploration seismology.
For geotechnical applications, surface-wave methods are one of the most important techniques for gathering information about the strength of the near surface, but these methods are sensitive to noise. Often only the fundamental mode is considered, but higher modes can also provide more information on deeper parts of the subsurface. We investigate the use of iterative supervirtual interferometry (SVI) to increase the signal-to-noise ratio and to extract more information about the higher modes. A big advantage is that the method results in data in the same domain as the original data, such that similar multichannel analysis of surface waves–based procedures can be used afterwards. SVI utilises seismic interferometry to extract the extra information in consecutive folds. Generally, the strongest event in the data is boosted the most. Although surface waves are usually the strongest, they consist of sub-events (the modes) with different amplitudes. Previously, iterative SVI has been applied to further boost the result of SVI. For surface waves, this would further diminish the presence of weaker modes. Instead, we subtract the results of SVI from the original data and use the remainder as input for the next iteration, which gives potential for highlighting different modes. We apply our methodology to three different datasets to show different properties of the iterative SVI. When the level of random noise is high, further iterations retrieve mostly the dominant event and no extra information on different modes is gained. When the level of noise is relatively low, we show that higher modes can be found in a wider frequency band in the results of each separate iteration. In both cases, the combination of all iterations provides a more complete description of the surface waves. The presence of strong random noise or spikes in the data can have negative effects on the methodology in the form of spurious events originating from the spikes.
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For geotechnical applications, surface-wave methods are one of the most important techniques for gathering information about the strength of the near surface, but these methods are sensitive to noise. Often only the fundamental mode is considered, but higher modes can also provide more information on deeper parts of the subsurface. We investigate the use of iterative supervirtual interferometry (SVI) to increase the signal-to-noise ratio and to extract more information about the higher modes. A big advantage is that the method results in data in the same domain as the original data, such that similar multichannel analysis of surface waves–based procedures can be used afterwards. SVI utilises seismic interferometry to extract the extra information in consecutive folds. Generally, the strongest event in the data is boosted the most. Although surface waves are usually the strongest, they consist of sub-events (the modes) with different amplitudes. Previously, iterative SVI has been applied to further boost the result of SVI. For surface waves, this would further diminish the presence of weaker modes. Instead, we subtract the results of SVI from the original data and use the remainder as input for the next iteration, which gives potential for highlighting different modes. We apply our methodology to three different datasets to show different properties of the iterative SVI. When the level of random noise is high, further iterations retrieve mostly the dominant event and no extra information on different modes is gained. When the level of noise is relatively low, we show that higher modes can be found in a wider frequency band in the results of each separate iteration. In both cases, the combination of all iterations provides a more complete description of the surface waves. The presence of strong random noise or spikes in the data can have negative effects on the methodology in the form of spurious events originating from the spikes.
This study introduces a novel quaternion-based approach to solving the scalar Helmholtz equation in a 3D heterogeneous environment, using an analytical Green’s function. The general Green’s function is constructed and validated through comparison with Finite-Difference Time-Domain (FDTD) simulations for a seismic example. The results demonstrate that the quaternionic method accurately models wave propagation, achieving the same performance as the established Green’s function in homogeneous media. Furthermore, the analytical approach offers significant advantages, achieving computational speeds approximately 1000 times faster than FDTD while inherently maintaining numerical stability. The findings highlight the potential of quaternion-based methods for efficient and precise wave modeling, paving the way for future applications in complex heterogeneous media.
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This study introduces a novel quaternion-based approach to solving the scalar Helmholtz equation in a 3D heterogeneous environment, using an analytical Green’s function. The general Green’s function is constructed and validated through comparison with Finite-Difference Time-Domain (FDTD) simulations for a seismic example. The results demonstrate that the quaternionic method accurately models wave propagation, achieving the same performance as the established Green’s function in homogeneous media. Furthermore, the analytical approach offers significant advantages, achieving computational speeds approximately 1000 times faster than FDTD while inherently maintaining numerical stability. The findings highlight the potential of quaternion-based methods for efficient and precise wave modeling, paving the way for future applications in complex heterogeneous media.
Surface-related multiple elimination is a fundamental step in seismic data processing, typically relying on a two-stage procedure: multiple prediction followed by adaptive subtraction. While the prediction step is physically robust, the adaptive subtraction stage often struggles to resolve complex non-stationary discrepancies and overlapping primary-multiple events using conventional energy minimization criteria. In this paper, we propose a physics-guided deep learning (PGDL) framework to address these limitations by treating adaptive subtraction as a non-linear, physics-constrained mapping task. We utilize a U-Net architecture with a specialized dual-channel input: the original recorded full wavefield and the globally estimated multiples derived from the wave equation–based multi-dimensional convolution. By explicitly incorporating the multiple models, we inject robust kinematic constraints (i.e., physics) into the network, allowing the learning process to focus on the non-linear residual mapping required to correct amplitude and phase errors rather than learning wave propagation from scratch. We validate the proposed framework through three comprehensive scenarios: (1) synthetic-to-synthetic generalization, (2) field-to-field application using pseudo-labels and (3) a cross-data-distribution test training on synthetic data and applying it to field data. Our results demonstrate that the PGDL framework effectively suppresses surface-related multiples while preserving weak primary energy that is often damaged by traditional methods. Furthermore, we show that a transfer learning strategy using minimal field data effectively bridges the data distribution gap between synthetic training sets and real-world field acquisition, offering a scalable and computationally efficient way for industrial deployment.
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Surface-related multiple elimination is a fundamental step in seismic data processing, typically relying on a two-stage procedure: multiple prediction followed by adaptive subtraction. While the prediction step is physically robust, the adaptive subtraction stage often struggles to resolve complex non-stationary discrepancies and overlapping primary-multiple events using conventional energy minimization criteria. In this paper, we propose a physics-guided deep learning (PGDL) framework to address these limitations by treating adaptive subtraction as a non-linear, physics-constrained mapping task. We utilize a U-Net architecture with a specialized dual-channel input: the original recorded full wavefield and the globally estimated multiples derived from the wave equation–based multi-dimensional convolution. By explicitly incorporating the multiple models, we inject robust kinematic constraints (i.e., physics) into the network, allowing the learning process to focus on the non-linear residual mapping required to correct amplitude and phase errors rather than learning wave propagation from scratch. We validate the proposed framework through three comprehensive scenarios: (1) synthetic-to-synthetic generalization, (2) field-to-field application using pseudo-labels and (3) a cross-data-distribution test training on synthetic data and applying it to field data. Our results demonstrate that the PGDL framework effectively suppresses surface-related multiples while preserving weak primary energy that is often damaged by traditional methods. Furthermore, we show that a transfer learning strategy using minimal field data effectively bridges the data distribution gap between synthetic training sets and real-world field acquisition, offering a scalable and computationally efficient way for industrial deployment.
Journal article
(2026)
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Alvaro Balderrama, Alessandra Luna-Navarro, Jian Kang, Hussam Al Basha, Eric Verschuur, Daniel Arztmann, Ulrich Knaack
As automation is progressively more present in buildings, it is important to understand its impact on people’s experience of urban environments. This case study investigated how one automated shading system with motorized venetian blinds affects sound perception of the acoustic environment in the immediate outdoor surroundings of a university campus building in Detmold, Germany. Procedures of ISO 12913 series for soundscape research were followed to conduct acoustic measurements and derive psychoacoustic indicators. Additionally, soundwalks involving 41 participants and a laboratory study using immersive virtual reality (VR) with 34 participants were conducted to assess how individuals perceive the acoustic environment. The results show that the operation of automated blinds increases sound levels above reference thresholds at short distances, and has a consistent effect on the soundscape, shifting it from pleasant and calm to annoying and chaotic. These trends are notable in both on-site and laboratory data. Additionally, when participants evaluated the soundscape in direct visual connection with the operating façade shading system, they perceived the sounds to be less chaotic than when they were not seeing the façade. These findings highlight that automated buildings and façades are active sound sources capable of influencing environmental quality through acoustic and non-acoustic effects; therefore, their influence on the soundscape should be considered within performance assessments, smart/automation policy, and sustainability frameworks.
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As automation is progressively more present in buildings, it is important to understand its impact on people’s experience of urban environments. This case study investigated how one automated shading system with motorized venetian blinds affects sound perception of the acoustic environment in the immediate outdoor surroundings of a university campus building in Detmold, Germany. Procedures of ISO 12913 series for soundscape research were followed to conduct acoustic measurements and derive psychoacoustic indicators. Additionally, soundwalks involving 41 participants and a laboratory study using immersive virtual reality (VR) with 34 participants were conducted to assess how individuals perceive the acoustic environment. The results show that the operation of automated blinds increases sound levels above reference thresholds at short distances, and has a consistent effect on the soundscape, shifting it from pleasant and calm to annoying and chaotic. These trends are notable in both on-site and laboratory data. Additionally, when participants evaluated the soundscape in direct visual connection with the operating façade shading system, they perceived the sounds to be less chaotic than when they were not seeing the façade. These findings highlight that automated buildings and façades are active sound sources capable of influencing environmental quality through acoustic and non-acoustic effects; therefore, their influence on the soundscape should be considered within performance assessments, smart/automation policy, and sustainability frameworks.
Time-lapse (4D) seismic is a critical technique for reliable monitoring of geological CO₂ storage. Its effectiveness depends on converting time-lapse seismic observations into quantitative estimates of CO2 saturation. We propose a neural-network-based framework trained with a physics-guided objective that embeds seismic and rock-physics relationships. The network adopts a U-Net-style encoder–decoder architecture with residual convolutional blocks and a Transformer bottleneck. The network predicts the non-wetting-phase saturation field from observed angle-dependent seismic reflection data. The utilized loss function compares physics-based predicted angle-dependent 4D seismic images with observed ones and also includes smoothness priors to enforce lateral continuity while suppressing spatial blurring in the estimated saturation fields. We evaluate the proposed framework on synthetically ‘observed’ time-lapse multi-angle seismic images generated from reference saturation fields produced by an 800-year flow simulation. The DNN is trained in a self-supervised manner on the first 720 years, with the remaining 80 years held out for further evaluation. The framework reproduces the large-scale evolution of two migrating plumes over the full 800-year sequence, consistently recovering plume locations, migration paths, and extents. These results indicate the framework is practical for monitoring tasks emphasizing plume localization and extent, enabling fast, stable inference of saturation changes from time-lapse multi-angle images.
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Time-lapse (4D) seismic is a critical technique for reliable monitoring of geological CO₂ storage. Its effectiveness depends on converting time-lapse seismic observations into quantitative estimates of CO2 saturation. We propose a neural-network-based framework trained with a physics-guided objective that embeds seismic and rock-physics relationships. The network adopts a U-Net-style encoder–decoder architecture with residual convolutional blocks and a Transformer bottleneck. The network predicts the non-wetting-phase saturation field from observed angle-dependent seismic reflection data. The utilized loss function compares physics-based predicted angle-dependent 4D seismic images with observed ones and also includes smoothness priors to enforce lateral continuity while suppressing spatial blurring in the estimated saturation fields. We evaluate the proposed framework on synthetically ‘observed’ time-lapse multi-angle seismic images generated from reference saturation fields produced by an 800-year flow simulation. The DNN is trained in a self-supervised manner on the first 720 years, with the remaining 80 years held out for further evaluation. The framework reproduces the large-scale evolution of two migrating plumes over the full 800-year sequence, consistently recovering plume locations, migration paths, and extents. These results indicate the framework is practical for monitoring tasks emphasizing plume localization and extent, enabling fast, stable inference of saturation changes from time-lapse multi-angle images.
Journal article
(2025)
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Faezeh Shirmohammadi, Deyan Draganov, Ranajit Ghose, Eric Verschuur, Jan Thorbecke, Kees Wapenaar
High-resolution seismic reflections are essential for imaging and monitoring applications. In seismic land surveys using sources and receivers at the surface, surface waves often dominate, masking the reflections. In this study, we demonstrate the efficacy of a two-step procedure to suppress surface waves in an active-source reflection seismic data set. First, we apply seismic interferometry (SI) by cross-correlation, turning receivers into virtual sources to estimate the dominant surface waves. Then, we perform adaptive subtraction to minimize the difference between the surface waves in the original data and the result of SI. We propose a new approach where the initial suppression results are used for further iterations, followed by adaptive subtraction. This technique aims to enhance the efficacy of data-driven surface-wave suppression through an iterative process. We use a 2-D seismic reflection data set from Scheemda, situated in the Groningen province of the Netherlands, to illustrate the technique’s efficiency. A comparison between the data after recursive interferometric surface-wave suppression and the original data across time and frequency–wavenumber domains shows significant suppression of the surface waves, enhancing visualization of the reflections for subsequent subsurface imaging and monitoring studies.
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High-resolution seismic reflections are essential for imaging and monitoring applications. In seismic land surveys using sources and receivers at the surface, surface waves often dominate, masking the reflections. In this study, we demonstrate the efficacy of a two-step procedure to suppress surface waves in an active-source reflection seismic data set. First, we apply seismic interferometry (SI) by cross-correlation, turning receivers into virtual sources to estimate the dominant surface waves. Then, we perform adaptive subtraction to minimize the difference between the surface waves in the original data and the result of SI. We propose a new approach where the initial suppression results are used for further iterations, followed by adaptive subtraction. This technique aims to enhance the efficacy of data-driven surface-wave suppression through an iterative process. We use a 2-D seismic reflection data set from Scheemda, situated in the Groningen province of the Netherlands, to illustrate the technique’s efficiency. A comparison between the data after recursive interferometric surface-wave suppression and the original data across time and frequency–wavenumber domains shows significant suppression of the surface waves, enhancing visualization of the reflections for subsequent subsurface imaging and monitoring studies.
This study examines the applicability of seismic methods for monitoring hydrogen storage and detecting potential leakage in sandstone reservoirs, with a particular focus on amplitude variations in angle-dependent image gathers. Using the FluidFlower benchmark model as a controlled geological framework, two types of sandstone—mildly consolidated and unconsolidated—are considered. Gassmann’s fluid substitution is used to model elastic property changes under different hydrogen saturation and leakage scenarios, and seismic responses are generated using Kennett’s reflectivity method.
The analysis shows that seismic amplitudes are sensitive to both fluid saturation and lithology. In mildly consolidated sandstones, hydrogen injection leads to observable increases in amplitude at reservoir interfaces. In unconsolidated sandstones, elastic contrasts are more pronounced, resulting in stronger and more detectable seismic responses. These findings highlight the need to account for lithological characteristics when designing seismic monitoring strategies for underground hydrogen storage. ...
The analysis shows that seismic amplitudes are sensitive to both fluid saturation and lithology. In mildly consolidated sandstones, hydrogen injection leads to observable increases in amplitude at reservoir interfaces. In unconsolidated sandstones, elastic contrasts are more pronounced, resulting in stronger and more detectable seismic responses. These findings highlight the need to account for lithological characteristics when designing seismic monitoring strategies for underground hydrogen storage. ...
This study examines the applicability of seismic methods for monitoring hydrogen storage and detecting potential leakage in sandstone reservoirs, with a particular focus on amplitude variations in angle-dependent image gathers. Using the FluidFlower benchmark model as a controlled geological framework, two types of sandstone—mildly consolidated and unconsolidated—are considered. Gassmann’s fluid substitution is used to model elastic property changes under different hydrogen saturation and leakage scenarios, and seismic responses are generated using Kennett’s reflectivity method.
The analysis shows that seismic amplitudes are sensitive to both fluid saturation and lithology. In mildly consolidated sandstones, hydrogen injection leads to observable increases in amplitude at reservoir interfaces. In unconsolidated sandstones, elastic contrasts are more pronounced, resulting in stronger and more detectable seismic responses. These findings highlight the need to account for lithological characteristics when designing seismic monitoring strategies for underground hydrogen storage.
The analysis shows that seismic amplitudes are sensitive to both fluid saturation and lithology. In mildly consolidated sandstones, hydrogen injection leads to observable increases in amplitude at reservoir interfaces. In unconsolidated sandstones, elastic contrasts are more pronounced, resulting in stronger and more detectable seismic responses. These findings highlight the need to account for lithological characteristics when designing seismic monitoring strategies for underground hydrogen storage.
With a focus on geo-imaging applications for the energy transition, we are looking for affordable, but still accurate seismic imaging methodologies. One of those recently developed methods is Joint Migration Inversion, which involves the joint estimation of the seismic reflectivity image and the background propagation velocity model. This method operates in the frequency domain and is based on recursive wavefield propagation, while including all scattering and transmission effects. The involved full wavefield modeling engine is the most time-consuming part of the JMI process, so accelerating this has direct impact on the overall costs. One option is making use of the fact that the modeling can be done independently per frequency component, such that we can model the data for a subset of these frequencies and use interpolation to obtain the data at missing frequencies. This papers studies the use of a neural network (NN) approach for this interpolation process. We investigate the accuracy of the interpolation process under different sub-sampling ratios and using regular or irregular subsampling. The counter-intuitive result is that regular subsampling gives slightly better results. Moreover, we demonstrate that we can go down to 66% missing frequencies with the currently used NN based on the cGAN approach.
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With a focus on geo-imaging applications for the energy transition, we are looking for affordable, but still accurate seismic imaging methodologies. One of those recently developed methods is Joint Migration Inversion, which involves the joint estimation of the seismic reflectivity image and the background propagation velocity model. This method operates in the frequency domain and is based on recursive wavefield propagation, while including all scattering and transmission effects. The involved full wavefield modeling engine is the most time-consuming part of the JMI process, so accelerating this has direct impact on the overall costs. One option is making use of the fact that the modeling can be done independently per frequency component, such that we can model the data for a subset of these frequencies and use interpolation to obtain the data at missing frequencies. This papers studies the use of a neural network (NN) approach for this interpolation process. We investigate the accuracy of the interpolation process under different sub-sampling ratios and using regular or irregular subsampling. The counter-intuitive result is that regular subsampling gives slightly better results. Moreover, we demonstrate that we can go down to 66% missing frequencies with the currently used NN based on the cGAN approach.
Seismic data acquired on land face multiple challenges due to the near-surface complexity. One of the challenges is the weathering layers’ influence due to the low velocity and rapidly varying nature of these layers. To overcome that, dense source-receiver sampling can be used to characterize the near-surface. However, that increases the acquisition costs, which makes it less attractive for large-scale seismic acquisition. An alternative approach is to use compressive sensing to acquire the data. Although it enables economical data acquisition, compressive sensing requires data reconstruction, which is another challenge in the presence of complex weathering layers. To overcome that, we propose a joint reconstruction and near-surface correction algorithm using a model-independent low-rank-based approach. We apply the method to synthetic and real data, which shows superior results compared with the conventional approach of near-surface correction followed by data reconstruction.
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Seismic data acquired on land face multiple challenges due to the near-surface complexity. One of the challenges is the weathering layers’ influence due to the low velocity and rapidly varying nature of these layers. To overcome that, dense source-receiver sampling can be used to characterize the near-surface. However, that increases the acquisition costs, which makes it less attractive for large-scale seismic acquisition. An alternative approach is to use compressive sensing to acquire the data. Although it enables economical data acquisition, compressive sensing requires data reconstruction, which is another challenge in the presence of complex weathering layers. To overcome that, we propose a joint reconstruction and near-surface correction algorithm using a model-independent low-rank-based approach. We apply the method to synthetic and real data, which shows superior results compared with the conventional approach of near-surface correction followed by data reconstruction.
Conventional reflection waveform inversion solves a two-parameter seismic inverse problem alternately for subsurface reflectivity and acoustic background velocity as the model parameters. It seeks to reconstruct a low-wavenumber velocity model of the subsurface from pure reflection data cyclically, through alternating migration and tomography loops, such that the remodelled data fits the observed data. Low-resolution seismic images with unpreserved amplitudes, full-wave inconsistency in the short-offset data and cycle skipping in the long-offset are perceived as the main reasons for suboptimal tomographic updates and slow convergence in conventional reflection waveform inversion. In the context of one-way reflection waveform inversion, this paper addresses the listed limitations through four main components. First, it augments one-way reflection waveform inversion with a computationally affordable preconditioned least-squares wave equation migration algorithm to ensure high-resolution reflectors with preserved amplitudes. Second, the paper verifies how well the full-wave consistency condition in the short-offset data is satisfied in one-way reflection waveform inversion and suggests muting inconsistent short-offset residual waveforms in the tomography loop to attenuate their adverse imprint. Third, the paper suggests extending the migration offset beyond short offsets to improve both the illumination and the signal-to-noise ratio of the reflectors. Fourth, the paper presents a data-selection algorithm to exclude the damaging effect of the cycle-skipped long-offset data in the tomography loop. The effectiveness of the proposed one-way reflection waveform inversion algorithm is finally validated through three numerical examples, demonstrating its capability to recover high-fidelity tomograms.
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Conventional reflection waveform inversion solves a two-parameter seismic inverse problem alternately for subsurface reflectivity and acoustic background velocity as the model parameters. It seeks to reconstruct a low-wavenumber velocity model of the subsurface from pure reflection data cyclically, through alternating migration and tomography loops, such that the remodelled data fits the observed data. Low-resolution seismic images with unpreserved amplitudes, full-wave inconsistency in the short-offset data and cycle skipping in the long-offset are perceived as the main reasons for suboptimal tomographic updates and slow convergence in conventional reflection waveform inversion. In the context of one-way reflection waveform inversion, this paper addresses the listed limitations through four main components. First, it augments one-way reflection waveform inversion with a computationally affordable preconditioned least-squares wave equation migration algorithm to ensure high-resolution reflectors with preserved amplitudes. Second, the paper verifies how well the full-wave consistency condition in the short-offset data is satisfied in one-way reflection waveform inversion and suggests muting inconsistent short-offset residual waveforms in the tomography loop to attenuate their adverse imprint. Third, the paper suggests extending the migration offset beyond short offsets to improve both the illumination and the signal-to-noise ratio of the reflectors. Fourth, the paper presents a data-selection algorithm to exclude the damaging effect of the cycle-skipped long-offset data in the tomography loop. The effectiveness of the proposed one-way reflection waveform inversion algorithm is finally validated through three numerical examples, demonstrating its capability to recover high-fidelity tomograms.
The phenomenon of elastic wave conversions, where acoustic, pressure (P-) waves are converted to elastic, shear (S-) waves and vice-versa, is commonly disregarded in seismic imaging. This can lead to lower quality images in regions with strong contrasts in elastic parameters. While a number of methods exist that do take wave conversions into account, they either deal with P and S waves separately, or are prohibitively computationally expensive, as is the case for elastic full-waveform inversion. In this paper an alternative approach to taking converted waves into account is presented by extending full wavefield migration (FWM) to account for wave conversions. FWM is a full-wavefield inversion method based on explicit, convolutional, one-way propagation and reflection operators in the space–frequency domain. By applying these operators recursively, multiscattering data can be modelled. Using these operators, the FWM algorithm aims to reconstruct the reflection properties of the subsurface (i.e. the ‘image’). In this paper, the FWM method is extended by accounting for wave conversions due to angle-dependent reflections and transmissions using an extended version of Shuey’s approximation. The resulting algorithm is tested on two synthetic models to give a proof of concept. The results of these tests show that the proposed extension can model wave conversions accurately and yields better inversion results than applying conventional, acoustic FWM.
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The phenomenon of elastic wave conversions, where acoustic, pressure (P-) waves are converted to elastic, shear (S-) waves and vice-versa, is commonly disregarded in seismic imaging. This can lead to lower quality images in regions with strong contrasts in elastic parameters. While a number of methods exist that do take wave conversions into account, they either deal with P and S waves separately, or are prohibitively computationally expensive, as is the case for elastic full-waveform inversion. In this paper an alternative approach to taking converted waves into account is presented by extending full wavefield migration (FWM) to account for wave conversions. FWM is a full-wavefield inversion method based on explicit, convolutional, one-way propagation and reflection operators in the space–frequency domain. By applying these operators recursively, multiscattering data can be modelled. Using these operators, the FWM algorithm aims to reconstruct the reflection properties of the subsurface (i.e. the ‘image’). In this paper, the FWM method is extended by accounting for wave conversions due to angle-dependent reflections and transmissions using an extended version of Shuey’s approximation. The resulting algorithm is tested on two synthetic models to give a proof of concept. The results of these tests show that the proposed extension can model wave conversions accurately and yields better inversion results than applying conventional, acoustic FWM.
Reflection waveform inversion (RWI) is a technique that uses pure reflection data to estimate subsurface background velocity, relying on evolving seismic images. Conventional RWI operates in a cyclic workflow, with two key components in each cycle—migration and reflection tomography. Conventional RWI may result in suboptimal background velocity estimation, partly due to limited or unresolved resolution within each component in each cycle. While gradient pre-conditioning with the reciprocal of Hessian information helps resolve this issue in both components of RWI, it becomes impractical for a large number of model parameters. One-way reflection waveform inversion (ORWI) is a reflection waveform inversion technique in which the forward modelling scheme operates in one direction (downward and then upward) via virtual parallel depth levels within the medium. Leveraging the ORWI framework, we decompose and reduce the linear Hessian operator (also known as the approximate Hessian or Gauss–Newton Hessian) into multiple smaller suboperators. In particular, the diagonal blocks of the monofrequency approximate Hessian operators, each corresponding to a single depth level within the medium, are extracted and inverted to pre-condition the corresponding monofrequency gradients in both the migration and reflection tomography components of ORWI. This depth-dependent gradient pre-conditioning transforms standard ORWI into a high-resolution, yet computationally feasible version aimed at addressing suboptimal velocity estimation, referred to as high-resolution ORWI. The effectiveness of the proposed approach is demonstrated through successful applications to synthetic data examples.
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Reflection waveform inversion (RWI) is a technique that uses pure reflection data to estimate subsurface background velocity, relying on evolving seismic images. Conventional RWI operates in a cyclic workflow, with two key components in each cycle—migration and reflection tomography. Conventional RWI may result in suboptimal background velocity estimation, partly due to limited or unresolved resolution within each component in each cycle. While gradient pre-conditioning with the reciprocal of Hessian information helps resolve this issue in both components of RWI, it becomes impractical for a large number of model parameters. One-way reflection waveform inversion (ORWI) is a reflection waveform inversion technique in which the forward modelling scheme operates in one direction (downward and then upward) via virtual parallel depth levels within the medium. Leveraging the ORWI framework, we decompose and reduce the linear Hessian operator (also known as the approximate Hessian or Gauss–Newton Hessian) into multiple smaller suboperators. In particular, the diagonal blocks of the monofrequency approximate Hessian operators, each corresponding to a single depth level within the medium, are extracted and inverted to pre-condition the corresponding monofrequency gradients in both the migration and reflection tomography components of ORWI. This depth-dependent gradient pre-conditioning transforms standard ORWI into a high-resolution, yet computationally feasible version aimed at addressing suboptimal velocity estimation, referred to as high-resolution ORWI. The effectiveness of the proposed approach is demonstrated through successful applications to synthetic data examples.
In recent years, deep learning (DL) has emerged as a promising alternative approach for various seismic processing tasks, including primary estimation (or multiple elimination), a crucial step for accurate subsurface imaging. In geophysics, DL methods are commonly based on supervised learning from large amounts of high-quality labelled data. Instead of relying on traditional supervised learning, in the context of free-surface multiple elimination, we propose a method in which the DL model learns to effectively parameterize the free-surface multiple-free wavefield from the full wavefield by incorporating the underlying physics into the loss computation. This, in turn, yields high-quality estimates without ever being shown any ‘ground truth’ data. Currently, the network reparameterization is performed independently for each dataset. We demonstrate its effectiveness through tests on both synthetic and field data. We employ industry-standard Surface-Related Multiple Elimination (SRME) using, respectively, global least-squares adaptive subtraction and local least-squares adaptive subtraction as benchmarks. The comparison shows that the proposed method outperforms the benchmarks in estimation accuracy, achieving the most complete primary estimation and the least multiple energy leakage, but at the cost of a higher computational burden.
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In recent years, deep learning (DL) has emerged as a promising alternative approach for various seismic processing tasks, including primary estimation (or multiple elimination), a crucial step for accurate subsurface imaging. In geophysics, DL methods are commonly based on supervised learning from large amounts of high-quality labelled data. Instead of relying on traditional supervised learning, in the context of free-surface multiple elimination, we propose a method in which the DL model learns to effectively parameterize the free-surface multiple-free wavefield from the full wavefield by incorporating the underlying physics into the loss computation. This, in turn, yields high-quality estimates without ever being shown any ‘ground truth’ data. Currently, the network reparameterization is performed independently for each dataset. We demonstrate its effectiveness through tests on both synthetic and field data. We employ industry-standard Surface-Related Multiple Elimination (SRME) using, respectively, global least-squares adaptive subtraction and local least-squares adaptive subtraction as benchmarks. The comparison shows that the proposed method outperforms the benchmarks in estimation accuracy, achieving the most complete primary estimation and the least multiple energy leakage, but at the cost of a higher computational burden.
Conference paper
(2025)
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A. Rahimi Dalkhani, F. Balestrini, A. Nath, V. Socco, G. Drijkoningen, E. Verschuur
This study investigates the feasibility of acquiring and analyzing Scholte waves within the water layer to estimate shallow shear-wave velocity profiles of marine sediments. Using a realistic subsurface model, 2D elastic wave simulations are performed with sources and receivers placed at varying heights above the seabed. Dispersion analysis confirmed that both fundamental and higher-mode Scholte waves remain detectable when the source and receivers are positioned just two meters above the seafloor. Even with added Gaussian noise, Scholte waves are clearly observable, demonstrating the practical potential of this approach. A Bayesian Markov Chain Monte Carlo inversion successfully recovers key features of the shear-wave velocity structure from the dispersion curves, though resolution diminishes with noise and reduced high-frequency content. These results suggest that effective Scholte wave acquisition and inversion are achievable without placing equipment directly on the seabed, offering an environmentally friendly alternative for shallow marine sediment characterization. However, implementing this setup in practice presents technical challenges that require further investigation.
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This study investigates the feasibility of acquiring and analyzing Scholte waves within the water layer to estimate shallow shear-wave velocity profiles of marine sediments. Using a realistic subsurface model, 2D elastic wave simulations are performed with sources and receivers placed at varying heights above the seabed. Dispersion analysis confirmed that both fundamental and higher-mode Scholte waves remain detectable when the source and receivers are positioned just two meters above the seafloor. Even with added Gaussian noise, Scholte waves are clearly observable, demonstrating the practical potential of this approach. A Bayesian Markov Chain Monte Carlo inversion successfully recovers key features of the shear-wave velocity structure from the dispersion curves, though resolution diminishes with noise and reduced high-frequency content. These results suggest that effective Scholte wave acquisition and inversion are achievable without placing equipment directly on the seabed, offering an environmentally friendly alternative for shallow marine sediment characterization. However, implementing this setup in practice presents technical challenges that require further investigation.