227 research outputs found
High-Resolution One-Way Reflection Waveform Inversion
No full textReflection waveform inversion (RWI) is a method that relies on primary pure reflection data to recover the subsurface background velocity based on the associated evolving seismic images. Background velocity updates estimated by conventional RWI are nonoptimal, which is partly attributed to low-resolution tomographic wavepaths and migration isochrones. Preconditioning RWI sensitivity kernels using Hessian information solves this problem but is not practical for a large number of model parameters. One-way reflection waveform inversion (ORWI) is a reflection waveform tomography technique in which the forward modeling scheme operates in one direction (downward and then upward) via virtual parallel data levels in the medium. The ORWI framework allows us to break down the Hessian matrix into smaller operators, which makes the preconditioning operation more efficient and less computationally expensive. This extended abstract turns conventional ORWI into a high-resolution but computationally feasible ORWI (Gauss-Newton ORWI) to improve the nonoptimal background velocity updates.ImPhys/Verschuur groupApplied Geophysics and Petrophysic
Full wavefield migration based on eigen-decomposition propagation operators
No full textSeismic imaging is crucial for subsurface exploration and monitoring, with a focus on deep and complex structures. Seismic wave migration solves the wave equation, and an accurate propagator is essential. Full Wavefield Modeling (FWMod) was developed based on recursive and iterative up/down wavefield propagation, modeling both primaries and multiples. Embedded within Full Wavefield Migration (FWM) it can be used to image data including multiples, resulting in better illumination in case primary illumination is not sufficient. FWM can be efficient and effective, but conventional one-way wave operators, such as Phase Shift Plus Interpolation Migration, have limitations in strongly inhomogeneous media. Local velocity-based one-way operator based on eigen decomposition was proposed and integrated within FWMod and FWM in this study, improving image amplitudes and fidelity and improving converage speed in the least-squares inversion process.ImPhys/Verschuur groupApplied Geophysics and Petrophysic
Novel method for UHR streamer shape reconstruction and improved receiver positioning: a conceptual overview
No full textPoor knowledge of source and receiver positions in ultra-high-resolution marine seismic data is the cause of severe damage which requires novel processing techniques to mitigate. This type of seismic data is highly relevant for ultra-shallow subsurface imaging in geo-engineering projects both offshore and in harbours. Current positioning technologies are limited partly by their accuracy but also the fact that they are only placed on head and tail buoys of the towed arrays. This leaves receiver locations on the length of the streamer cable to be interpolated. Rather than developing additional processing methods, we propose to improve the quality of the data by introducing a complimentary receiver positioning system to reconstruct the shape of the streamer cable in 4D using Fiber Optic Shape Sensing (FOSS) technology. In this abstract, we outline the key features of FOSS technology and provide a conceptual overview of our efforts to bring this technology to the field.Applied Geophysics and PetrophysicsImPhys/Verschuur grou
Seismic data interpolation using an anti-over-fitting mixed-scale dense convolutional neural network
No full textSeismic data interpolation is a topic well suited for deep learning (DL) applications. Scaling operation-based DL neural networks, e.g., U-Net, have been popular since its booming development in the field of seismic data processing. Although many successful studies using U-Net on seismic data, scientists start to realize the downside of its implementation, i.e., large trainable parameters (normally larger than 1 million), the potential risks of over-fitting, and tedious hyper-parameter selection. Therefore, in this abstract, we introduce a mixed-scale dense convolutional neural network (MS-DCNN) for seismic data interpolation with relatively few trainable parameters to reduce the risk of over-fitting. This MS-DCNN was originally developed for biomedical image processing. In addition, this neural network can be trained with relatively small training set. Via a field data case study, the different behavior of U-Net and MS-DCNN is analyzed and compared for a specific interpolation problem, where 9 consecutive shot records were missing from a 2D line of marine seismic data.Applied Geophysics and PetrophysicsImPhys/Verschuur grou
Focal deblending: Marine data processing experiences
No full textIn contrast to conventional acquisition practices, simultaneous source acquisition allows for overlapping wavefields to be recorded. Relaxing the shot schedule in this manner has certain advantages, such as allowing for faster acquisition and/or denser shot sampling. This flexibility usually comes at the cost of an extra step in the processing workflow, where the wavefields are deblended, that is, separated. An inversion-type algorithm for deblending, based on the focal transform, is investigated. The focal transform uses an approximate velocity model to focus seismic data. The combination of focusing with sparsity constraints is used to suppress blending noise in the deblended wavefield. The focal transform can be defined in different ways to better match the spatial sampling of different types of marine surveys. To avoid solving a large inverse problem, involving a large part of the survey simultaneously, the input data can be split into sub-sets that are processed independently. We discuss the formation of such sub-sets for ocean bottom node and streamer-type acquisitions. Two deblending experiments are then carried out. The first is on numerically blended ocean bottom node field data. The second is on field-blended towed streamer data with a challenging signal overlap. The latter experiment is repeated using curvelet-based deblending for comparison purposes, showing the virtues of the focal deblending process. Several challenges of basing deblending around the focal transform are discussed as well as some suggestions for improved implementations.ImPhys/Verschuur groupApplied Geophysics and Petrophysic
Towards 3D near-surface correction without NMO: A rank-based approach
No full textTo avoid multiple iterations of normal moveout (NMO) velocity estimation followed by short-wavelength statics estimation usually performed on land data, and to also improve the accuracy and computational efficiency of the latter, a low-rank-based residuals statics (LR-ReS) estimation and correction framework has been recently proposed. The method iteratively promotes the low-rank structure in the midpoint-offset-frequency domain of 2D data as statics-free data can be approximated by low-rank matrices, while data influenced by the weathering layers exhibits slow singular values decay. For 3D data, there exist different options to organize it into 2D matrices to be able to compute the singular value decomposition (SVD) required for low-rank approximation. It is also essential to find an organization that reveals the rank structure. We examine the different organization options. Based on finding a suitable sorting domain, we extend the LR-ReS estimation and correction to 3D data. We demonstrate the performance of the method on simulated data and will show field data results during the presentation.ImPhys/Medical ImagingImPhys/Verschuur groupApplied Geophysics and Petrophysic
Imaging with surface-related multiples using linear and non-linear modelling
No full textSeismic reflection imaging aims to generate a representation of the subsurface of the earth using acoustic or elastic waves recorded in the form of seismic data. During the processing of the data for imaging, we pick a part of the data as signal and discard the rest as noise. Since so-called primary wavefields carry the single-scattering reflection response of the subsurface, they are assumed to be sufficient to carry out reflection imaging. This also means that significant effort is devoted towards removing not only the noise but also the multiple reflection events to avoid imaging artefacts, also known as cross-talk. Surface-related multiples are the multiples generated during the marine acquisition by at least one downward reflection at the water-air boundary, and tend to be the strongest in amplitude compared to the other multiples. Over the past several decades, many novel techniques have been developed to remove the surface-related multiples effectively. While we are getting better at primary-multiple separation, it is still a very challenging and expensive problem. In recent years, multiples have gained recognition as valuable signals, not just noise. Multiples also contain the reflection responses of the subsurface and since they travel different paths they often contain additional information about the subsurface compared to the primaries-only wavefields. Imaging with primaries and multiples without separation is the way forward as it avoids the expensive multiple removal steps along with providing (potentially) additional illumination from the multiples...ImPhys/Verschuur grou
Geologic stratigraphic scenario testing via deep learning: towards imaging beyond seismic resolution
No full textIn the process of seismic subsurface imaging, there is no acceptable forward model reflecting the AVO response in a laterally inhomogeneous medium for reservoir characterization. This means that even when inversion is performed in full waveform, local heterogeneity is typically not fully incorporated while emplying a local 1.5D assumption. Thus, it is impossible to image and classify the subsurface features with these local heterogeneities. Still, the angle-dependent response encodes heterogeneity information that assists overcoming this issue if used properly. To exploit its capabilities, we present a way for identifying reservoir characteristics in the presence of local heterogeneity by linking encoded angle-dependent responses created using angle-dependent Full Wavefield Migration with their originating source - the relevant geological context. To accomplish this purpose, a pipeline technique that integrates the produced angle-dependent responses with a pattern categorization deep-learning tool is proposed. For a basic test on synthetic data, the method successfully identified the produced different stratigraphic architectures and classified them in the training stage. The method is then validated on angle gathers generated from different models with comparable geological circumstances.Applied Geophysics and PetrophysicsApplied GeologyImPhys/Verschuur grou
Estimation of primaries by sparse inversion incuding the ghost
No full textToday, the problem of surface-related multiples, especially in shallow water, is not fully solved. Although surface-related multiple elimination (SRME) method has proved to be successful on a large number of data cases, the involved adaptive subtraction acts as a weak link in this methodology, where primaries can be distorted due to their interference with multiples. Therefore, recently, SRME has been redefined as a large-scale inversion process, called estimation of primaries by sparse inversion (EPSI). In this process the multi-dimensional primary impulse responses are considered as the unknowns in a largescale inversion process. By parameterizing these impulse responses as spikes in the space-time domain, and using a sparsity constraint in the update step, the algorithm looks for those primaries that, together with their associated multiples, explain the total input data. As the objective function in this minimization process truly goes to zero, the tendency for distorting primaries is greatly reduced. An additional advantage is that imperfections in the data can be included in the forward model and resolved simultaneously, such as the missing near offsets. In this paper it is demonstrated that the ghost effect can also be included in the EPSI formulation after which a ghost-free primary estimate can be obtained, even in the case the ghost notch is within the desired spectrum.IST/Imaging Science and TechnologyApplied Science
Low-rank-based residual statics estimation and correction
No full textSurface consistency forms the basis for short-wavelength statics estimation. When raypaths in the near surface diverge from a normal incidence or when the normal moveout (NMO) velocity is inaccurate, surface-consistent methods may fail to estimate accurate statics. Existing nonsurface-consistent techniques can be prone to errors due to the need to construct pilot traces or pick horizons while imposing additional computational costs. To overcome these limitations and correct for the surface- and nonsurface-consistent statics, we have developed a low-rank-based residual statics (LR-ReS) estimation and correction framework. The method makes use of the redundant nature of seismic data by using its low-rank structure in the midpoint-offset-frequency domain. Due to the near-surface effect, the low-rank structure is destroyed. Therefore, we estimate the statics by means of low-rank approximation and crosscorrelation. To alleviate the need for accurate rank selection for low-rank approximation and improved statics estimation, we implement the method in an iterative and multiscale fashion. Because the low-rank approximation deteriorates at high frequencies, we use its better performance at low frequencies and exploit the common statics among the different frequency bands. The LR-ReS estimation and correction can be applied to data without an NMO correction, which makes statics estimation independent of the NMO velocity errors. Consequently, it can reduce the multiple iterations of the NMO velocity estimation and short-wavelength statics correction commonly needed for conventional methods to improve their performance. Moreover, the LR-ReS estimation does not require windowing of a noise-free area containing aligned primaries or mute to avoid the NMO stretch effect, which enables statics correction of the wavefield of all offsets. To evaluate the performance of our method, we apply it to simulated data and a challenging field data set affected by complex weathering layers and noise, which indicate a substantial improvement compared with conventional short-wavelength statics correction.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.ImPhys/Medical ImagingImPhys/Verschuur grou
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