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Compressive sensing introduces novel perspectives on non‐uniform sampling, leading to substantial reductions in acquisition cost and cycle time compared to current seismic exploration practices. Non‐uniform spatial sampling, achieved through source and/or receiver areal distributions, and non‐uniform temporal sampling, facilitated by simultaneous‐source acquisition schemes, enable compression and/or reduction of seismic data acquisition time and cost. However, acquiring seismic data using compressive sensing may encounter challenges such as an extremely low signal‐to‐noise ratio and the generation of interference noise from adjacent sources. A significant challenge to this innovative approach is to demonstrate the translation of theoretical gains in sampling efficiency into operational efficiency in the field. In this study, we propose a spatial compression scheme based on compressive sensing theory, aiming to obtain an undersampled survey geometry by minimizing the mutual coherence of a spatial sampling operator. Building upon an optimised spatial compression geometry, we subsequently consider temporal compression through a simultaneous‐source acquisition scheme. To address challenges arising from the recorded compressed seismic data in the non‐uniform temporal and spatial domains, such as missing traces and crosstalk noise, we present a joint deblending and reconstruction algorithm. Our proposed algorithm employs the iterative shrinkage‐thresholding method to solve an ℓ2–ℓ1 optimization problem in the frequency–wavenumber–wavenumber (ω–kx–ky) domain. Numerical experiments demonstrate that the proposed algorithm produces excellent deblending and reconstruction results, preserving data quality and reliability. These results are compared with non‐blended and uniformly acquired data from the same location illustrating the robustness of the application. This study exemplifies how the theoretical improvements based on compressive sensing principles can significantly impact seismic data acquisition in terms of spatial and temporal sampling efficiency. [ABSTRACT FROM AUTHOR]

Seismic blended source acquisition, also referred to as simultaneous source acquisition, is a cost‐effective technology that achieves a significant reduction in acquisition cycle time and increases seismic crew field productivity. The dispersed source array is a blended acquisition field technique that simultaneously employs sources emitting different types of sweeps (i.e. multi‐sweep), in terms of frequency bandwidth and length, which ultimately will result in a full broadband seismic data. In this paper, deblending of 3D multi‐sweep seismic blended data and the subsequent merging of the data volumes having different frequency bandwidths will be discussed. In specific data domains where the signal component is coherent, interference shots (i.e. blending noise) are randomly distributed in the data space according to its own shot firing time. Therefore, the deblending process, which separates interference shots from a signal component, becomes a noise attenuation problem. A sparse inversion methodology is applied in the frequency–wavenumber–wavenumber (f–kx–ky) domain to attenuate blending noise. By applying this deblending methodology to both dispersed source array's low‐ and mid‐high‐frequency bandwidths, we obtained high‐quality deblending results. For both frequency bandwidths of the deblended dispersed source array data, additional effort was made to combine the two datasets to a single broadband data volume. Consequently, deblending and merging of the dispersed source array blended data generated a broadband, deblended and well‐balanced seismic volume suitable for further processing and reservoir characterization applications. [ABSTRACT FROM AUTHOR]

We propose a workflow of deblending methodology comprised of rank‐reduction filtering followed by a signal enhancing process. This methodology can be used to preserve coherent subsurface reflections and at the same time to remove incoherent and interference noise. In pseudo‐deblended data, the blending noise exhibits coherent events, whereas in any other data domain (i.e. common receiver, common midpoint and common offset), it appears incoherent and is regarded as an outlier. In order to perform signal deblending, a robust implementation of rank‐reduction filtering is employed to eliminate the blending noise and is referred to as a joint sparse and low‐rank approximation. Deblending via rank‐reduction filtering gives a reasonable result with a sufficient signal‐to‐noise ratio. However, for land data acquired using unconstrained simultaneous shooting, rank‐reduction–based deblending applications alone do not completely attenuate the interference noise. A considerable amount of signal leakage is observed in the residual component, which can affect further data processing and analyses. In this study, we propose a deblending workflow via a rank‐reduction filter followed by post‐processing steps comprising a nonlinear masking filter and a local orthogonalization weight application. Although each application shows a few footprints of leaked signal energy, the proposed combined workflow restores the signal energy from the residual component achieving significantly signal‐to‐noise ratio enhancement. These hierarchical schemes are applied on land simultaneous shooting acquisition data sets and produced cleaner and reliable deblended data ready for further data processing. [ABSTRACT FROM AUTHOR]

A number of deblending methods and workflows have been reported in the past decades to eliminate the source interference noise recorded during a simultaneous shooting acquisition. It is common that denoising algorithms focusing on optimizing coherency and weighting down/ignoring outliers can be considered as deblending tools. Such algorithms are not only enforcing coherency but also handling outliers either explicitly or implicitly. In this paper, we present a novel approach based on detecting amplitude outliers and its application on deblending based on a local outlier factor that assigns an outlier‐ness (i.e. a degree of being an outlier) to each sample of the data. A local outlier factor algorithm quantifies outlier‐ness for an object in a data set based on the degree of isolation compared with its locally neighbouring objects. Assuming that the seismic pre‐stack data acquired by simultaneous shooting are composed of a set of non‐outliers and outliers, the local outlier factor algorithm evaluates the outlier‐ness of each object. Therefore, we can separate the data set into blending noise (i.e. outlier) and signal (i.e. non‐outlier) components. By applying a proper threshold, objects having high local outlier factors are labelled as outlier/blending noise, and the corresponding data sample could be replaced by zero or a statistically adequate value. Beginning with an explanation of parameter definitions and properties of local outlier factor, we investigate the feasibility of a local outlier factor application on seismic deblending by analysing the parameters of local outlier factor and suggesting specific deblending strategies. Field data examples recorded during simultaneous shooting acquisition show that the local outlier factor algorithm combined with a thresholding can detect and attenuate blending noise. Although the local outlier factor application on deblending shows a few shortcomings, it is consequently noted that the local outlier factor application in this paper obviously achieves benefits in terms of detecting and attenuating blending noise and paves the way for further geophysical applications. [ABSTRACT FROM AUTHOR]

Although narrow‐azimuth towed‐streamer data provide good image quality for structural interpretation, it is generally accepted that for wide‐azimuth marine surveys seabed receivers deliver superior seismic reflection measurements and seismically derived reservoir attributes. However, seabed surveys are not widely used due to the higher acquisition costs when compared to streamer acquisition. In recent years, there have been significant engineering efforts to automate receiver deployment and retrieval in order to minimize the cost differential and conduct cost‐efficient seabed receiver seismic surveys. These engineering efforts include industrially engineered nodes, nodes on a rope deployment schemes and even robotic nodes, which swim to and from the deployment location. This move to automation is inevitable, leading to robotization of seismic data acquisition for exploration and development activities in the oil and gas industry. We are developing a robotic‐based technology, which utilizes autonomous underwater vehicles as seismic sensors without the need of using a remotely operated vehicle for deployment and retrieval. In this paper, we describe the autonomous underwater vehicle evolution throughout the project years from initial heavy and bulky nodes to fully autonomous light and flexible underwater receivers. Results obtained from two field pilot tests using different generations of autonomous underwater vehicles indicate that the seismic coupling, and navigation based on underwater acoustics are very reliable and robust. [ABSTRACT FROM AUTHOR]

Abstract P-wave and S-wave separation methods have been widely applied to recorded and modelled multi-component wavefields in the time- and space-domains. Traditional methodologies, which employ divergence and curl operators, have given reasonable results; however, substantial residual energy or crosstalk remains. Application of the divergence and curl operators will alter the physical properties of the respective separated wavefields in terms of amplitude and phase information. Numerous studies have suggested more robust methods to separate P- and S-waves, such as decoupled wave equations to preserve the physical properties of the decomposed wavefields. These formulations produce intrinsic artifacts from S-wave reflections and conversions at where the non-smoothed velocity exists. In this paper, we introduce a filtering methodology using signal and noise orthogonalization to reduce unwanted artifacts occurring during the P-wave and S-wave separation. By combining robust wavefield separation and orthogonalization filtering, the residual reflection events, mainly from the S-wave conversions and reflections, are attenuated. We demonstrate using synthetic and real data examples a decoupled elastic wave propagation modelling scheme followed by local orthogonalization filtering and show its application in elastic full waveform inversion (FWI), which attempts to handle interference noise between mode-converted waves. Consequently, from the numerical examples presented, it is shown that using cleaner and separated P- and S-wavefields obtained from the filtered elastic wave propagation modelling and inversion, the solution converges faster and reduces the risk of falling into local minima during elastic multi-parametric inversion. Highlights • Decoupled elastic wave equation is employed to separate elastic wavefield. • Filtering wavefields by local orthogonalization weights (LOW) during the elastic wavefield separation is proposed. • Proposed scheme achieves enhancement in signal-to-noise ratio of the decoupled elastic wave equation modelling. • Enhanced wavefield separation wih LOW applied plays a proper role in elastic modelling and full waveform inversion. [ABSTRACT FROM AUTHOR]

Abstract The main objective of the full waveform inversion (FWI) is to deliver a velocity model which produces accurate and high resolution depth images. An integral part of FWI is the iterative optimization process aiming at minimizing an objective function describing the misfit between the observed and modelled data with respect to the velocity model parameters. However, FWI commonly suffers from local minima due to errors in data fitting and the starting velocity model. In this study, we develop a methodology to enhance convergence and mitigate the risk of having solutions falling into local minima. We employ optical flow to decompose the source and residual receiver wavefields and map them into the subsurface angle domain. Having evaluated the propagation vectors of the respective cleaner wavefields, subsurface reflection and azimuth angles can be robustly computed. Using such information we can apply angle-dependent preconditioning within the inversion algorithm. During a conventional FWI workflow, larger angles (i.e., transmission wavepaths) are mainly responsible for long wavelength velocity anomalies whereas narrow angles (i.e., reflection wavepaths) influence short wavelength velocity anomalies. In the proposed FWI methodology, we apply an optimized weighting function based on these estimated subsurface reflection angles in order to weight certain angle directions needed for gradient estimation using in parameter updating. Following a hierarchical approach, allowing for larger angles at the early stage of the FWI application, we initially solve for the smooth and longer wavelength velocity updates. Relaxing the opening angle from large to mid to narrow, the gradient direction will first estimate long wavelength velocity model updates (transmission-based wavepaths), following by short wavelength velocity model updates (reflection-based wavepaths). Moreover, angle-dependent-based gradient preconditioning can reduce cycle skips and improve convergence during FWI iterations. Highlights • Decomposing wavefields by optical flow during full waveform inversion is proposed. • A gradient direction is pre-conditioned based on the subsurface reflection angle. • Proposed pre-conditioning could decompose the wavenumber components. • Pre-conditioning provides smoother and less noisy updates in velocity model building. [ABSTRACT FROM AUTHOR]

During ocean bottom seismic acquisition, seafloor multicomponent geophones located in rugose and sloping water bottom can be affected by skewed energy distribution, such as leaked shear energy on the vertical geophones and leaked compressional energy on the horizontal geophones. To correct for the tilted energy distribution, which is one of most effective preprocessing steps, a geophone reorientation step is applied. This is a simple and straightforward process that applies a 3‐dimensional rotation matrix with respect to the orientation angles. Since the reorientation process highly affects the outcome of the entire data processing workflow, it has to be accompanied by a careful quality control process to verify its validity for the whole survey area. In this study, we propose a quality control workflow for the geophone reorientation by using unsupervised machine learning. A correlation analysis is employed to compare numerical versus analytical solutions of both the azimuth and the incidence angles for the direct arrivals. A comparison of both solutions aims to generate correlation coefficients that are indicative of the accuracy of geophone orientation. The correlation coefficients are subsequently investigated by the k‐means clustering algorithm to differentiate and identify normally and abnormally deployed/reoriented geophones. Numerical experiments on a field ocean bottom seismic data set confirm that the proposed workflow effectively provides reliable labels for normally and abnormally deployed/reoriented geophones. The labelling assigned by the proposed quality control workflow is a suitable indicator for abnormalities in the geophone reorientation step and will be helpful for further investigation, such as re‐correction or removal of abnormally reoriented geophones. [ABSTRACT FROM AUTHOR]

This study introduces a new attribute to identify seismic erratic noise, i.e. outlier, in the context of unsupervised anomaly detection and is defined as local outlier probabilities. The local outlier probabilities calculate scores of degrees of isolation, i.e. outlier‐ness, for each object in a data set, which represents how far an object is deviated from its surrounding objects. Since the local outlier probabilities combines a density‐based outlier detection method with a statistically oriented scheme, its scoring system provides regularized outlier‐ness, which is an outlier probability, to be used for making a binary decision to do inclusion or exclusion of an object; such a decision only requires a simple and straightforward threshold on a probability. Based on the binary decision that flags outliers versus non‐outliers, local outlier probabilities‐denoising workflows are developed by combining multiple steps to complete an application of the local outlier probabilities to attenuate seismic erratic noise. Higher stability and improved robustness in the detection and rejection of seismic erratic noise have been achieved by implementing moving windows and decision tree‐based processes. To avoid loss of useful signal energy, signal enhancement applications are additionally suggested. Numerical experiments on synthetic data investigate the applicability of the proposed algorithms to seismic erratic noise attenuation. Field data examples demonstrate the feasibility of a local outlier probabilities‐denoising application as an effective tool in seismic denoising portfolio. [ABSTRACT FROM AUTHOR]

Abstract Separation of signal and noise is a challenging process in seismic processing, particularly for seismic data acquired in a desert environment, where the near surface is very complex and the recorded data suffers from signal scattering and ambient noise contamination. Classic transforms, such as the Fourier or the wavelet transform, have a number of limitations in representing complex seismic wavefields, because of the nonstationarity of seismic data, as they operate on a one-dimensional scale. Advanced algorithms are needed to address these challenges. In recent years, multiscale methods, such as the seislet transform, have started to be utilized by the industry to address these challenges. The seislet transform is an effective sparse multiscale wavelet-like transform specifically tailored for optimal representation of seismic data. The transform computes multiscale orthogonal basis functions, which are aligned along varying slopes or dips of seismic events in the input data. To effectively separate signal and noise, one needs to further estimate any signal component that has leaked into the noise estimate and restore it back to the separated signal. This is achieved by an innovative and recently developed signal-and-noise orthogonalization technique. In this approach, we first estimate the signal component using one of the transforms. We then apply the orthogonalization scheme to retrieve the leaked signal energy and restore it back to the initial signal estimate. We have applied the seislet transform and signal and noise orthogonalization to several field datasets with the aim of improving the signal-to-noise ratio, increasing the frequency bandwidth and enhancing the image quality. Highlights • Seislet transform is wavelet-like transform specifically tailored for optimal representation of seismic data. • Separation of signal and noise is challenging on dataset acquired in a desert environment. • Successfully applied the seislet transform using seismic data examples from both marine and land environments. • The seislet transform along with the orthogonalization scheme is found to be very effective in suppressing random noise. [ABSTRACT FROM AUTHOR]