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.

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.

Numerous field acquisition examples and case studies have demonstrated the importance of recording, processing, and interpreting broadband land data. In most seismic acquisition surveys, three main objectives should be considered: (1) dense spatial source and receiver locations to achieve optimum subsurface illumination and wavefield sampling; (2) coverage of the full frequency spectrum, i.e., broadband acquisition; and (3) cost efficiency. Consequently, an effort has been made to improve the manufacturing of seismic vibratory sources by providing the ability to emit both lower (approximately 1.5 Hz) and higher frequencies (approximately 120 Hz) and of receivers by utilizing single, denser, and lighter digital sensors. All these developments achieve both operational (i.e., weight, optimized power consumption) and geophysical benefits (i.e., amplitude and phase response, vector fidelity, tilt detection). As part of the effort to reduce the acquisition cycle time, increase productivity, and improve seismic imaging and resolution while optimizing costs, a novel seismic acquisition survey was conducted employing 24 vibrators generating two different types of sweeps in a 3D unconstrained decentralized and dispersed source array field configuration. During this novel blended acquisition design, the crew reached a maximum of 65,000 vibrator points during 24 hours of continuous recording, which represents significantly higher productivity than a conventional seismic crew operating in the same area using a nonblended centralized source mode. Applying novel and newly developed deblending algorithms, high-resolution images were obtained. In addition, two data sets (i.e., low-frequency and medium-high-frequency sources) were merged to obtain full-bandwidth broadband seismic images. Data comparisons between the distributed blended and nonblended conventional surveys, acquired by the same crew during the same time over the same area, showed that the two data sets are very similar in the poststack and prestack domains.

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.

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.