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ABSTRACT: In order to deconvolve the ghost response from marine seismic data, an estimate of the ghost operator is required. Typically, this estimate is made using a model of in‐plane propagation, i.e., the ray path at the receiver falls in the vertical plane defined by the source and receiver locations. Unfortunately, this model breaks down when the source is in a crossline position relative to the receiver spread. In this situation, in‐plane signals can only exist in a small region of the signal cone. In this paper, we use Bayes' theory to model the posterior probability distribution functions for the vertical component of the ray vector given the known source–receiver azimuth and the measured inline component of the ray vector. This provides a model for the ghost delay time based on the acquisition geometry and the dip of the wave in the plane of the streamer. The model is fairly robust with regard to the prior assumptions and controlled by a single parameter that is related to the likelihood of in‐plane propagation. The expected values of the resulting distributions are consistent with the deterministic in‐plane model when in‐plane likelihood is high but valid everywhere in the signal cone. Relaxing the in‐plane likelihood to a reasonable degree radically simplifies the shape of the expected‐value surface, lending itself for use in deghosting algorithms. The model can also be extended to other plane‐wave processing problems such as interpolation. [ABSTRACT FROM AUTHOR]

Transient disturbances in process measurements compromise the accuracy of some methods for plant-wide oscillation analysis. This paper presents a method to remove such transients while maintaining the dynamic features of the original measurement. The method is based on a nearest neighbors imputation technique. It replaces the removed transient with an estimate which is based on the time series of the whole measurement. The method is demonstrated on experimental and industrial case studies. The results demonstrate the efficacy of the method and recommended parameters. Furthermore, inconsistency indices are proposed which facilitate the automation of the method. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-rate method to identify the propagation path of a persistent disturbance in an enlarged system envelope which includes the process plant and its electromechanical equipment. The need to integrate process and equipment diagnosis has been highlighted by industrial commentators. However, process and electromechanical measurements often have different sampling rates. The multi-rate method proposed extends a state-of-the-art propagation path method so that it combines fast-sampled electromechanical measurements and slow-sampled process measurements. The method is based on non-linear mutual prediction, which yields the directionality in the relationship between two time series. The method was demonstrated and validated, giving the expected outcome in an experimental case study, in which the root cause and propagation path of the disturbance were known. [ABSTRACT FROM AUTHOR]

Transient disturbances are increasingly relevant in process industries which rely on electromechanical equipment. Existing data-driven methods for detecting transient disturbances assume a distinct amplitude or time-frequency component. This paper proposes a detection method which is more generic and handles any short-term deviation of a measurement from its overall trend, regardless of whether the trend incorporates features such as oscillations, noise or changes in operation level. The method is based on a nearest neighbors technique and builds a vector of anomaly indices which are high for the period with the transient disturbance. The paper includes analyses of the statistical significance of the threshold proposed and of the sensitivity of the parameters, and it also suggests a color map to visualize the detection results. The method is demonstrated on experimental and industrial case studies. [ABSTRACT FROM AUTHOR]