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Denoising is always an important step in seismic processing, in order to produce high-quality data for subsequent imaging and inversion. Different types of noise can be suppressed using targeted denoising methods. For outlier noise with singular amplitudes, many classical denoising methods suffer from signal leakage. To mitigate this issue, we developed a statistics-based mask method and incorporated it into the compressive sensing (CS) framework, in order to remove outlier noise. A statistical analysis for seismic data amplitudes was first used to identify the locations of traces containing outlier noise. Then, the outlier trace locations were compared with a mask matrix generated by jitter sampling, and we replaced the sampled traces of the jitter mask that had the outlier noise with their nearby unsampled traces. The optimized sampling matrix enabled us to effectively identify and remove outliers. This optimized mask strategy converts an outlier denoising problem into a data reconstruction problem. Finally, a sparsely constrained inverse problem was solved using a soft-threshold iteration solver to recover signals at the null locations. The feasibility and adaptability of the proposed method were demonstrated through numerical experiments for synthetic and field data. The results showed that the proposed method outperformed the conventional f-x deconvolution and median filter method, and could accurately suppress outlier noise and recover missed expected signals. [ABSTRACT FROM AUTHOR]

The Autoregressive Moving Average (ARMAX) model with exogenous input is a widely used discrete time series model, but its special structure allows outliers of its process to affect multiple output data items, thereby significantly affecting the output. In this paper, a regularized model predictive control (MPC) is proposed for an ARMAX process affected by outlier noise. The outlier noise is modeled as an auxiliary variable in the ARMAX model, and the MPC cost function is reconstructed to reduce the influence of outlier noise on multiple data items. The stability of the proposed method and the convergence of output/input and state are guaranteed. The degree to which regularization affects the system can be adjusted by an optional parameter. This paper provides some helpful insights on how to choose this optional parameter in the cost function. The effectiveness of the proposed method is demonstrated by the results of 200 repeated simulations. [ABSTRACT FROM AUTHOR]

Denoising is always an important step in seismic processing, in order to produce high-quality data for subsequent imaging and inversion. Different types of noise can be suppressed using targeted denoising methods. For outlier noise with singular amplitudes, many classical denoising methods suffer from signal leakage. To mitigate this issue, we developed a statistics-based mask method and incorporated it into the compressive sensing (CS) framework, in order to remove outlier noise. A statistical analysis for seismic data amplitudes was first used to identify the locations of traces containing outlier noise. Then, the outlier trace locations were compared with a mask matrix generated by jitter sampling, and we replaced the sampled traces of the jitter mask that had the outlier noise with their nearby unsampled traces. The optimized sampling matrix enabled us to effectively identify and remove outliers. This optimized mask strategy converts an outlier denoising problem into a data reconstruction problem. Finally, a sparsely constrained inverse problem was solved using a soft-threshold iteration solver to recover signals at the null locations. The feasibility and adaptability of the proposed method were demonstrated through numerical experiments for synthetic and field data. The results showed that the proposed method outperformed the conventional f-x deconvolution and median filter method, and could accurately suppress outlier noise and recover missed expected signals.

Abstract: If a visual observer moves through an environment, the patterns of light that impinge its retina vary leading to changes in sensed brightness. Spatial shifts of brightness patterns in the 2D image over time are called optic flow. In contrast to optic flow visual motion fields denote the displacement of 3D scene points projected onto the camera’s sensor surface. For translational and rotational movement through a rigid scene parametric models of visual motion fields have been defined. Besides ego-motion these models provide access to relative depth, and both ego-motion and depth information is useful for visual navigation. In the past 30 years methods for ego-motion estimation based on models of visual motion fields have been developed. In this review we identify five core optimization constraints which are used by 13 methods together with different optimization techniques. [1] Matlab (R) implementations of these 13 methods are available at the internet page http://cns.bu.edu/∼fraudies/EgoMotion/EgoMotion.html. 1 In the literature methods for ego-motion estimation typically have been evaluated by using an error measure which tests only a specific ego-motion. Furthermore, most simulation studies used only a Gaussian noise model. Unlike, we test multiple types and instances of ego-motion. One type is a fixating ego-motion, another type is a curve-linear ego-motion. Based on simulations we study properties like statistical bias, consistency, variability of depths, and the robustness of the methods with respect to a Gaussian or outlier noise model. In order to achieve an improvement of estimates for noisy visual motion fields, part of the 13 methods are combined with techniques for robust estimation like m-functions or RANSAC. Furthermore, a realistic scenario of a stereo image sequence has been generated and used to evaluate methods of ego-motion estimation provided by estimated optic flow and depth information. [Copyright &y& Elsevier]