5D reconstruction via robust tensor completion
نویسندگان
چکیده
Tensor completion techniques (including tensor denoising) can be used to solve the ubiquitous multidimensional data reconstruction problem. We present a robust tensor reconstruction method that can tolerate the presence of erratic noise. The method is derived by minimizing a robust cost function with the addition of low rank constraints. Our presentation is based on the Parallel Matrix Factorization (PMF) tensor completion method that we modify to cope with erratic noise.
منابع مشابه
A Nuclear Norm Minimization Algorithm with Application
In this paper we present a new algorithm to reconstruct prestack (5D) seismic data. If one considers seismic data at a given frequency and, for instance, in the x midpoint, y midpoint, offset and azimuth domain, the data volume can be represented via a 4th order tensor. Seismic data reconstruction can be posed as a tensor completion problem where it is assumed that the fully sampled data can be...
متن کاملEvaluation of a New 5D Seismic Volume Reconstruction Method: Tensor Completion versus Fourier Reconstruction
Multi-dimensional Fourier interpolators have become the industry standard for 5D seismic volume reconstruction. However, room for improvement exists and a few key aspects of seismic data reconstruction do require additional study. The latter includes stability in the presence of coherent noise and statics, recovery conditions for extremely sparse data sets and computational efficiency. This pre...
متن کاملTensor completion based on nuclear norm minimization for 5D seismic data reconstruction
Prestack seismic data are multidimensional signals that can be described as a low-rank fourth-order tensor in the frequency − space domain. Tensor completion strategies can be used to recover unrecorded observations and to improve the signal-to-noise ratio of prestack volumes. Additionally, tensor completion can be posed as an inverse problem and solved using convex optimization algorithms. The...
متن کامل5D and 4D Pre-stack seismic data completion using tensor nuclear norm (TNN)
In this paper we present novel strategies for completion of 5D pre-stack seismic data, viewed as a 5D tensor or as a set of 4D tensors across temporal frequencies. In contrast to existing complexity penalized algorithms for seismic data completion, which employ matrix analogues of tensor decompositions such as HOSVD or use overlapped Schatten norms from different unfoldings (or matricization) o...
متن کاملNeuron Mathematical Model Representation of Neural Tensor Network for RDF Knowledge Base Completion
In this paper, a state-of-the-art neuron mathematical model of neural tensor network (NTN) is proposed to RDF knowledge base completion problem. One of the difficulties with the parameter of the network is that representation of its neuron mathematical model is not possible. For this reason, a new representation of this network is suggested that solves this difficulty. In the representation, th...
متن کامل