From Recommender Systems to 5D Seismic Data Reconstruction

In recent years, the development of recommendation systems has become an important area of research for data scientists. A recommendation system (or recommender system) is an algorithm that attempts to predict the rating that a user or costumer will give to an item. Recommendation systems have become quite popular in the field of e-commerce for predicting ratings of movies, books, news, research articles, etc. Research in the area of data analytics and recommendation systems have lead to important efforts toward solving the so called matrix completion problem. The latter entails estimating the missing elements of a matrix containing customer ratings. The aforementioned problem can be extended to the recovery of the missing elements of a multilinear array or tensor. Prestack seismic data in midpoint-offset domain can be represented by a 5th order tensor. Therefore, tensor completion methods can be applied to the recovery of unrecorded traces. Furthermore, tensor completion methodologies can also be applied for multidimensional signal-to-noise-ratio enhancement. Mauricio will discuss the implementation of tensor completion algorithms to reconstruct and enhance 5D volumes. He will also discuss the successful application of tensor completion techniques to the reconstruction of field data sets.