LSHSIM: A Locality Sensitive Hashing based method for multiple-point geostatistics. (October 2017)

• Record Type: Journal Article
• Authors: Moura, Pedro; Laber, Eduardo; Lopes, Hélio; Mesejo, Daniel; Pavanelli, Lucas; Jardim, João; Thiesen, Francisco; Pujol, Gabriel
• Is Part Of: Computers & geosciences. Volume 107(2017)
• Page Start: 49
• Page End: 60
• Publication Date: 2017-10
• Subjects: Multiple-point geostatistics -- Pattern modeling -- Training image -- Locality Sensitive Hashing -- Run-Length Encoding; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2017.06.013
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Reservoir modeling is a very important task that permits the representation of a geological region of interest, so as to generate a considerable number of possible scenarios. Since its inception, many methodologies have been proposed and, in the last two decades, multiple-point geostatistics (MPS) has been the dominant one. This methodology is strongly based on the concept of training image (TI) and the use of its characteristics, which are called patterns. In this paper, we propose a new MPS method that combines the application of a technique called Locality Sensitive Hashing (LSH), which permits to accelerate the search for patterns similar to a target one, with a Run-Length Encoding (RLE) compression technique that speeds up the calculation of the Hamming similarity. Experiments with both categorical and continuous images show that LSHSIM is computationally efficient and produce good quality realizations. In particular, for categorical data, the results suggest that LSHSIM is faster than MS-CCSIM, one of the state-of-the-art methods. Abstract : Highlights: We propose a new Multiple-Point Geostatistical method. It combines the Locality Sensitive Hashing and Run-Length Encoding techniques. It shows good computational time performance in categorical and continuous images. It also generates realizations with good quality.