Produção Nacional
Revisado por pares
LSHSIM: A Locality Sensitive Hashing based method for multiple-point geostatistics
2017; Elsevier BV; Volume: 107; Linguagem: Inglês
10.1016/j.cageo.2017.06.013
ISSN1873-7803
AutoresPedro Moura, Eduardo Sany Laber, Hélio Lopes, Daniel Mesejo, Lucas Pavanelli, João Gabriel Jardim, Francisco Thiesen, Gabriel Ensenyat Pujol,
Tópico(s)Soil Geostatistics and Mapping
ResumoReservoir 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.
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