Geomodelling of a fluvial system with semi-supervised support vector regression

Fluvial deposits are a challenge for modelling flow in sub-surface reservoirs. Connectivity and continuity of permeable bodies have a major impact on fluid flow in porous media. Contemporary object-based and multipoint statistics methods face a problem of robust representation of connected structures. An alternative approach to model petrophysical properties is based on machine learning algorithm ? Support Vector Regression (SVR). Semi-supervised SVR is able to establish spatial connectivity taking into account the prior knowledge on natural similarities. SVR as a learning algorithm is robust to noise and captures dependencies from all available data. Semi-supervised SVR applied to a synthetic fluvial reservoir demonstrated robust results, which are well matched to the flow performance

William Smith - 200 years of geomodelling celebrated at IAMG

The International Association for Mathematical Geosciences (IAMG) commemorated William Smith (23rd March 1769 - 28th August 1839) and 200 years of geomodelling with geological surveys and academics across the globe at the 17th annual conference of the IAMG in Freiberg, Germany from the 5th to 13th September 2015. The aim of the IAMG is to promote the use of mathematics, statistics and geoinformatics in the geosciences. The annual IAMG conference is an opportunity for geoscientists to collaborate with mathematicians and statisticians and present their recent work. The

17th annual IAMG conference, with 300 participants from across the world, differed from previous IAMG conferences in that it included a special ‘Day of Surveys’ to acknowledge 200 years of science and methodologies to construct maps.

Técnicas de modelagem 3D aplicadas a dados paleobatimétricos das bacias de Santos e Campos e à simulação deformacional de objetos geológicos

Pós-graduação em Geologia Regional - IGCE