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Bertoncello Antoine Oppenheim Georges Cordier Philippe Gourvénec Sébastien Mathieu Jean-Philippe Chaput Eric Kurth Tobias 《Mathematical Geosciences》2020,52(5):637-637
Mathematical Geosciences - Unfortunately, in the original version of the article the first and second name of the fourth author were wrong. 相似文献
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Antoine Bertoncello Tao Sun Hongmei Li Gregoire Mariethoz Jef Caers 《Mathematical Geosciences》2013,45(7):873-893
Geostatistical simulation methods aim to represent spatial uncertainty through realizations that reflect a certain geological concept by means of a spatial continuity model. Most common spatial continuity models are either variogram, training image, or Boolean based. In this paper, a more recent spatial model of geological continuity is developed, termed the event, or surface-based model, which is specifically applicable to modeling cases with complex stratigraphy, such as in sedimentary systems. These methods rely on a rule-based stacking of events, which are mathematically represented by two-dimensional thickness variations over the domain, where positive thickness is associated with deposition and negative thickness with erosion. Although it has been demonstrated that the surface-based models accurately represent the geological variation present in complex layered systems, they are more difficult to constrain to hard and soft data as is typically required of practical geostatistical techniques. In this paper, we develop a practical methodology for constraining such models to hard data from wells and thickness data interpreted from geophysics, such as seismic data. Our iterative methodology relies on a decomposition of the parameter optimization problem into smaller, manageable problems that are solved sequentially. We demonstrate this method on a real case study of a turbidite sedimentary basin. 相似文献
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Bertoncello Antoine Oppenheim Georges Cordier Philippe Gourvénec Sébastien Mathieu Jean-Philippe Chaput Eric Kurth Tobias 《Mathematical Geosciences》2020,52(5):619-635
Mathematical Geosciences - In the current era of big data and machine learning, a strong focus exists on prediction and classification. In industrial applications, however, many important questions... 相似文献
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