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Simulation of Geological Contacts from Interpreted Geological Model Using Multiple-Point Statistics

机译:使用多点统计从解释的地质模型模拟地质接触

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摘要

Applications of multiple-point statistics (mps) algorithms to large nonrepetitive geological objects such as those found in mining deposits are difficult because most mps algorithms rely on pattern repetition for simulation. In many cases, an interpreted geological model built from a computer-aided design system is readily available but suffers as a training image due to the lack of patterns repetitiveness. Porphyry copper deposits and iron ore formations are good examples of such mining deposits with non-repetitive patterns. This paper presents an algorithm called contactsim that focuses on reproducing the patterns of the contacts between geological types. The algorithm learns the shapes of the lithotype contacts as interpreted by the geologist, and simulates their patterns at a later stage. Defining a zone of uncertainty around the lithological contact is a critical step in contactsim, because it defines both the zones where the simulation is performed and where the algorithm should focus to learn the transitional patterns between lithotypes. A larger zone of uncertainty results in greater variation between realizations. The definition of the uncertainty zone must take into consideration the geological understanding of the deposit, and the reliability of the contact zones. The contactsim algorithm is demonstrated on an iron ore formation.
机译:将多点统计(mps)算法应用于大型非重复地质对象(例如在矿床中发现的那些对象)非常困难,因为大多数mps算法都依赖于模式重复进行模拟。在许多情况下,从计算机辅助设计系统构建的经解释的地质模型很容易获得,但由于缺乏图案重复性而遭受训练图像的困扰。斑岩型铜矿床和铁矿石地层是这类具有非重复型矿床的很好的例子。本文提出了一种称为contactsim的算法,该算法专注于重现地质类型之间的接触模式。该算法学习地质学家解释的岩性接触的形状,并在以后的阶段模拟它们的模式。定义岩性接触周围的不确定性区域是contactsim中的关键步骤,因为它同时定义了执行模拟的区域和算法应重点学习岩石类型之间过渡模式的区域。较大的不确定性区域会导致实现之间的差异更大。不确定区域的定义必须考虑对矿床的地质认识以及接触区域的可靠性。 contactsim算法在铁矿石地层上得到了证明。

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