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The Kappa model of probability and higher-order rock sequences

机译:概率和高阶岩石序列的Kappa模型

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摘要

In any depositional environment, the sequence of sediments follows specific high- and low-frequency patterns of rock occurrences or events. The occurrence of a rock in a spatial location is conditional to a prior rock event at a distant location. Subsequently, a third rock occurs between the two locations. This third event is conditional to both prior events and is driven by a third-order conditional probability P(C(A n B)). Such probability has to be characterized beyond the classic conditional independence model, and this research has found that exact computation requires a third-order co-cumulant term. The co-cumulants provide the higher-order redundancy among multiple indicator variables. A Bayesian analysis has been performed with "known" numerical co-cumulants yielding a novel model of conditional probability that is called the "Kappa model." This model was applied to three-point variables, and the concept has been extended for multiple events P(G|A ∩B∩C∩D...∩N), allowing the reproduction of complex transitions of rocks in sequence stratigraphy. The Kappa model and co-cumulants have been illustrated with simple numerical examples for clastic rock sequences. In addition, the co-cumulant has been used to discover an extension of the variogram called the indicator cumulogram. In this way, multiple prior events are no longer ignored for evaluating the probability of a posterior event with higher-order co-cumulant considerations.
机译:在任何沉积环境中,沉积物的序列都遵循特定的高频率和低频模式的岩石事件或事件。空间位置中岩石的出现是在较远位置发生先前岩石事件的条件。随后,在两个位置之间出现了第三块岩石。该第三事件以两个先前事件为条件,并且由三阶条件概率P(C(A n B))驱动。这种概率的特征必须超越经典的条件独立性模型,并且这项研究发现,精确的计算需要一个三阶共累积项。协累积量在多个指标变量之间提供了更高阶的冗余。贝叶斯分析已经用“已知的”数字协累积量进行了分析,产生了一种新的条件概率模型,称为“ Kappa模型”。该模型已应用于三点变量,并且该概念已扩展为适用于多个事件P(G |A∩B∩C∩D...∩N),从而可以再现层序地层中岩石的复杂过渡。用简单的数值示例说明了碎屑岩层序的Kappa模型和共累积量。此外,协累积量已被用于发现称为指示剂累积图的变异函数的扩展。通过这种方式,在评估具有较高阶累积量考虑因素的后发事件的可能性时,不再忽略多个先验事件。

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