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When Zero Doesn't Mean it and Other Geomathematical Mischief.

机译:当零并不意味着它和其他基因数学上的恶作剧。

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There is almost not a case in exploration geology, where the studied data doesn't includes below detection limits and/or zero values, and since most of the geological data responds to lognormal distributions, these "zero data" represent a mathematical challenge for the interpretation.We need to start by recognizing that there are zero values in geology. For example the amount of quartz in a syenite is zero, since quartz cannot co-exists with nepheline. Another common essential zero is a North azimuth, however we can always change that zero for the value of 360°. These are known as "Essential zeros", but what can we do with "Rounded zeros" that are the result of below the detection limit of the equipment?Amalgamation, e.g. adding Na2O and K2O, as total alkalis is a solution, but sometimes we need to differentiate between a sodic and a potassic alteration. Pre-classification into groups requires a good knowledge of the distribution of the data and the geochemical characteristics of the groups which is not always available. Considering the zero values equal to the limit of detection of the used equipment will generate spurious distributions, especially in ternary diagrams. Same situation will occur if we replace die zero values by a small amount using non-parametric or parametric techniques (imputation).The method that we are proposing takes into consideration the well known relationships between some elements. For example, in copper porphide deposits, there is always a good direct correlation between the copper values and the molybdenum ones, but while copper will always be above the limit of detection, many of the molybdenum values will be "rounded zeros". So, we will take the lower quartile of the real molybdenum values and establish a regression equation with copper, and then we will estimate the "rounded" zero values of molybdenum by their corresponding copper values.The method could be applied to any type of data, provided we establish first their correlation dependency.One of the main advantages of this method is that we do not obtain a fixed value for the "rounded zeros", but one that depends on the value of the other variable.
机译:勘探地质几乎没有一种情况,即研究数据不包括低于检测极限和/或零值的数据,并且由于大多数地质数据对对数正态分布做出响应,因此这些“零数据”代表了数学上的挑战。解释。 我们需要从认识到地质学中的零值开始。例如,由于石英不能与霞石共存,所以正长岩中石英的数量为零。另一个常见的基本零是北方位角,但是我们始终可以将其更改为360°的值。这些被称为“基本零”,但是对于低于设备检测极限的结果“圆角零”,我们该怎么办? 合并,例如加入Na2O和K2O,因为总碱是一种解决方案,但是有时我们需要区分钠盐和钾盐变化。将组预先分类需要对数据的分布和组的地球化学特征有很好的了解,而这并非总是可用的。认为零值等于所用设备的检测极限将产生杂散分布,尤其是在三元图中。如果我们使用非参数或参数技术(输入)少量替换零值,也会发生相同的情况。 我们提出的方法考虑了一些元素之间众所周知的关系。例如,在铜的硫化物矿床中,铜值和钼值之间始终存在良好的直接相关性,但是尽管铜将始终高于检测极限,但许多钼值将被“舍入为零”。因此,我们将采用实际钼值的较低四分位数,并与铜建立回归方程,然后通过相应的铜值来估算钼的“四舍五入”零值。 该方法可以应用于任何类型的数据,前提是我们首先确定它们的相关性。 此方法的主要优点之一是,对于“四舍五入的零”,我们不会获得固定值,而是取决于另一个变量的值。

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