Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm
Data-driven predictive mapping of gold prospectivity, Baguio district, Philippines: Application of Random Forests algorithm
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DOI：
10.1016/j.oregeorev.2014.08.010
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发表时间：
2015-12-01
影响因子：
3.3
通讯作者：
Laborte, Alice G.
中科院分区：
文献类型：
--
作者：
Carranza, Emmanuel John M.;Laborte, Alice G.
随机森林（RF）算法是近年来一种新兴的数据驱动的矿产找矿预测成图方法，因此进一步研究其在该领域的有效性具有指导意义。本研究使用碧瑶金矿区（菲律宾）进行，检验了(a)射频算法对不同的矿床和非矿床位置集作为训练数据的敏感性，以及(b)与数据驱动的矿产前景预测映射的既定方法相比，射频建模的性能。我们发现，使用不同的沉积物/非沉积物位置训练集的RF建模是稳定的和可重复的，并且它准确地捕捉了预测变量与训练沉积物/非沉积物位置之间的空间关系。对于碧瑶地区浅成热液金矿远景预测作图，我们发现：(a)射频建模的成功率优于证据权、证据信念和逻辑回归模型；(b)射频建模的预测率优于证据权模型，但与证据信念和逻辑回归模型的预测率大致相等。因此，RF算法可能比目前用于数据驱动的矿物远景预测制图的现有方法有用得多。但是，需要在其他领域对该方法进行进一步测试，以充分探索其在数据驱动的矿物远景预测制图方面的有用性。(C) 2014 Elsevier B.V.版权所有
The Random Forests (RF) algorithm has recently become a fledgling method for data-driven predictive mapping of mineral prospectivity, and so it is instructive to further study its efficacy in this particular field. This study, carried out using Baguio gold district (Philippines), examines (a) the sensitivity of the RF algorithm to different sets of deposit and non-deposit locations as training data and (b) the performance of RF modeling compared to established methods for data-driven predictive mapping of mineral prospectivity. We found that RF modeling with different training sets of deposit/non-deposit locations is stable and reproducible, and it accurately captures the spatial relationships between the predictor variables and the training deposit/non-deposit locations. For data-driven predictive mapping of epithermal Au prospectivity in the Baguio district, we found that (a) the success-rates of RF modeling are superior to those of weights-of-evidence, evidential belief and logistic regression modeling and (b) the prediction-rate of RF modeling is superior to that of weights-of-evidence modeling but approximately equal to those of evidential belief and logistic regression modeling. Therefore, the RF algorithm is potentially much more useful than existing methods that are currently used for data-driven predictive mapping of mineral prospectivity. However, further testing of the method in other areas is needed to fully explore its usefulness in data-driven predictive mapping of mineral prospectivity. (C) 2014 Elsevier B.V. All rights reserved.