How do non-deposit sites influence the performance of machine learning-based gold prospectivity mapping? A study case in the Pitangui Greenstone Belt, Brazil

IF 3.4 2区 地球科学 Q1 GEOCHEMISTRY & GEOPHYSICS
Brener Otávio Luiz Ribeiro , Danilo Barbuena , Gustavo Henrique Coelho de Melo , João Gabriel Motta , Eduardo Duarte Marques , Marcelo de Souza Marinho
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Abstract

One of the greatest challenges in mineral prospectivity mapping (MPM) research nowadays is to find a solid methodology that ensures the reliability of the prospectivity model during the learning and prediction procedures. Multiple uncertainties such as the location of non-deposit sites or the type of machine learning algorithm (MLA) can bias the MPM. To investigate these effects, we used multiple training datasets with different non-deposits locations, randomly created, and MLAs such as Artificial Neural Network (ANN), Random Forests (RF) and Support Vector Machine (SVM), to model orogenic-Au prospectivity in the Pitangui Greenstone Belt (PGB, Brazil). Regarding the implications in the methodology for MPM, there are great differences between the models' performances in mapping prospective zones when there is a slightly change in the location of negative samples. These changes can be observed by using the Shapley additive explanation metrics (SHAP values), which can help mitigate such effects by choosing an optimal model among all randomly created datasets. The SHAP values of non-deposit sites also showed that ANN and SVM present overfitting problems despite the use of balanced data. RF on the other hand outperformed in all ten datasets and showed great recognition and adjustment to the negative samples. The results presented in this research are also promising to the prospective studies in the PGB, as it shows a map capable to correctly predict 97 % of the known deposits and occurrences in 3 % of the total area and points the new frontiers for gold exploration in the PGB.

非矿床地点如何影响基于机器学习的金矿远景测绘?巴西皮坦吉绿岩带研究案例
目前,矿产远景测绘(MPM)研究面临的最大挑战之一是找到一种可靠的方法,确保远景模型在学习和预测过程中的可靠性。多种不确定因素,如非矿床地点的位置或机器学习算法(MLA)的类型,都可能使 MPM 产生偏差。为了研究这些影响,我们使用了随机创建的具有不同非矿床位置的多个训练数据集,以及人工神经网络(ANN)、随机森林(RF)和支持向量机(SVM)等机器学习算法,来模拟皮坦吉绿岩带(PGB,巴西)的造山金矿远景。关于该方法对 MPM 的影响,当负样本的位置稍有变化时,各模型在绘制远景区方面的表现存在很大差异。使用沙普利加法解释度量(SHAP 值)可以观察到这些变化,通过在所有随机创建的数据集中选择一个最佳模型,有助于减轻这些影响。非存款网站的 SHAP 值还表明,尽管使用了平衡数据,ANN 和 SVM 仍然存在过度拟合问题。另一方面,RF 在所有十个数据集中的表现都优于 ANN 和 SVM,并对负样本有很好的识别和调整能力。这项研究的结果也为 PGB 的前瞻性研究带来了希望,因为它显示了一张能够正确预测 97% 的已知矿床和占总面积 3% 的矿点的地图,并为 PGB 的金矿勘探指明了新的前沿。
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来源期刊
Journal of Geochemical Exploration
Journal of Geochemical Exploration 地学-地球化学与地球物理
CiteScore
7.40
自引率
7.70%
发文量
148
审稿时长
8.1 months
期刊介绍: Journal of Geochemical Exploration is mostly dedicated to publication of original studies in exploration and environmental geochemistry and related topics. Contributions considered of prevalent interest for the journal include researches based on the application of innovative methods to: define the genesis and the evolution of mineral deposits including transfer of elements in large-scale mineralized areas. analyze complex systems at the boundaries between bio-geochemistry, metal transport and mineral accumulation. evaluate effects of historical mining activities on the surface environment. trace pollutant sources and define their fate and transport models in the near-surface and surface environments involving solid, fluid and aerial matrices. assess and quantify natural and technogenic radioactivity in the environment. determine geochemical anomalies and set baseline reference values using compositional data analysis, multivariate statistics and geo-spatial analysis. assess the impacts of anthropogenic contamination on ecosystems and human health at local and regional scale to prioritize and classify risks through deterministic and stochastic approaches. Papers dedicated to the presentation of newly developed methods in analytical geochemistry to be applied in the field or in laboratory are also within the topics of interest for the journal.
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