Self-organizing maps (SOMs) are a type of unsupervised artificial neural networks clustering tool. SOMs are used to cluster large multivariate data sets. They can identify patterns and trends in the geophysical maps of an area and generate proxy geology maps, known as remote predictive mapping. We have applied SOMs to magnetic, radiometric, and gravity data sets compiled from multiple modern and legacy data sources over the Baie Verte Peninsula, Newfoundland, Canada. The regional and local geologic maps available for this area and knowledge from numerous geologic studies has enabled the accuracy of SOM-based predictive mapping to be assessed. Proxy geology maps generated by primary clustering directly from the SOMs and secondary clustering using a k-means approach reproduced many geologic units identified by previous traditional geologic mapping. Of the combinations of data sets tested, the combination of magnetic data, primary radiometric data and their ratios, and Bouguer gravity data gave the best results. We found that using reduced-to-the-pole residual intensity or using the analytic signal as the magnetic data were equally useful. The SOM process was unaffected by gaps in the coverage of some of the data sets. The SOM results could be used as input into k-means clustering because this method requires no gaps in the data. The subsequent k-means clustering resulted in more meaningful proxy geology maps than were created by the SOM alone. In regions where the geology is poorly known, these proxy maps can be useful in targeting where traditional, on-the-ground geologic mapping would be most beneficial, which can be especially useful in parts of the world where access is difficult and expensive.

中文翻译：

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使用自组织地图根据地球物理数据进行预测地质绘图：来自加拿大纽芬兰 Baie Verte 的案例研究

自组织图 (SOM) 是一种无监督的人工神经网络聚类工具。SOM 用于对大型多元数据集进行聚类。他们可以识别一个地区地球物理图中的模式和趋势，并生成代理地质图，称为远程预测制图。我们已将 SOM 应用于从加拿大纽芬兰贝维特半岛的多个现代和遗留数据源汇编而来的磁、辐射和重力数据集。该地区可用的区域和当地地质图以及来自众多地质研究的知识，使基于 SOM 的预测绘图的准确性得以评估。通过直接从 SOM 进行初级聚类和使用k 的二级聚类生成的代理地质图-means 方法再现了以前传统地质绘图所确定的许多地质单元。在测试的数据集组合中，磁数据、原始辐射数据及其比率以及布格重力数据的组合给出了最好的结果。我们发现使用归一极剩余强度或使用解析信号作为磁数据同样有用。SOM 过程不受某些数据集覆盖范围差距的影响。SOM 结果可用作k均值聚类的输入，因为此方法不需要数据中的间隙。随后的k-means 聚类产生了比 SOM 单独创建的更有意义的代理地质图。在对地质知之甚少的地区，这些代理地图可用于定位传统的实地地质测绘最有利的地方，这在世界上访问困难且成本高昂的地区尤其有用。