Projection-Based Classification of Chemical Groups for Provenance Analysis of Archaeological Materials
Projection-Based Classification of Chemical Groups for Provenance Analysis of Archaeological Materials
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DOI：
10.1109/access.2020.3016244
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发表时间：
2020-08
期刊：
影响因子：
3.9
通讯作者：
P. López-García;D. Argote;M. Thrun
中科院分区：
文献类型：
--
作者：
P. López-García;D. Argote;M. Thrun
在来源分析中，确定考古文物的来源起着重要的作用。通常，这个问题是通过发现光谱技术测量的数据中的天然基团来解决的。然后，主成分和经典的分区聚类分析，揭示了群体，理应定义的调查文物的起源。然而，这项工作表明，最大限度地提高方差和寻找特定的集群结构可能会产生误导，因为它不能清楚地区分不同的考古来源。相比之下，新方法通过利用涌现和群体智能揭示了材料中存在的几个公认的地质来源，而无需事先假设数据结构。将无监督和半监督机器学习与化学计量学相结合，应用于从墨西哥Xalasco考古遗址收集的中美洲地质资源和黑曜石文物样本。对文物的分析表明，哈拉斯科居民对当地黑曜石矿床有偏好。结果表明，这种方法，在鲁棒性方面，是适合于处理无偏定量光谱分析的考古材料揭示自然组的考古数据。
In provenance analysis, identifying the origin of the archaeological artifacts plays a significant role. Usually, this problem is addressed by discovering natural groups in data measured with spectroscopic techniques. Then, principal component and classical partitioning cluster analysis are employed to reveal the groups that supposedly define the origin of the investigated artefacts. However, this work shows that maximizing the variance and searching for specific cluster structures can be misleading because it fails to discriminate clearly the different archeological sources. In contrast, the new methodology reveals several acknowledged geological sources present in the materials through the exploitation of emergence and swarm intelligence without prior assumptions about the data structures. A combination of unsupervised and semi-supervised machine learning and chemometric is applied on samples of Mesoamerican geological sources and obsidian artefacts collected from the archaeological site of Xalasco in Mexico. The analysis of the artifacts showed a preference of Xalasco inhabitants to local obsidian deposits. The results show that this approach, in terms of robustness, is suitable for handling unbiased quantitative spectral analysis of archaeological materials revealing the natural groups of archeological data.