Learning under uncertainty for interpreting the pattern of volcanic eruptions

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外文摘要：The overall goal of the research presented in this paper is to design an intelligent system to aid geologists in processing complex rock characteristics for interpreting eruption patterns， and thereby to aid eruption forecasting for volcanic chains and fields。 The objective of this paper is to introduce a belief-based partially supervised classification method designed to deal with high uncertainty of geological data。 A case study developed to show the feasibility of the presented method for correlation of tephra layers based on geochemical characteristics is also described。 This method is not specific to geological data and can be used in other applications。

外文关键词：

belief functionsdescision fusiongeochemical datatephrathe Transferable Belief Modeluncertaintyvolcanic eruptions

作者：

Rogova, Galina L.、Bursik, Marcus I.、Pouget, Solene

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作者单位：

Geol. Dept., State Univ. of New York at Buffalo Amherst, Amherst, NY, USA

会议名称：

International Conference on Information Fusion

会议地点：

Washington, DC(US)

会议母体文献：

2015 18th International Conference on Information Fusion

页码：

375-382

出版时间：

2015