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A Simple and Efficient Feature Extraction Algorithm for Geophysical Phenomena

机译:一种简单有效的地球物理现象特征提取算法

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A phenomenon is defined as any state or process known through the senses rather than by intuition or reasoning, and thus is an observable event, especially something special or unusual. A geophysical phenomenon in the context of geoscience data can be characterized as a spatial region which is significantly different from the rest of the image; having higher/lower than average background intensity value; and having higher variation in intensity when compared to the remaining data points. This paper will describe two variations of the Phenomena Extraction Algorithm (PEA). The PEA consists of three components: a hierarchical splitting to efficiently decompose geoscience data into smaller regions; a set of statistical tests to determine whether decomposed region meets the definition of a geophysical phenomenon and an optimization algorithm to determine the best thresholds needed by these statistical tests. The two variations of the algorithm were tested on a synthetic dataset in a series of experiments. The results from these experiments will be presented in this paper. The use of PEA in a proof-of-concept effort within Linked Environment for Atmospheric Discovery (LEAD), a large NSF funded Information Technology Research project, will also be described.
机译:现象定义为通过感官而不是通过直觉或推理所知的任何状态或过程,因此是可观察的事件,尤其是特殊或不寻常的东西。地球科学数据背景中的地球物理现象可以表征为空间区域,其与图像的其余部分显着不同;具有更高/低于平均背景强度值;与剩余数据点相比,强度变化更高。本文将描述现象提取算法(PEA)的两种变体。豌豆由三个组成部分组成:分层分裂,以将地球科学数据有效地分解为较小的区域;一组统计测试,以确定分解区域是否符合地球物理现象的定义和优化算法,以确定这些统计测试所需的最佳阈值。在一系列实验中在合成数据集上测试了算法的两个变化。本文将提出这些实验的结果。还将描述一项大型NSF资助信息技术研究项目的大气发现(领导)在联系环境中使用豌豆在概念上的验证工作中。

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