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首页> 外文期刊>Journal of Volcanology and Geothermal Research >Parallel System Architecture (PSA): An efficient approach for automatic recognition of volcano-seismic events
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Parallel System Architecture (PSA): An efficient approach for automatic recognition of volcano-seismic events

机译：并行系统架构（PSA）：一种自动识别火山地震事件的有效方法

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摘要

Automatic recognition of volcano-seismic events is becoming one of the most demanded features in the early warning area at continuous monitoring facilities. While human-driven cataloguing is time-consuming and often an unreliable task, an appropriate machine framework allows expert technicians to focus only on result analysis and decision-making. This work presents an alternative to serial architectures used in classic recognition systems introducing a parallel implementation of the whole process: configuration, feature extraction, feature selection and classification stages are independently carried out for each type of events in order to exploit the intrinsic properties of each signal class. The system uses Gaussian Mixture Models (GMMs) to classify the database recorded at Deception Volcano Island (Antarctica) obtaining a baseline recognition rate of 84% with a cepstral-based waveform parameterization in the serial architecture. The parallel approach increases the results to close to 92% using mixture-based parameterization vectors or up to 91% when the vector size is reduced by 19% via the Discriminative Feature Selection (DFS) algorithm. Besides the result improvement, the parallel architecture represents a major step in terms of flexibility and reliability thanks to the class-focused analysis, providing an efficient tool for monitoring observatories which require real-time solutions.
机译：在连续监视设施中，自动识别火山地震事件已成为预警领域最需要的功能之一。人为驱动的编目既费时又常常是不可靠的任务，但是适当的机器框架使专家技术人员只能将精力集中在结果分析和决策上。这项工作提出了经典识别系统中使用的串行体系结构的替代方案，并引入了整个过程的并行实现：为每种事件类型独立执行配置，特征提取，特征选择和分类阶段，以利用每种事件的内在特性信号类别。该系统使用高斯混合模型（GMM）对在Deception Volcano Island（Antarctica）上记录的数据库进行分类，从而在串行体系结构中使用基于倒频谱的波形参数化获得了84％的基线识别率。使用基于混合的参数化矢量，并行方法可将结果提高到接近92％，而通过区分特征选择（DFS）算法将矢量大小减少19％时，并行方法可将结果提高到91％。除了结果的改进之外，由于以类为中心的分析，并行体系结构在灵活性和可靠性方面也迈出了重要的一步，这为监视需要实时解决方案的天文台提供了有效的工具。

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