Advanced signal recognition methods applied to seismo-volcanic events from Planchon Peteroa Volcanic Complex: Deep Neural Network classifier
Advanced signal recognition methods applied to seismo-volcanic events from Planchon Peteroa Volcanic Complex: Deep Neural Network classifier
复制标题
适用于 Planchon Peteroa 火山群地震火山事件的先进信号识别方法：深度神经网络分类器
DOI：
10.1016/j.jsames.2020.103115
复制
发表时间：
2021
影响因子：
1.8
通讯作者：
J. Ibáñez
中科院分区：
文献类型：
--
作者：
Verónica L. Martínez;M. Titos;C. Benítez;G. Badi;J. A. Casas;Victoria Hipatia Olivera Craig;J. Ibáñez
在研究活火山时，识别和分类地震火山事件的先进技术是超越的，不仅因为它们作为精确的真实的时间地震监测程序的重要性，而且因为它们的结果用于模拟火山环境的动态。众所周知，真实的实时地震监测处理如此大量的数据，这将成为一个压倒性的工作，为操作员做手工。因此，使用基于机器学习方法的自动检测和分类技术是合适的，在满足这样的挑战。这项工作的目的是测试的深度神经网络（DNN）的能力，通过使用不同的事件参数化作为一个有信心的分类工具，可以允许一个可靠的地震目录建立在一个新的和未经分析的火山场景。我们测试了不同的配置，以建立一种尽可能简单的方法，在有限数量的事件中使用这种分类器。在这方面，探索特征空间，以选择地震信号的最重要的参数。用于此分析的数据对应于位于南美洲智利和阿根廷之间的过渡南部火山带（TSVZ）的Planchon Peteroa火山复合体（PPVC）。这项工作最重要的结果不仅是它提供了对该算法性能的分析，特别是当训练，验证和测试数据集可靠时，尽管肯定会减少，但它也让我们深入了解最佳事件参数化如何显着提高地震火山事件的自动检测和分类。
Advanced techniques in the recognition and classification of seismo-volcanic events are transcendental when studying active volcanoes, not only for their importance as an accurate real time seismic monitoring procedure but also for the use of their results in modeling the dynamics of the volcanic environment. It is well known that real time seismic monitoring deals with such a large amount of data that it would become an overwhelming job for an operator to do manually. Therefore the use of automatic detection and classification techniques based on the Machine Learning approach are suitable in meeting such a challenge.The aim of this work is to test the capability of the Deep Neural Network (DNN) by using different event parametrization as a confident classifier tool that could permit a reliable seismic catalog to be built in a new and un-analyzed volcanic scenario. We tested different configurations in order to build an approach that was as simple as possible to use this classifier with a limited number of events. In this regard, the feature space was explored in order to select the most significant parameters of the seismic signals. The data used for this analysis corresponds to the Planchon Peteroa Volcanic Complex (PPVC) located in the Transitional Southern Volcanic Zone (TSVZ) between Chile and Argentina, South America. The most significant result of this work was not only that it provided an analysis in terms of performance of this algorithm, especially when the training, validation and test dataset is reliable although definitely reduced, but it also gave an insight of into how an optimal event parametrization can significantly improve the automatic detection and classification of seismo-volcanic events.
PICOSS：用于地震信号分类的 Python 接口
影响因子：
4.4
作者：
Bueno A
通讯作者：
Bueno A
DOI：
10.1109/tgrs.2019.2941494
发表时间：
2020-02-01
影响因子：
8.2
作者：
Bueno, Angel;Benitez, Carmen;Ibanez, Jesus M.
通讯作者：
Ibanez, Jesus M.
影响因子：
5.3
作者：
Lange, Dietrich;Tilmann, Frederik;Beck, Susan
通讯作者：
Beck, Susan