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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Classification of Isolated Volcano-Seismic Events Based on Inductive Transfer Learning
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Classification of Isolated Volcano-Seismic Events Based on Inductive Transfer Learning

机译：基于归纳转移学习的分离火山地震事件分类

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摘要

Domain-specific problems where data collection is an expensive task are often represented by scarce or incomplete data. From a machine learning perspective, this type of problems has been addressed using models trained in different specific domains as the starting point for the final objective-model. The transfer of knowledge between domains, known as transfer learning (TL), helps to speed up training and improve the performance of the models in problems with limited amounts of data. In this letter, we introduce a TL approach to classify isolated volcano-seismic signals at "Volcan de Fuego", Colima (Mexico). Using the well-known convolutional architecture (LeNet) as a feature extractor and a representative data set containing regional earthquakes, volcano-tectonic earthquakes, long-period events, volcanic tremors, explosions, and collapses, our proposal compares the generalization capabilities of the models when we only fine-tune the upper layers and fine-tune overall of them. Compared with the other state-of-the-art techniques, classification systems based on TL approaches provide good generalization capabilities (attaining nearly 94% of events correctly classified) and decreasing computational time resources.
机译：域特定问题，数据收集是昂贵的任务通常由稀缺或不完整的数据表示。从机器学习的角度来看，使用在不同特定域中培训的模型作为最终目标模型的起点来解决这种问题。域之间的知识转移，称为转移学习（TL），有助于加快培训并提高模型的性能，以有限的数据存在。在这封信中，我们介绍了一个TL方法，在科里马（墨西哥火山）（墨西哥火山德福加）分类孤立的火山地震信号。使用众所周知的卷积架构（LENET）作为特征提取器和包含区域地震，火山构造地震，长期事件，火山震颤，爆炸和崩溃的代表性数据集，我们的提案比较了模型的泛化能力当我们只微调上层和它们的微调。与其他最先进的技术相比，基于TL方法的分类系统提供了良好的泛化能力（实现了近94％的事件正确分类）和降低计算时间资源。

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