Extremely and very low frequency (ELF/VLF) radio waves are generated from a variety of natural geophysical sources. Ground-based observations often contain signals of interest; however, the signals are typically immersed in a noisy environment due to lightning-generated sferics and additional anthropogenic sources. Although automated detection algorithms have been employed successfully in the past, extraction of arbitrary and broadband signal classes has been a challenge. In this work, we employ a mask-scoring regional convolutional neural network (MSRCNN) for automated extraction of whistlers from ground measurements at Palmer station, Antarctica. Statistics of several hundred thousand whistler receptions are evaluated to determine seasonal and diurnal variations at Palmer station along with strong correlations to lightning activity in the conjugate hemisphere. Although MSRCNN has been employed for whistler extraction in this work, the method has can be easily extended to other signal classes including chorus, hiss, and VLF triggered emissions.

中文翻译：

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使用掩码评分区域卷积神经网络自动大规模提取吹口哨

极低频和极低频 (ELF/VLF) 无线电波由各种自然地球物理源产生。地面观测通常包含感兴趣的信号；然而，由于闪电产生的 sferics 和其他人为来源，信号通常会沉浸在嘈杂的环境中。尽管过去已成功采用自动检测算法，但任意和宽带信号类别的提取一直是一个挑战。在这项工作中，我们采用掩模评分区域卷积神经网络 (MSRCNN) 从南极洲帕尔默站的地面测量中自动提取哨声。评估了数十万惠斯勒接收的统计数据，以确定帕尔默站的季节性和昼夜变化，以及与共轭半球闪电活动的强相关性。尽管 MSRCNN 在这项工作中被用于吹口哨提取，但该方法可以很容易地扩展到其他信号类别，包括合唱、嘶嘶声和 VLF 触发发射。