Predicting disaster events from seismic data is of paramount importance and can save thousands of lives, especially in earthquake-prone areas and habitations around volcanic craters. The drastic rise in the number of seismic monitoring stations in recent years has allowed the collection of a huge quantity of data, outpacing the capacity of seismologists. Due to the complex nature of the seismological data, it is often difficult for seismologists to detect subtle patterns with major implications. Machine learning algorithms have been demonstrated to be effective in classification and prediction tasks for seismic data. It has been widely known that some animals can sense disasters like earthquakes from seismic signals well before the disaster strikes. Mel spectrogram has been widely used for speech recognition as it scales the actual frequencies according to human hearing. In this paper, we propose a variant of the Mel spectrogram to scale the raw frequencies of seismic data to the hearing of such animals that can sense disasters from seismic signals. We are using a Computer vision algorithm along with clustering that allows for the classification of unlabelled seismic data.
翻译:从地震数据中预测灾害事件至关重要,可以拯救数千人的生命,特别是在地震易发地区和火山坑周围的居住区。近年来地震监测站数目的急剧增加使得能够收集大量数据,超过了地震学家的能力。由于地震数据的复杂性,地震学家往往难以发现具有重大影响的微妙模式。机器学习算法已证明在地震数据的分类和预测任务方面是有效的。众所周知,有些动物在灾害袭击前很早就能够从地震信号中感知到地震等灾害。Mel光谱仪被广泛用于语音识别,因为它根据人类听力量实际频率。在本文件中,我们建议采用梅尔光谱仪变量,将地震数据的原始频率缩到能够从地震信号中感知到灾害的动物的听觉中。我们正在使用计算机视觉算法,同时进行集群,以便能够对无标记的地震数据进行分类。