Research on Seismic Signal Analysis Based on Machine Learning
基于机器学习的地震信号分析研究
Vedio
https://www.bilibili.com/video/bv1ZV4y1p75v?vd_source=9dad485ab167164358578deecb64a255
Code
Source Code could be accessed from https://github.com/sndnyang/LCL-SSS.
Abstract and figures
In this paper, the time series classification frontier method MiniRocket was used to classify earthquakes, blasts, and background noise. From supervised to unsupervised classification, a comprehensive analysis was carried out, and finally, the supervised method achieved excellent results. The relatively simple model, MiniRocket, is only a one-dimensional convolutional neural network structure which has achieved the best comprehensive results, and its computational efficiency is far stronger than other supervised classification methods. Through our experimental results, we found that the MiniRocket model could well-extract the decisive features of the seismic sensing signal. In order to try to eliminate the tedious work of making data labels, we proposed a novel lightweight collaborative learning for seismic sensing signals (LCL-SSS) based on the method of feature extraction in MiniRocket combined with unsupervised classification. The new method gives new vitality to the unsupervised classification method that could not be used originally and opens up a new path for the unsupervised classification of seismic sensing signals.
本文采用时间序列分类前沿方法MiniRocket对地震、爆炸和背景噪声进行分类。从有监督分类到无监督分类，进行了综合分析，最后，有监督方法取得了很好的效果。相对简单的模型MiniRocket只是一个一维卷积神经网络结构，取得了最好的综合效果，其计算效率远远强于其他有监督分类方法。通过实验结果，我们发现MiniRocket模型可以很好地提取地震感应信号的决定性特征。为了尝试消除制作数据标签的繁琐工作，我们在MiniRocket的特征提取方法的基础上，结合无监督分类，提出了一种新型的地震感应信号轻量级协作学习（LCL-SSS）。这种新方法给原来无法使用的无监督分类方法注入了新的活力，为地震传感信号的无监督分类开辟了一条新路。