Research on Seismic Signal Analysis Based on Machine Learning
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Seismology and Earthquake Studies · Earthquake Detection and Analysis · Time Series Analysis and Forecasting
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Lanzhou Geophysics National Observation and Research Station, Lanzhou 730000, China
Institute of Geophysics, China Earthquake Administration, Beijing 100081, China
Institute of AI for Education, East China Normal University, Shanghai 200062, China
School of Computer Science and Technology, East China Normal University, Shanghai 200062, China
Institute of Artificial Intelligence and Change Management, Shanghai University of International Business and Economics, Shanghai 200062, China
Chengdu Surveying Geotechnical Research Institute Co., Ltd. of MCC, Chengdu 610063, China
Department of Computer Science, Georgia State University, Atlanta, GA 30314, USA
Funding
science and technology development fund of Gansu Seismological Bureau · science and technology plan of Gansu Province · Fundamental Research Funds for the Central Universities
References (39)
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