An Earthquake Forecast Model Based on Multi-Station PCA Algorithm
Source abstract
Abstract
With the continuous development of human society, earthquakes are becoming more and more dangerous to the production and life of human society. Researchers continue to try to predict earthquakes, but the results are still not significant. With the development of data science, sensing and communication technologies, there are increasing efforts to use machine learning methods to predict earthquakes. Our work raises a method that applies big data analysis and machine learning algorithms to earthquakes prediction. All data are accumulated by the Acoustic and Electromagnetic Testing All in One System (AETA). We propose the multi-station Principal Component Analysis (PCA) algorithm and extract features based on this method. At last, we propose a weekly-scale earthquake prediction model, which has a 60% accuracy using LightGBM (LGB).
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Earthquake Detection and Analysis · Seismology and Earthquake Studies · earthquake and tectonic studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
The Key Laboratory of Integrated Microsystems, Peking University Shenzhen Graduate School, Shenzhen 518055, China
Faculty of Engineering, Shenzhen MSU-BIT University, Shenzhen 518172, China
School of Software and MicroElectronics, Peking University, Beijing 100871, China
Funding
Shenzhen Science and Technology Innovation Commission
References (28)
Show source-provided references
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