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http://dx.doi.org/10.9720/kseg.2022.3.377

Application of Dimensional Expansion and Reduction to Earthquake Catalog for Machine Learning Analysis  

Jang, Jinsu (Department of Geophysics, Kangwon National University)
So, Byung-Dal (Department of Geophysics, Kangwon National University)
Publication Information
The Journal of Engineering Geology / v.32, no.3, 2022 , pp. 377-388 More about this Journal
Abstract
Recently, several studies have utilized machine learning to efficiently and accurately analyze seismic data that are exponentially increasing. In this study, we expand earthquake information such as occurrence time, hypocentral location, and magnitude to produce a dataset for applying to machine learning, reducing the dimension of the expended data into dominant features through principal component analysis. The dimensional extended data comprises statistics of the earthquake information from the Global Centroid Moment Tensor catalog containing 36,699 seismic events. We perform data preprocessing using standard and max-min scaling and extract dominant features with principal components analysis from the scaled dataset. The scaling methods significantly reduced the deviation of feature values caused by different units. Among them, the standard scaling method transforms the median of each feature with a smaller deviation than other scaling methods. The six principal components extracted from the non-scaled dataset explain 99% of the original data. The sixteen principal components from the datasets, which are applied with standardization or max-min scaling, reconstruct 98% of the original datasets. These results indicate that more principal components are needed to preserve original data information with even distributed feature values. We propose a data processing method for efficient and accurate machine learning model to analyze the relationship between seismic data and seismic behavior.
Keywords
earthquake catalog; machine learning; dimensional expansion; feature scaling; dimensional reduction; feature extraction;
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Times Cited By KSCI : 2  (Citation Analysis)
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