Application of Empirical Orthogonal Function on Geodetic Time-Series Data

Abstract

Growing amount of geodetic data through sophisticated data collection techniques in recent times has led to substantial challenges in data analysis and interpretation. The empirical orthogonal function (EOF), commonly known as principal component analysis (PCA), is one of the dominant tools in analyzing coherent space-time dataset. The EOF method belongs to the family of factor analysis and has application in dimensionality reduction and pattern extraction, especially in the field of Geophysics, Atmospheric Sciences and Oceanography. This paper provides some basic formulation of the EOF technique with an emphasis on the step-by-step implementation to extract dominant modes from any time-series data. For instance, the EOF-based results show that the deformation pattern of the 2016, M w 7.8, Kaikoura earthquake of New Zealand is in the North-East direction. A few variations of the conventional EOF method are also discussed.