Exploratory Data Analysis of Earthquake Moment Tensor Catalog using Non-linear Graph-based Dimensionality Reduction

Abstract

Abstract The information on spatial location, origin time and fault mechanism type of earthquakes is contained in moment tensor solutions, which are routinely estimated. The catalog of moment tensor solutions has been interpreted by researchers based on a wealth of past knowledge; however, the long-term routine analyses have led to the accumulation of a huge amount of data in moment tensor catalog, and it is necessary to move away from the artisanal approach. In this study, using dimensionality reduction of unsupervised machine learning, we performed exploratory data analysis of the moment tensor catalog to objectively obtain images of seismic activity and to acquire knowledge on the spatial and temporal characteristics of the earthquake mechanism. Source parameters of the moment tensor catalog in Japan, spatial location (latitude, longitude, and depth) and source-mechanism diagram information, were embedded in two-dimensional space via a non-linear graph-based dimensionality-reduction method, Uniform Manifold Approximation and Projection. On the embedding map, earthquakes in eastern and western Japan are distributed separately and are further embedded to reflect their characteristic fault mechanism and depth in each region. The similarity degree of the earthquakes can be obtained as the distance on the embedding map. This study demonstrates that the data visualization using dimensionality reduction is useful for intuitively and objectively understanding the regional characteristics of earthquake mechanisms. The embedding map can also be employed to visualize temporal changes in regional seismic activity, and to perform a similarity search with a past event.