Single-Component/Single-Station–Based Machine Learning for Estimating Magnitude and Location of an Earthquake: A Support Vector Machine Approach

Chanda, Sarit and Somala, Surendra Nadh (2021) Single-Component/Single-Station–Based Machine Learning for Estimating Magnitude and Location of an Earthquake: A Support Vector Machine Approach. Pure and Applied Geophysics. ISSN 0033-4553

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Abstract

Traditional approaches require a velocity model to compute travel times for estimating the location of earthquakes. Moreover, the velocity model typically assumed is layered in nature, ignoring the perturbations around the background velocity model. In this study, we propose a novel method that does not require any assumption on a velocity model. Also, triangulation estimates of location require three-station recordings. Even if the single station is used, three components of recordings are needed. In this work, we propose a machine learning method, based on support vector machines (SVM), that can predict earthquake location and magnitude with just one component (vertical) of a single station. Machine learning methods are typically used for either regression or classification. Literature shows that the SVM algorithm is promising for the classification of an arbitrary signal. This research demonstrates that, by using complementary input features from a seismic recording, a comparable performance can be obtained, with a substantial reduction in detection time. Furthermore, only the vertical component of a single recording station data is used to train the network. The SVM algorithm is applied on synthetic seismograms from 400 earthquakes of different focal mechanisms and magnitudes. This falls under supervised machine learning, as we give the input features and use them for training the model. Overfitting is avoided by tenfold cross-validation, i.e., the algorithm is repeated ten times, each time giving a different 10% as a testing portion. The classifier's performance is quite good, on the synthetic noise-free data, in predicting the magnitude, elevation angle, and hypocentral distance, but not the azimuthal angle. The trained model in the present work can be readily used where noise levels are quite low without sophisticated ray-tracing or Green's function computation. The performance of the algorithm is checked by adding additive noise to the data. The novelty of this work is that knowledge of the velocity model is not required, and the estimation of magnitude and the origin of the earthquake are done using the single-station/single-component earthquake records.

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