[SSS14-09] Two sprouting researches to characterize postseismic deformation: Neural network learning and modified Omori-law
Keywords:postseismic deformation, GNSS, machine learning
In this presentation, we introduce two different approaches to characterizing the postseismic deformation. As a test case, we use GNSS time-series data from Geospatial Information Authority of Japan, GEONET, for the 2011 Tohoku earthquake. One approach (Yamagata and Mitsui, 2019) used a recurrent neural network (RNN), a method of machine learning. RNN was made to learn the postseismic time-series data 1 year after the mainshock, and predict the postseismic deformation by the end of 2018 at observation points which was not used for the learning, to compare with the actual data. In the other approach, we converted the displacement of the postseismic deformation to the velocity, and tried its characterization by a power law similar to the improved Omori law of aftershocks (Ingleby and Wright, 2018). Using the high-rate data of 30-second sampling, we regressed the time decay of the postseismic deformation rate from just after the Tohoku earthquake to 2018, and obtained a small estimated value of the power index p of about 0.7. Both approaches suggested that the dominant physical mechanism of the postseismic deformation changed around 2013.