← Back to Gallery

A b-value Based Analysis of Earthquake Sequences for Japan Using Deep Learning

The b-value from the Gutenberg-Richter relation captures the ratio between small and large earthquakes triggering for a region. Several researchers have observed fluctuation in b-values prior to an earthquake especially during the aftershock sequence analysis. By leveraging the ever-increasing earthquake database and the development in the field of deep learning computations, this study focuses on analyzing the spatial-temporal b-value series for Japan.

We follow a two step approach, in the first step a spatial-temporal b-value series is calculated from the earthquake catalog, which takes the shape of a series of [32 × 32] pixel images of spatial b-value distributions. On this we train an autoencoder, which compresses and then decompresses the input to learn the normal behavior and relationships within the data. In the second step, we then take the pixel by pixel reconstruction error as input for a Dilated ResNet-like classifier, which predicts an earthquake probability. We expect that this study will improve the understanding of earthquake occurrences which could further be beneficial for Early Warning, rapid response and mitigation plans, especially for sustainable human habitats in earthquake-prone regions.

Session: New Developments in Physics- and Statistics-based Earthquake Forecasting I

Type: Oral

Room: Grand B

Date: 4/22/2022

Presentation Time: 08:15 AM Pacific

Presenting Author: Jonas Köhler

Student Presenter: Yes

Additional Authors