Prediction of Seismic Intensity Distributions Using Neural Networks

08/16/2022
by   Koyu Mizutani, et al.
0

The ground motion prediction equation is commonly used to predict the seismic intensity distribution. However, it is not easy to apply this method to seismic distributions affected by underground plate structures, which are commonly known as abnormal seismic distributions. This study proposes a hybrid of regression and classification approaches using neural networks. The proposed model treats the distributions as 2-dimensional data like an image. Our method can accurately predict seismic intensity distributions, even abnormal distributions.

READ FULL TEXT

page 1

page 2

research
12/28/2017

Future Frame Prediction for Anomaly Detection -- A New Baseline

Anomaly detection in videos refers to the identification of events that ...
research
01/07/2022

Forecasting emissions through Kaya identity using Neural Ordinary Differential Equations

Starting from the Kaya identity, we used a Neural ODE model to predict t...
research
06/04/2018

Neural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas

Parts of Texas, Oklahoma, and Kansas have experienced increased rates of...
research
09/30/2021

Using neural networks to estimate parameters in spatial point process models

In this paper, I show how neural networks can be used to simultaneously ...
research
04/20/2023

Fourier Neural Operator Surrogate Model to Predict 3D Seismic Waves Propagation

With the recent rise of neural operators, scientific machine learning of...
research
11/21/2018

Multivariate Forecasting of Crude Oil Spot Prices using Neural Networks

Crude oil is a major component in most advanced economies of the world. ...

Please sign up or login with your details

Forgot password? Click here to reset