Prediction of Seismic Intensity Distributions Using Neural Networks
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.
Code (0)
등록된 구현이 없습니다.
Tasks
motion predictionPredictionregressionSimilar Papers 제목 키워드 기반
Data-Driven Prediction of Seismic Intensity Distributions Featuring Hybrid Classification-Regression Models
Earthquakes are among the most immediate and deadly natural disasters that humans face. Accurately forecasting the extent of earthquake damage and assessing potential risks can be instrumental in saving numerous lives. I…
motion predictionregressionReal-time Seismic Intensity Prediction using Self-supervised Contrastive GNN for Earthquake Early Warning
Seismic intensity prediction from early or initial seismic waves received by a few seismic stations can enhance Earthquake Early Warning (EEW) systems, particularly in ground motion-based approaches like PLUM. While many…
Contrastive LearningGraph Neural NetworkA Deep Convolutional Network for Seismic Shot-Gather Image Quality Classification
Deep Learning-based models such as Convolutional Neural Networks, have led to significant advancements in several areas of computing applications. Seismogram quality assurance is a relevant Geophysics task, since in the …
General ClassificationGeophysicsNeural Network-Based Equations for Predicting PGA and PGV in Texas, Oklahoma, and Kansas
Parts of Texas, Oklahoma, and Kansas have experienced increased rates of seismicity in recent years, providing new datasets of earthquake recordings to develop ground motion prediction models for this particular region o…
motion predictionPredictionEfficient Seismic fragility curve estimation by Active Learning on Support Vector Machines
Fragility curves which express the failure probability of a structure, or critical components, as function of a loading intensity measure are nowadays widely used (i) in Seismic Probabilistic Risk Assessment studies, (ii…
Active LearningBinary Classification