학술논문
Domain Knowledge Informed Multitask Learning for Landslide-Induced Seismic Classification
Document Type
Periodical
Author
Source
IEEE Geoscience and Remote Sensing Letters IEEE Geosci. Remote Sensing Lett. Geoscience and Remote Sensing Letters, IEEE. 20:1-5 2023
Subject
Language
ISSN
1545-598X
1558-0571
1558-0571
Abstract
Automatic seismic signal classification methods are extensively investigated to reduce or replace manual interpretation, with great potential in previous research. Discriminative seismic wave propagation physical characteristics, such as velocities and accelerations, are rarely considered for classification. A multitask learning scheme is proposed that utilizes the seismic wave equation and 3-D P-wave velocity Vp model for signal representation learning. The classifier uses the obtained latent feature maps on a convolutional neural network (CNN) architecture for classification of rockfall, slide quake, earthquake, and natural/anthropogenic noise events, recorded at an ongoing landslide. Our experimental results show that our approach outperforms state-of-the-art methods.