Estimation of Magnitude and Epicentral Distance From Seismic Waves Using Deeper CRNN

Article Geochemistry & Geophysics

Complex Neural Networks for Estimating Epicentral Distance, Depth, and Magnitude of Seismic Waves

Nicolae-Catalin Ristea et al.

Summary: This research utilizes deep learning techniques for earthquake characterization, extracting information from a large volume of seismic data. A novel approach is proposed to estimate epicentral distance, depth, and magnitude directly from raw seismograms observed by single stations. Experimental results show the effectiveness of the proposed method in estimating epicentral distance, depth, and magnitude.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2022)

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Article Geochemistry & Geophysics

Multifeature Fusion-Based Earthquake Event Classification Using Transfer Learning

Gwantae Kim et al.

Summary: This research proposes a multi-feature fusion model using deep convolutional neural networks and transfer learning for earthquake event classification, which outperforms state-of-the-art methods according to experimental results on earthquake databases.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021)

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Article Geochemistry & Geophysics

Earthquake Event Classification Using Multitasking Deep Learning

Bonhwa Ku et al.

Summary: This study proposes an attention-based convolutional neural network architecture for multitasking learning to improve performance in earthquake event classification. Experimental results show the effectiveness of this method with earthquake databases from the Korean peninsula and the Circum-Pacific belt.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS (2021)

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Article Geosciences, Multidisciplinary