SVInvNet: A Densely Connected Encoder-Decoder Architecture for Seismic Velocity Inversion

This study presents a deep learning (DL)-based approach to the seismic velocity inversion problem, focusing on both noisy and noiseless training datasets of varying sizes. Our seismic velocity inversion network (SVInvNet) introduces a novel architecture that contains a multiconnection encoder-decode...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 63; S. 1 - 12
Hauptverfasser:	Najafi Khatounabad, Mojtaba, Yalim Keles, Hacer, Kadioglu, Selma
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Ambient noise Benchmark testing Comparative analysis Computer architecture Convolutional neural network (CNN) Data models Datasets Decoding Deep learning deep learning (DL) densenet encoder-decoder architecture Encoders-Decoders Mathematical models Predictive models Receivers Salt Salt domes Seismic velocities seismic velocity inversion Surface waves Training Velocity
ISSN:	0196-2892, 1558-0644
Online-Zugang:	Volltext
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