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Ditemukan 3 dari pencarian Anda melalui kata kunci: subject="Autoencoder"
cover
Deep convolutional autoencoders as generic feature extractors in seismologica…
Komentar Bagikan
Qingkai KongAndrea ChiangAna C. AguiarM. Giselle Fern andez-GodinoStephen C. MyersDonald D. Lucas

The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing. In this paper, we designed tests to evaluate this idea of using autoencoders as feature extractors for different seismological applications, such as event discrimination (i.e., earthquake vs. noise waveforms, earthquake vs. explosion waveforms), and ph…

Edisi
Vol.2, December 2021
ISBN/ISSN
2666-5441
Deskripsi Fisik
11 hlm PDF, 1.991 KB
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
cover
Seismic labeled data expansion using variational autoencoders
Komentar Bagikan
Guangmin HuKunhong LiSong Chen

Supervised machine learning algorithms have been widely used in seismic exploration processing, but the lack of labeled examples complicates its application. Therefore, we propose a seismic labeled data expansion method based on deep variational Autoencoders (VAE), which are made of neural networks and contains two parts-Encoder and Decoder. Lack of training samples leads to overfitting of the …

Edisi
Vol.1, December 2020
ISBN/ISSN
2666-5441
Deskripsi Fisik
7 hlm PDF, 3.230 KB
Judul Seri
Artificial Intelligence in Geosciences
No. Panggil
551
cover
Long-term temperature prediction with hybrid autoencoder algorithms
Komentar Bagikan
J. Pérez-AracilD. FisterC.M. MarinaC. Peláez-RodríguezL. Cornejo-BuenoP.A. GutiérrezA. CastelletiS. Salcedo-Sanz

This paper proposes two hybrid approaches based on Autoencoders (AEs) for long-term temperature prediction. The first algorithm comprises an AE trained to learn temperature patterns, which is then linked to a second AE, used to detect possible anomalies and provide a final temperature prediction. The second proposed approach involves training an AE and then using the resulting latent space as i…

Edisi
Vol.23, September 2024
ISBN/ISSN
2590-1974
Deskripsi Fisik
13 hlm PDF, 1.864 KB
Judul Seri
Applied Computing and Geoscience - Open Access
No. Panggil
551.136