MLReal: Bridging the gap between training on synthetic data and real data app…
Among the biggest challenges we face in utilizing neural networks trained on waveform (i.e., seismic, electromagnetic, or ultrasound) data is its application to real data. The requirement for accur…
- Edisi
- Vol.3, December 2022
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 14 hlm PDF, 3.698 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Wavefield solutions from machine learned functions constrained by the Helmhol…
Solving the wave equation is one of the most (if not the most) fundamental problems we face as we try to illuminate the Earth using recorded seismic data. The Helmholtz equation provides wavefield …
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 9 hlm PDF, 3.131 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
The potential of self-supervised networks for random noise suppression in sei…
Noise suppression is an essential step in many seismic processing workflows. A portion of this noise, particularly in land datasets, presents itself as random noise. In recent years, neural network…
- Edisi
- Vol.2, December 2021
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 13 hlm PDF, 7.813 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
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