Blockly earthquake transformer: A deep learning platform for custom phase pic…
Deep-learning (DL) algorithms are increasingly used for routine seismic data processing tasks, including seismic event detection and phase arrival picking. Despite many examples of the remarkable p…
- Edisi
- Vol.4, December 2023
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 11 hlm PDF, 3.595 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
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
Local earthquakes detection: A benchmark dataset of 3-component seismograms b…
Machine learning is becoming increasingly important in scientific and technological progress, due to its ability to create models that describe complex data and generalize well. The wealth of publi…
- Edisi
- Vol.1, December 2020
- ISBN/ISSN
- 2666-5441
- Deskripsi Fisik
- 10 hlm PDF, 3.299 KB
- Judul Seri
- Artificial Intelligence in Geosciences
- No. Panggil
- 551
Knowledge graphs for seismic data and metadata
The increasing scale and diversity of seismic data, and the growing role of big data in seismology, has raised interest in methods to make data exploration more accessible. This paper presents the …
- Edisi
- Vol.21, March 2024
- ISBN/ISSN
- 2590-1974
- Deskripsi Fisik
- 12 hlm PDF, 2.532 KB
- Judul Seri
- Applied Computing and Geoscience - Open Access
- No. Panggil
- 551.136
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