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Artificial Intelligence in Geosciences is a peer-reviewed journal published by Elsevier, covering Seismic Imaging and Inversion Techniques, Hydrocarbon exploration and reservoir analysis, Hydraulic Fracturing and Reservoir Analysis.
| Publisher | Elsevier |
|---|---|
| Reference style | Numbered (Elsevier) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, C. Lee, A representative article title, Artificial Intelligence in Geosciences 12 (2023) 45–58.
Formats any DOI in Artificial Intelligence in Geosciences style. No sign-up. |
| Publishes research in | Seismic Imaging and Inversion Techniques Hydrocarbon exploration and reservoir analysis Hydraulic Fracturing and Reservoir Analysis Seismic Waves and Analysis Geochemistry and Geologic Mapping |
| ISSN | 2666-5441 |
| Citation impact (2-yr) | 2.26 |
| h-index | 18 |
| i10-index | 44 |
| Total citations | 1,352 |
| Article processing charge | $800 |
| Open access | Yes |
| Top institutions publishing here | University of Electronic Science and Technology of China |
| Journal website | www.sciencedirect.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
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 solutions that are dimensionally reduced, per frequency, compared to the time domain, which is useful for many applications, like full waveform inversion.…
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 networks have been successfully used to denoise seismic data in a supervised fashion. However, supervised learning always comes with the often unachievable requirement of having noisy-clean…
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 accurate labels often forces us to train our networks using synthetic data, where labels are readily available. However, synthetic data often fail to capture the reality of…
Flood incidents can massively damage and disrupt a city economic or governing core. However, flood risk can be mitigated through event planning and city-wide preparation to reduce damage. For, governments, firms, and civilians to make such preparations, flood susceptibility predictions are required. To predict flood susceptibility nine environmental related factors have been identified. They are…
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 publicly-available seismic data nowadays requires automated, fast, and reliable tools to carry out a multitude of tasks, such as the detection of small, local earthquakes in areas…
