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Journal of Geophysical Research: Machine Learning and Computation is a peer-reviewed journal published by AGU (American Geophysical Union), covering Meteorological Phenomena and Simulations, Seismic Imaging and Inversion Techniques, Seismology and Earthquake Studies. Over its lifetime it has published 394 papers, which have received 801 citations.
| Publisher | AGU (American Geophysical Union) |
|---|---|
| Reference style | Author–year (APA) Author–year — (Smith, 2023) in the text Smith, A., Jones, B., & Lee, C. (2023). A representative article title. Journal of Geophysical Research: Machine Learning and Computation, 12(3), 45–58.
Formats any DOI in Journal of Geophysical Research: Machine Learning and Computation style. No sign-up. |
| Publishes research in | Meteorological Phenomena and Simulations Seismic Imaging and Inversion Techniques Seismology and Earthquake Studies Tropical and Extratropical Cyclones Research Climate variability and models |
| ISSN | 2993-5210 |
| Citation impact (2-yr) | 2.03 |
| h-index | 12 |
| i10-index | 17 |
| Open access | Yes — indexed in DOAJ |
| Top institutions publishing here | Chinese Academy of Sciences |
| 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.
Abstract Prediction of tropical cyclones (TCs) beyond a week is challenging but of great importance for disaster prevention and mitigation. We propose a hybrid machine learning (ML)/physics‐based modeling framework to extend TC forecasts to 2 weeks. This framework integrates a recently launched ML‐based global weather prediction model (Pangu) and the high‐resolution physics‐based regional weather research…
Abstract Full waveform inversion (FWI) is capable of generating high‐resolution subsurface parameter models, but it is susceptible to cycle‐skipping when the data lack low‐frequency components. Unfortunately, such components (<5.0 Hz) are often tainted by noise in real seismic exploration, which hinders the application of FWI. To address this issue, we develop a novel self‐supervised low‐frequency…
Abstract Elastic full waveform inversion (EFWI) promises to account for the Earth's elastic nature and corresponding reflectivity, which is often disregarded in the commonly used acoustic FWI. However, EFWI usually requires a more sophisticated recording apparatus (beyond the usual single‐component data). Even in the presence of multicomponent recordings, an empirical formulation that relates the elastic…
Abstract The most adopted definition of landslide hazard combines spatial information about landslide location (susceptibility), threat (intensity), and frequency (return period). Only the first two elements are usually considered and estimated when working over vast areas. Even then, separate models constitute the standard, with frequency being rarely investigated. Frequency and intensity are dependent on each…
Abstract The ocean oxygen (O 2 ) inventory has declined in recent decades but the estimates of O 2 trend are uncertain due to its sparse and irregular sampling. A refined estimate of deoxygenation rate is developed using machine learning techniques and biogeochemical Argo array. The source data includes historical shipboard (bottle and CTD‐O 2…
