Academic Journal
Q1Artificial Intelligence in Geosciences
About Artificial Intelligence in Geosciences
Artificial Intelligence in Geosciences is a scholarly journal published by KeAi Communications Co.. SCImago 2025 lists it in Q1, with an SJR of 0.869 and H-index of 17.
Coverage: 2020-2026. Research categories: Control and Systems Engineering (Q1); Earth and Planetary Sciences (miscellaneous) (Q1); Artificial Intelligence (Q2); Computers in Earth Sciences (Q2).
Open-access policies and author information
Reported in the official DOAJ public CSV snapshot (2026-09-01), downloaded 2026-10-03. Record updated 2024-04-04. This snapshot does not establish today’s listing status or fee quotation.
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Source-backed journal facts
Topics in published research
Seismic Imaging and Inversion Techniques; Hydrocarbon exploration and reservoir analysis; Hydraulic Fracturing and Reservoir Analysis; Seismic Waves and Analysis; Geochemistry and Geologic Mapping; Seismology and Earthquake Studies.
OpenAlex classifies topics from published works. These topics are not the publisher’s official aims and scope.
Source: OpenAlex source record. Retrieved 2026-10-03. Source record updated 2026-10-01. OpenAlex metrics are different from SCImago metrics and the Clarivate Journal Impact Factor.
Journal Metrics
Quartile, SJR and the listed SCImago H-index use the 2025 imported SCImago dataset. A quartile may vary by subject category. Values without a source or reporting year are unverified historical entries. Verify the current Journal Impact Factor with Clarivate or the publisher before using it.
Aims & Scope
The publisher’s official aims and scope have not yet been verified for this profile. Use the journal website to check subject fit and accepted article types before submitting.
Recent Research Articles
Latest publications matched automatically by ISSN.
Multi-agent collaborative knowledge question answering for mineralogy
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2026-09 · DOI: 10.1016/j.aiig.2026.100255Simultaneous estimation of hyperparameters and basement depth in gravity data inversion using the JAYA algorithm
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2026-09 · DOI: 10.1016/j.aiig.2026.100245Vision-language models for automated carbonate petrography and depositional environment interpretation
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2026-09 · DOI: 10.1016/j.aiig.2026.100230Stability prediction of footings on slopes with dense sand using Bolton model, FELA, XGBoost, Random Forest, and Evolutionary Polynomial Regression
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2026-09 · DOI: 10.1016/j.aiig.2026.100254Optimizing the potential of iterative bilateral proposed U-Net for advanced forest segmentation techniques
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2026-09 · DOI: 10.1016/j.aiig.2026.100227A hybrid Prophet–LSTM–BPNN framework for seasonal drought prediction
Hossein Shafizadeh-Moghadam, Erfan Mor, Tingting Xu, Mehdi Homaee et al.
2026-09 · DOI: 10.1016/j.aiig.2026.100251Spatial intersection of soil texture classes and landscape features: An XGBoost-enhanced digital soil mapping approach
Farshid Pashaezadeh, Farzin Shahbazi, Shahin Oustan, Tobias Karl David Weber et al.
2026-09 · DOI: 10.1016/j.aiig.2026.100235Deep hybrid vision transformers for improved landslide mapping in geospatial remote sensing
S. Sreelakshmi, S.S. Vinod Chandra
2026-09 · DOI: 10.1016/j.aiig.2026.100252Feature-data collaborative inversion: A Siamese convolutional neural network method for shallow-subsurface EFWI
Keyu Huo, Guangzhou Shao, Jing Hu, Weishuai Chang et al.
2026-09 · DOI: 10.1016/j.aiig.2026.100250Reviews
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October 2, 2026 at 8:33 pm
October 2, 2026