The Earth is the space that human beings depend on. Earth observation and remote sensing technology use modern information technology to carry out disaster and threat early warning, mineral and resource detection, climate change and other real-time monitoring, prediction and distribution law exploration of adjacent space, surface and internal structure of the Earth. It has been widely used in military, civil, energy and other fields, and has become an indispensable information technology means for the development of human society.
In recent years, with the continuous development of Earth Science and remote sensing technology, especially the continuous emergence of different detection sensors and new detection systems, and the continuous accumulation of historical data and samples, it is possible to use artificial intelligence (AI) for big data analysis, and it has become a research hotspot in this field.
In the field of oil and gas seismic exploration, technologies such as seismic data processing and reservoir prediction have shifted from classic signal processing methods to data-driven artificial intelligence methods, specifically including: 1) seismic data processing. In this Research Topic, newly developed artificial intelligence models are utilized to solve seismic denoising (He et al.), velocity analysis (Wang D. et al.), data reconstruction, etc., in order to minimize the negative impact of perceived factors as much as possible. 2) Reservoir parameter inversion and oil and gas prediction, automatic fault tracking shear wave velocity prediction (Wang H. et al.), logging modeling, seismic wave field forward modeling, seismic impedance inversion(), rock fracture detection, etc.
For remote sensing super-resolution image restoration and reconstruction, authors proposed a novel Auto-weighted low-rank Tensor Ring Factorization with Hybrid Smoothness regularization (ATRFHS) for mixed noise removal in HIS (Wang Z. et al.).
For Remote sensing target detection and recognition, authors presented a system which uses multiple sensors and a convolutional neural network (CNN) architecture to test cross-sensor object detection resiliency (Mohan and Simske, 2023).
In the future fields of Earth science and remote sensing, artificial intelligence may play a more important role and have greater development space. Especially artificial intelligence models driven by sufficient knowledge, which do not rely on the neural network structure of large models and targeted interpretable networks, are worthy of attention.
A total of 20 submissions were received for the advances and applications of artificial intelligence in geoscience and remote sensing, and after peer review, 13 manuscripts were accepted, involving 55 authors.
Thanks all authors for sharing their latest achievements and contributions to promoting the application of artificial intelligence technology in the field of geoscience and remote sensing.
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Author contributions
All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
1
MohanV.SimskeS. J. (2023). Cross-sensor vision system for maritime object detection. Front. Mar. Sci.10, 1112955. 10.3389/fmars.2023.1112955
Summary
Keywords
intelligent exploration geophysics, Earth observation, optical remote sensing, artificial intelligence, object detection
Citation
Peng Z, Yuan S, Qiu X, Zhang W and Sowizdzal A (2023) Editorial: Advances and applications of artificial intelligence in geoscience and remote sensing. Front. Earth Sci. 11:1234360. doi: 10.3389/feart.2023.1234360
Received
04 June 2023
Accepted
10 July 2023
Published
20 July 2023
Volume
11 - 2023
Edited by
Taskin Kavzoglu, Gebze Technical University, Türkiye
Reviewed by
Bangyu Wu, Xi’an Jiaotong University, China
Updates
Copyright
© 2023 Peng, Yuan, Qiu, Zhang and Sowizdzal.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Zhenming Peng, zmpeng@uestc.edu.cn
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.