Automated high-resolution aerial mapping with UAVs for seismic survey scouting

References

1. BagainiC., BuntingT., El-EmanA., LaakeA., andStrobbia C. [2010] Land Seismic Techniques for High-Quality Data. Oilfield Review, 22, 28–39.
   [Google Scholar]
2. DeanT., Tulett, J., and Barnwell, R. [2018] Nodal land seismic acquisition: The next generation, First Break, 36, 47–52
   [Google Scholar]
3. Goodfellow, I., J.Pouget-Abadie, M.Mirza, B.Xu, D.Warde-Farley, S.Ozair, A.Courville, and Y.Bengio [2014] Generative adversarial networks: Commun. ACM, v. 63, p. 139–144.
   [Google Scholar]
4. Isola, P., J.Zhu, T.Zhou, and A. A.Efros [2017] Image-to-Image Translation with Conditional Adversarial Networks: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p. 5967–5976.
   [Google Scholar]
5. JiangT., EickP., JiangY., LiT., HaoH., ChuW., HoltR., BlymyerD., KosterK., and EnnsD., [2019] Compressive sensing seismic processing tests on a high density blended land data set. SEG Technical Program Expanded Abstracts, 4505–4509.
   [Google Scholar]
6. Lord, B. [2017] Remote sensing techniques for onshore oil and gas exploration: The Leading Edge, 36, 24–32.
   [Google Scholar]
7. Mirza, M., and S.Osindero [2014] Conditional Generative Adversarial Nets: ArXiv, v. abs/1411.1784.
   [Google Scholar]
8. Mitchell, G. [2013] Improving land seismic field crew safety on field operations in hazardous areas of the world through the use of highly accurate stereo satellite topographic mapping: 83th Annual International Meeting, SEG, Expanded Abstract, 241–243.
   [Google Scholar]
9. TellierN., OllivrinG., LarocheS., andDonval C. [2022] Mastering the highest Vibroseis productivity while preserving seismic data quality. First Break, 40, 81–86.
   [Google Scholar]
10. TsingasC., AlmubarakM., JeongW., Al ShuhailA., and TrzesniowskiZ., [2020], 3D distributed and dispersed source array acquisition and data processing. The Leading Edge, 39, 392–400.
    [Google Scholar]