Artificial intelligence in geophysics: Opportunities and risks
DOI:
https://doi.org/10.24028/gj.v47i2.322463Keywords:
artificial intelligence, algorithm, seismology, Tomsk school of Geothermy, interpretation of logging dataAbstract
The article briefly reviews some artificial intelligence methods successfully used to process and interpret logging data and for seismology and geothermy. The possibilities of artificial neural networks, the Support Vector Machines, the Random Forest method, and genetic algorithms are highlighted. The basic information about the advantages and limitations of artificial intelligence tools is given.
AI is not self-sufficient for geological and geophysical research. It is important to adapt its algorithms to work with large volumes of geophysical data. If the algorithm has too high computational complexity, calculations can be simplified by manually processing the input data or using conventional software. Sometimes, several algorithms are used to solve a single problem. In such cases, each network is trained several times. When comparing the results with approximately equal control errors, a computationally simpler neural network is chosen.
For the purpose of better orientation in the computing world, information is provided on the computational adaptation of artificial intelligence to geophysical data.
Attention is drawn to the possibility of financial risks associated with the use of an insufficiently powerful network when modeling a particular dependence.
References
Li, Z., Meier, M.-A., Hauksson, E., Zhan, Z., & Andrews, J. (2018). Machine Learning Seismic Wave Discrimination: Application to Earthquake Early Warning. Geophysical Research Letters, 45(10), 4773—4779. https://doi.org/10. 1029/2018GL077870.
Liu, H, Song, J., & Li, S. (2022). Seismic Event Identification Based on a Generative Adversarial Network and Support Vector Machine. Frontiers in Earth Science, 10, 814655. https://doi.org/10.3389/feart.2022.814655.
Kong, Q., Allen, R.M., Schreier, L., & Kwon, Y.-W. (2016). MyShake: A Smartphone Seismic Network for Earthquake Early Warning and beyond. Science Advances, 2(2), e1501055. https://doi.org/10.1126/sciadv.1501055.
Perol, T., Gharbi, M., & Denolle, M. (2018). Convolutional neural network for earthquake detection and location. Science Advances, 4(2), e1700578. https://doi.org/10.1126/sciadv.170 0578.
Rouet-Leduc, B., Hulbert, C., Lubbers, N., Barros, K., Humphreys, C.J., & Johnson, P.A. (2017). Machine learning predicts laboratory earthquakes. Geophysical Research Letters, 44, 9276—9282. https://doi.org/10.1002/ 2017GL 074677.
Wang, D., Peng, J., Yu, Q., Chen, Y., & Yu, H. (2019). Support vector machine algorithm for automatically identifying depositional microfacies using well logs. Sustainability, 11(7). https://doi.org/10.3390/su11071919
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 N.I. Bakhova
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
