Application of Artificial Intelligence Algorithms to Optimal Planning of Offshore Seismic Works

In this article, we present the algorithm, that allows to optimise the time and cost of marine seismic surveys. The application of a heuristic, algorithm is proposed for solving the traveling salesman problem on a series of graphs, to which the seismic observation system is reduced. The proposed alg...

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Bibliographic Details
Published in:Doklady earth sciences 2021-12, Vol.501 (2), p.1074-1080
Main Authors: Zaytsev, S. V., Tikhotskiy, S. A., Silaev, S. V., Ananiev, A. A., Orlov, R. V., Uzhegov, D. N., Kudryashev, I. Yu, Vasekin, B. V., Kondrashenko, S. I., Bazilevich, S. O.
Format: Article
Language:English
Subjects:
Algorithms
Artificial intelligence
Earth and Environmental Science
Earth Sciences
Genetic algorithms
Geophysical methods
Geophysics
Graphs
Marine geophysics
Offshore
Optimization
Planning
Seismic activity
Seismic surveys
Seismology
Surveys
Traveling salesman problem
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Summary:In this article, we present the algorithm, that allows to optimise the time and cost of marine seismic surveys. The application of a heuristic, algorithm is proposed for solving the traveling salesman problem on a series of graphs, to which the seismic observation system is reduced. The proposed algorithm assumes work with towed equipment and use of bottom stations. The algorithm provides a path close to the minimum, taking into account the real geometry of ships and their speed of passing various sections. The scientific novelty of the work lies in the application of the genetic algorithm for optimal planning, taking into account the closure of the work zones, to the problems of marine geophysics. The use of artificial intelligence methods allowed for the first time to develop a system that provides the ability to promptly adjust work plans depending on the changing meteorological and other conditions, including when working with several vessels using bottom stations. The paper describes the methodology, development of the algorithm and results in the form of applied software, and examples of planning on test data.
ISSN:1028-334X
1531-8354
DOI:10.1134/S1028334X21120187