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Application of Optimization Algorithms for Solving Marine Seismic Survey Planning Problems with Bottom Stations in the Arctic Shelf

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Abstract

An algorithm for optimizing the trajectories and movement sequence of a fleet of marine seismic survey vessels in solving the problem of marine seismic surveys using bottom stations is presented. The algorithm is based on solving the traveling salesman problem with mixed deliveries and collections of goods (TSPDC). A description of the algorithm extension to a problem that takes into account static closed zones that simulate ice and meteorological conditions unsuitable for the ship movement is given. The Dubins path algorithm provides a path close to the minimum and takes into account real characteristics of the ship movement and its speed when performing various types of work (installing bottom stations, collecting stations, maneuvering, etc.). The scientific novelty of the study lies in applying the solution of the TSPDC to problems of marine geophysics with the condition of presence of closed zones and developing an algorithm for optimizing the work of seismic vessels with the use of bottom stations, which is relevant in the conditions of the Arctic shelf during the period of limited navigation. The algorithm described in the article makes it possible to take into account the return of the vessel for collecting the equipment when working with bottom stations in the transition zone. The developed algorithm for planning marine seismic surveys formed the basis of the application software. The formalization of the problem, the results of the algorithm operation, and examples of planning on test data are presented. The possible limitations for the proposed algorithm are raised. The obtained results are applicable for further use in the implementation of tasks on optimizing the work plan for marine seismic surveys with several vessels, both when planning seismic surveys and when adjusting plans directly on the ship. The use is also justified if it is necessary to reenter the profile (for example, when reworking out a defective work area).

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Notes

  1. http://www.math.uwaterloo.ca/tsp/concorde.html.

  2. U.S. Geological Survey https://walrus.wr.usgs.gov/namss/survey/b-08-77-fl/ (accessed on Apr. 1, 2021).

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Funding

This work was carried out within the framework of the Program for the Creation and Development of the Competence Center of the National Technological Initiative at the premises of the Moscow Institute of Physics and Technology (National Research University) in the Artificial Intelligence direction, contract no. 13/1251/2019 dated September 11, 2019, on the topic of “Creation of a Software and Hardware Complex with an Integrated Expert System for Optimal Planning of Processes of Geological Exploration of Remote Objects in Real Time.”

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Correspondence to S. V. Zaitsev.

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Translated by A. Nikol’skii

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Zaitsev, S.V., Tikhotskiy, S.A., Silaev, A.V. et al. Application of Optimization Algorithms for Solving Marine Seismic Survey Planning Problems with Bottom Stations in the Arctic Shelf. Izv., Phys. Solid Earth 59, 1079–1088 (2023). https://doi.org/10.1134/S1069351323060253

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