Abstract
A drogue is used to stabilise and straighten seismic arrays so that seismic waves can be well-received. To embed the effect of a cone-shaped drogue into the numerical modelling of the deep-towed seismic survey system, one surrogate model that maps the relationship between the hydrodynamic characteristics of the drogue and towing conditions was obtained based on data-driven simulations. The sample data were obtained by co-simulation of the commercial software RecurDyn and Particleworks, and the modelling parameters were verified by physical experiments. According to the Morison formula, the rotational angle, angular velocity, angular acceleration, towing speed, and towing acceleration of the drogue were selected as the design variables and drag forces and aligning torque were selected as the research objectives. The sample data of more than 8500 sets were obtained from virtual manoeuvres. Subsequently, both polynomial and neural network regression algorithms were used to study these data. Finally, analysis results show that the surrogate model obtained by machine learning has good performance in predicting research objectives. The results also reveal that the neural network regression algorithm is superior to the polynomial regression algorithm, its largest error of mean square is less than 0.8 (N2/N2 mm2), and its R-squared is close to 1.
Highlights
A deep-towed multi-channel seismic survey system, consisting of mothership, towing cable, towed vehicle, seismic array, and drogue, is used for high-resolution surveys of the submarine stratum, as shown in Figure 1 [1,2]
The surrogate model that indicates the relationship between the design variables and the research objectives was generated by the training and validation sets, and five design variables of the testing set were input into the surrogate model to predicate the drag force Fx, lateral force Fy and torque Mz
Based on data-driven simulations, the surrogate model that maps the relationship between the hydrodynamic characteristics of the cone-shaped drogue and towing conditions was obtained in this paper, so that the effect of a cone-shaped drogue can be embedded into the numerical modelling of the deep-towed seismic survey system
Summary
A deep-towed multi-channel seismic survey system, consisting of mothership, towing cable, towed vehicle, seismic array, and drogue, is used for high-resolution surveys of the submarine stratum, as shown in Figure 1 [1,2]. The deeptowed multi-channel seismic survey system is mainly composed of slender cables that are the cone-shaped structure has superior alignability and returnability, a cone drogue with both circular sections opened was chosen for this study. Theodoropoulos et al [25] researched the development of deep-learning models that can be utilised to predict the propulsion power of a vessel. They evaluated feed-forward neural networks and recurrent neural networks. The relationship between the hydrodynamic characteristics of the cone-shaped drogue and towing conditions can be studied using data-driven simulations. Analysis results show that the surrogate model obtained by machine learning had good performance in predicting research objectives.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
