## 1 Introduction
### Literature Review  
Literature in the maritime scope has been set up predominantly on one of the data types, e.g., the network is exclusively trained by real-world recorded data [7; 11], model-scale data [12; 13], or only simulation data [14; 15]. The earliest effort that has tried to  
Figure 2: (**a**) Influence of entropy in classification of data. (**b**) Relationship between cost and accuracy for different surrogates’ fidelity levels [10]. utilize and discuss the application of different data generation processes with different fidelities can be attributed to [6], where a convolutional encoder-decoder network was tailored for predicting non-parametric wave spectra from wave radar data. The pre-trained network, initially trained with synthesized data, was applied to estimate in situ seaway data, resulting in unsatisfactory results. The study did not delve into the potential reasons behind the deficiencies. [PERSON] et al. [7] is the sole study to date that extensively discusses the disparities between features in field data and simulations. It was demonstrated how the entropy in real-world data positively impacts feature classification when the governing low-pass features are in common between different surrogates, highlighting a notable deficit of entropy in simulation data. Furthermore, it was discussed that the addition of generic white noise to the data cannot compensate for productive uncertainty, contributing to beneficial entropy. This is because the features are added in the entire bandwidth, while the in-depth scrutiny has shown that the feature level is not uniformly distributed in bandwidth. While not explicitly focused on ML, few studies have acknowledged the importance of high-frequency attributes in waves and the responses of floating bodies [16]. Apart from the aforementioned two studies, all other investigations are predominantly based on single data types, resulting in a lack of comparative literature to the current study. In a broader sense, data augmentation techniques vary significantly depending on the type of data and the specific network being trained. For instance, cropping and rotation are commonly used for pictorial data [17], while jittering is often employed for time-series data [18]. In the maritime context, prevalent techniques from general data science have been adapted to enhance data diversity. For example, generative adversarial networks (GANs) have been used by [19] to expand the inclusion of directional wave spectra for training a neural network tailored for sea state estimation. Similarly, jittering and slicing were employed by [20] to generate additional data for training a feedforward neural network used in automatic berthing and unberthing operations. Despite these advances, research in the maritime domain has largely been confined to data augmentation techniques originally developed for other fields, such as general data science and time-series analysis. However, for real-world applications aboard floating objects, the most viable data generation approach for training ML models remains synthesized data from low-fidelity simulations. This underscores the need for designing methodologies that enable effective feature augmentation. To address this gap, the current work introduces a methodology specifically tailored for hydrodynamic data augmentation, which can significantly enhance data inclusiveness for maritime applications. While primarily developed for ocean engineering and seakeeping, the proposed method is versatile and has potential applications in other domains characterized by data with inherently stochastic properties. The remaining content is systematically organized as follows: Section 2 provides insights into the data generation process. In Section 3, an innovative data engineering technique is introduced. To assess its feasibility, the technique undergoes initial testing with deterministic input (regular waves), with results presented in this section. Section 4 delves into the outcomes for irregular waves, accompanied by discussions on the new feature space.