# WaveConv-sLSTM-KET: A Novel Framework for the Multi-Task Analysis of Oil Spill Fluorescence Spectra
## 1 Introduction  
With the continuous growth of marine oil transportation, oil spill incidents occur frequently, causing severe damage to marine ecosystems and coastal economies [1, 2, 3]. Consequently, there is a pressing need for the rapid and accurate analysis of oil spills to guide subsequent pollution assessment and remediation efforts. Oil spill analysis primarily involves qualitative and quantitative methods. A qualitative analysis focuses on identifying the type of spilled oil, while a quantitative analysis predicts parameters such as oil thickness, area, and concentration. Among these, oil film thickness is a key parameter for assessing spill volume. In recent years, a range of non-contact detection methods based on remote sensing and spectroscopy have been widely used for oil spill analysis [4]. These include Synthetic Aperture Radar (SAR) [5, 6], hyperspectral remote sensing [7, 8], infrared remote sensing [9, 10], near-infrared spectroscopy [11, 12], and Laser-Induced Fluorescence (LIF) [13, 14]. Among these, LIF is regarded as one of the most effective techniques for marine oil spill detection due to its capability to accurately identify oil types and estimate oil film thickness [15, 16]. LIF is an active detection method, where laser light is directed onto the sea surface through an emission system. Oil spills and organic matter in seawater fluoresce upon excitation and the fluorescence signal are captured by a detection system, processed through a spectrometer, and analyzed on a computer for predictions. Oil spill identification leverages the distinct fluorescence spectra of different compounds inpetroleum products, such as polycyclic aromatic hydrocarbons, using chemometric and deep learning methods to achieve accurate classification [17; 18; 19]. Thickness prediction methods include fluorescence-based and Raman suppression-based approaches [20; 21; 22]. The fluorescence method establishes a correlation between fluorescence intensity and thickness, while the Raman suppression method relies on the attenuation of seawater Raman signals by the oil film. Existing LIF-based oil spill analysis methods face several limitations. First, data reliability is a major concern, because obtaining fluorescence spectra in actual oil spill scenarios is difficult. As a result, most datasets are collected under controlled laboratory conditions. Real-world oil spill spectra deviate from laboratory data due to environmental influences (e.g., waves, emulsification, and ambient light) and instrumental factors (e.g., noise and baseline drift), while variations in detector distance, angle, and transmission-reception modes further compromise model accuracy, particularly in thickness prediction [23; 24; 25]. Laser power selection also plays an important role. Insufficient power fails to produce distinct peaks, while excessive power leads to saturation and peak disappearance. Fluctuations in the relative distance and angle between the detector and the oil spill surface--driven by wind and waves--further alter the collected spectral data. As a result, spectral measurements taken solely under ideal conditions may not accurately reflect field conditions, potentially leading to model failure. This dynamic environment can even cause overlapping fluorescence intensities for oil films of similar thickness, despite the fact that the absolute thickness remains unchanged. Since most thickness estimation methods rely on fluorescence intensity as a key parameter, it is crucial to account for variations in detector positioning to improve model accuracy. [PERSON] et al. [26] stressed the need for more representative data and robust models. [PERSON] et al. [27] introduced transfer learning to improve model applicability and reduce training parameters, while [PERSON] et al. [28] expanded datasets using conditional variational autoencoders to address spectral variations in emulsified oil spills. However, these improvements were made under laboratory conditions and do not fully resolve field challenges.