Oil species identification based on fluorescence excitation-emission matrix and transformer-based deep learning

After oil spills are found at sea, the identification on oil species can help determine the source of leakage and form the plan of post-accident treatment. Since the fluorometric characteristics of petroleum hydrocarbon reflect its molecular structure, the composition of oil spills could potentially be inferred using the fluorescence spectroscopy method. The excitation-emission matrix (EEM) includes additional fluorescence information in the dimension of excitation wavelength, which could be useful to identify oil species. This study proposed an oil species identification model using transformer network. The EEMs of oil pollutants are reconstructed into sequenced patch input that consists of the fluorometric spectra obtained under the different excitation wavelengths. The comparative experiments show that the proposed model can reduce the incorrect predictions and achieve higher identification accuracies than the regular convolutional neural networks that have been used in the previous studies. According to the structure of transformer network, an ablation experiment is also designed to evaluate the contributions of different input patches and seek for the optimal excitation wavelengths for oil species identification. The proposed model is expected to identify oil species, and even other fluorescent materials, based on the fluorometric spectra collected under multiple excitation wavelengths.