## 1 Introduction  
Oil sheens are common, and their source and type need to be adequately characterized. An oil sheen is an iridescent appearance that forms on the water's surface when oil spreads on water [1]. Oil sheen can be produced from the release of small amounts of hydrocarbons that are entrapped in subaquatic sediments after they are disturbed or can occur from natural microbial activity, forming a film on the water surface. The hydrocarbons in the sediments can come from anthropogenic activities (e.g., oil and gas extraction, transportation, and petroleum exploration) or natural processes (e.g., natural oil seeps, erosion of sedimentary rocks, and organic matter from the soil) [2, 3]. Sheens are unsightly and hydrocarbon sheen can potentially be detrimental to wildlife [4], economic development [5], and the environment [6, 7]. The source and type of a sheen can have a strong effect on these potential impacts and needs to be characterized. Several imaging-based methods have been developed to detect, classify, and monitor oil sheens in nearshore, ocean, river, marsh, and mudflat ecosystems. Sensors have been used in oil sheen detection and mapping, including radar, laser, UV, visible, infrared, and thermal infrared sensors [8]. Satellite Synthetic Aperture Radar (SAR) images have also been used to detect and map oil sheens over large spatial areas [9, 10, 11, 12, 13]. Different machine learning methodologies have been developed to identify oil sheens from these sensor data, including classification [13], object detection [14], and segmentation [15, 16]. Convolution neural networks (CNNs) [17] are the most commonly used machine learning algorithm applied for analyzing visible light imagery and have been used for oil sheen monitoring [16; 18]. For example, visible light images of oil sheen taken by Unmanned Aerial Vehicles (UAV), along with convolutional neural networks (CNNs), have been used to detect oil spills on the water surface [19; 20]. One source of oil sheen is the release of hydrocarbons trapped in subaquatic sediments. This can occur when hydrocarbon-impacted sediments are disturbed with sufficient force to release trapped oil or through processes, such as ebullition (the trapped oil is transported to the surface as an intermediate wetting fluid with gas bubbles [21]). Sheening potential of a sediment is operationally defined as the potential for a sheen to form when the sediment is disturbed, e.g., by a storm or by human disturbance. Currently, there are no automated methods to determine the sheening potential of hydrocarbon-impacted subaquatic sediments. To quantify sheening potential, the sediment must be disturbed in a controlled manner and the water surface in the disturbed area must be monitored for oil sheen formation. To avoid widespread release of oil sheen, this disturbed oil, if released, needs to rise to the surface in a controlled environment, e.g., a small-diameter tube, and then imaged inside the tube at the surface. The appearance of the oil sheen in a small area may be different from that formed over large areas. The large sheep can be easily viewed and detected by satellites and drones with sensors (e.g., radar, laser, UV, visible, infrared, and thermal infrared [8; 9; 10; 11; 12; 13]), as the sheen covers a large area and stays for a longer time. Small-area sheen formation may be short lived and can only be observed at a close distance (within several meters). The appearance and color of a small-scale sheen can also be more diverse, as it can be easily influenced by several factors, including oil properties, sheen thickness, light conditions, and the angle of observation [22]. Thus, the current models and image datasets for remote oil monitoring are not appropriate for monitoring localized sheening potential, which is important to understand due to potential community aesthetic concerns. Automating the small-area oil sheen detection process will, therefore, require a robust method to identify sheen formation in various appearances in real time.