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Remote sensing techniques are widely used during petroleum exploration. However, limitations in spectral and spatial resolution mean that these techniques are used mainly to detect hydrocarbons either indirectly or under laboratory conditions. To investigate the spectral signatures of natural hydrocarbon-bearing features, we took spectral measurements from oil seeps and associated hydrocarbon-bearing materials in Karamay city, northwest China. The hydrocarbon content of 16 hydrocarbon-bearing samples was also determined to study the relationship between hydrocarbon content and spectral absorption. The results reveal a positive correlation between spectral absorption intensities at 1725 nm and hydrocarbon contents of >1 wt%. Thus, surface hydrocarbon content can be direct detection above a detection limit of around 1 wt% using this method. This result is not only instructive for petroleum exploration, but also important for environmental protection. [ABSTRACT FROM AUTHOR]

Standard traditional gem identification requires expert supervision, while sophisticated modern methods are time-consuming and expensive. In contrast, reflectance spectroscopy coupled with artificial intelligence is economical and convenient and does not require specialist supervision. This study established an artificial neural network model that consists of standard multilayered, feed-forward, and back-propagation neural networks, and obtained reflectance spectra of a transparent gem (almandine), an opaque gem (turquoise), several almandine imitations (agate, plastic, and glass), and several treated turquoise samples (dyed, impregnated, and Zachery treated) using an Analytical Spectral Devices spectrometer. The acquired spectra were used to train and test the artificial neural network model. The results show that the model can effectively discriminate between genuine and imitation gems of different classes. However, discrimination between natural and treated gems of same class is not as effective as discrimination of gems of different classes. The results suggest that an artificial neural network based on reflectance spectroscopy could serve as a useful tool for preliminary gem identification, and the advanced identification needs further training and investigation. [ABSTRACT FROM AUTHOR]