Use of simultaneous absorbance-transmittance and excitation-emission matrix (A-TEEM) spectroscopy to monitor source water contamination for waterworks

Conventional oil-in-water analyzers used by waterworks have hydrocarbon detection limits at mg/L levels and do not identify the type of oil compounds. The objectives of this study were to evaluate a more sensitive optical instrument and the analysis method to (1) determine the signature excitation and emission matrixs of each type of oil (such as diesel, heavy oil, gasoline and kerosene) or their indicator organic compounds and enter them into the instrument's software library and (2) test out the effectiveness of the instrument in detecting the above-mentioned oil in local waterworks’ source and treated water. The patented simultaneous absorbance-transmittance excitation-emission matrix (A-TEEM) instrument method was used to identify and quantify low levels of organic contaminants present in a much higher background of other dissolved organic matter components in raw and treated water. Multivariate regression and machine learning techniques were applied and shown to have potential for alerting plant operators to organic contamination events.

Hydrocarbon detection based on Phase decomposition in Chaoshan Depression, northern South China Sea

Located in northern South China Sea, Chaoshan Depression is mainly a residual Mesozoic depression, with a construction of Meso-Cenozoic strata over 7000m thick and good hydrocarbon accumulation conditions. Amplitude attribute of -90°phase component derived by phase decomposition is employed to detect Hydrocarbon in the zone of interest (ZOI) in Chaoshan Depression. And it is found that there are evident amplitude anomalies occurring around ZOI. Phase decomposition is applied to forward modeling results of the ZOI, and high amplitudes occur on the -90°phase component more or less when ZOI is charged with hydrocarbon, which shows that the amplitude abnormality in ZOI is probably caused by oil and gas accumulation.

Python Earth Engine API as a new open-source ecosphere for characterizing offshore hydrocarbon seeps and spills

The Python Earth Engine application programming interface (API) provides a new open-source ecosphere for testing hydrocarbon detection algorithms on large volumes of images curated with the Google Earth Engine. We specifically demonstrate the Python Earth Engine API by calculating three hydrocarbon indices: fluorescence, rotation absorption, and normalized fluorescence. The Python Earth Engine API provides an ideal environment for testing these indices with varied oil seeps and spills by (1) removing barriers of proprietary software formats and (2) providing an extensive library of data analysis tools (e.g., Pandas and Seaborn) and classification algorithms (e.g., Scikit-learn and TensorFlow). Our results demonstrate end-member cases in which fluorescence and normalized fluorescence indices of seawater and oil are statistically similar and different. As expected, predictive classification is more effective and the calculated probability of oil is more accurate for scenarios in which seawater and oil are well separated in the fluorescence space.