Hydrocarbon micro-seepage can cause oxidation reduction reactions and produce altered minerals in surface sediments and soil.The typical altered minerals mapping by their diagnostic spectral features on hyper-spectral images is an important tool for the petroleum exploration industry.In this study,the airborne hyper-spectral data were used to investigate the altered minerals induced by hydrocarbon micro-seepages by spectral feature fitting(SFF)in the loess coverage area of Xifeng Oilfield.The results reveal that the distribution region of the altered minerals induced by hydrocarbon micro-seepage is larger than the known oilfield exploration area.The potential hydrocarbon micro-seepage region was also revealed by the distribution of altered minerals besides the known hydrocarbon area.A fast index was proposed by the absorption depths of clay and carbonate minerals for assessment of hydrocarbon microseepage.And it gave much clearer boundaries for the hydrocarbon micro-seepage in the loess coverage area than those by the altered mineral mapping.In addition,some field samples were analyzed by X-ray diffraction(XRD)and atomic absorption spectrophotometer to validate the results.Within the extents of hydrocarbon micro-seepage,there are lower contents of ferric iron and higher contents of carbonate minerals in these samples.Therefore,it is satisfactory to have the airborne hyper-spectral data to outline the extents of hydrocarbon micro-seepage for further hydrocarbon exploration in the loess coverage area.
It is a key challenge to exploit the label coupling relationship in multi-label classification(MLC)problems.Most previous work focused on label pairwise relations,in which generally only global statistical information is used to analyze the coupled label relationship.In this work,firstly Bayesian and hypothesis testing methods are applied to predict the label set size of testing samples within their k nearest neighbor samples,which combines global and local statistical information,and then apriori algorithm is used to mine the label coupling relationship among multiple labels rather than pairwise labels,which can exploit the label coupling relations more accurately and comprehensively.The experimental results on text,biology and audio datasets shown that,compared with the state-of-the-art algorithm,the proposed algorithm can obtain better performance on 5 common criteria.
