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In order to improve the speed and accuracy of lithology identification with logging data, according to the advantage of the recognition of linear distribution with SOM network, a identification model was established to recognize lithology in northern Qaidam basin. Firstly, the input data of logging curves are normalized, and then continuously trained network separately for 30 times, 50 times, 100 times, 200 times, 500 times, 1000 times, and the final results had shown that it did the best when it’s on 200 times, and the accuracy almostly reached 93 percent. Finally, the model was applied in other district which had the same geology background, and it also apparently displayed more efficient than other traditional method.

The Qingshankou and Nenjiang group in upper cretaceous of Songliao basin is a set of dark shale. Analyze the shale thickness, organic matter type, organic matter abundance, vitrinite reflectance, mineral composition of the permo-carboniferous coal-bearing strata. Consider that the thickness of shale in the Songliao basin is larger, despite the organic matter abundance is good, but maturity is lower, less of the formation of oil and gas.

While the methods of gray image segmentation keeping innovation, researches on color image segmentation have a marked development, and the gray image segmentation provides research base for color image segmentation. At present, the problem of color image segmentation has become a fundamental one and had significant impact on both new pattern recognition algorithms and applications. In order to make the research in this field has a more comprehensive understanding, In this paper, several typical approaches are presented and discussed, and expected to seek an algorithm with better real-time and robustness. algorithm combining both SOFM and FCM clustering is selected to be better in robustness, and reduce the hassle of looking for a suitable feature space.

Yanji Depression was formed on the base of Upper Paleozoic strata. Distribution area of Carboniferous-Permian strata is about 1550km 2 , and the strata are preserved intact. By researching on the thermal evolution analysis of hydrocarbon source rock and the source-reservoir-cap combination, the hydrocarbon generation and exploration prospect was studied. The results show that the thickness and the depth of Carboniferous-Permian strata are great, and organic matter maturity is on the stage of high mature-past mature, and it has two stages of hydrocarbon generation process, and the residual hydrocarbon generation potential is low. Upper Paleozoic, Mesozoic and Cenozoic have extensive sandstone reservoir, and have favorable territorial cap rock. It forms two types of source- reservoir- cap assemblage, and has great Hydrocarbon Resource Potential. After analysis the unconventional oil and gas, it shows that Yanji depression has the formation condition of tight sandstone gas and shale gas.

The primary objective of this study was to develop a multivariate Poisson-lognormal regression model (MVPLN) to evaluate the effects of contributing factors to crash frequency and to identify the correlations among different types. Crash data were collected at 282 exit-ramps on freeways in Florida, US.The Markov chain Monte Carlo (MCMC) method was used to get the solutions of the MVPLN model.The effects of crash-related factors of different types were explored in the MVPLN model considering the correlation structure.It was found thatmainline lane number was the contributing factor for both single-lane and two-lane exit ramp.