Hou F.,Key Laboratory of Marine Hydrocarbon Resource and Geology |
Hou F.,CAS Qingdao Institute of Oceanology |
Zhang X.,Key Laboratory of Marine Hydrocarbon Resource and Geology |
Zhang X.,CAS Qingdao Institute of Oceanology |
And 7 more authors.
Shiyou Diqiu Wuli Kantan/Oil Geophysical Prospecting | Year: 2015
Located at the East Asia continental margin, the East China Sea Shelf Basin is a Mesozoic-Cenozoic composite basin superposed on the South China Block. Research on Mesozoic tectonic regime transition and evolution process in the basin is of great significance for good understanding tectonic evolution in the South China. This paper presents our research on the distribution of Mesozoic strata and tectonic characteristics using new seismic data acquired in recent years. Based on our research work, it is a depression basin in the Jurassic period and a fault depression basin in the Cretaceous period. Through the structural evolution section recovery and combined with regional sea-land tectonic contrast, the Mesozoic evolution process is divided into three stage: passive continental margin depression stage in the late Triassic-middle Jurassic, Andean active continental margin arc uplift stage in the late Jurassic-early Cretaceous, and West Pacific active continental margin back-arc stretch basin stage in the late early Cretaceous-late Cretaceous. © 2015, Science Press. All right reserved.
Fu J.,Key Laboratory of Marine Hydrocarbon Resource and Geology |
Fu J.,CAS Qingdao Institute of Oceanology |
Gu D.,State Oceanic Administration |
Yang H.,CAS Qingdao Institute of Oceanology
Proceedings of SPIE - The International Society for Optical Engineering | Year: 2011
Texture feature of image is one of the most important factors in the processing of information extraction from satellite scene image. In this paper the texture feature analysis was introduced in the processing of the classification of the objects in coastal zone. During the texture analysis process, how to extract effectively the texture features is the key factor. In the experiment of coastal classification, this paper introduced a method of a set of texture features selection based on step-by-step discriminance. Texture is described by Gray level co-occurrence matrix in this study, and there are 192 texture features to describe the characteristics of coastal objects. With the features selection method presented by this paper, five values were chosen as the representatives to classify the object texture feature. By means of the neural networks the object classification mode based on the texture features was defined and the object classifications of the southern coast of Laizhou Bay were carried out. Results show the step-by-step discriminance not only can decrease the dimension of the texture feature database, but also ensure and improve the accuracy of the classification, and the classification accuracy was up to 83.4%. The neural networks mode is the most effective method to account for the classification of the typical objects in coastal zone. © 2011 SPIE.