scholarly journals The Study of Important Fault Structure and Its Structural Character in Qikou Sag of Dagang Oilfield

2017 ◽  
Vol 07 (01) ◽  
pp. 8-16
Author(s):  
宪保 陈
Keyword(s):  
Fault Structure ◽  
Structural Character ◽  
Qikou Sag

scholarly journals Topset-to-forest rollover trajectories as reliable predictors of sediment-volume partitioning into deep-lake areas

2021 ◽  
Author(s):  
Xian-Zheng Zhao ◽  
Cheng-Lin Gong ◽  
Li-Hong Zhou ◽  
Dong-Wei Li ◽  
Xiu-Gang Pu ◽  
...  
Keyword(s):  
Bohai Bay ◽  
Deep Lake ◽  
Bohai Bay Basin ◽  
Widespread Occurrence ◽  
Sediment Volume ◽  
Terrestrial Sediments ◽  
Qikou Sag ◽  
Sand Budget ◽  
Stacking Patterns

AbstractTopset-to-forest rollover trajectories and their relation to sediment- and sand-budget partitioning into deep-lake areas are far from being well understood, as compared with their marine counterparts of shelf edges. Two quantitatively distinctive topset-to-forest rollover trajectories and clinothem-stacking patterns were recognized in the Oligocene Qikou Sag of the Bohai Bay Basin and are quantified in terms of trajectory angles (Tse), topset thickness (Tt), forest thickness (Tf), bottomset thickness (Tb), and clinothem-set relief (Rc). Rising topset-to-forest trajectories have positive Tse of 0.15°–0.51° (averaging 0.35°). Ranges in Tt, Tf, Tb, and Rc of their associated progradational and aggradational clinothem sets are, respectively, 32.4–58.7 m (averaging 42.7 m), 76.9–176.2 m (averaging 148.3 m), 0 m, and 167.8–320.8 m (averaging 272.9 m). Falling topset-to-forest rollover trajectories, in contrast, have negative Tse of − 0.12° to − 0.02° (averaging − 0.06°). Ranges in Tt, Tf, Tb, and Rc of their associated progradational and downstepping clinothem sets are, respectively, 0 m, 266.0–395.7 m (averaging 333.4 m), 441.1–542.5 m (averaging 464.1), and 874.9–922.6 m (averaging 892.5 m). These two topset-to-forest rollover trajectories and clinothem-stacking patterns are closely linked to two distinctive patterns of sediment- and sand-volume partitioning into deep-lake areas, which are quantified in terms of Tt, Tb, and differential sediment aggradation of topset segments and forest-to-bottomset compartments (As/Ad). Rising topset-to-forest rollover trajectories and associated progradational and aggradational clinothem sets are characterized by aggradational topsets (reported as Tt of 32.4–58.7 m), a lack of time-equivalent bottomsets, and As/Ad of 0.22–0.87 (averaging 0.33), and are fronted by mud-dominated depositional deposits, with sporadic occurrence of thinner and regionally localized forest sands. They are, therefore, inefficient at delivering terrestrial sediments or sands into deep-lake settings. Falling topset-to-forest rollover trajectories and associated progradational and downstepping clinothem sets, in contrast, are characterized by toplap, erosional terminations but aggradational bottomsets (reported as Tb of 266.0–473.4 m), and As/Ad of 0, and are fronted by sand-rich depositional deposits, with widespread occurrence of thicker and regionally extensive time-equivalent deep-lake bottomset sands. They are, thus, efficient at delivering terrestrial sediments or sands into deep-lake settings. Topset-to-forest rollover trajectories and associated clinothem-stacking patterns are thus reliable predictors of sediment- and sand-volume partitioning into deep-lake areas, assisting greatly in developing a more dynamic stratigraphy.

Design of Fault Diagnosis Expert System for Certain Type of Aircraft Electrical Power

2012 ◽  
Vol 466-467 ◽  
pp. 1186-1190
Author(s):  
Jun Bin Cao ◽  
Er Min Guo ◽  
Yan Li
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Electrical Power ◽  
Fault Models ◽  
Electrical System ◽  
Diagnosis System ◽  
Fault Structure ◽  
Fault Diagnosis System ◽  
Fault Mechanism ◽  
Analytical Technology

In order to diagnose and exempt the fault of aircraft electrical system accurately and fast, on the basis of analyzing the lost mode and fault mechanism of certain aircraft electrical system, fault structure are built and structure are built and fault models are analyzed by adopting the analytical technology based on regular fault structure. Two induction mechanisms, namely directional and anti-directional inference are introduced and the component methods are studied based on the knowledge corpus of data corpus technique. The result shows the inference results of fault diagnosis system are in accordance to reality and improve the intelligentized level of fault diagnosis system for aircraft electrical power.

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