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Deformation kinematics are a fundamental part of providing a comprehensive understanding of the development of the topography of an orogen. Along a compressive orogen with a single mountain range, critical wedge theory (e.g., Dahlen, 1990;Davis et al., 1983) has been often applied to explain the development of the deformation and the building of the topography. However, for orogens with a wider range, other deformation hypotheses have been proposed. For the northeastern Tibetan Plateau, based on a study of a sedimentation proxy in adjacent basins, the hypothesis was proposed that the entire orogen was uplifted synchronously (Bovet et al., 2009;Zhuang et al., 2011), with the kinematic characteristics of the distributed faults and folds being active since their onset (Figure 1a). This hypothesis is supported by evidence of a gradually decreasing crustal shortening rate across the range, based on decades of GPS measurements (G. Zheng et al., 2017;W. Zheng et al., 2013). Another end-member argument is that the deformation front is gradually propagating outwards (Cheng et al., 2019;Yin et al., 2002;Yuan et al., 2013), similar to the wedge theory, with the kinematic characteristics of the most active deformation occurring at the leading-edge fault, and the fault within the orogen being inactive or less active (Figure 1b). This is evidenced by a fast slip rate (Champagnac et al., 2010) and a high strain rate of shortening along the boundary fault (Zuza et al., 2016).The Qilian Orogen, located in the northeastern margin of the Tibetan Plateau, is composed of several NW-SE -trending parallel mountain ranges separated by thrust-fold systems; it has a width of ∼300 km from the Qaidam Basin to the Hexi Corridor. The uplift of this wide range is caused by the continuous extrusion of the Tibetan Plateau (

The uplifting of the Tibetan Plateau has significantly changed the environment in surrounding areas by delivering abundant water and sediment. The Heihe River draining the Qilian Shan in the NE Tibetan Plateau acts as a dominant sediment routing system from the uplifted NE Tibetan Plateau to the Hexi Corridor as well as the Badain Jaran Desert. Reconstructing the evolution of the Heihe River could provide evidence for the birth of the Badain Jaran Desert and enhance the understanding of sedimentary basin fill and the relationship between tectonism, drainage evolution and environmental changes. With this aim, two parallel cores (DWJ and XKJD with depths of 140 and 68.2 m, respectively) were drilled in the floodplain of the Heihe River. The facies analysis of the sedimentary sequences from the drilling cores showed that the sedimentary environment changed from the lake system to a delta system and finally to a fluvial system at the depths of ~133.3 and ~68 m, respectively. The magnetostratigraphic results revealed ages of approximately 1.75 and 1.12 Ma for the DWJ and XKJD cores, respectively, and an age of approximately 1.1 Ma for the transition from delta to fluvial environment in both the cores. The change of the sedimentary environment at approximately 1.1 Ma was caused by the formation of the integrated Heihe River. The integrated Heihe River may have developed via mechanisms such as river capture and river diversion due to the uplifting of the North Qilian Shan and the Longshou Shan. The present study suggested that the formation of large inland rivers, such as the Heihe River

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A new method of designing BP neural networks based on evolutionary algorithm (EA) is proposed for Knowledge Management in Electricity Supply Industry. The mechanisms of diversity maintaining and antibody density regulation exhibited in evolutionary system are introduced into evolutionary algorithm (EA). The proposed algorithm overcomes the problems of EA on search efficiency, individual diversity and premature and enhances the convergent performance effectively. In order to solve the problem of random initial weights, neuro fuzzy system for diversity is used to initialize weight vectors, and the detailed design steps of the algorithm are given. Simulated results show that the BP neural networks designed by EA have better performance in convergent speed and global convergence compared with hybrid evolutionary algorithm and the method is more accurate than other ones.