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Paleohydrological investigations were carried out in the Yellow River source area on the northeast (NE) Tibetan Plateau. During our fieldwork investigations, two units of overbank flood deposits (OFD) were found in the Maqu-Kesheng reach. These OFD units were studied using a multi-index approach, including magnetic susceptibility, hygroscopic water, grain size distribution and micro-morphological features. It can be inferred that they have recorded two episodes of overbank flooding of the Yellow River. Using the optically stimulated luminescence (OSL) and AMS 14C dating techniques, the first episode was dated to 34,680 ± 1880-29000 ± 1790 a and correlated with the late Marine Isotope Stage 3a (MIS 3a) with a warm and wet climate. The second episode occurred at 16,500 ± 1,250-14000 ± 1,280 a, which is coincided with the Bølling-Allerød warm stage during the last deglaciation, a period of coexisting global warming and rapidly shrinking ice sheets. At these two periods, the water was mainly derived from the accelerated melting of mountain glaciers surrounding the basin and/or the large-scale precipitation, which led to the overbank flooding events. At the same time, the related overbank flood deposits were interbedded within glacial outwash/flashflood deposits in the valley bottom of the Yellow River, which intensified the valley aggradation. These results are of great significance in enriching the paleoflood records in the Yellow River source area, recognizing the effect of river system on the development of valley landform, as well as understanding the hydro-climatic response of the Tibetan Plateau to global climate warming occurring presently.

Over 90% of the global hydrate resources are stored in very-low-permeability clayey silt reservoirs. The low permeability significantly restricts the efficiency of gas and water flow into the production well. To enhance gas production efficiency in low-permeability hydrate reservoirs, the high-pressure rotating water jets (HPRWJ) technology is proposed to construct near wellbore artificial fractures (NWAFs) in hydrate reservoirs. The HPRWJ avoid the risks of hydraulic fracturing as well as large-scale reservoir damage, which makes it more suitable for constructing fractures in hydrate-bearing sediments (HBS). In this article, the site SH7 in the South China Sea is studied to evaluate the feasibility of this technology for enhancing gas production of low-permeability hydrate reservoirs by numerical simulation. The results show that the gas productivity is increased by approximately three times by using the HPRWJ technology to construct NWAFs with a depth of 3 m. It is suggested that the proposed technology is a promising method for improving gas production from the low-permeability hydrate reservoirs. Furthermore, the gas production performance is closely related to NWAF depth, NWAF permeability, and NWAF spacing. For the site SH7 in the South China Sea, the NWAF depth, permeability, and spacing are recommended as 3 m, 3D, and 3 m, respectively.

The geological structure of the reservoir bank landslide is complex and intricate. After deformation and damage, it causes river blockage, surges, and loss of people's lives and property, posing a huge threat. At present, in academia and engineering applications, a large number of techniques such as inclinometers, rain gauges, and surface GNSS deformation monitoring are still used for monitoring landslides on reservoir banks. This type of monitoring method has the problem of "point to surface" which can easily lead to missed detection and reporting in some areas, some disaster points are close to water and steep, difficult to reach, and equipment installation is difficult. This work designs and implements a non-contact video quantitative monitoring system for surface deformation of geological disaster. By constructing a deep learning neural network, deformation area recognition and displacement quantitative calculation are achieved; By obtaining continuous images for a long time, draw the surface displacement-time curve, and output the surface deformation data and landform changes of the disaster. Meanwhile, this work explores the impact of different lighting conditions on the recognition results of target areas. This work can provide non-contact monitoring methods and dynamic warning support for large-scale monitoring of geological disasters. [ABSTRACT FROM AUTHOR]