Search

[Objective] Jiaojia-type gold deposit is a new type of gold mine discovered by Shandong geologists in northwestern Jiaodong in the 1960s, and its output is controlled by regional tectonic fracture zones. Pyrite sericitized alteration generally occurred during the ore-forming period, which is characterized by a large scale, distinct alteration zones, concentration of mineralization, simple morphology of the ore body, relatively uniform grade, and simple mineral combinations. The deposit has gone through 12 years of exploration and scientific research, from discovery to establishment. [Methods] This new type of gold deposit has been rapidly promoted since its establishment and has achieved fruitful prospecting results in China. Deep exploration achievements of the Jiaojia-type gold deposit have been coming up in the Jiaodong area since the 21st century. [Results] The accumulated gold resource reserves submitted have been 3617.12 tons by 2020, which has helped the Jiaodong area become the third largest gold cluster in the world, and gold reserves of China have risen to the second rink in the world. Research on the Jiaojia-style gold mine has also made considerable progress, with the age of mineralization accurately limited to 126~120 Ma and formation occurring under a strong regional extension background. The source of mineralization material has multiple origins, with the Archean tholeiite being the initial source rock of gold-bearing minerals. The ore-forming process is divided into four stages, and the “thermo-extension”metallogenic theory and the step metallogenic model are proposed. The metallogenic depth is about 5~10 km, and the post-metallogenic denudation thickness is 5.2±1.2 km, with the deposits well preserved. [Conclusion] The metallogenic prediction study concluded that the total gold resources in the three major metallogenic zones of Sanshandao, Jiaojia, and Zhaoping in the northwestern Jiaozhou area were 7258~10150 tons of metal at a depth of 5000 m, which demonstrates the immense potential of the deeper part of the Jiaojia-type gold deposits for exploration. A set of key exploration techniques was summarized for different exploration stages, from shallow to deep. [Significance] These studies enriched the metallogenic theory of the Jiaojia-type gold deposit, making the metallogenic prediction results highly accurate, reliable, and effective in guiding the gold exploration in the Jiaodong peninsula.

With the continuous surge of production data in the field of oil exploration and development, the difficulty of integration and analysis is increasing, and manual analysis methods are difficult to meet the development needs of oil and gas fields on site. Machine learning can accurately and efficiently process data, and is a key technology in the field of data analysis and optimization. Utilizing machine learning to solve difficult problems in oil and gas field development has become the main focus of the industry. However, due to limitations in standardization construction, sample library establishment, and on-site integration, there are still many challenges and shortcomings in the application of machine learning in the field of oil exploration and development. In the future, efforts should be made to strengthen the standardization construction of machine learning, improve the data processing ability of deep learning algorithms, and pro-mote interdisciplinary research in the field of deep machine learning. Through multiple approaches, we can jointly promote the significant development of machine learning in the oil and gas field industry. [ABSTRACT FROM AUTHOR]

Surface runoff is a major component of the hydrological cycle, and it is essential for supporting the ecosystem services provided by grassland and forest ecosystems. It is of practical importance to understand the mechanisms and the dynamic processes of runoff in a river's basin, and in this study, we focused on the restored montane Pailugou Basin in the Qilian Mountains, Gansu Province, China, since its water status is extremely important for the large arid area and local economies therein. Our purpose was to determine the annual variation in the surface runoff in the Pailugou Basin because it is important to understand the influence of climate fluctuations on surface water resources and the economy of the basin. In addition, little is known about the annual variations in precipitation and runoff in this region of the world. Daily atmospheric precipitation, air temperature and runoff data from 2000 to 2019 were analyzed by the calculation of the uneven annual distribution of surface runoff, the calculation of the complete adjustment coefficient, and the vector accumulation expressed by the concentration degree. We also used the cumulative anomaly approach to determine the interannual variation trend of runoff, while the change trend was quantified by the sliding average method. Finally, we used the Mann–Kendall mutation test method and regression analysis to establish the time-series trend for precipitation and runoff and to determine the period of abrupt runoff changes. The results indicated concentrated and positive distributions of surface runoff on an annual basis, with a small degree of dispersion, and an explicit concentration of extreme flows. The relative variation ranges exhibited a decreasing trend, and the distribution of the surface runoff gradually was uniform over the year. The runoff was highest from July to September (85% of the annual total). We also determined that annual surface runoff in the basin fluctuated over the 20-year period but showed an overall increasing trend, increasing by 3.94 × 105 m3, with an average increase rate of 0.42 × 105 m3 every ten years. From 2005 to 2014, the annual runoff and the proportion of runoff in the flood season (July to September) to the annual runoff fluctuated greatly. The correlation between the runoff and precipitation was significant (r = 0.839, p < 0.05), whereas the correlation between air temperature and surface runoff was low (r = 0.421, p < 0.05). [ABSTRACT FROM AUTHOR]