The Hangjinqi area was explored for natural gas around 40 years ago, but the efficient consideration in this area was started around a decade ago for pure gas exploration. Many wells have been drilled, yet the Hangjinqi area remains an exploration area, and the potential zones are still unclear. The Lower Shihezi Formation is a proven reservoir in the northern Ordos Basin. This study focuses on the second and third members of the Lower Shihezi Formation to understand the controlling factors of faults and sedimentary facies distribution, aimed to identify the favorable zones of gas accumulation within the Hangjinqi area. The research is conducted on a regional level by incorporating the 3D seismic grid of about 2500 km 2 , 62 well logs, and several cores using seismic stratigraphy, geological modeling, seismic attribute analysis, and well logging for the delineation of gas accumulation zones. The integrated results of structural maps, thickness maps, sand-ratio maps, and root mean square map showed that the northwestern region was uplifted compared to the southern part. The natural gas accumulated in southern zones was migrated through Porjianghaizi fault toward the northern region. Well J45 from the north zone and J77 from the south zone were chosen to compare the favorable zones of pure gas accumulation, proving that J45 lies in the pure gas zone compared to J77. Based on the faults and sedimentary facies distribution research, we suggest that the favorable zones of gas accumulation lie toward the northern region within the Hangjinqi area.
Xiangyun County is a typical mountainous county. At present, few people have studied geological hazards in Xiangyun County, so it is particularly important to choose appropriate methods for disaster assessment in the study area. (1) Research background: Xiangyun County is located in western Yunnan, China, where geological disasters such as landslides and collapses occur frequently. Not only endanger the safety of people's lives and property, but also destroy the ecological environment within the territory. (2) Methods: The distribution characteristics and influencing factors of geological hazards in Xiangyun County were studied and analyzed, and 9 influencing factors such as elevation, slope, slope direction, stratigraphic lithology, NDVI and rainfall were selected to evaluate the disaster susceptibility of the study area. Through the correlation analysis of evaluation factors, the evaluation system is constructed by combining the information model and the information—logistic regression model. Using ArcGIS software, the study was divided into 5 grades: extremely high, high, medium, low and extremely low prone areas. (3) Results: ROC curve was used to test the evaluation results of the two models respectively. The accuracy of the information model was 74%, and the accuracy of the coupling model was 83%. Extremely high and high-risk areas account for 18% and 24% of the total area, respectively. (4) Conclusion: The results show that the coupled model test has a high precision and can provide a reliable basis for this evaluation.
A coastline is the boundary zone between land and sea, an active zone of human social production activities and an area where the ecology is fragile and easy to change. The traditional method to analyze temporal and spatial changes in the coastline is to extract the coastline through remote sensing, LiDAR, and field sampling and analyze the temporal and spatial changes with statistical data. The coastline extracted by these methods has high spatial and temporal resolution, but it requires remote sensing images and data obtained by other sensors, so it is impossible to extract coastlines from before the emergence of remote sensing technology. This paper improves the coastline generation algorithm. Firstly, a triangulated irregular network is used to generate the preliminary rough coastline, and then, each line segment is optimized with Python language according to the influence range of the place names to further approach the real coastline. The accuracy of the coastline extracted by this method can reach 80% within 500 m, which is of great significance in the mapping and analysis of small- and medium-scale coastlines. This paper analyzes the changes in the coastline of Hainan Island before the founding of China (pre-founding) and in modern times and analyzes the impact of coastal development on coastline change. Through the analysis, it is found that, from before the founding of the People’s Republic of China to the present, the natural coastline of Hainan Island has become shorter, the artificial coastline has become longer, and the coastline generally presents a trend of advancing toward the ocean. This method realizes coastline construction under the condition of missing remote sensing images and puts forward a new way to study historical coastline changes.
The determination of the sustainable development of a region requires estimating its carrying capacity in terms of resources and environment. It is essential to investigate the carrying capacity of Kunming City to comprehend its rapid development and create a resource and environment-friendly society. This research involved the selection of a set of 35 evaluation indicators from three categories: resources, environment, and social economy. These indicators were chosen based on statistical data obtained from Kunming City between 2011 and 2020. An evaluation system was established using the entropy weight method to determine the weight of these indicators. Subsequently, the fuzzy matter–element analysis method was utilized to construct the European closeness model of Kunming’s resource and environmental carrying capacity. The correlation between the carrying capacity of resources and environment and sub-carrying capacities was analyzed using Pearson’s correlation coefficient to determine the degree of influence of different aspects on the carrying capacity of resources and environment in Kunming. The results show a consistent upward trend in the carrying capacity of resources and environment in Kunming City from 2011 to 2019. However, in 2020, due to national policy adjustments and the impact of COVID-19 on the social economy, the resource and environment carrying capacity index in Kunming City slightly decreased.
Vegetation is a main part of ecosystems and an essential indicator for monitoring changes in terrestrial ecosystems. It is crucial for us to discover the temporal and spatial features and potential drivers of vegetation change to promote regional ecological environment protection and management. However, it can be difficult to pinpoint the causes of vegetation change when considering both human activity and climate change. We used trend and stability method to study the temporal and spatial patterns of vegetation evolution in the South Sichuan Urban Agglomeration (SSUA) from 2001 to 2021 with the Google Earth Engine (GEE) platform. An optimal parameter-based geographical detector (OPGD) model was applied to optimize the spatial scale and zoning effect of geographic data, effectively solving the problem of spatial data heterogeneity. It compensates for the inadequacies of conventional approaches that neglect the modifiable areal units problem (MAUP), and improving the science and accuracy of quantitative analysis and identification of vegetation drivers. We studied demonstrate that (1) During the last 21 years, Fractional Vegetation Cover (FVC) has generally been in good condition, with a multi-year average FVC greater than 0.4 of 71.74 %, and the vegetation is significantly characterized by a low fluctuation of 78.16 %. However, there is a significant trend of vegetation degradation, accounting for 8.89 %, mainly in the main urban areas of Neijiang and Zizhong County, Lu County of Luzhou City, Gao County of Yibin City, and other areas with rapid urbanization. In general, FVC is low in the built-up areas of towns and along transportation roads, while the mountainous and agricultural areas have a high level of vegetation cover. (2) The OPGD model detection showed the optimal spatial scale of vegetation cover in this study region was 2 km. Optimal discrete parameter combinations for slope, elevation, temperature and GDP are quantile breaks with 9 intervals, which contribute to improved scientific accuracy and precision in studies of vegetation change and its drivers. (3) The explanatory power of urbanization rate, land use type, slope, GDP, population density, and average annual precipitation were all above 20 % and were the main drivers of vegetation change. Moreover, any two factors interacted in a nonlinear enhancement and a bi-variable enhancement, increasing the impact on vegetation spatial variation. When the slope is 26.9°∼87.4°, the elevation is 967 m ∼ 4207 m, the average annual temperature is 0.18 °C ∼ 13.6 °C, the average annual precipitation is 328 mm ∼ 439 mm, the GDP is 4.07 ∼ 5.23 million yuan km−2, the population density is 12.7 ∼ 21.1 people/km2, the urbanization rate is 33.4 %∼37.7 %, and the land-use type is forest land, the FVC value is the highest and suitable for vegetation growth. The study showed that using the OPGD model to detect the zoning effects and spatial scale of the explanatory variables solves for the shortcomings of the previous methods for variable regional units and discrete methods, may more precisely explore the features of temporal and spatial changes in the vegetation and the driving mechanisms, offers scientific references for environmental conservation and long-term economic growth in the region.
The Niubiziliang Ni-(Cu) deposit is the first magmatic Ni-Cu sulfide deposit in the North Qaidam Orogenic Belt (NQOB), NW China, and plays a significant role in geological evolution, Ni-Cu mineralization, and exploration in the NQOB. Here, we report on the mineral chemistry, S-Pb-O isotopes, and S/Se ratios of the mafic-ultramafic complex, which provide insights on the parental magma, evolution, and sulfur saturation mechanism. The Niubiziliang mafic-ultramafic intrusion contains four ore blocks and about ten Ni-(Cu) ore/mineralization bodies. Olivines in Niubiziliang belong to the species of chrysolite with Fo values of 88~89, and the pyroxenes are mainly orthopyroxene (En = 79~82) and clinopyroxene (En = 44~40). The olivines and some pyroxenes likely crystallized in a magma chamber at a depth of 35.45~36.55 km at a high temperature (1289~1369 °C) and pressure (9.38~9.67 kbar), whereas the Niubiziliang complex formed at a moderate depth (8.13~8.70 km) with a temperature and pressure of 1159~1253 °C and 2.15~2.30 kbar, respectively. The parental magma was considered to be high-Mg picritic basalt with MgO and NiO contents of 14.95~16.58% and 0.053~0.068%, respectively, which indicated high-degree partial melting of the depleted mantle. The mantle-derived primary magma underwent significant fractional crystallization and crustal assimilation and contamination, which was strongly supported by S-Pb-O isotope data and S/Se ratios, resulting in sulfur saturation and sulfide immiscibility in the magma. Crustal assimilation and contamination contributed more to sulfur saturation than fractional crystallization.
This study considered Shilin World Geopark as the research object and constructed a landscape ecological risk assessment model based on the landscape pattern index by using remote sensing image data during five periods between 2000 and 2020. In addition, it analyzed the spatial and temporal changes of landscape ecological risk in the region. Spatial autocorrelation analysis was utilized to study spatial differences in the landscape ecological risk in the park. The results showed that during the study period, (1) cultivated land, forest land, and rocky desertification land were the main landscape types, different landscape types differed, and the area of rocky desertification land and building land increased by 37.47 km 2 and 14.29 km 2 , respectively, while the area of cultivated land and grassland decreased significantly, with changes of 34.11 km 2 and 18.67 km 2 ; (2) landscape ecological risk of the park showed significant spatial differences, the ‘high–high’ risk areas have been concentrated mainly in the central and northern parts of the park, the ‘low–low’ risk areas have been concentrated in the central part and the southwest-southeast area of the park; and (3) landscape ecological risk of the geopark has been increasing, with the degree of landscape ecological risk being spatially positively correlated. The results of the study are of great significance for maintaining ecosystem health of the Shilin World Geopark and optimizing the ecological risk management of the park.
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