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Landslides are common natural disasters that cause serious damage to ecosystems and human societies. To effectively prevent and mitigate these disasters, an accurate assessment of landslide hazards is necessary. However, most traditional landslide hazard assessment methods rely on static assessment factors while ignoring the dynamic changes in landslides, which may lead to false-positive errors in the assessment results. This paper presents a novel landslide hazard assessment method for the Zagunao River basin, China. In this study, an updated landslide inventory was obtained for the Zagunao River basin using data from interferometric synthetic aperture radar (InSAR) and optical images. Based on this inventory, a landslide susceptibility map was developed using a random forest algorithm. Finally, an evaluation matrix was created by combining the results of deformation rates from both ascending and descending data to establish a hazard level that considers surface deformation. The method presented in this study can reflect recent landslide hazards in the region and produce dynamic assessments of regional landslide hazards. It provides a basis for the government to identify and manage high-risk areas. [ABSTRACT FROM AUTHOR]

Siliceous minerals of the Dengying Formation in the Gaoshiti–Moxi area in the central Sichuan Basin exhibit four types of quartz crystals (cryptocrystalline quartz, chalcedony, microcrystalline quartz, and megacrystalline quartz) and three structural types: cryptocrystalline, microcrystalline, and mosaic (laminated mosaic, window-hole interrupted mosaic, and arc-laminated mosaic). Siliceous minerals have a great influence on the storage performance of the reservoirs in the Dengying Formation. According to the petrophysical parameters of the Dengying Formation and porosity intersection diagrams, the siliceous dolomite and the reservoirs have low impedance characteristics, which makes it difficult to distinguish between them and leads to difficulties in the characterization and prediction of the reservoirs. The transverse wave velocity is favorable for reservoir characterization. Currently, the main method used to estimate the transverse wave velocity is petrophysical modeling, which establishes a relationship between the elastic and physical parameters of the reservoir. In this paper, the siliceous minerals in the dolomite in the study area are regarded as solid inclusions, and the calculation method of the rock matrix modulus is improved by using solid replacement. Then, an improved petrophysical model is constructed by combining the KT (Kuster–Toksöz) model, the DEM (Discrete Element Method) model, the Gassmann equation, and the Wood equation. The transverse wave velocity is estimated using the improved model under the constraint of the longitudinal wave velocity. The shapes of the transverse wave velocity curves obtained by the improved model and the deviations from the measured velocities are significantly better than those of the Xu–Payne model and other models. The results show that the improved model can effectively estimate the transverse wave velocity of the reservoir in this area, which provides a basis for future reservoir predictions in this area. [ABSTRACT FROM AUTHOR]

The ion-adsorption rare earth deposit developed on the Mosuoying granite in the Panxi area of southwestern China represents a significant advancement in the exploration of ion-adsorption rare earth deposits in Sichuan. Being the first and currently the sole ion-adsorption rare earth deposit in Sichuan, studying its rare earth mineralization characteristics holds great importance. This paper aims to investigate the geochemical properties of the Mosuoying granite and its overlying weathered crust using rock geochemical methods based on field geological investigations. The findings reveal that the deposit belongs to the light rare earth type, with the ore-forming parent rock attributed to the high-potassium calc-alkaline series. It exhibits a high rock REE content ranging from 419 to 578 ppm, indicating favorable mineralization potential. Hydrothermal alteration reduces the REE content of the parent rock, leading to a notable increase in the LREE/HREE ratio, thus impacting the partitioning of rare earth elements and subsequent ore formation. The distribution characteristics of rare earth elements in each layer of the weathered crust are controlled by the parent rock and exhibit a light rare earth distribution pattern. The completely weathered layer is the main enrichment zone for rare earth elements, and the migration and enrichment patterns of rare earth elements in the weathered crust are evident. From the semi-weathered layer to the completely weathered layer, all REEs were gained, with a higher degree of migration for LREE. From the completely weathered layer to the clay layer, all REEs were lost, and the vertical distribution of rare earth content shows a "low-high-low" pattern. [ABSTRACT FROM AUTHOR]

In the face of the new reform of China's "unified electricity market", the frequency regulation ancillary service market in each region of China is still divided between using the MCP and PAB pricing mechanisms. The purpose of this paper is to explore the different impact the laws of the above two pricing mechanisms have on the frequency regulation market and to provide relevant suggestions for this electricity market reform. This paper simulates the competitive activities of the frequency regulation market based on the multi-agent simulation model, conducts a comparative experiment by changing the pricing mechanism to a single variable based on the rules of the Sichuan frequency regulation market in China, and concludes that MCP can make the market fast and stable, and that its market settlement price is low, which is suitable for the "unified electricity market". Although PAB makes the market settlement price high, it can ensure the retention of high-performance units in the market, and the stable settlement price makes this model able to accurately reflect the "price signal", making it suitable for late adoption in the "unified electricity market". [ABSTRACT FROM AUTHOR]

The rapid and accurate extraction of wide-area coseismic landslide locations is critical in earthquake emergencies. At present, the extraction of coseismic landslides is mainly based on post-earthquake site investigation or the interpretation of human–computer interactions based on remote sensing images. However, the identification efficiency is low, which seriously delays the earthquake emergency response. On the basis of the available multisource and multiscale remote sensing data, numerous studies have been carried out on the methods of coseismic landslide extraction, such as pixel analysis, object-oriented analysis, change detection, and machine learning. However, the effectiveness of coseismic landslide extraction was low in wide areas with complex topographic and geomorphic backgrounds. Therefore, this paper offers a comprehensive study of the factors influencing coseismic landslides and researches rapid and accurate wide-area coseismic landslide extraction methods with multisource remote sensing and geoscience technology. These techniques include digital elevation modeling (DEM) and its derived slopes and aspects. An embedded multichannel spectral–topographic feature fusion model for coseismic landslide extraction based on DeepLab V3+ is proposed, and a knowledge-enhanced deep learning information extraction method integrating geological knowledge is formed. Using the Jiuzhaigou Ms7.0 earthquake (seismic intensity VIII) in Sichuan Province, China, a comparison of landslide extraction models and strategies is carried out. The results show that the model proposed in this paper achieves the best balance in the accuracy and efficiency of wide-area extractions. Using multiple feature data of coseismic landslides, the problem of mixed pixels is solved. The rate of the misidentification of landslides as clouds, snow, buildings, and roads is significantly lower than in other methods. The identified landslide boundaries are smoother and more accurate, and the connectivity is better. Compared with other methods, ours can more accurately eliminate landslides not triggered by the Jiuzhaigou earthquake. While using the image block strategy to ensure extraction efficiency, it also improves the extraction accuracy of wide-area coseismic landslides in complex backgrounds. [ABSTRACT FROM AUTHOR]

The geological storage of CO2 is a critical technique for reducing emissions, which significantly contributes to the mitigation of the greenhouse effect. Currently, CO2 is often geologically stored in coal seams, hydrocarbon reservoirs, and saline aquifers in order to store CO2 and improve the oil and gas recovery simultaneously. Shale formations, as candidates for CO2 storage, are drawing more attention because of their rich volumes. CO2 storage through shale formations in the Sichuan Basin, China, has tremendous potential because of the readily available CO2 injection equipment, such as abandoned shale gas wells. Therefore, we review the potential of using these wells to store CO2 in this paper. Firstly, we review the status of the geological storage of CO2 and discuss the features and filed applications for the most studied storage techniques. Secondly, we investigate the formation properties, shale gas field development process, and characteristics of the abandoned wells in the Sichuan Basin. Additionally, after carefully studying the mechanism and theoretical storage capacity, we evaluate the potential of using these abandoned wells to store CO2. Lastly, recommendations are proposed based on the current technologies and government policies. We hope this paper may provide some insights into the development of geological CO2 storage using unconventional reservoirs. [ABSTRACT FROM AUTHOR]

The giant panda (Ailuropoda melanoleuca) is a relic species in China and a flagship species in the field of endangered wildlife conservation. The conservation of the giant panda's habitat has gained widespread attention for this reason. Historically, Chinese Giant Panda Nature Reserves are surrounded by communities and the resource utilization behavior of households disturbs the giant panda habitat. Changes in these communities and in Giant Panda Nature Reserves began around 2010, with the feminization and aging of the farm labor force. These changes brought different resource utilization behaviors that led to different evolutionary tendencies in giant panda habitats. This research study assesses the impact of these tendencies based on data from the fourth survey of the giant panda in the Sichuan Province and from geographic information data. The paper aims to uncover the internal mechanisms of farmers' resource utilization behavior in terms of the changes wrought to giant panda habitats. The paper simulates the future habitat of the giant panda based on the LUCC (land use/cover change) model to identify anticipated changes in future landscape patterns and the habitat quality of giant pandas under the current scenarios. The paper analyzes the spatial-temporal change of landscape patterns through the land use transfer matrix, based on the Markov model. The results of the scenario analyses illustrate the spatial and temporal difference in habitat quality. The driving mechanism for landscape pattern change is explored using the logistic regression model. The paper simulates the variation tendency of giant panda habitats under differential labor force structures and resource utilization behavior based on the CA (cellular automata) model, with the robustness of the results verified by participatory experiment. Through four scenarios of simulated farm labor force structure and resource utilization behavior, results suggest that the quality of giant panda habitats in the future will be relatively high when workforce feminization and aging is intense and resource utilization behavior is weakened. The simulated results confirm that the current agricultural labor force structure can reduce the intensity of resource utilization behavior. In this scenario, disturbance to giant panda habitats would decrease and their quality would improve. [ABSTRACT FROM AUTHOR]

Research on government service quality can help ensure the success of digital government services and has been the focus of numerous studies that proposed different frameworks and approaches. Most of the existing studies are based on traditional researcher-led methods, which struggle to capture the needs of citizens. In this paper, a citizen-feedback-based analysis framework was proposed to explore citizen demands and analyze the service quality of digital government. Citizen feedback data are a direct expression of citizens' demands, so the citizen-feedback-based framework can help to obtain more targeted management insights and improve citizen satisfaction. Efficient machine learning methods used in the framework make data collection and processing more efficient, especially for large-scale internet data. With the crawled user feedback data from the Q&A e-government portal of Luzhou, Sichuan Province, China, we conducted experiments on the proposed framework to verify its feasibility. From citizens' online feedback on Q&A services, we extracted five service quality factors: efficiency, quality, attitude, compliance, and execution of response. The analysis of five service quality factors provides some management insights, which can provide a guide for improvements in Q&A services. [ABSTRACT FROM AUTHOR]

In this paper, a Comprehensive Diagram Method (CDM) for a Multi-Layer Perceptron Neuron Network (MLPNN) is proposed to realize natural gas energy metering using temperature, pressure, and the speed of sound from an ultrasonic flowmeter. Training and testing of the MLPNN model were performed on the basis of 1003 real data points describing the compression factors (Z-factors) and calorific values of the three main components of natural gas in Sichuan province, China. Moreover, 20 days of real tests were conducted to verify the measurements' accuracy and the adaptability of the new intelligent method. Based on the values of the Mean Relative Errors and the Root Mean Square errors for the learning and test errors calculated on the basis of the actual data, the best-quality MLP 3-5-1 network for the metering of Z-factors and the new CDM methods for the metering of calorific values were experimentally selected. The Bayesian regularized MLPNN (BR-MLPNN) 3-5-1 network showed that the Z-factors of natural gas have a maximum relative error of −0.44%, and the new CDM method revealed calorific values with a maximum relative error of 1.90%. In addition, three local tests revealed that the maximum relative error of the daily cumulative amount of natural gas energy was 2.39%. [ABSTRACT FROM AUTHOR]

The Jiajika rare metal deposit contains the largest area of granitic pegmatite-type rare metal deposits in China. The X03 vein is an immense rare metal deposit dominated by lithium, which was found in the deposit in recent years. The contact metamorphic belt of tourmalinization and petrochemistry is widely developed in its wall rocks, and the altered rocks formed contain Li and other rare metal mineralization. In this paper, the tourmaline found in the different rocks of the Jiajika X03 vein is divided into four types: two-mica quartz schist (Tur-Ⅰ), tourmaline hornfels (Tur-Ⅱ), tourmaline-bearing granite pegmatite (Tur-Ⅲ) and spodumene-bearing granite pegmatite (Tur-Ⅳ); their in situ major element, trace element and boron isotope data are systematically studied. The results show that all tourmalines in the Jiajika X03 vein deposit belong to the alkali group, and are schorl–Oxy/Fluor–schorl, dravite–Hydroxy-dravite and foitite–Oxy foitite solid solutions, among which Tur-Ⅰ are dravite, Tur-Ⅱ are foitite of hydrothermal origin and Tur-Ⅲ and Tur-Ⅳ are schorl of magmatic origin. The boron isotope values show that the boron involved the formation process of tourmaline mainly originates from the Majingzi S-type granite, and the boron isotope variations in tourmaline are controlled by melt fluid and Rayleigh fractionation. Moreover, there is a clear correlation between the B isotope value of tourmaline and the Li, Mn, Zn, Mg, and V contents, showing that these contents in tourmaline are good indicators of the mineralization type of pegmatite. [ABSTRACT FROM AUTHOR]

High graphitization is responsible for low-resistivity shale development with poor reservoir quality. This paper provides an explanation of organic matter graphitization and determines the impact of high graphite content on low-resistivity shale reservoir quality at the Wufeng-Longmaxi Formation in the Southern Sichuan Basin. Fine veins are frequently developed at shale samples with Ro > 3.5%, graphitized organic matter > 25%, and resistivity < 5 Ω•m, which are dominated by three mineral assemblages: brunsvigite, barite-hyalophane-barium feldspar-potassium feldspar-anhydrite, and calcite-ankerite. These filling minerals are characterized by an Eu positive anomaly and high Ba, Fe, and Mn contents, suggesting that low-resistivity shale was modified by magmatic-related low-temperature hydrothermal fluid. Temperature measurements of brine inclusions and a semi-empirical geothermometer of chlorite show that low-temperature hydrothermal fluid experienced the chlorite stage (150–180 °C), the low-sulfidation stage (120–150 °C), and the low-temperature calcitization stage. Paleozoic fault systems and late Permian hydrothermal activities associated with the Emeishan mantle plume control the graphitization of low-resistivity shale. The water formation and seawater infiltrated into the deep crust along the Paleozoic basement faults under gravity, developing alkaline hot brine through mantle plume heating and then causing a water-rock reaction with basement rocks. They migrated upward along deep and large Paleozoic faults through convective thermal circulation in the Tiangongtang area, the Shuanglong-Luochang area, and the Xuyong area. Cation exchange and redox reactions occurred during the interaction between high-temperature hydrothermal fluid and cool wall rocks. The migration of alkaline hot brine via the Wufeng-Longmaxi shale introduced a subsequent water-rock reaction, resulting in the development of hydrothermal mineral assemblages that intricately filled fractures. It increased formation temperature and enhanced thermal maturity and graphitization of organic matter at the Wufeng-Longmaxi low-resistivity shale, resulting in a wide distribution of low-resistivity shale at the Changning Block. [ABSTRACT FROM AUTHOR]

The Lala copper area in Huili County, Sichuan Province, China, is favored by superior regional metallogenic geological conditions due to its location in an extremely important copper–iron metallogenic belt in southwest China, and it has witnessed the formation of a series of unique iron–copper deposits following the superposition of multiple tectonic events. In recent years, major mineral exploration breakthroughs have been achieved in the deep and peripheral zones of this area. Using the Lala copper mining area in Sichuan as an example, this paper describes metallogenic prediction research carried out based on multivariate geoscience information (geological information, geophysics, geochemistry, and remote sensing data) and the application of geographic information system (GIS) technology and the radial basis function neural network (RBFLN) model. The five specific aspects covered in this paper are as follows: (1) we collected geology–geophysics–geochemistry remote sensing data and other information, adopted GIS technology to extract multivariate geoscience ore-forming anomaly information, and established a geoscience prospecting information database; (2) we applied the RBFLN algorithm for information on integrated analysis of ore-forming anomalies in the study area; (3) we applied a statistical method to divide the threshold value to delineate favorable ore-prospecting target areas; (4) we applied three-dimensional (3D) visualization technology, through which sample assistance was verified, to evaluate the performance of the RBFLN model; and (5) the results revealed that the RBFLN model can integrate multivariate and multi-type geoscience information and effectively predict metallogenic prospective areas and delineate favorable target areas. The metallogenic prediction method based on RBFLN technology provides a scientific basis for the exploration and deployment of minerals in the study area. It is obvious that the methods to predict and evaluate mineral resources are developing towards model integration and information intelligent analysis. [ABSTRACT FROM AUTHOR]

The Silurian Longmaxi (S1l) marine shale and Jurassic Dongyuemiao (J1d) lacustrine shale in the Sichuan Basin, West China have attracted considerable attention from the oil-and-gas industry in China. Currently, the differences in pore types and pore structures between them are poorly understood, which has limited shale-resource exploration in the Sichuan Basin. This paper systemically compares the pore characteristics of Longmaxi shale and Dongyuemiao shale and investigates their impact on shale-gas enrichment by integrating field-emission scanning electron microscopy (FE–SEM), X-ray diffraction (XRD), low-pressure gas (CO2 and N2) adsorption and mercury-intrusion porosimetry, high-pressure sorption isotherms, gas-saturation measurement, molecular-dynamics simulation, etc. The results show that the S1l organic-rich marine shale and the J1d lacustrine shale have different pore types and pore structures. The S1l shale is dominated by organic pores, mainly micropores and mesopores with ink-bottle-like pore shapes, while the J1d shale is primarily composed of clay-mineral pores, mainly mesopores and macropores with slit- or plate-like pore shapes. Organic pores can provide considerable storage space for shale-gas enrichment in S1l marine shale, which also determines the adsorption capacity of shale reservoirs. Although organic pores are not the most prevalent in the Dongyuemiao lacustrine shale, they also play an important role in enhancing reservoir quality and absorbed-gas enrichment. Clay-mineral pores contribute weakly to the storage space of J1d-lacustrine-shale reservoirs. Mesopores are the most important form of storage space in both S1l shale and J1d shale, contributing significantly to shale-gas enrichment. Micropores are secondary in importance in S1l marine shale, while macropores are secondary contributors to pore volume in J1d lacustrine shale. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study, we describe the well-preserved fossil seed cones of Nothotsuga found in the Mula Basin, Litang County, Sichuan Province, southwestern China. By detailed morphological comparisons, a new fossil species, Nothotsuga mulaensis Z. Li et J. L. Dong sp. nov. is named. This discovery suggests that Nothotsuga had a more northern distribution during the Miocene. In addition, it suggests that the Mula region had a humid and warm climate during this period. The ancient landform presents an intermountain lake basin, which would have been surrounded by higher mountains. After the Miocene, due to the uplift of the Tibetan Plateau, Nothotsuga was either unable to adapt to the extinction of the local environmental or was able to migrate to several scattered places in southern China. In this paper, we describe a new fossil species, Nothotsuga mulaensis Z. Li & J.L. Dong sp. nov. The discovery of the fossil species was based on well-preserved fossil seed cones that were found in the Mula Basin in Xiamula village, Litang County, Sichuan Province, southwestern China. The shapes of these fossils were characterized by ovate seed cones, rhombic or suborbicular scales with auriculate bases, and the bracts were ligulate-spathulate in shape. This finding suggests that Nothotsuga once had a wide distribution range in China and that it also inhabited the eastern Tibetan Plateau (TP). Nothotsuga mulaensis was distributed in an intermountain lake basin, at altitudes from 2000 to 2300 m, in a warm and humid environment. This finding also suggests that the eastern TP may have provided good habitat for Nothotsuga during the Miocene. In addition, we propose that the uplift, accompanied by the severe cooling and aridification that occurred after the Miocene, caused the disappearance of this species of Nothotsuga in the eastern TP. [ABSTRACT FROM AUTHOR]

Due to global warming and other climate changes, it is increasingly important to study the response of regional environmental changes and dynamic changes in vegetation to climate change. Based on meteorological data from the last 60 years, this paper calculates the humidity index of western China under a wide range of long time series in different regions and explores the cross-correlation effect between series by offering a comparison with NDVI data, to analyze the cross-correlation between wet and dry changes and changes in vegetation in western China on a spatial scale. The results show that the spatial distribution of the interdecadal humidity index is different between different regions in western China. For example, the semi-arid and the semi-humid zones of the Weihe River region exhibit significant changes, while the Xinjiang and Qinghai–Tibet regions show a trend of constant wetness, on the whole, and the Sichuan and Yunnan–Guizhou regions are relatively humid and the distribution of wetness and dryness is relatively stable. The distribution of high and low values of the humidity index is very obvious and consistent with that of the distribution of desert bare land and precipitation in western China. In common with the distribution in the humidity index, the maximum correlation number between the NDVI and the humidity index in the whole western region is also significantly different in spatial distribution. There is a positive correlation between the NDVI and the humidity index in 99% of the study area. However, the delay in response time of the NDVI to changes in the humidity index in each region is inconsistent. For example, changes in the NDVI lag changes in the humidity index in the Menggan region by generally either 2 months or 5 months, while in the Sichuan region the delay in response time is generally 3 months. The variation and trend in dry and wet areas are closely related to the geographical location, climate zone, and topographic terrain, which may be the reason for the differences in the distribution of vegetation types and the response time to dry and wet changes. There is significant interaction between the humidity index and the vegetation type or precipitation distribution in western China. The positive correlation between the NDVI and the humidity index means that the positive effect is more sensitive, and the response of grassland is the most sensitive in the ecosystem. [ABSTRACT FROM AUTHOR]

To explore the causes of pollution formation and changes in the complex topography of the Sichuan Basin, China, and improve the comprehensive simulation capability of pollution models, we use two online coupling models, WRF/Chem and WRF/CUACE, to simulate two heavy pollution episode that successively occurred in the southern part of Sichuan Province from 15 December 2016 to 11 January 2017 in this paper. Additionally, two sets of meteorological physics parameterization schemes MET1 and MET2 are designed, and four groups of experiments are carried out. The results suggest that the two models are good at simulating the static weather parameters such as temperature, low speed of winds and boundary layer height. The four groups of tests can accurately simulate the beginning, maintenance and turning point of the two pollution episodes' life cycles. CUACE shows better performance in terms of higher correlation coefficients and lower errors in most of the particles and particulate components evaluations. It also performs better in the competitive mechanism of sulfate and nitrate against ammonium in the thermodynamic equilibrium mechanism. In addition, the evaluation of PM2.5 and the component simulation show that CUACE is more capable of simulating the mechanisms of heavy pollutions in southern Sichuan. Meanwhile, MET2 scheme is more appropriate for the simulation than MET1 dose. Based on the simulated concentrations of components and their precursors, the models overestimate the conversion of NO2 to nitrate but underestimate the conversion of SO2 to sulfate, which is the essential cause of the general overestimation of nitrate. Therefore, reducing the overestimation of nitrate is one major target for future model improvement. [ABSTRACT FROM AUTHOR]

The unidirectional consolidation theory of soils is widely used in certain conditions and approximate calculations. The multidirectional theory of soil consolidation is more reasonable than the unidirectional theory in practical applications but is much more complicated in terms of index determination and solution. To address the above problem, in this paper, we propose a deep learning method using physics-informed neural networks (PINN) to predict the excess pore water pressure of two-dimensional soil consolidation. In the proposed method, (1) a fully connected neural network is constructed; (2) the computational domain, partial differential equation (PDE), and constraints are defined to generate data for model training; and (3) the PDE of two-dimensional soil consolidation and the model of the neural network are connected to reduce the loss of the model. The effectiveness of the proposed method is verified by comparison with the numerical solution of PDE for two-dimensional consolidation. Moreover, the FEM and the proposed PINN-based method are applied to predict the consolidation of foundation soils in a real case of Sichuan Railway in China, and the results are quite consistent. The proposed deep learning approach can be used to investigate large and complex multidirectional soil consolidation. [ABSTRACT FROM AUTHOR]

This paper collects and analyzes the 1954–2017 monthly average reanalysis data from the U.S. National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR), the 1954–2017 haze days observation data from the National Meteorological Information Center/China Meteorological Administration (NMIC/CMA) and the PM2.5 daily average mass concentration data in 2013–2017 from the China Air Quality Online Monitoring Platform. The atmospheric apparent heat source Q1 (negative Q1 means cold source in winter) over the Tibetan Plateau in December of 1954–2017 is estimated based on thermodynamic equations, and the East Asian winter monsoon index (EAWMI) is calculated. In addition, the discrepancies of the air quality among China's five typical regions (Beijing–Tianjin–Hebei, Fen-Wei Plain, Yangtze River Delta, Pearl River Delta and Sichuan-Chongqing regions) under the joint influence of the Q1 and EAWMI are studied. Results show that when it is a strong cold source year, abnormal downdrafts and "temperature inversion covers" occur in areas far from the high terrain, resulting in increased pollution, while the opposite is true in weak cold source years. In strong EAWMI years, there is an abnormal northerly sinking cold flow in the lower layers of mid-high latitudes, which increases the pollution in the area south of 30° N, and the opposite is true in weak EAWMI years. Affected by the combined activities of the Q1 and the EAWMI, the meteorological conditions of the five typical regions are different, and thus present different air pollution characteristics. [ABSTRACT FROM AUTHOR]

The mountainous area of southwest China is characterized by significant topography and complex geological conditions, which pose great challenges to the airport's site selection, construction, and safe operation. Suining Anju Airport, one of the key projects under construction in southwest China, is essential in alleviating and dredging the air passenger flow in Sichuan Province. Because the overlying quaternary strata's physical and mechanical properties, thickness, and distribution range are fairly different in the longitudinal and transverse directions, the Anju Airport's foundation in the hilly area has typical inhomogeneity. Large-scale excavation and filling pose a challenge to the ground stability of the airport. To comprehensively monitor Anju Airport's uneven ground subsidence during the construction period, this paper selected SAR image data collected by the Sentinel-1A satellite from May 2018 to June 2021 to extract time-series ground subsidence measurements based on the SBAS-InSAR method. Furthermore, based on the simulation of roadbed filling in the airport's parallel slide fill area, the dynamic evolution analysis of soil stress field and internal subsidence caused by roadbed filling activities was carried out to further reveal the occurrence mechanism of ground subsidence. The monitoring results show that the subsidence centers of Anju Airport are mainly distributed in the filling areas, and the average annual subsidence is −20~−75 mm/yr from May 2018 to June 2021. Comparative analysis with in situ data indicates that the RMSE of InSAR monitoring results was ±6.12 mm. The numerical simulation shows that the subsidence of the airport parallel slide is mainly caused by a load of subgrade filling body and the compression of its weight. The results of this study can provide reference methodology and data support for the construction and future safe operation of Suining Anju Airport. [ABSTRACT FROM AUTHOR]

This paper proposes a semiparametric solution to estimate the intensity (hazard) function of modulated renewal processes: a nonparametric estimate for the baseline intensity function together with a parametric estimate of the model parameters of the covariate processes. Based on the martingale property associated with the conditional intensity, we construct a statistic from a residual analysis to estimate the baseline renewal intensity function, when the model parameters of the covariate processes are known. In addition, when the baseline intensity is obtained, the model parameters can be estimated using the usual maximum likelihood estimation. In practice, both the baseline intensity and model parameters are suggested to be estimated simultaneously via an expectation–maximization (E–M)-type iterative algorithm. A more important feature of the newly proposed algorithm is that, given n events in the observation dataset, its computation time is of order O (n 2) , while the Nelson–Aalen–Breslow estimator takes a computation time of order O (n 3) . For illustration, we apply the proposed estimation procedure to a set of data simulated from a modulated gamma renewal process and the aftershock sequence following the M s 8 Wenchuan earthquake, which occurred in Sichuan Province, China on 12 May 2008. [ABSTRACT FROM AUTHOR]

The southwest mountainous area of China is one of the areas with the most landslides in the world. In this paper, we used Ya'an City and Garzê Tibetan Autonomous Prefecture in Sichuan Province as the research areas to explore the identification application effects of large-area potential landslides using synthetic aperture radar (SAR) data with different wavelength types (Sentinel-1, ALOS-2), different processing methods (SBAS-InSAR, Stacking-InSAR), and different geological environmental conditions. The results show the following: (1) The effect of identifying landslides with different slope directions is largely affected by the satellite orbit direction; when we identify landslide hazards across a large area, the joint monitoring mode of ascending and descending orbit data is required. (2) The period of monitoring affects the identification effect of potential landslides when landslide identification is carried out in southwestern China; the InSAR monitoring period is recommended to be more than 2 years. (3) In different geological environmental regions, SBAS technology and Stacking technology have their own advantages; Stacking technology identifies more potential landslides, and SBAS technology identifies potential landslides with higher accuracy; (4) the degree of vegetation coverage has a great impact on the landslide identification effect of different SAR data sources. In low-density vegetation coverage areas, the landslide identification result using Sentinel-1 data seems to be better than the result using ALOS-2 data. In high-density vegetation coverage areas, the landslide identification result using ALOS-2 data is better than that using Sentinel-1 data. [ABSTRACT FROM AUTHOR]

This paper documents depositional structures and their reservoir characteristics in the Wufeng–Longmaxi shale from outcrops and cores using thin sections, X-ray powder diffraction (XRD) analysis, carbon–sulfur analyzer, helium porosimeter, decay permeameter, and focused ion beam scanning electron microscope (FIB-SEM). In the study area, clayey and silty laminae abound in the shale. Clayey laminae are rich in bedding parallel fractures, microfractures, and organic pore networks. Silty laminae are rich in isolated inorganic pores and limited amounts of bedding non-parallel fractures. Various inter-lamination of clayey and silty laminae form five depositional structure types which are closely related to the ancient hydrodynamics, paleoredox condition, and sedimentation rate and have significant impacts on shale fractures, microfractures, pore types, pore-size distribution, and porosity. For the paper lamination (PL) and grading lamination composed of siltstone and claystone (GL-SC), organic pores account for 71.6% and 61.4% of the total, and dense bedding parallel and non-parallel fractures intersect to form connective networks. In the interlaminated lamination composed of siltstone and claystone (IL), grading lamination composed of claystone (GL-C) and structureless beds (SB), organic pores merely account for 20% to 51.8% of the total and minor isolated bedding parallel fractures occur. Among five depositional structure types, the PL and GL-SC have the highest porosity, permeability, TOC content, siliceous content, organic pore proportions, and ratios of horizontal to vertical permeability, which help them become shale gas exploration and development targets. [ABSTRACT FROM AUTHOR]

The overpressure phenomenon is a widespread occurrence in unconventional shale gas reservoirs. Multiple overpressure shale gas fields were discovered in southern China, and there is no doubt that the gas production per well increases with increasing pressure coefficient (the ratio of the reservoir fluid pressure to the corresponding normal hydrostatic pressure). Thus, successful evaluation and production strategies of organic-rich shale deposits require an understanding of the evolution of the pressure coefficient and its controlling factors in these deposits. In this paper, drilling engineering data of a typical well were collected, and clay mineral tests and overburden diffusion coefficient experiments were conducted. Based on multiphysics simulations, this paper analyzes the overpressure characteristics and formation mechanisms of overpressure in the Longmaxi Formation shale, as well as its geological evolution and controlling factors. The results show that the large amount of shale gas is the cause of overpressure formation rather than disequilibrium compaction. The simulation results show that pressure coefficients of the typical well range from 0.84 to 1.49. The current pressure coefficient increases with increasing pressure coefficient after the last hydrocarbon generation. A large initial pressure coefficient (>1.9), short lifting time (<100 Ma) and small lifting amplitude (<4000 m) are favorable for the preservation of shale gas. [ABSTRACT FROM AUTHOR]

Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides. [ABSTRACT FROM AUTHOR]

Due to the volatility of natural resources, the power fluctuations of photovoltaic (PV) generation have serious negative impacts on the power quality. This paper reports a novel approach to resolve this problem in a combined cascade hydro-PV-pumped storage (CH-PV-PS) generation system through appropriate power distribution on a small time scale. According to the characteristics of power generation systems and multi-constraints, how to obtain the optimal smoothing effects with the small cost is a key challenge. For this purpose, wavelet packet decomposition is modified by constructing cost function and ideal power trajectory sequences to obtain a new adaptive power distribution method for the CH-PV-PS generation system considering the boundary conditions of the system in this paper. At the same time, to meet the real-time control requirements of actual systems, an additive function is presented to improve the optimization speed of the adaptive power distribution method. In the meantime, a fuzzy controller is designed to optimize the inevitable delay time of the power scheduling system, and the starting threshold is set to avoid the frequently mode conversion of pumped storage. Finally, the performance of the method is evaluated based on the PV station data in Xiaojin County, Sichuan Province, China. Simulation results based on Matlab-simulink indicate that the proposed method can effectively suppress PV power fluctuations and ensure normal operation of the CH-PV-PS generation system within the multiple constraints. [ABSTRACT FROM AUTHOR]

Tomographic Synthetic Aperture Radar (TomoSAR) is a breakthrough of the traditional SAR, which has the three-dimentional (3D) observation ability of layover scenes such as buildings and high mountains. As an advanced system, the airborne array TomoSAR can effectively avoid temporal de-correlation caused by long revisit time, which has great application in high-precision mountain surveying and mapping. The 3D reconstruction using TomoSAR has mainly focused on low targets, while there are few literatures on 3D mountain reconstruction. Due to the layover phenomenon, surveying in high mountain areas remains a difficult task. Consequently, it is meaningful to carry out the research on 3D mountain reconstruction using the airborne array TomoSAR. However, the original TomoSAR mountain point cloud faces the problem of elevation ambiguity. Furthermore, for mountains with complex terrain, the points located in different elevation periods may intersect. This phenomenon increases the difficulty of solving the problem. In this paper, a novel elevation ambiguity resolution method is proposed. First, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Gaussian Mixture Model (GMM) are combined for point cloud segmentation. The former ensures coarse segmentation based on density, and the latter allows fine segmentation of the abnormal categories caused by intersection. Subsequently, the segmentation results are reorganized in the elevation direction to reconstruct all possible point clouds. Finally, the real point cloud can be extracted automatically under the constraints of the boundary and elevation continuity. The performance of the proposed method is demonstrated by simulations and experiments. Based on the airborne array TomoSAR experiment in Leshan City, Sichuan Province, China in 2019, the 3D model of the surveyed mountain is presented. Moreover, three kinds of external data are applied to fully verify the validity of this method. [ABSTRACT FROM AUTHOR]

A series of small-magnitude earthquakes (Mw 2.9~Mw 4.9) occurred in Rong County, Sichuan Province, China between 30 March 2018 and December 2020, which threatened the safety of local residents. Determining the surface displacement and estimating the damage caused by these earthquakes are significant for earthquake relief, post-earthquake disaster assessment and hazard elimination. This paper integrates the Generic Atmospheric Correction Online Service (GACOS) with interferometry synthetic aperture radar (InSAR) to accurately detect the displacement of the series of small-magnitude earthquakes in Rong County based on 45 Sentinel-1 ascending/descending images acquired from January 2018 to December 2020. We analyze the influence of some factors involved in surface displacement, including earthquake magnitude, focal depth and the distance from the epicenter to the fault. The above measurement for small-magnitude earthquakes and statistics analysis for the displacement have not been performed before, so this can help better understand the displacement features of small-magnitude earthquakes, which are important for post-earthquake hazard assessment and disaster prevention. [ABSTRACT FROM AUTHOR]

The mismatching between the multi-scale feature of complex fracture networks (CFNs) in unconventional reservoirs and their current numerical approaches is a conspicuous problem to be solved. In this paper, the CFNs are divided into hydraulic macro-fractures, induced fractures, and natural micro-fractures according to their mode of origin. A hybrid model coupling various numerical approaches is proposed to match the three-dimensional multi-scale fracture networks. The macro-fractures with high-conductivity and wide-aperture are explicitly characterized by a mimetic Green element method-based hierarchical fracture model. The induced fractures and natural micro-fractures that have features of low-conductivity and small-openings are upscaled to the dual-medium grid and enhanced matrix grid through the equivalent continuum-medium method, respectively. Subsequently, some benchmark cases are implemented to confirm the high-precision and high-robustness of the proposed hybrid model that indeed accomplishes accurate modeling of fluid flow in multi-scale CFNs by comparing with commercial software tNavigator®. Furthermore, an integrated workflow of simulation modeling for multiscale CFNs combined with a field example in Sichuan from China is used to analyzing the production information of fractured horizontal wells in shale gas reservoirs. Compared with the field production data from this typical well, it can be proved that the hybrid model has strong reliability and practicability. [ABSTRACT FROM AUTHOR]

From 7 July to 11 July 2013, an extreme rainstorm occurred in the Sichuan Basin (SCB) of China, which is located at the eastern edge of the Tibetan Plateau, causing severe floods and huge economic losses. The rainstorm event was associated with mesoscale convection systems (MCSs). In this paper, we analyze the evolution characteristics and formation conditions of the MCSs, and the results show that: (1) the continuous activity of MCSs was a direct cause of the formation of extreme rainstorms. Under an "east high and west low" circulation mode, the MCSs formed a "cloud cluster wave train" phenomenon from the plateau to the basin; that is, the MCSs over the basin developed strongly in the process of the MCSs over the plateau area weakening. (2) The activities of MCSs over the rainstorm area was related to ascending branches of the two vertical circulations and topographic gravity wave. Under the influence of meridional vertical circulation, MCSs could move in the south–north direction in the western SCB, while under the influence of zonal circulation, it was difficult for MCSs to develop in the descending airflow east of 106°E. (3) In the mountainous area of the western part of the SCB, the gravity wave stress was obvious and its direction was opposite to the direction of the lower southeast warm–moist airflow. This configuration was able to form a drag effect in the low-level airflow, which was conducive to the convergence of the wind field and strengthening of the vertical ascending movement. These findings help in further understanding the effects of vertical circulation and terrain on MCSs and extreme rainstorms. [ABSTRACT FROM AUTHOR]

This paper deals with the study of multi-channel adaptive noise cancellation with a focus on its application in electromagnetic (EM) telemetry. We presented new variable step-size least mean square (LMS) techniques: regularized variable step-size LMS and regularized sigmoid variable size LMS, for electromagnetic telemetry data processing. Considering the complexity and spatial distribution of environmental noise, algorithms with multiple reference signals were used to retrieve transmitted EM signals. The feasibility of the regularized variable step size LMS algorithms with numerical simulation was analyzed and presented. The adaptive processing techniques were applied in the recovery of frequency and binary phase shift key modulated signal. The proposed multi-channel adaptive technique achieves fast convergence speed, low mean squared error and is shown to have good convergence characteristics compared to conventional methods. In addition to attaining good results from the multi-channel adaptive filter and performing the signal analysis in real-time, we implemented combined fast effective impulse noise removal techniques. The combination of median and mean filters was effective in removing a wide range of impulsive noises without distorting any other data points. Further, electromagnetic telemetry data were acquired during a drilling operation in Sichuan province, China, for real field application. Data processing workflow was designed for EM telemetry data processing based on the noise characteristics, simulation results and expected result for demodulation. To establish a comprehensive overview, a performance comparison of the acquisition array system is also provided. Conclusively, the introduced multichannel adaptive noise canceling techniques are very effective in recovering transmitted EM telemetry signals. [ABSTRACT FROM AUTHOR]

This paper deals with the study of multi-channel adaptive noise cancellation with a focus on its application in electromagnetic (EM) telemetry. We presented new variable step-size least mean square (LMS) techniques: regularized variable step-size LMS and regularized sigmoid variable size LMS, for electromagnetic telemetry data processing. Considering the complexity and spatial distribution of environmental noise, algorithms with multiple reference signals were used to retrieve transmitted EM signals. The feasibility of the regularized variable step size LMS algorithms with numerical simulation was analyzed and presented. The adaptive processing techniques were applied in the recovery of frequency and binary phase shift key modulated signal. The proposed multi-channel adaptive technique achieves fast convergence speed, low mean squared error and is shown to have good convergence characteristics compared to conventional methods. In addition to attaining good results from the multi-channel adaptive filter and performing the signal analysis in real-time, we implemented combined fast effective impulse noise removal techniques. The combination of median and mean filters was effective in removing a wide range of impulsive noises without distorting any other data points. Further, electromagnetic telemetry data were acquired during a drilling operation in Sichuan province, China, for real field application. Data processing workflow was designed for EM telemetry data processing based on the noise characteristics, simulation results and expected result for demodulation. To establish a comprehensive overview, a performance comparison of the acquisition array system is also provided. Conclusively, the introduced multichannel adaptive noise canceling techniques are very effective in recovering transmitted EM telemetry signals. [ABSTRACT FROM AUTHOR]

A variable sedimentary environment and accumulation process leads to a complex pore structure in deep carbonate gas reservoirs, and the physical properties are quite different between layers. Moreover, some pores and throats are filled with solid bitumen (SB), which not only interferes with reservoir analysis, but also affects efficient development. However, previous studies on SB mainly focused on the accumulation process and reservoir analysis, and there are few reports about the influence on development. In this paper, through scanning electron microscope analysis, SB extraction, gas flow experiments and depletion experiments, and a similar transformation between experimental results and reservoir production, the production characteristics of carbonate gas reservoirs with different pore structures were studied, and the influence of SB on pore structure, reservoir analysis and development were systematically analyzed. The results show that permeability is one of the key factors affecting gas production rate and recovery, and the production is mainly contributed by high-permeability layers. Although the reserves are abundant, the gas production rate and recovery of layers with a low permeability are relatively low. The SB reduces the pore and throat radius, resulting in porosity and permeability being decreased by 4.73–6.28% and 36.02–3.70%, respectively. With the increase in original permeability, the permeability loss rate decreases. During development, the loss rate of gas production rate is much higher than that of permeability. Increasing the production pressure difference is conducive to reducing the influence. SB also reduces the recovery, which leads to the loss rate of gas production being much higher than that of porosity. For reservoirs with a high permeability, the loss rates of gas production rate and the amount produced are close to those of permeability and porosity. Therefore, in the reservoir analysis and development of carbonate gas reservoirs bearing SB, it is necessary and significant to analyze the influence of reservoir types. [ABSTRACT FROM AUTHOR]

How to acquire landslide disaster information quickly and accurately has become the focus and difficulty of disaster prevention and relief by remote sensing. Landslide disasters are generally featured by sudden occurrence, proposing high demand for emergency data acquisition. The low-altitude Unmanned Aerial Vehicle (UAV) remote sensing technology is widely applied to acquire landslide disaster data, due to its convenience, high efficiency, and ability to fly at low altitude under cloud. However, the spectrum information of UAV images is generally deficient and manual interpretation is difficult for meeting the need of quick acquisition of emergency data. Based on this, UAV images of high-occurrence areas of landslide disaster in Wenchuan County and Baoxing County in Sichuan Province, China were selected for research in the paper. Firstly, the acquired UAV images were pre-processed to generate orthoimages. Subsequently, multi-resolution segmentation was carried out to obtain image objects, and the barycenter of each object was calculated to generate a landslide sample database (including positive and negative samples) for deep learning. Next, four landslide feature models of deep learning and transfer learning, namely Histograms of Oriented Gradients (HOG), Bag of Visual Word (BOVW), Convolutional Neural Network (CNN), and Transfer Learning (TL) were compared, and it was found that the TL model possesses the best feature extraction effect, so a landslide extraction method based on the TL model and object-oriented image analysis (TLOEL) was proposed; finally, the TLOEL method was compared with the object-oriented nearest neighbor classification (NNC) method. The research results show that the accuracy of the TLOEL method is higher than the NNC method, which can not only achieve the edge extraction of large landslides, but also detect and extract middle and small landslides accurately that are scatteredly distributed. [ABSTRACT FROM AUTHOR]

The abundant reserve of shale gas in Sichuan Basin has become a significant natural gas component in China. To achieve efficient development of shale gas, it is necessary to analyze the stress state, pore pressure, and reservoir mechanical properties such that an accurate geomechanical model can be established. In this paper, Six wells of Neijiang-Dazu and North Rongchang (NDNR) Block were thoroughly investigated to establish the geomechanical model for the study area. The well log analysis was performed to derive the in-situ stresses and pore pressure while the stress polygon was applied to constrain the value of the maximum horizontal principal stress. Image and caliper data, mini-frac test and laboratory rock mechanics test results were used to calibrate the geomechanical model. The model was further validated by comparing the model prediction against the actual wellbore failure observed in the field. It was found that it is associated with the strike-slip (SS) stress regime; the orientation of SHmax was inferred to be 106–130° N. The pore pressure appears to be approximately hydrostatic from the surface to 1000 m true vertical depth (TVD), but then becomes over-pressured from the Xujiahe formation. The geomechanical model can provide guidance for the subsequent drilling and completion in this area and be used to effectively avoid complex drilling events such as collapse, kick, and lost circulation (mud losses) along the entire well. Also, the in-situ stress and pore pressure database can be used to analyze wellbore stability issues as well as help design hydraulic fracturing operations. [ABSTRACT FROM AUTHOR]

In this paper, the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) monthly average reanalysis data from 1954 to 2017, haze days observation data from 1954 to 2017, and PM2.5 daily average mass concentration data from 2013 to 2017 are collected and collated. Firstly, the atmospheric apparent heat source on the Qinghai-Tibet Plateau is estimated based on thermodynamic equations. The correlation between the atmospheric apparent heat source (Q1) on the Qinghai-Tibet Plateau and the air quality in China, especially in the five typical regions (Beijing-Tianjin-Hebei, Fen-Wei Plain, Yangtze River Delta, Pearl River Delta, and Sichuan-Chongqing regions) is analyzed and studied. Through comprehensive diagnosis and synthesis, the differences of the three-dimensional spatial distribution of the circulation field and temperature field (planes and sections) in China and the typical regions in the strong and weak years of the apparent heat source Q1 on the Qinghai-Tibet Plateau in winter are compared, and the different distribution characteristics of the climate circulation background causing the strong and weak years of Q1 on the Qinghai-Tibet Plateau and the influence mechanism on the air quality in different regions in China are discussed. The results show that the spatial distribution of correlation between Q1 on the Qinghai-Tibet Plateau and PM2.5 in December has a northeast-southwest boundary. There is a negative correlation in the southeast region of the boundary, with heavy pollution when the cold source is strong and light pollution when the cold source is weak, while there is a positive correlation in the northwest region of the boundary, with light pollution when the cold source is strong and heavy pollution when the cold source is weak. The Q1 on Qinghai-Tibet Plateau is negatively correlated with air pollution in Beijing-Tianjin-Hebei and Fen-Wei Plain located in the northwest region of the boundary but positively correlated with air pollution in the Yangtze River Delta, Pearl River Delta, and Sichuan-Chongqing regions located in the southeast region of the boundary. In the cold source strong year, the northerly winds are stronger in the middle and high latitudes, and there is an abnormal northerly downward flow in the southeast region, thus the pollution is aggravated by the suppression of convection–diffusion in a downward flow. However, abnormal updraft in the northwest region exists, reducing pollution. In the cold source weak year, the situation is just the opposite. [ABSTRACT FROM AUTHOR]

With the rapid development of big data, numerous industries have turned their focus from information research and construction to big data technologies. Earth science and geographic information systems industries are highly information-intensive, and thus there is an urgent need to study and integrate big data technologies to improve their level of information. However, there is a large gap between existing big data and traditional geographic information technologies. Owing to certain characteristics, it is difficult to quickly and easily apply big data to geographic information technologies. Through the research, development, and application practices achieved in recent years, we have gradually developed a common geospatial big data solution. Based on the formation of a set of geospatial big data frameworks, a complete geospatial big data platform system called BiGeo was developed. Through the management and analysis of massive amounts of spatial data from Sichuan Province, China, the basic framework of this platform can be better utilized to meet our needs. This paper summarizes the design, implementation, and experimental experience of BiGeo, which provides a new type of solution to the research and construction of geospatial big data. [ABSTRACT FROM AUTHOR]