Microstructure and water-swelling mechanism of red-bed mudstone in the Xining region,Northeastern Tibetan Plateau

This paper examines the effect of the microstructure and electrical conductivity(EC)on the swelling ratio and pressure in red-bed mudstone sampled from arid areas in the Xining region in the northeastern Tibetan Plateau.A series of laboratory tests,including swelling experiments,X-ray diffraction(XRD),and scanning electron microscope(SEM),was carried out for mechanical and microstructural analysis.The coupled influence of the EC and microstructural parameters on the expansion ratio and pressure was investigated,and the weight coefficients were discussed by the entropy weight method.The results revealed an increasing exponential trend in EC,and the maximum swelling speed occurred at an EC of approximately 10 μS/cm.In addition,a method for predicting the expansion potential is proposed based on the microstructure,and its reliability is verified by comparing with swelling experimental results.In addition,according to the image analysis results,the ranges of the change in the clay minerals content(CMC),the fractal dimension(FD),the average diameter(AD)of pores,and the plane porosity(PP)are 23.75%-53%,1.08-1.17,7.53-22.45 mm,and 0.62%-1.25%,respectively.Moreover,mudstone swelling is negatively correlated with the plane porosity,fractal dimension and average diameter and is linearly correlated with the clay mineral content.Furthermore,the weight values prove that the microstructural characteristics,including FD,AD,and PP,are the main factors influencing the expansion properties of red-bed mudstones in the Xining region.Based on the combination of macro and micro-analyses,a quantitative analysis of the swelling process of mudstones can provide a better reference for understanding the mechanism of expansion behavior.

Current status and construction scheme of smart geothermal field technology

To address the key problems in the application of intelligent technology in geothermal development,smart application scenarios for geothermal development are constructed.The research status and existing challenges of intelligent technology in each scenario are analyzed,and the construction scheme of smart geothermal field system is proposed.The smart geothermal field is an organic integration of geothermal development engineering and advanced technologies such as the artificial intelligence.At present,the technology of smart geothermal field is still in the exploratory stage.It has been tested for application in scenarios such as intelligent characterization of geothermal reservoirs,dynamic intelligent simulation of geothermal reservoirs,intelligent optimization of development schemes and smart management of geothermal development.However,it still faces many problems,including the high computational cost,difficult real-time response,multiple solutions and strong model dependence,difficult real-time optimization of dynamic multi-constraints,and deep integration of multi-source data.The construction scheme of smart geothermal field system is proposed,which consists of modules including the full database,intelligent characterization,intelligent simulation and intelligent optimization control.The connection between modules is established through the data transmission and the model interaction.In the next stage,it is necessary to focus on the basic theories and key technologies in each module of the smart geothermal field system,to accelerate the lifecycle intelligent transformation of the geothermal development and utilization,and to promote the intelligent,stable,long-term,optimal and safe production of geothermal resources.

System Reliability Analysis of Reservoir Landslides:Insights from Long-Term Reservoir Operation

The reservoir operation awakens numerous landslides with multiple sliding surfaces known as reservoir landslides,and the systematic stability analysis for such landslides is becoming increasingly urgent.Taking the Majiagou landslide as an example,this paper analyses the comprehensive performance of the landslide from a probabilistic point of view.Under a reservoir operation cycle,a series of numerical analyses are carried out to simulate the migration of the seepage field,then the dynamic stability of the landslide is quantified accordingly.Subsequently,the wetting-drying cycles test is used to model the weakening of strength parameters in hydro-fluctuation belt under the long-term reservoir operation.Considering the weakening effect of long-term reservoir operation on the hydrofluctuation belt,the system reliability is evaluated using the Ditlevsen's bounds.The results suggest that the reservoir operation can affect the stability of the landslide by changing the seepage field.The system failure probability gradually rises as the number of wetting-drying cycles increases.Compared with conventional probabilistic analysis that calculates the failure probability of each sliding surface mechanically,analyzing the landslide in terms of system reliability can effectively narrow the failure probability range,which provides an insightful idea for evaluating the systematic stability of analogous reservoir landslides.

Glacial debris flow susceptibility mapping based on combined models in the Parlung Tsangpo Basin,China

Machine learning(ML)-based prediction models for mapping hazard(e.g.,landslide and debris flow)susceptibility have been widely developed in recent research.However,in some specific areas,ML models have limited application because of the uncertainties in identifying negative samples.The Parlung Tsangpo Basin exemplifies a region prone to recurrent glacial debris flows(GDFs)and is characterized by a prominent landform featuring deep gullies.Considering the limitations of the ML model,we developed and compared two combined statistical models(FA-WE and FA-IC)based on factor analysis(FA),weight of evidence(WE),and the information content(IC)method.The final GDF susceptibility maps were generated by selecting 8 most important static factors and considering the influence of precipitation.The results show that the FA-IC model has the best performance.The areas with a very high susceptibility to GDFs are primarily located in the narrow valley section upstream,on both sides of the valley in the middle and downstream of the Parlung Tsangpo River,and in the narrow valley section of each tributary.These areas encompass 86 gullies and are characterized as"narrow and steep".

Reliability analysis of multimodal scene matching based on correlation peaks

The navigation system plays a pivotal role in guiding aircraft along designated routes,ensuring precise and punctual arrival at destinations.The integration of scene matching with an inertial navigation system enhances the capability of providing a dependable guarantee for success-ful accomplishment of flight missions.Nonetheless,assuring reliability in scene matching encoun-ters significant challenges in areas characterized by repetitive or weak textures.To tackle these challenges,we propose a novel method to assess the reliability of scene matching based on the dis-tinctive characteristics of correlation peaks.The proposed method leverages the fact that the sim-ilarity of the optimal matching result is significantly higher than that of the surrounding area,and three novel indicators(e.g.,relative height,slope of a correlation peak,and ratio of a sub peak to the main peak)are determined to conjointly evaluate the reliability of scene matching.The pro-posed method entails matching a real-time image with a reference image to generate a correlation surface.A correlation peak is then obtained by extracting the portion of the correlation surface exhibiting a significant gradient.Additionally,the matching reliability is determined by considering the relative height,slope,and ratio of the peak collectively.Exhaustive experimental results with two sets of data demonstrate that the proposed method significantly outperforms traditional approaches in terms of precision,recall,and F1-score.These experiments also establish the efficacy of the proposed method in achieving reliable matching in challenging environments characterized by repetitive and weak textures.This enhancement holds the potential to significantly elevate scene-matching-based navigation.