This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou Ci...This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.展开更多
In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken a...In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.展开更多
Pipeline hydraulic transport is a highly efficient and low energy-consumption method for transporting solids and is commonly used for tailing slurry transport in the mining industry.Erosion wear(EW)remains the main ca...Pipeline hydraulic transport is a highly efficient and low energy-consumption method for transporting solids and is commonly used for tailing slurry transport in the mining industry.Erosion wear(EW)remains the main cause of failure in tailings slurry pipeline systems,particularly at bends.EW is a complex phenomenon influenced by numerous factors,but research in this area has been limited.This study performs numerical simulations of slurry transport at the bend by combining computational fluid dynamics and fluid particle tracking using a wear model.Based on the validation of the feasibility of the model,this work focuses on the effects of coupled inlet velocity(IV)ranging from 1.5 to 3.0 m·s^(-1),particle size(PS)ranging from 50 to 650μm,and bend angle(BA)ranging from 45°to 90°on EW at the bend in terms of particle kinetic energy and incidence angle.The results show that the maximum EW rate of the slurry at the bend increases exponentially with IV and PS and first increases and then decreases with the increase in BA with the inflection point at 60°within these parameter ranges.Further comprehensive analysis reveals that the sensitivity level of the three factors to the maximum EW rate is PS>IV>BA,and when IV is 3.0 m/s,PS is 650μm,and BA is 60°,the bend EW is the most severe,and the maximum EW rate is 5.68×10^(-6)kg·m^(-2)·s^(-1).In addition,When PS is below or equal to 450μm,the maximum EW position is mainly at the outlet of the bend.When PS is greater than 450μm,the maximum EW position shifts toward the center of the bend with the increase in BA.Therefore,EW at the bend can be reduced in practice by reducing IV as much as possible and using small particles.展开更多
To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method propose...To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.展开更多
Most literature related to landslide susceptibility prediction only considers a single type of landslide,such as colluvial landslide,rock fall or debris flow,rather than different landslide types,which greatly affects...Most literature related to landslide susceptibility prediction only considers a single type of landslide,such as colluvial landslide,rock fall or debris flow,rather than different landslide types,which greatly affects susceptibility prediction performance.To construct efficient susceptibility prediction considering different landslide types,Huichang County in China is taken as example.Firstly,105 rock falls,350 colluvial landslides and 11 related environmental factors are identified.Then four machine learning models,namely logistic regression,multi-layer perception,support vector machine and C5.0 decision tree are applied for susceptibility modeling of rock fall and colluvial landslide.Thirdly,three different landslide susceptibility prediction(LSP)models considering landslide types based on C5.0 decision tree with excellent performance are constructed to generate final landslide susceptibility:(i)united method,which combines all landslide types directly;(ii)probability statistical method,which couples analyses of susceptibility indices under different landslide types based on probability formula;and(iii)maximum comparison method,which selects the maximum susceptibility index through comparing the predicted susceptibility indices under different types of landslides.Finally,uncertainties of landslide susceptibility are assessed by prediction accuracy,mean value and standard deviation.It is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang County.The united method has the best susceptibility prediction performance,followed by the probability method and maximum susceptibility method.More cases are needed to verify this result in-depth.LSP considering different landslide types is superior to that taking only a single type of landslide into account.展开更多
Phosphogypsum(PG)is a typical by-product of phosphoric acid and phosphate fertilizers during acid digestion.The application of cemented paste backfill(CPB)has been feasibly investigated for the remediation of PG.The p...Phosphogypsum(PG)is a typical by-product of phosphoric acid and phosphate fertilizers during acid digestion.The application of cemented paste backfill(CPB)has been feasibly investigated for the remediation of PG.The present study evaluated fluorine immobilization mechanisms and attempted to construct a related thermodynamic and geochemical modeling to describe the related stabilization performance.Physico-chemical and mineralogical analyses were performed on PG and hardened PG-based CPB(PCPB).The correlated macro-and micro-structural properties were obtained from the analysis of the combination of unconfined compressive strength and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy imaging.Acid/base-dependent leaching tests were performed to ascertain fluoride leachab-ility.In addition,Gibbs Energy Minimization Software and PHREEQC were applied as tools to characterize the PCPB hydration and deduce its geochemical characteristics.The results proved that multiple factors are involved in fluorine stabilization,among which the calcium silicate hy-drate gel was found to be associated with retention.Although the quantitative comparison with the experimental data shows that further modi-fication should be introduced into the simulation before being used as a predictive implement to determine PG management options,the im-portance of acid/base concentration in regulating the leaching behavior was confirmed.Moreover,the modeling enabled the identification of the impurity phases controlling the stability and leachability.展开更多
The aim of this research is to deepen the knowledge of the role of friction on the dynamics of granular media; in particular the friction angle is taken into consideration as the physical parameter that drives stabili...The aim of this research is to deepen the knowledge of the role of friction on the dynamics of granular media; in particular the friction angle is taken into consideration as the physical parameter that drives stability, motion and deposition of a set of grains of any nature and size. The idea behind this work is a question: is the friction angle really that fundamental and obvious physical parameter which rules stability and motion of granular media as it seems from most works which deal with particle dynamics? The experimental study tries to answer this question with a series of laboratory tests, in which different natural and artificial granular materials have been investigated in dry condition by means of a tilting flume. The characteristic friction angles, both in deposition(repose) and stability limit(critical) conditions, were measured and checked against size, shape, density and roughness of the considered granular material. The flume tests have been preferred to "classical" geotechnical apparatuses(e.g. shear box) since the flume experimental conditions appear closer to the natural ones of many situations of slope stability interest(e.g. a scree slope). The results reveal that characteristic friction angles depend on size and shape of grains while mixtures of granules of different size show some sorting mechanism with less clear behaviour.展开更多
Salt marshes are among the most common morphological features found in tidal landscapes and provide ecosystem services of primary ecological and economic importance.However,the continued rise in relative sea level and...Salt marshes are among the most common morphological features found in tidal landscapes and provide ecosystem services of primary ecological and economic importance.However,the continued rise in relative sea level and increasing anthropogenic pressures threaten the sustainability of these environments.The alarmingly high rates of salt marsh loss observed worldwide,mainly dictated by the lateral erosion of their margins,call for new insights into the mutual feedbacks among physical,biological,and morphological processes that take place at the critical interface between salt marshes and the adjoining tidal flats.We combined field measurements,remote sensing data,and numerical modeling to investigate the interplays between wind waves and the morphology,ecology,and planform evolution of salt marsh margins in the Venice Lagoon of Italy.Our results confirm the existence of a positive linear relationship between incoming wave power density and rates of salt marsh lateral retreat.In addition,we show that lateral erosion significantly decreases when halophytic vegetation colonizes the marsh margins,and that different erosion rates in vegetated margins are associated with different halophytes.High marsh cliffs and smooth shorelines are expected along rapidly eroding margins,whereas erosion rates are reduced in gently sloped,irregular edges facing shallow tidal flats that are typically exposed to low wind-energy conditions.By highlighting the relationships between the dynamics and functional forms of salt marsh margins,our results represent a critical step to address issues related to conservation and restoration of salt marsh ecosystems,especially in the face of changing environmental forcings.展开更多
A recent fascinating development in the study of high-grade metamorphic basements is represented by the finding of tiny inclusions of crystallized melt(nanogranitoid inclusions) hosted in peritectic phases of migmatit...A recent fascinating development in the study of high-grade metamorphic basements is represented by the finding of tiny inclusions of crystallized melt(nanogranitoid inclusions) hosted in peritectic phases of migmatites and granulites. These inclusions have the potential to provide the primary composition of crustal melts at the source. A novel use of the recently-published nanogranitoid compositional database is presented here. Using granulites from the world-renowned Ivrea Zone(NW Italy) on which the original melt-reintegration approach has been previously applied, it is shown that reintegrating melt inclusion compositions from the published database into residual rock compositions can be a further useful method to reconstruct a plausible prograde history of melt-depleted rocks. This reconstruction is fundamental to investigate the tectonothermal history of geological terranes.展开更多
Synergistic multi-factor early warning of large-scale landslides is a crucial component of geohazard prevention and mitigation efforts in reservoir areas.Landslide forecasting and early warning based on surface displa...Synergistic multi-factor early warning of large-scale landslides is a crucial component of geohazard prevention and mitigation efforts in reservoir areas.Landslide forecasting and early warning based on surface displacements have been widely investigated.However,the lack of direct subsurface real-time observations limits our ability to predict critical hydrometeorological conditions that trigger landslide acceleration.In this paper,we leverage subsurface strain data measured by high-resolution fiber optic sensing nerves that were installed in a giant reservoir landslide in the Three Gorges Reservoir(TGR)region,China,spanning a whole hydrologic year since February 2021.The spatiotemporal strain profile has preliminarily identified the slip zones and potential drivers,indicating that high-intensity short-duration rainstorms controlled the landslide kinematics from an observation perspective.Considering the time lag effect,we reexamined and quantified potential controls of accelerated movements using a data-driven approach,which reveals immediate response of landslide deformation to extreme rainfall with a zero-day shift.To identify critical hydrometeorological rules in accelerated movements,accounting for the dual effect of rainfall and reservoir water level variations,we thus construct a landslide prediction model that relies upon the boosting decision tree(BDT)algorithm using a dataset comprising daily rainfall,rainfall intensity,reservoir water level,water level fluctuations,and slip zone strain time series.The results indicate that landslide acceleration is most likely to occur under the conditions of mid-low water levels(i.e.,<169.700 m)and large-amount and high-intensity rainfalls(i.e.,daily rainfall>57.9 mm and rainfall intensity>24.4 mm/h).Moreover,this prediction model allows us to update hydrometeorological thresholds by incorporating the latest monitoring dataset.Standing on the shoulder of this landslide case,our study informs a practical and reliable pathway for georisk early warning based on subsurface observations,particularly in the context of enhanced extreme weather events.展开更多
The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning mode...The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.展开更多
This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of ...This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.展开更多
The Mufushan massif, as continental intra-plate magmatites located in the Jiangnan-Xuefeng orogenic belt of the South China. The Mufushan massif constitutes the largest Mesozoic intrusive complex, intruded the Mesopro...The Mufushan massif, as continental intra-plate magmatites located in the Jiangnan-Xuefeng orogenic belt of the South China. The Mufushan massif constitutes the largest Mesozoic intrusive complex, intruded the Mesoproterozoic Lengjiaxi Formation. Multiple geochronometric dating was used to reconstruct their evolution from emplacement to exhumation. The Mufushan granitoids were emplaced at ~150 Ma(U-Pb zircon) as post-orogenic magmatites contributing to Triassic crustal thickening. Onset of regional extension at ~128 Ma(40Ar/39Ar white mica and biotite) manifests a tectonic regime switch. Intense exhumation prior to ~55 Ma was followed by slow denudation and peneplanation for the next 37 Ma(~55–18 Ma). Accelerated cooling since ~18 Ma may have been caused by a far-field effect of the collision between IndiaAsia Plate or the Pacific-Plate subduction. Through a multi-geochronometric approach, this study provides a new comprehensive model for the cause of the intra-plate magmatism formation in the South China, and also established a reliable geochronological framework of the post-orogenic tectonic evolutions of the Jiangnan-Xuefeng orogenic belt.展开更多
Mockina slovakensis,thought to have evolved from Epigondolella praeslovakensis,is an important species of the Norian(Upper Triassic),generally considered as the representative of the uppermost Alaunian to upper Sevati...Mockina slovakensis,thought to have evolved from Epigondolella praeslovakensis,is an important species of the Norian(Upper Triassic),generally considered as the representative of the uppermost Alaunian to upper Sevatian in the Tethys.The previous description of M.slovakensis was incomplete,thus has led to some misidentifications.We thus update the description of M.slovakensis and discuss its comparisons and occurrence based on the new conodont investigations in Dolomia di Forni and the data from previous literatures.The conodont assemblage in the succession of Dolomia di Forni is dominated by M.slovakensis,along with rare M.postera and E.praeslovakensis.We described two morphotypes of M.slovakensis(morphotypes A and B),on the basis of shape of the lateral profile.These two morphotypes can also be observed in the E.praeslovakensis.Moreover,M.slovakensis is usually documented as almost monospecific conodont association in intraplatform basins,thus its paleogeographic implications are also discussed.展开更多
Based on a study of 49 conodont and 57 geochemical samples from the Upper Triassic,carbonate-dominated Dengdengqiao Formation,Qinling Basin,China,the Carnian conodonts and carbon isotope records are first reported.Two...Based on a study of 49 conodont and 57 geochemical samples from the Upper Triassic,carbonate-dominated Dengdengqiao Formation,Qinling Basin,China,the Carnian conodonts and carbon isotope records are first reported.Two genera and four species have been identified amongst 87 conodont elements:Mosherella praebudaensis,Mo.longnanensis sp.nov.,Mo.sp.,and"Misikella"longidentata.The presence of Mo.praebudaensis indicates that the lower part(bed 2)of the formation is attributable to the Julian(lower Carnian)substage.A radiolarian fauna identified in a previous study belongs to the upper Carnian,but the sampling horizon is unclear.Theδ13Ccarb curve shows a~1.8‰negative excursion beginning from upper part of bed 3,but its stratigraphic location is uncertain.The Dengdengqiao Formation is clearly at least partly of Carnian age but could include younger strata.The abundant calcareous algae at the section is probably due to some transport rather than preserved in site.The unusual ecosystem with rare marine organisms may reflect long-term stressful and unfavorable conditions at Dengdengqiao.展开更多
During their last phase of evolution,the pectiniform conodont elements manifested an evident trend of simplification and miniaturization.This phase started from the late Norian(Sevatian)in the Late Triassic and the ev...During their last phase of evolution,the pectiniform conodont elements manifested an evident trend of simplification and miniaturization.This phase started from the late Norian(Sevatian)in the Late Triassic and the evolutionary process of genus Mockina to Parvigondolella,in particular between Mockina bidentata and Parvigondolella andrusovi,is one of the most significant examples.Parvigondolella has been reported worldwide since it was first described in the early 1970s.However,it has recently been suggested that genus Parvigondolella is an ecostratigraphic morphotype of genus Mockina,and thus a phenotype controlled by the environmental conditions,and not an independent taxon.In the Pizzo Mondello Section(Sicily,Italy),transitional forms between M.bidentata and P.andrusovi have been found at different evolutionary stages.We have investigated the oceanic conditions at the time by using redox-sensitive elements(Mn,Fe,V,Cr,and Ni)and seawater temperatures from biogenetic δ^(18)Ophos to understand the possible environmental influences on the phylogenetic evolution between Mockina and Parvigondolella.The geochemical and isotope analyses indicate that the redox condition and temperature were stable during the evolution of genus Parvigondolella in Pizzo Mondello,confirming that genus Parvigondolella is a real taxon and not a phenotype.A new conodont species named Parvigondolella ciarapicae n.sp.is described here for the first time.展开更多
Conodonts are elements of a feeding apparatus of jawless eel-like animals belonging to the clade Vertebrata.They are very important microfossils,ubiquitous in the Paleozoic and Early Mesozoic marine sequences,and they...Conodonts are elements of a feeding apparatus of jawless eel-like animals belonging to the clade Vertebrata.They are very important microfossils,ubiquitous in the Paleozoic and Early Mesozoic marine sequences,and they occurred in different habitats,from deep-ocean to shallow-shelf waters.展开更多
基金the Natural Science Foundation of China(41807285)Interdisciplinary Innovation Fund of Natural Science,NanChang University(9167-28220007-YB2107).
文摘This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.
基金This work is funded by the National Natural Science Foundation of China(Grant Nos.42377164 and 52079062)the National Science Fund for Distinguished Young Scholars of China(Grant No.52222905).
文摘In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.
基金financially supported by the National Natural Science Foundation of China (Nos.52104156,52074351 and 52004330)the Science and Technology Innovation Program of Hunan Province,China (No.2021RC3125).
文摘Pipeline hydraulic transport is a highly efficient and low energy-consumption method for transporting solids and is commonly used for tailing slurry transport in the mining industry.Erosion wear(EW)remains the main cause of failure in tailings slurry pipeline systems,particularly at bends.EW is a complex phenomenon influenced by numerous factors,but research in this area has been limited.This study performs numerical simulations of slurry transport at the bend by combining computational fluid dynamics and fluid particle tracking using a wear model.Based on the validation of the feasibility of the model,this work focuses on the effects of coupled inlet velocity(IV)ranging from 1.5 to 3.0 m·s^(-1),particle size(PS)ranging from 50 to 650μm,and bend angle(BA)ranging from 45°to 90°on EW at the bend in terms of particle kinetic energy and incidence angle.The results show that the maximum EW rate of the slurry at the bend increases exponentially with IV and PS and first increases and then decreases with the increase in BA with the inflection point at 60°within these parameter ranges.Further comprehensive analysis reveals that the sensitivity level of the three factors to the maximum EW rate is PS>IV>BA,and when IV is 3.0 m/s,PS is 650μm,and BA is 60°,the bend EW is the most severe,and the maximum EW rate is 5.68×10^(-6)kg·m^(-2)·s^(-1).In addition,When PS is below or equal to 450μm,the maximum EW position is mainly at the outlet of the bend.When PS is greater than 450μm,the maximum EW position shifts toward the center of the bend with the increase in BA.Therefore,EW at the bend can be reduced in practice by reducing IV as much as possible and using small particles.
基金funded by the Natural Science Foundation of China(Grant Nos.41807285,41972280 and 52179103).
文摘To perform landslide susceptibility prediction(LSP),it is important to select appropriate mapping unit and landslide-related conditioning factors.The efficient and automatic multi-scale segmentation(MSS)method proposed by the authors promotes the application of slope units.However,LSP modeling based on these slope units has not been performed.Moreover,the heterogeneity of conditioning factors in slope units is neglected,leading to incomplete input variables of LSP modeling.In this study,the slope units extracted by the MSS method are used to construct LSP modeling,and the heterogeneity of conditioning factors is represented by the internal variations of conditioning factors within slope unit using the descriptive statistics features of mean,standard deviation and range.Thus,slope units-based machine learning models considering internal variations of conditioning factors(variant slope-machine learning)are proposed.The Chongyi County is selected as the case study and is divided into 53,055 slope units.Fifteen original slope unit-based conditioning factors are expanded to 38 slope unit-based conditioning factors through considering their internal variations.Random forest(RF)and multi-layer perceptron(MLP)machine learning models are used to construct variant Slope-RF and Slope-MLP models.Meanwhile,the Slope-RF and Slope-MLP models without considering the internal variations of conditioning factors,and conventional grid units-based machine learning(Grid-RF and MLP)models are built for comparisons through the LSP performance assessments.Results show that the variant Slopemachine learning models have higher LSP performances than Slope-machine learning models;LSP results of variant Slope-machine learning models have stronger directivity and practical application than Grid-machine learning models.It is concluded that slope units extracted by MSS method can be appropriate for LSP modeling,and the heterogeneity of conditioning factors within slope units can more comprehensively reflect the relationships between conditioning factors and landslides.The research results have important reference significance for land use and landslide prevention.
基金funded by the Natural Science Foundation of China(Grant Nos.52079062 and 41807285)the Interdisciplinary Innovation Fund of Natural Science,Nanchang University,China(Grant No.9167-28220007-YB2107).
文摘Most literature related to landslide susceptibility prediction only considers a single type of landslide,such as colluvial landslide,rock fall or debris flow,rather than different landslide types,which greatly affects susceptibility prediction performance.To construct efficient susceptibility prediction considering different landslide types,Huichang County in China is taken as example.Firstly,105 rock falls,350 colluvial landslides and 11 related environmental factors are identified.Then four machine learning models,namely logistic regression,multi-layer perception,support vector machine and C5.0 decision tree are applied for susceptibility modeling of rock fall and colluvial landslide.Thirdly,three different landslide susceptibility prediction(LSP)models considering landslide types based on C5.0 decision tree with excellent performance are constructed to generate final landslide susceptibility:(i)united method,which combines all landslide types directly;(ii)probability statistical method,which couples analyses of susceptibility indices under different landslide types based on probability formula;and(iii)maximum comparison method,which selects the maximum susceptibility index through comparing the predicted susceptibility indices under different types of landslides.Finally,uncertainties of landslide susceptibility are assessed by prediction accuracy,mean value and standard deviation.It is concluded that LSP results of the three coupled models considering landslide types basically conform to the spatial occurrence patterns of landslides in Huichang County.The united method has the best susceptibility prediction performance,followed by the probability method and maximum susceptibility method.More cases are needed to verify this result in-depth.LSP considering different landslide types is superior to that taking only a single type of landslide into account.
基金This work was financially supported by the Natural Science Foundation of Hunan Province,China(No.2020JJ5718)a scholarship granted by the China Scholarship Council(No.CSC201906370062)the National Natural Science Foundation of China(Nos.52004330 and 52074351).
文摘Phosphogypsum(PG)is a typical by-product of phosphoric acid and phosphate fertilizers during acid digestion.The application of cemented paste backfill(CPB)has been feasibly investigated for the remediation of PG.The present study evaluated fluorine immobilization mechanisms and attempted to construct a related thermodynamic and geochemical modeling to describe the related stabilization performance.Physico-chemical and mineralogical analyses were performed on PG and hardened PG-based CPB(PCPB).The correlated macro-and micro-structural properties were obtained from the analysis of the combination of unconfined compressive strength and scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy imaging.Acid/base-dependent leaching tests were performed to ascertain fluoride leachab-ility.In addition,Gibbs Energy Minimization Software and PHREEQC were applied as tools to characterize the PCPB hydration and deduce its geochemical characteristics.The results proved that multiple factors are involved in fluorine stabilization,among which the calcium silicate hy-drate gel was found to be associated with retention.Although the quantitative comparison with the experimental data shows that further modi-fication should be introduced into the simulation before being used as a predictive implement to determine PG management options,the im-portance of acid/base concentration in regulating the leaching behavior was confirmed.Moreover,the modeling enabled the identification of the impurity phases controlling the stability and leachability.
文摘The aim of this research is to deepen the knowledge of the role of friction on the dynamics of granular media; in particular the friction angle is taken into consideration as the physical parameter that drives stability, motion and deposition of a set of grains of any nature and size. The idea behind this work is a question: is the friction angle really that fundamental and obvious physical parameter which rules stability and motion of granular media as it seems from most works which deal with particle dynamics? The experimental study tries to answer this question with a series of laboratory tests, in which different natural and artificial granular materials have been investigated in dry condition by means of a tilting flume. The characteristic friction angles, both in deposition(repose) and stability limit(critical) conditions, were measured and checked against size, shape, density and roughness of the considered granular material. The flume tests have been preferred to "classical" geotechnical apparatuses(e.g. shear box) since the flume experimental conditions appear closer to the natural ones of many situations of slope stability interest(e.g. a scree slope). The results reveal that characteristic friction angles depend on size and shape of grains while mixtures of granules of different size show some sorting mechanism with less clear behaviour.
文摘Salt marshes are among the most common morphological features found in tidal landscapes and provide ecosystem services of primary ecological and economic importance.However,the continued rise in relative sea level and increasing anthropogenic pressures threaten the sustainability of these environments.The alarmingly high rates of salt marsh loss observed worldwide,mainly dictated by the lateral erosion of their margins,call for new insights into the mutual feedbacks among physical,biological,and morphological processes that take place at the critical interface between salt marshes and the adjoining tidal flats.We combined field measurements,remote sensing data,and numerical modeling to investigate the interplays between wind waves and the morphology,ecology,and planform evolution of salt marsh margins in the Venice Lagoon of Italy.Our results confirm the existence of a positive linear relationship between incoming wave power density and rates of salt marsh lateral retreat.In addition,we show that lateral erosion significantly decreases when halophytic vegetation colonizes the marsh margins,and that different erosion rates in vegetated margins are associated with different halophytes.High marsh cliffs and smooth shorelines are expected along rapidly eroding margins,whereas erosion rates are reduced in gently sloped,irregular edges facing shallow tidal flats that are typically exposed to low wind-energy conditions.By highlighting the relationships between the dynamics and functional forms of salt marsh margins,our results represent a critical step to address issues related to conservation and restoration of salt marsh ecosystems,especially in the face of changing environmental forcings.
基金supported by the Italian Ministry of Education, University, Research (Grant SIR RBSI14Y7PF to O.B.)
文摘A recent fascinating development in the study of high-grade metamorphic basements is represented by the finding of tiny inclusions of crystallized melt(nanogranitoid inclusions) hosted in peritectic phases of migmatites and granulites. These inclusions have the potential to provide the primary composition of crustal melts at the source. A novel use of the recently-published nanogranitoid compositional database is presented here. Using granulites from the world-renowned Ivrea Zone(NW Italy) on which the original melt-reintegration approach has been previously applied, it is shown that reintegrating melt inclusion compositions from the published database into residual rock compositions can be a further useful method to reconstruct a plausible prograde history of melt-depleted rocks. This reconstruction is fundamental to investigate the tectonothermal history of geological terranes.
基金supported by the National Science Fund for Distinguished Young Scholars(Grant No.42225702)the National Natural Science Foundation of China(Grant No.42077235)+1 种基金the Maria Sklodowska-Curie Action(MSCA)-UPGRADE(mUltiscale IoT equipPed lonG linear infRastructure resilience built and sustAinable DevelopmEnt)project HORIZON-MSCA-2022-SE-01(Grant No.101131146)the China Scholarship Council(CSC)for funding his research period at UNIPD and CNRIRPI。
文摘Synergistic multi-factor early warning of large-scale landslides is a crucial component of geohazard prevention and mitigation efforts in reservoir areas.Landslide forecasting and early warning based on surface displacements have been widely investigated.However,the lack of direct subsurface real-time observations limits our ability to predict critical hydrometeorological conditions that trigger landslide acceleration.In this paper,we leverage subsurface strain data measured by high-resolution fiber optic sensing nerves that were installed in a giant reservoir landslide in the Three Gorges Reservoir(TGR)region,China,spanning a whole hydrologic year since February 2021.The spatiotemporal strain profile has preliminarily identified the slip zones and potential drivers,indicating that high-intensity short-duration rainstorms controlled the landslide kinematics from an observation perspective.Considering the time lag effect,we reexamined and quantified potential controls of accelerated movements using a data-driven approach,which reveals immediate response of landslide deformation to extreme rainfall with a zero-day shift.To identify critical hydrometeorological rules in accelerated movements,accounting for the dual effect of rainfall and reservoir water level variations,we thus construct a landslide prediction model that relies upon the boosting decision tree(BDT)algorithm using a dataset comprising daily rainfall,rainfall intensity,reservoir water level,water level fluctuations,and slip zone strain time series.The results indicate that landslide acceleration is most likely to occur under the conditions of mid-low water levels(i.e.,<169.700 m)and large-amount and high-intensity rainfalls(i.e.,daily rainfall>57.9 mm and rainfall intensity>24.4 mm/h).Moreover,this prediction model allows us to update hydrometeorological thresholds by incorporating the latest monitoring dataset.Standing on the shoulder of this landslide case,our study informs a practical and reliable pathway for georisk early warning based on subsurface observations,particularly in the context of enhanced extreme weather events.
基金funded by the National Natural Science Foundation of China (41807285)。
文摘The numerical simulation and slope stability prediction are the focus of slope disaster research.Recently,machine learning models are commonly used in the slope stability prediction.However,these machine learning models have some problems,such as poor nonlinear performance,local optimum and incomplete factors feature extraction.These issues can affect the accuracy of slope stability prediction.Therefore,a deep learning algorithm called Long short-term memory(LSTM)has been innovatively proposed to predict slope stability.Taking the Ganzhou City in China as the study area,the landslide inventory and their characteristics of geotechnical parameters,slope height and slope angle are analyzed.Based on these characteristics,typical soil slopes are constructed using the Geo-Studio software.Five control factors affecting slope stability,including slope height,slope angle,internal friction angle,cohesion and volumetric weight,are selected to form different slope and construct model input variables.Then,the limit equilibrium method is used to calculate the stability coefficients of these typical soil slopes under different control factors.Each slope stability coefficient and its corresponding control factors is a slope sample.As a result,a total of 2160 training samples and 450 testing samples are constructed.These sample sets are imported into LSTM for modelling and compared with the support vector machine(SVM),random forest(RF)and convo-lutional neural network(CNN).The results show that the LSTM overcomes the problem that the commonly used machine learning models have difficulty extracting global features.Furthermore,LSTM has a better prediction performance for slope stability compared to SVM,RF and CNN models.
基金This research is funded by the National Natural Science Foundation of China(41807285,41762020,51879127 and 51769014E)Natural Science Foundation of Hebei Province(D2022202005).
文摘This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction(LSP),namely the spatial resolution,proportion of model training and testing datasets and selection of machine learning models.Taking Yanchang County of China as example,the landslide inventory and 12 important conditioning factors were acquired.The frequency ratios of each conditioning factor were calculated under five spatial resolutions(15,30,60,90 and 120 m).Landslide and non-landslide samples obtained under each spatial resolution were further divided into five proportions of training and testing datasets(9:1,8:2,7:3,6:4 and 5:5),and four typical machine learning models were applied for LSP modelling.The results demonstrated that different spatial resolution and training and testing dataset proportions induce basically similar influences on the modeling uncertainty.With a decrease in the spatial resolution from 15 m to 120 m and a change in the proportions of the training and testing datasets from 9:1 to 5:5,the modelling accuracy gradually decreased,while the mean values of predicted landslide susceptibility indexes increased and their standard deviations decreased.The sensitivities of the three uncertainty issues to LSP modeling were,in order,the spatial resolution,the choice of machine learning model and the proportions of training/testing datasets.
基金the NSFC projects(Nos.41972152,41672140,41372140)the“Thirteenth Five-Year”Major National Science and Technology Programs(Nos.2017ZX05032-002-004,2016ZX05024-002-005)+2 种基金the Special Fund for Basic Scientific Research of Central Colleges,China University of Geosciences,Wuhan(No.CUGCJ1820)the“111”Program(No.B14031)the Natural Science Foundation of Hubei Province(No.2016CFA055)。
文摘The Mufushan massif, as continental intra-plate magmatites located in the Jiangnan-Xuefeng orogenic belt of the South China. The Mufushan massif constitutes the largest Mesozoic intrusive complex, intruded the Mesoproterozoic Lengjiaxi Formation. Multiple geochronometric dating was used to reconstruct their evolution from emplacement to exhumation. The Mufushan granitoids were emplaced at ~150 Ma(U-Pb zircon) as post-orogenic magmatites contributing to Triassic crustal thickening. Onset of regional extension at ~128 Ma(40Ar/39Ar white mica and biotite) manifests a tectonic regime switch. Intense exhumation prior to ~55 Ma was followed by slow denudation and peneplanation for the next 37 Ma(~55–18 Ma). Accelerated cooling since ~18 Ma may have been caused by a far-field effect of the collision between IndiaAsia Plate or the Pacific-Plate subduction. Through a multi-geochronometric approach, this study provides a new comprehensive model for the cause of the intra-plate magmatism formation in the South China, and also established a reliable geochronological framework of the post-orogenic tectonic evolutions of the Jiangnan-Xuefeng orogenic belt.
基金supported by the National Natural Science Foundation of China(Nos.41830320,45172002,41661134047)。
文摘Mockina slovakensis,thought to have evolved from Epigondolella praeslovakensis,is an important species of the Norian(Upper Triassic),generally considered as the representative of the uppermost Alaunian to upper Sevatian in the Tethys.The previous description of M.slovakensis was incomplete,thus has led to some misidentifications.We thus update the description of M.slovakensis and discuss its comparisons and occurrence based on the new conodont investigations in Dolomia di Forni and the data from previous literatures.The conodont assemblage in the succession of Dolomia di Forni is dominated by M.slovakensis,along with rare M.postera and E.praeslovakensis.We described two morphotypes of M.slovakensis(morphotypes A and B),on the basis of shape of the lateral profile.These two morphotypes can also be observed in the E.praeslovakensis.Moreover,M.slovakensis is usually documented as almost monospecific conodont association in intraplatform basins,thus its paleogeographic implications are also discussed.
基金supported by the National Natural Science Foundation of China(Nos.41830320,45172002,41661134047)。
文摘Based on a study of 49 conodont and 57 geochemical samples from the Upper Triassic,carbonate-dominated Dengdengqiao Formation,Qinling Basin,China,the Carnian conodonts and carbon isotope records are first reported.Two genera and four species have been identified amongst 87 conodont elements:Mosherella praebudaensis,Mo.longnanensis sp.nov.,Mo.sp.,and"Misikella"longidentata.The presence of Mo.praebudaensis indicates that the lower part(bed 2)of the formation is attributable to the Julian(lower Carnian)substage.A radiolarian fauna identified in a previous study belongs to the upper Carnian,but the sampling horizon is unclear.Theδ13Ccarb curve shows a~1.8‰negative excursion beginning from upper part of bed 3,but its stratigraphic location is uncertain.The Dengdengqiao Formation is clearly at least partly of Carnian age but could include younger strata.The abundant calcareous algae at the section is probably due to some transport rather than preserved in site.The unusual ecosystem with rare marine organisms may reflect long-term stressful and unfavorable conditions at Dengdengqiao.
基金supported by the grants PRIN to Manuel Rigo(No.2017W2MARE)the China Scholarship Council to Yixing Du(No.201708510096)Development of conodont oxygen isotopic analysis by SHRIMP was supported by Australian Research Council Discovery to Ian S.Williams(No.DP1096252)。
文摘During their last phase of evolution,the pectiniform conodont elements manifested an evident trend of simplification and miniaturization.This phase started from the late Norian(Sevatian)in the Late Triassic and the evolutionary process of genus Mockina to Parvigondolella,in particular between Mockina bidentata and Parvigondolella andrusovi,is one of the most significant examples.Parvigondolella has been reported worldwide since it was first described in the early 1970s.However,it has recently been suggested that genus Parvigondolella is an ecostratigraphic morphotype of genus Mockina,and thus a phenotype controlled by the environmental conditions,and not an independent taxon.In the Pizzo Mondello Section(Sicily,Italy),transitional forms between M.bidentata and P.andrusovi have been found at different evolutionary stages.We have investigated the oceanic conditions at the time by using redox-sensitive elements(Mn,Fe,V,Cr,and Ni)and seawater temperatures from biogenetic δ^(18)Ophos to understand the possible environmental influences on the phylogenetic evolution between Mockina and Parvigondolella.The geochemical and isotope analyses indicate that the redox condition and temperature were stable during the evolution of genus Parvigondolella in Pizzo Mondello,confirming that genus Parvigondolella is a real taxon and not a phenotype.A new conodont species named Parvigondolella ciarapicae n.sp.is described here for the first time.
文摘Conodonts are elements of a feeding apparatus of jawless eel-like animals belonging to the clade Vertebrata.They are very important microfossils,ubiquitous in the Paleozoic and Early Mesozoic marine sequences,and they occurred in different habitats,from deep-ocean to shallow-shelf waters.