This paper explored and discussed the cognition of informatization geomatics innovative talents cultivating laws and the basic principles and teaching system of informatization experiment teaching in the informatizati...This paper explored and discussed the cognition of informatization geomatics innovative talents cultivating laws and the basic principles and teaching system of informatization experiment teaching in the informatization experimental teaching innovation.展开更多
Geomatics is an interdisciplinary subject.Many disciplines have teaching demands in this field.A new course on“Geomatics Technology”has been suggested by the Weiyang College of Tsinghua University of China for the m...Geomatics is an interdisciplinary subject.Many disciplines have teaching demands in this field.A new course on“Geomatics Technology”has been suggested by the Weiyang College of Tsinghua University of China for the major of“Mathematical and Scientific Basic Science+Civil,Hydraulic and Marine Engineering”.This paper offers a data-led geomatics teaching mode,developing a customized teaching cloud platform,to explore the cross-integrated innovative teaching methods.Teachers and students can assign and submit assignments on this platform.The platform constitutes a data flow with the data download,data processing and result sharing.It encourages communication among students in various majors,grades and units using data as the medium,from data processing to application upstream and downstream.In the“Geomatics Technology”course,geospatial data has emerged as a vital element of the multidisciplinary approach.This kind of teaching mode has been used in the postgraduate remote sensing course offered by Tsinghua University’s Department of Civil Engineering and Construction Management.Furthermore,the mode will be used for the first time in the autumn semester of 2022 in the undergraduate teaching of Weiyang College and civil engineering,to offer a novel idea for the reform of courses linked to geospatial informatics.展开更多
Landslides are one of the most significant natural damaging disasters in hilly environment [1]. The location of our study area is to the north of Tunisia, home to several manifestations of land instabilities, we ...Landslides are one of the most significant natural damaging disasters in hilly environment [1]. The location of our study area is to the north of Tunisia, home to several manifestations of land instabilities, we bring to study this area of interest by GIS and geomatic approach to reduce social economic losses due to landslides. The performance of a cartographic data base for the landslide study in the Cap-Bon region was realized through studying geologic 1/50,000 and topographic 1/25,000 maps, aster optic Remote Sensing, land observation, and climatologic seismic data. These data will be digitalized, georeferenced, vectorized, spatially analyzed, classified and geotreated in order to produce a landslides card. The findings have shown that fields with smooth and friable lithology which are located at rather important seismic zones (more than 4 at Richter’s scale) have some stability. However, the most endangered zones are in the North-West around Oued El Kbir and El Ain. Realizing this work helps to determine the most hazardous zones so that policy makers have an effective field intervention.展开更多
Mining activities often cause dramatic changes in landscapes, particularly in the dump sites and its surrounding environment. Land rehabilitation is the process of renovating damaged land to some extent of its origina...Mining activities often cause dramatic changes in landscapes, particularly in the dump sites and its surrounding environment. Land rehabilitation is the process of renovating damaged land to some extent of its original shape and aims to minimize and mitigate the environmental effects to allow new land uses. The success of different rehabilitation strategy and newly suggested urban and architecture modeling depends on the landscape characterization (topography of the study area and its derivatives such as slope and aspects, geological and geomorphologic nature of the study area). The aim of this study is to demonstrate the utility of different methodologies based on geomatics techniques (Photogrammetry, Remote Sensing, Global Positioning System (GPS) and three dimensional Geographic Information System (GIS)) for highlighting landscape characterization which is needed for rehabilitation of Mahis area. Photogrammetric adjustment procedures were used to create digital elevation model and Orth-Photo model for the study area using aerial images. Remote sensing data were used for land classification to provide vital information for rehabilitation planning. GPS field observations were used to build spatial network for the study area based on ground control point collections. Finally, realistic representation of the study area with three dimensional GIS was prepared for the study area considering ease and flexible updating of the geo-spatial database.展开更多
The GRACE(Gravity Recovery and Climate Experiment)space mission recorded temporal variation characteristics of the global gravity field at decadal timescales.The gravity data have been shown to capture the dynamics of...The GRACE(Gravity Recovery and Climate Experiment)space mission recorded temporal variation characteristics of the global gravity field at decadal timescales.The gravity data have been shown to capture the dynamics of flows within the outer core and their effects on the core-mantle boundary.We first aim to remove global surface process gravity signals from the GRACE data.We then construct the global core magnetic field according to the CHAOS-7 model.Finally,we apply the blind source separation method to decompose the processed gravity signals and core magnetic signals and compute the power spectral density of the gravity and magnetic field signals by using the Lomb-Scargle periodogram approach.We have discovered a signal cycle(of~6 years)in the principal components of the core magnetic and gravity signals,potentially as a result of deep Earth processes.The main principal components of the core magnetic and gravity signals reveal that the variation trends in the second-order time derivative of the core magnetic field are similar to those in the gravity field.After 2014,the second-order time derivative of the core magnetic field exhibited linear and rapid change characteristics,which were the same as the change in the gravity field and are consistent with existing research results.展开更多
In Global Navigation Satellite System(GNSS)meteo rology,the atmospheric weighted mean temperatu re(T_(m))is a critical intermediate parameter for converting zenith wet delay(ZWD)to precipitable water vapor(PWV),essent...In Global Navigation Satellite System(GNSS)meteo rology,the atmospheric weighted mean temperatu re(T_(m))is a critical intermediate parameter for converting zenith wet delay(ZWD)to precipitable water vapor(PWV),essential for accurate atmospheric water content estimation.However,global models often overlook regional climatic variability,leading to reduced accuracy in localized applications.This study introduces an improved T_(m)model developed using radiosonde observations across Iran and GNSS radio occultation(RO)profiles from CHAMP,GRACE,MetOp-A/B/C,COSMIC,TerraSAR-X,and TanDEM-X missions collected between 2007 and 2022.A novel integral formulation was proposed to estimate T_(m)more accurately by incorporating vertical water vapor distribution and temperature linearity.Based on this formulation,three regional T_(m)models were constructed using annual,semiannual,and diurnal periodicities,along with surface temperature(T_(s)),each varying in structure and complexity.Validation against independent radiosonde observations from 2022 showed that Models Two and Three outperformed the Bevis model,reducing RMSE by 30.7%.When evaluated against GNSS RO profiles,Model One—excluding T_(s)due to its inaccessibility in RO data—yielded the highest accuracy,with a 42.6%improvement in RMSE over the Bevis model.To evaluate the practical effectiveness of the proposed T_(m)model,PWV was derived from GNSS data at the tehn and tabz stations during the second half of 2022and compared with PWV values obtained from co-located radiosonde observations in Tehran and Tabriz.Using T_(m)from Model One improved PWV estimation compared to the Bevis model,reducing RMSE and MAE by up to 54%and 53.8%in Tabriz and 50.6%and 52.9%in Tehran,respectively.These results demonstrate that regionalized T_(m)modeling,particularly approaches that avoid dependence on T_(s),can significantly enhance GNSS-based PWV estimation in areas with limited surface data.展开更多
To characterize the spatial patterns of vertical crustal movement of Chinese mainland,GNSS imaging technology was applied to map the tectonic deformation of the region.In this study,the vertical crustal velocities inf...To characterize the spatial patterns of vertical crustal movement of Chinese mainland,GNSS imaging technology was applied to map the tectonic deformation of the region.In this study,the vertical crustal velocities inferred from GNSS data for Chinese mainland over two decades were rigorously estimated.First,by analyzing the vertical displacement time series from continuous GNSS stations and environmental load data,we found that the annual and semi-annual vertical displacements are highly correlated.This indicates that the vertical seasonal variations on the ground surface are mainly caused by environmental loading.After removing the seasonal variations caused by environmental loads from the GNSS time series,we applied an improved PCA technique to filter out common mode errors.Next,we estimated the optimal noise models for the filtered time series and derived the vertical velocity field of Chinese mainland.Finally,we employed an empirical Spatial Structure Function(SSF)to image the tectonic deformation of Chinese mainland.This method effectively mitigates issues with abrupt circular arc-shaped boundaries in GNSS imaging caused by sparse station networks.The imaging results show that vertical crustal deformation in Chinese mainland generally ranges from-3 to 3 mm/yr,with significant spatial variability.The central and northern parts of Qinghai-Xizang Plateau are identified as primary subsidence zones,indicating that plate boundaries and tectonic compression continue to shape the crustal movement in these regions.The major uplift zones are located in northern and central China,likely linked to regional tectonic activity and plate compression.Subsidence deformation in parts of eastern China appears to be influenced by human activities.展开更多
Comprehending the flow behavior of deep-sea mining plumes is paramount for precise predictions of their propagation range and holds immense significance in advancing the commercial exploitation of deep-sea minerals.As...Comprehending the flow behavior of deep-sea mining plumes is paramount for precise predictions of their propagation range and holds immense significance in advancing the commercial exploitation of deep-sea minerals.As deep-sea mining plumes propagate,they can transition from high-density non-Newtonian fluids to low-density Newtonian fluids.However,a comprehensive rheological model capable of accurately describing this intricate evolutionary process is currently lacking.This study explores the variations in rheological properties observed during the propagation of deep-sea mining plumes,utilizing rheological test data obtained from kaolin clay plumes.Utilizing the Power Law model,we established a power exponential function correlating the plume rheological parameters(consistency index and flow behavior index)with a density range from 1.00 to 1.50 g/cm3 through data fitting,developing a rheological model of deep-sea mining plumes considering the variations in plume density.Subsequently,taking into account the differences in sediment properties,the effects of clay content and clay mineral composition on the rheological parameters of natural sediment plumes were compared and analyzed.This model provides a reference for understanding the rheological properties of deep-sea mining plumes during their propagation.展开更多
Central Sumatra,Indonesia,is historically known for its significant seismic activities,most notably the devastating 1883 earthquake.In this study,we measured the interseismic deformation using continuous GNSS observat...Central Sumatra,Indonesia,is historically known for its significant seismic activities,most notably the devastating 1883 earthquake.In this study,we measured the interseismic deformation using continuous GNSS observation data for three years from 2018 to 2021.5.The results show that the derived velocity fields indicate that the Central Sumatra deformation is primarily characterized by crustal strain shortening due to interaction between the India-Australian plate and the Sundaland plate.High strain values are observed along the Sumatran Fault Zone(SFZ),which is characterized by a history of significant seismic activity.Interseismic locking is divided into two segments.Segment A,located in the northern part of Siberut Island has an estimated moment magnitude of MW7.44 with a return period of200 years leading to a potential earthquake magnitude of MW8.98.Segment B in the southern part of Siberut Island has an estimated moment magnitude of MW7.26 with a return period of 200 years,resulting in a potential earthquake magnitude of MW8.79.The findings highlight critical seismic hazard implications,emphasizing the potential for a major earthquake in the Central Sumatra.展开更多
The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navi...The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.展开更多
Slow Slip Events(SSEs)are critical for understanding subduction zone tectonics and earthquake prediction;however their detection is challenged by low-magnitude-offsets and data gaps.To address these challenges,this pa...Slow Slip Events(SSEs)are critical for understanding subduction zone tectonics and earthquake prediction;however their detection is challenged by low-magnitude-offsets and data gaps.To address these challenges,this paper introduces an optimization-based signal decomposition(OSD)fra mework capable of automatically processing signals with missing data.We applied and validated this framework with GNSS coordinate time series in the Cascadia subduction zone,benchmarking its perfo rmance against the existing SSEs catalog.The proposed high-magnitude-offset detection method achieved an accuracy of67.21%in single-station SSE detection,significantly outperforming traditional methods such as the Relative Strength Index(RSI;32.24%)and deep learning methods like bidirectional Long Short-Term Memory(bi-LSTM;44.41%).Additionally,we proposed a complementary velocity-based screening strategy that successfully identified low-magnitude-offset SSEs and events obscured by data gaps.Through cluster analysis of single-station detection results,we successfully identified the spatiotemporal boundary of the majority of SSEs.Finally,we established an anomaly catalog for uncataloged period from 2018 to 2024,which further demonstrates the method's efficacy in characterizing the spatiotemporal features of SSEs.The OSD-based SSEs detection framework identified SSEs with diverse kinematic patterns using raw geodetic data,facilitating the construction of high-quality SSEs catalogs.These advancements enhance our understanding of subduction zone dynamics and provide a robust technical foundation for seismic hazard assessment.展开更多
An MW6.0 earthquake struck Jishishan County in Linxia Prefecture,Gansu Province,on December 18,2023.In this research,Sentinel-1A satellite radar observations were used to obtain the field of coseismic deformation of t...An MW6.0 earthquake struck Jishishan County in Linxia Prefecture,Gansu Province,on December 18,2023.In this research,Sentinel-1A satellite radar observations were used to obtain the field of coseismic deformation of the Jishishan earthquake in 2023,and the geometric and fine slip distribution of the seismogenic fault were inverted using this as a constraint.The results show that the earthquake is characterized by thrust movement.The coseismic slip distribution results show that the maximum slip of this earthquake is 0.3 m.The Coulomb stress distribution shows that the whole section of the southern edge of Lajishan fault,the NWW trending segment of the northern edge of Lajishan fault and its NNW trending segment to the south of the epicenter,the northern edge of the West Qinling fault and the segment to the east of the epicenter of the Daotanghe Linxia fault are under stress loading,which indicates an increase in the potential risk of earthquakes.This research discussed the seismogenic characteristics of earthquakes and the tendency of faults.We speculate that the Jishishan earthquake is the result of the joint action of regional faults and tectonic stress.Based on the observation of seismic data,geodesy,and other geological and geophysical data,we believe that the earthquake was caused by the activation of weak areas under the crust by the local stress from the driving mechanism of the northeast expansion of Qinghai-Xizang Plateau.The seismogenic fault of this earthquake is more likely to be northeast dipping under the comprehensive consideration of various factors,which occurred on the concealed fault belonging to the eastern edge of the Jishishan fault zone.展开更多
Terrestrial ecosystems are vital for maintaining equilibrium in the global carbon cycle.Land use and land cover change(LUCC),which is influenced mainly by urbanization and ecological policies,impacts terrestrial ecosy...Terrestrial ecosystems are vital for maintaining equilibrium in the global carbon cycle.Land use and land cover change(LUCC),which is influenced mainly by urbanization and ecological policies,impacts terrestrial ecosystem carbon storage significantly.In this study,spatiotemporal carbon storage changes in the urban belt along the Yellow River in the Ningxia Hui Autonomous Region,China,were estimated through a model that integrated patch-generating land use simulation(PLUS)and integrated valuation of ecosystem services and tradeoffs(InVEST)models from 1993 to 2033.The results revealed that:(1)from 1993 to 2023,the expansion of built-up land and cropland was derived mainly from unused land and grassland,whereas water body and woodland remained relatively stable.Projections to 2033 have indicated that LUCC will continue and be concentrated primarily in the Ningxia Plain;(2)carbon storage increased by a net 5.01×10^(6) Mg C from 1993 to 2023;(3)the spatial distribution of carbon storage revealed that high-value areas were predominantly located in the Helan Mountains and the Ningxia Plain,whereas low-value areas were found in the Tengger Desert;(4)scenario projections indicated that by 2033,the ecological protection scenario(EPS)would achieve a 0.18×10^(6) Mg C increase by reducing the conversion of woodland to cropland and grassland to built-up land,while increasing the conversion of unused land to grassland.In contrast,the natural development scenario(NDS),cropland protection scenario(CPS),and urban development scenario(UDS)decreased carbon storage by 0.60×10^(6),0.21×10^(6),and 0.42×10^(6) Mg C,respectively;and(5)spatial autocorrelation analysis revealed that high–high carbon storage clusters formed belt-like patterns along the Ningxia Plain and the Helan Mountains,whereas the low–low carbon storage clusters were concentrated in northern Zhongwei City,western Qingtongxia City,western Dawukou District,and the urbanized areas within the central Ningxia Plain.Overall,the study results revealed the close coupling relationship between LUCC and carbon storage functions.Furthermore,the study establishes a framework for carbon management that balances ecological protection with coordinated urban development for the urban belt as well as for similar arid and semi-arid areas.On the basis of these findings,this study provides decision-makers with guidance to optimize ecosystem carbon storage via land use,which plays a key role in developing future land use policies and achieving the"dual carbon"goals.展开更多
The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified,...The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified,despite the fact that the scenario exists in practice.A series of laboratory-scale experiments were conducted in this study to investigate the smoke back-layering length in a model tunnel with cross-passage.The heat release rate,the velocity of longitudinal air flow,and the location of the fire source were all varied.It was found that the behavior of smoke backflow for the fire source located at the upstream of bifurcation point resembles a single-hole tunnel fire.As the fire source’s position shifts downstream from the bifurcation point,the length of smoke back-layering progressively increases.A competitive interaction exists between airflow diversion and smoke diversion during smoke backflow,significantly affecting the smoke back-layering length in the main tunnel.The dimensionless smoke back-layering length model was formulated in a tunnel featuring a cross-passage,taking into account the positions of longitudinal fire sources.The dimensionless smoke back-layering length exhibits a positive correlation with the 17/18 power of total heat release rate Q and a negative correlation with the 5/2 power of longitudinal ventilation velocity V.展开更多
The Sichuan-Yunnan region,located at the southeastern margin of the Qinghai-Xizang Plateau,serves as a key channel for the southeastward extrusion of plateau material.The characteristics of crustal deformation and the...The Sichuan-Yunnan region,located at the southeastern margin of the Qinghai-Xizang Plateau,serves as a key channel for the southeastward extrusion of plateau material.The characteristics of crustal deformation and the mechanisms of deep material flow have been central topics of interest in geoscience research.In this work,a three-dimensional viscoelastic-plastic finite element model including the upper and mid-lower crust was established,constrained by GNSS horizontal crustal velocity observations and incorporating maj or active faults and geophysical survey data to explore the contribution of mid-lower crustal flow to surface deformation and its coupling with faults.Comparison of modeling experiments shows that relying solely on boundary loading or uniform layering assumptions fails to reproduce the GNSS observed velocities.We introduce a mid-lower crustal low-velocity weak zone,derived from the latest seismic velocity structure models.The new model improves the fit to GNSS observations.Tests of different viscosity coefficients in the low-velocity zone indicate an optimal viscosity range of 7.5×10^(19)-1×10^(20)Pa·s.Vertical profiles reveal that mid-lower crustal material motion is mainly concentrated at depths of 20-40 km,forming localized channelized flow in low-velocity zone with a typical Poiseuille velocity profile which indicates a ductile,fluid-like behavior with the lowvelocity zone serving as primary pathways for deep material transport.The results further show that under the geometric constraints of upper-crustal faults,the mid-lower crustal flow contributes approximately 1-3 mm/a to surface deformation,primarily concentrated along major faults.This indicates that faults play a key role in constraining and modulating the transmission of deep-seated dynamics to shallow surface deformation.However,the contribution of mid-lower crustal flow is also significant;neglecting its influence on surface deformation would lead to an incomplete understanding of the deformation pattern and bias the interpretation of block boundaries and crustal kinematic segmentation.展开更多
Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-st...Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-stationarity,and short-term interruptions of SCB data under complex environments,this paper proposes an enhanced SCB prediction model combining Temporal Convolutional Networks(TCN)and Transformers.Experimental results indicate that,in a 24-h prediction task,the proposed model reduces root mean square error(RMSE)and range error(RE)by 95.6%,86.0%,and 61.3%,and93.7%,86.3%,and 58.8%,respectively,compared with LSTM,Transformer,and CNN-BiGRU-Attention models,while improving computational efficiency by 48.6%over the Transformer.Moreover,although the clock bias products generated by the proposed method result in slightly higher static PPP positioning errors than the International GNSS Service(IGS)rapid clock products,the error differences are generally at the millimeter level,demonstrating the feasibility of using predicted clock bias products to replace rapid clock products in the short term.This method addresses the PPP positioning issue during short-term network service interruptions from the perspective of time series prediction and provides potential solutions for engineering applications such as landslide,earthquake,and subsidence monitoring.展开更多
By integrating self-localization,environment mapping,and dynamic object tracking into a unified framework,visual simultaneous localization and mapping with multiple object tracking(SLAMMOT)enhances decision-making and...By integrating self-localization,environment mapping,and dynamic object tracking into a unified framework,visual simultaneous localization and mapping with multiple object tracking(SLAMMOT)enhances decision-making and interaction capabilities in applications such as autonomous driving,robotic navigation,and augmented reality.While numerous outstanding visual SLAMMOT methods have been proposed,the majority rely only on point features,overlooking the abundant and stable planar features in artificial objects that can provide valuable constraints.To address this limitation,we propose OP(object planar)-SLAM,an RGB-D SLAMMOT system that leverages planar features to improve object pose estimation and reconstruction accuracy.Specifically,we introduce an accurate object planar feature extraction and association method using normal images,alongside a novel object bundle adjustment framework that incorporates planar constraints for enhanced optimization.The proposed system is evaluated on both synthetic and public real-world datasets,including Oxford multimotion dataset(OMD)and KITTI tracking dataset.Especially on the OMD,where planar features are prominent,our method improves object pose estimation accuracy by approximately 60%.Extensive experiments demonstrate its effectiveness in enhancing object pose estimation and reconstruction,achieving notable performance compared with existing methods.Furthermore,OP-SLAM runs in real time,making it suitable for practical robots and augmented reality applications.展开更多
This study addresses the challenge of predicting zinc(Zn)recovery from carbonate ores via sodium hydroxide(NaOH)leaching.This complex process influenced by variable ore composition,surface passivation effects,and nonl...This study addresses the challenge of predicting zinc(Zn)recovery from carbonate ores via sodium hydroxide(NaOH)leaching.This complex process influenced by variable ore composition,surface passivation effects,and nonlinear reaction dynamics,which complicate reagent optimization and process control in hydrometallurgical operations.To tackle this,a dataset containing 422 experimental observations was compiled from previous studies,incorporating ore composition and process parameters,such as NaOH concentration,leaching time,temperature,and solid-to-liquid ratio.Four regression models(decision tree,neural network,generalized additive model,and random forest)were trained and evaluated using performance metrics,such as coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and symmetrical mean absolute percentage error(SMAPE).Among these,the random forest model achieved the best predictive accuracy,with R^(2)value of 0.8541 on the test set and the lowest error rates,demonstrating its effectiveness in capturing the complex relationships between input variables and Zn recovery.Explainable artificial intelligence,particularly SHapley additive exPlanations(SHAP)analysis,revealed that NaOH concentration,leaching time,and solid-to-liquid ratio had the most positive influence on Zn recovery,whereas elements such as Ca,Fe,and Pb had inhibitory effects.These findings align with known geochemical behavior and provide valuable insights for reagent optimization and process effi-ciency in leaching processes.This study demonstrates the practical potential of machine learning in mineral processing,offering a scalable framework for optimizing Zn recovery from non-sulfide ores and a data-driven approach to enhance decision-making in hydrometallurgical applications.展开更多
Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that empl...Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.展开更多
The correlation between the Soil Moisture and Ocean Salinity(SMOS)L-band brightness temperature and thin sea ice thickness has been widely exploited using semi-empirical retrieval approaches based on a single-tie poin...The correlation between the Soil Moisture and Ocean Salinity(SMOS)L-band brightness temperature and thin sea ice thickness has been widely exploited using semi-empirical retrieval approaches based on a single-tie point(STP).However,due to pronounced spatial heterogeneity in seawater and sea ice properties across the Arctic,the use of an STP often leads to regionally biased.To address this limitation,this study proposes a multi-tie point(MTP)sea ice thickness retrieval method based on SMOS brightness temperature and sea ice concentration time series.Multiple seawater and sea ice tie-point values are identified through point-by-point time series analysis,quality control,and statistical hypothesis testing,allowing spatial variability in radiometric properties to be explicitly considered.The MTP-based retrieval is applied to Arctic freeze-up conditions.Validation against independent SMOS thin sea ice thickness products shows that the MTP approach yields significantly reduced bias and root mean square error compared with the conventional STP method,with statistically significant improvements confirmed by paired t-tests.While retrieval accuracy stabilizes beyond a certain number of tie points,the preprocessing cost associated with tie-point selection increases substantially.Considering both accuracy and efficiency,the MTP framework provides a practical and robust approach for large-scale Arctic thin sea ice thickness retrieval and enables improved characterization of regional freezing processes and maximum ice thickness.展开更多
文摘This paper explored and discussed the cognition of informatization geomatics innovative talents cultivating laws and the basic principles and teaching system of informatization experiment teaching in the informatization experimental teaching innovation.
基金Teaching Reform Project of Tsinghua University(No.DX0702)National Natural Science Foundation of China(No.41971379)。
文摘Geomatics is an interdisciplinary subject.Many disciplines have teaching demands in this field.A new course on“Geomatics Technology”has been suggested by the Weiyang College of Tsinghua University of China for the major of“Mathematical and Scientific Basic Science+Civil,Hydraulic and Marine Engineering”.This paper offers a data-led geomatics teaching mode,developing a customized teaching cloud platform,to explore the cross-integrated innovative teaching methods.Teachers and students can assign and submit assignments on this platform.The platform constitutes a data flow with the data download,data processing and result sharing.It encourages communication among students in various majors,grades and units using data as the medium,from data processing to application upstream and downstream.In the“Geomatics Technology”course,geospatial data has emerged as a vital element of the multidisciplinary approach.This kind of teaching mode has been used in the postgraduate remote sensing course offered by Tsinghua University’s Department of Civil Engineering and Construction Management.Furthermore,the mode will be used for the first time in the autumn semester of 2022 in the undergraduate teaching of Weiyang College and civil engineering,to offer a novel idea for the reform of courses linked to geospatial informatics.
文摘Landslides are one of the most significant natural damaging disasters in hilly environment [1]. The location of our study area is to the north of Tunisia, home to several manifestations of land instabilities, we bring to study this area of interest by GIS and geomatic approach to reduce social economic losses due to landslides. The performance of a cartographic data base for the landslide study in the Cap-Bon region was realized through studying geologic 1/50,000 and topographic 1/25,000 maps, aster optic Remote Sensing, land observation, and climatologic seismic data. These data will be digitalized, georeferenced, vectorized, spatially analyzed, classified and geotreated in order to produce a landslides card. The findings have shown that fields with smooth and friable lithology which are located at rather important seismic zones (more than 4 at Richter’s scale) have some stability. However, the most endangered zones are in the North-West around Oued El Kbir and El Ain. Realizing this work helps to determine the most hazardous zones so that policy makers have an effective field intervention.
文摘Mining activities often cause dramatic changes in landscapes, particularly in the dump sites and its surrounding environment. Land rehabilitation is the process of renovating damaged land to some extent of its original shape and aims to minimize and mitigate the environmental effects to allow new land uses. The success of different rehabilitation strategy and newly suggested urban and architecture modeling depends on the landscape characterization (topography of the study area and its derivatives such as slope and aspects, geological and geomorphologic nature of the study area). The aim of this study is to demonstrate the utility of different methodologies based on geomatics techniques (Photogrammetry, Remote Sensing, Global Positioning System (GPS) and three dimensional Geographic Information System (GIS)) for highlighting landscape characterization which is needed for rehabilitation of Mahis area. Photogrammetric adjustment procedures were used to create digital elevation model and Orth-Photo model for the study area using aerial images. Remote sensing data were used for land classification to provide vital information for rehabilitation planning. GPS field observations were used to build spatial network for the study area based on ground control point collections. Finally, realistic representation of the study area with three dimensional GIS was prepared for the study area considering ease and flexible updating of the geo-spatial database.
基金the National Natural Science Foundation of China(Grant Nos.42274003,41974007,and 41774019).
文摘The GRACE(Gravity Recovery and Climate Experiment)space mission recorded temporal variation characteristics of the global gravity field at decadal timescales.The gravity data have been shown to capture the dynamics of flows within the outer core and their effects on the core-mantle boundary.We first aim to remove global surface process gravity signals from the GRACE data.We then construct the global core magnetic field according to the CHAOS-7 model.Finally,we apply the blind source separation method to decompose the processed gravity signals and core magnetic signals and compute the power spectral density of the gravity and magnetic field signals by using the Lomb-Scargle periodogram approach.We have discovered a signal cycle(of~6 years)in the principal components of the core magnetic and gravity signals,potentially as a result of deep Earth processes.The main principal components of the core magnetic and gravity signals reveal that the variation trends in the second-order time derivative of the core magnetic field are similar to those in the gravity field.After 2014,the second-order time derivative of the core magnetic field exhibited linear and rapid change characteristics,which were the same as the change in the gravity field and are consistent with existing research results.
文摘In Global Navigation Satellite System(GNSS)meteo rology,the atmospheric weighted mean temperatu re(T_(m))is a critical intermediate parameter for converting zenith wet delay(ZWD)to precipitable water vapor(PWV),essential for accurate atmospheric water content estimation.However,global models often overlook regional climatic variability,leading to reduced accuracy in localized applications.This study introduces an improved T_(m)model developed using radiosonde observations across Iran and GNSS radio occultation(RO)profiles from CHAMP,GRACE,MetOp-A/B/C,COSMIC,TerraSAR-X,and TanDEM-X missions collected between 2007 and 2022.A novel integral formulation was proposed to estimate T_(m)more accurately by incorporating vertical water vapor distribution and temperature linearity.Based on this formulation,three regional T_(m)models were constructed using annual,semiannual,and diurnal periodicities,along with surface temperature(T_(s)),each varying in structure and complexity.Validation against independent radiosonde observations from 2022 showed that Models Two and Three outperformed the Bevis model,reducing RMSE by 30.7%.When evaluated against GNSS RO profiles,Model One—excluding T_(s)due to its inaccessibility in RO data—yielded the highest accuracy,with a 42.6%improvement in RMSE over the Bevis model.To evaluate the practical effectiveness of the proposed T_(m)model,PWV was derived from GNSS data at the tehn and tabz stations during the second half of 2022and compared with PWV values obtained from co-located radiosonde observations in Tehran and Tabriz.Using T_(m)from Model One improved PWV estimation compared to the Bevis model,reducing RMSE and MAE by up to 54%and 53.8%in Tabriz and 50.6%and 52.9%in Tehran,respectively.These results demonstrate that regionalized T_(m)modeling,particularly approaches that avoid dependence on T_(s),can significantly enhance GNSS-based PWV estimation in areas with limited surface data.
基金National Natural Science Foundation of China(42274012,42004001)the Science and Technology Innovation Project of Anhui Surveying and Mapping Bureau(2025-KJ-08)+1 种基金the Open Fund of Wuhan Gravitation and Solid Earth Tides,National Observation and Research Station(WHYWZ202107)the Fundamental Research Funds for the Central Universities(JZ2022HGTB0268)。
文摘To characterize the spatial patterns of vertical crustal movement of Chinese mainland,GNSS imaging technology was applied to map the tectonic deformation of the region.In this study,the vertical crustal velocities inferred from GNSS data for Chinese mainland over two decades were rigorously estimated.First,by analyzing the vertical displacement time series from continuous GNSS stations and environmental load data,we found that the annual and semi-annual vertical displacements are highly correlated.This indicates that the vertical seasonal variations on the ground surface are mainly caused by environmental loading.After removing the seasonal variations caused by environmental loads from the GNSS time series,we applied an improved PCA technique to filter out common mode errors.Next,we estimated the optimal noise models for the filtered time series and derived the vertical velocity field of Chinese mainland.Finally,we employed an empirical Spatial Structure Function(SSF)to image the tectonic deformation of Chinese mainland.This method effectively mitigates issues with abrupt circular arc-shaped boundaries in GNSS imaging caused by sparse station networks.The imaging results show that vertical crustal deformation in Chinese mainland generally ranges from-3 to 3 mm/yr,with significant spatial variability.The central and northern parts of Qinghai-Xizang Plateau are identified as primary subsidence zones,indicating that plate boundaries and tectonic compression continue to shape the crustal movement in these regions.The major uplift zones are located in northern and central China,likely linked to regional tectonic activity and plate compression.Subsidence deformation in parts of eastern China appears to be influenced by human activities.
基金Shandong Provincial Key Laboratory of Marine Environment and Geological Engineering with grant at Ocean University of China,Grant/Award Numbers:MEGE2024001,MEGE2024002National Natural Science Foundation of China,Grant/Award Number:42207181+2 种基金Fundamental Research Funds for the Central Universities,Grant/Award Number:202441003Opening Fund of the State Key Laboratory of Coastal and Offshore Engineering at Dalian University of Technology,Grant/Award Number:LP2310National Key Research and Development Program of China,Grant/Award Number:2024YFC2815400。
文摘Comprehending the flow behavior of deep-sea mining plumes is paramount for precise predictions of their propagation range and holds immense significance in advancing the commercial exploitation of deep-sea minerals.As deep-sea mining plumes propagate,they can transition from high-density non-Newtonian fluids to low-density Newtonian fluids.However,a comprehensive rheological model capable of accurately describing this intricate evolutionary process is currently lacking.This study explores the variations in rheological properties observed during the propagation of deep-sea mining plumes,utilizing rheological test data obtained from kaolin clay plumes.Utilizing the Power Law model,we established a power exponential function correlating the plume rheological parameters(consistency index and flow behavior index)with a density range from 1.00 to 1.50 g/cm3 through data fitting,developing a rheological model of deep-sea mining plumes considering the variations in plume density.Subsequently,taking into account the differences in sediment properties,the effects of clay content and clay mineral composition on the rheological parameters of natural sediment plumes were compared and analyzed.This model provides a reference for understanding the rheological properties of deep-sea mining plumes during their propagation.
文摘Central Sumatra,Indonesia,is historically known for its significant seismic activities,most notably the devastating 1883 earthquake.In this study,we measured the interseismic deformation using continuous GNSS observation data for three years from 2018 to 2021.5.The results show that the derived velocity fields indicate that the Central Sumatra deformation is primarily characterized by crustal strain shortening due to interaction between the India-Australian plate and the Sundaland plate.High strain values are observed along the Sumatran Fault Zone(SFZ),which is characterized by a history of significant seismic activity.Interseismic locking is divided into two segments.Segment A,located in the northern part of Siberut Island has an estimated moment magnitude of MW7.44 with a return period of200 years leading to a potential earthquake magnitude of MW8.98.Segment B in the southern part of Siberut Island has an estimated moment magnitude of MW7.26 with a return period of 200 years,resulting in a potential earthquake magnitude of MW8.79.The findings highlight critical seismic hazard implications,emphasizing the potential for a major earthquake in the Central Sumatra.
基金supported by the National Natural Science Foundation of China(No.41971339)the SDUST Research Fund(No.2019TDJH103)。
文摘The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes.Accurate remote sensing data on sea ice concentration(SIC)is crucial for effective route planning of ships and ensuring navigational safety.Despite the availability of numerous SIC products in China,these datasets still lag behind mainstream international products in terms of data accuracy,spatiotemporal resolution,and time span.To enhance the accuracy of China's domestic SIC remote sensing data,this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen(BRM-SIC)as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets:the dataset derived from the microwave radiation imager(MWRI)aboard the FY-3D satellite,provided by the National Satellite Meteorological Center(FY-SIC),and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite,provided by Ocean University of China(OUC-SIC).Based on the evaluation results,a TransUnet fusion correction model was developed.The performance of this model was then compared against Ordinary Least Squares(OLS),Random Forest(RF),and UNet correction models,through spatial and temporal analyses.Results indicate that,compared to FY-SIC data,the RMSE of the OUC-SIC data and the standard data is reduced by24.245%,while the R is increased by 12.516%.Overall,the accuracy of OUC-SIC data is superior to that of FY-SIC data.During the research period(2020–2022),the standard deviation(SD)and coefficient of variation(CV)of OUC-SIC were 3.877%and 10.582%,respectively,while those for FY-SIC were 7.836%and 7.982%,respectively.In the study area,compared with OUC-SIC data,FYSIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas.These results indicate that the OUC-SIC data exhibit better temporal and spatial stability,whereas the FY-SIC data show stronger relative dimensionless stability.Among the four correction models,all showed improvements over the original,unfused corrected data.The fusion corrections using the OLS,RF,UNet,and TransUnet models reduced RMSE by 5.563%,14.601%,42.927%,and48.316%,respectively.Correspondingly,R increased by 0.463%,1.176%,3.951%,and 4.342%,respectively.Among these models,TransUnet performed the best,effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.
基金supported by the National Natural Science Foundation of China(Grant No.42274035)the Major Program(JD)of Hubei Province(Grant No.2023BAA026)the Hunan Provincial Land Surveying and Mapping Project(HNGTCH-2023-05)。
文摘Slow Slip Events(SSEs)are critical for understanding subduction zone tectonics and earthquake prediction;however their detection is challenged by low-magnitude-offsets and data gaps.To address these challenges,this paper introduces an optimization-based signal decomposition(OSD)fra mework capable of automatically processing signals with missing data.We applied and validated this framework with GNSS coordinate time series in the Cascadia subduction zone,benchmarking its perfo rmance against the existing SSEs catalog.The proposed high-magnitude-offset detection method achieved an accuracy of67.21%in single-station SSE detection,significantly outperforming traditional methods such as the Relative Strength Index(RSI;32.24%)and deep learning methods like bidirectional Long Short-Term Memory(bi-LSTM;44.41%).Additionally,we proposed a complementary velocity-based screening strategy that successfully identified low-magnitude-offset SSEs and events obscured by data gaps.Through cluster analysis of single-station detection results,we successfully identified the spatiotemporal boundary of the majority of SSEs.Finally,we established an anomaly catalog for uncataloged period from 2018 to 2024,which further demonstrates the method's efficacy in characterizing the spatiotemporal features of SSEs.The OSD-based SSEs detection framework identified SSEs with diverse kinematic patterns using raw geodetic data,facilitating the construction of high-quality SSEs catalogs.These advancements enhance our understanding of subduction zone dynamics and provide a robust technical foundation for seismic hazard assessment.
基金National Natural Science Foundation of China(Grant Nos.41930101 and 42101096)the China Postdoctoral Science Foundation(No.2019M660091XB)+8 种基金the Key Research and Development Project of Ecological Civilization Construction in Gansu Province(No.24YFFA054)the Natural Science Foundation of Gansu Province(Grant Nos.23JRRA857,23JRRG0015,and 21JR7RA317)the Gansu Province Higher Education Institutions Young Doctor(2024QB-046)the Open Fund of Wuhan,Gravitational Field and Solid Tides,National Field Observation and Research Station(WHYWZ202403)the National Cryosphere Desert Data Center(No.E01Z790201/2021kf07)the Lanzhou Talent Innovation and Entrepreneurship(No.2022-RC-73)the Experimental Teaching Reform Project of Lanzhou Jiaotong University(2024002)the Undergraduate Teaching Reform Project of Lanzhou Jiaotong University(JGY202416)"Young Scientific and Technological Talents Supporting Project"Project of Gansu Province(Li Wei)。
文摘An MW6.0 earthquake struck Jishishan County in Linxia Prefecture,Gansu Province,on December 18,2023.In this research,Sentinel-1A satellite radar observations were used to obtain the field of coseismic deformation of the Jishishan earthquake in 2023,and the geometric and fine slip distribution of the seismogenic fault were inverted using this as a constraint.The results show that the earthquake is characterized by thrust movement.The coseismic slip distribution results show that the maximum slip of this earthquake is 0.3 m.The Coulomb stress distribution shows that the whole section of the southern edge of Lajishan fault,the NWW trending segment of the northern edge of Lajishan fault and its NNW trending segment to the south of the epicenter,the northern edge of the West Qinling fault and the segment to the east of the epicenter of the Daotanghe Linxia fault are under stress loading,which indicates an increase in the potential risk of earthquakes.This research discussed the seismogenic characteristics of earthquakes and the tendency of faults.We speculate that the Jishishan earthquake is the result of the joint action of regional faults and tectonic stress.Based on the observation of seismic data,geodesy,and other geological and geophysical data,we believe that the earthquake was caused by the activation of weak areas under the crust by the local stress from the driving mechanism of the northeast expansion of Qinghai-Xizang Plateau.The seismogenic fault of this earthquake is more likely to be northeast dipping under the comprehensive consideration of various factors,which occurred on the concealed fault belonging to the eastern edge of the Jishishan fault zone.
基金supported by the National Natural Sciences Foundation of China(42261026)the Open Foundation of Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone(XJYS0907-2023-01)the Light of the West Program for Young Scholars,Chinese Academy of Sciences(25JR6KA005).
文摘Terrestrial ecosystems are vital for maintaining equilibrium in the global carbon cycle.Land use and land cover change(LUCC),which is influenced mainly by urbanization and ecological policies,impacts terrestrial ecosystem carbon storage significantly.In this study,spatiotemporal carbon storage changes in the urban belt along the Yellow River in the Ningxia Hui Autonomous Region,China,were estimated through a model that integrated patch-generating land use simulation(PLUS)and integrated valuation of ecosystem services and tradeoffs(InVEST)models from 1993 to 2033.The results revealed that:(1)from 1993 to 2023,the expansion of built-up land and cropland was derived mainly from unused land and grassland,whereas water body and woodland remained relatively stable.Projections to 2033 have indicated that LUCC will continue and be concentrated primarily in the Ningxia Plain;(2)carbon storage increased by a net 5.01×10^(6) Mg C from 1993 to 2023;(3)the spatial distribution of carbon storage revealed that high-value areas were predominantly located in the Helan Mountains and the Ningxia Plain,whereas low-value areas were found in the Tengger Desert;(4)scenario projections indicated that by 2033,the ecological protection scenario(EPS)would achieve a 0.18×10^(6) Mg C increase by reducing the conversion of woodland to cropland and grassland to built-up land,while increasing the conversion of unused land to grassland.In contrast,the natural development scenario(NDS),cropland protection scenario(CPS),and urban development scenario(UDS)decreased carbon storage by 0.60×10^(6),0.21×10^(6),and 0.42×10^(6) Mg C,respectively;and(5)spatial autocorrelation analysis revealed that high–high carbon storage clusters formed belt-like patterns along the Ningxia Plain and the Helan Mountains,whereas the low–low carbon storage clusters were concentrated in northern Zhongwei City,western Qingtongxia City,western Dawukou District,and the urbanized areas within the central Ningxia Plain.Overall,the study results revealed the close coupling relationship between LUCC and carbon storage functions.Furthermore,the study establishes a framework for carbon management that balances ecological protection with coordinated urban development for the urban belt as well as for similar arid and semi-arid areas.On the basis of these findings,this study provides decision-makers with guidance to optimize ecosystem carbon storage via land use,which plays a key role in developing future land use policies and achieving the"dual carbon"goals.
基金funded by the National Natural Science Foundation of China(NSFC)under Grant No.52278415the National Key Research and Development Program of China under Grant No.2022YFC3801104+2 种基金Hebei Provincial Department of Education Project under Grant No.QN2025304the Innovation Fund Project of Hebei University of Engineering under Grant No.SJ2401002066the Sichuan Science and Technology Program under Grant No.2023YFS0407。
文摘The diversion effect caused by the linked structure in a metro tunnel with cross-passage complicates the impact of longitudinal fire source location on the smoke backflow layering behavior that has not been clarified,despite the fact that the scenario exists in practice.A series of laboratory-scale experiments were conducted in this study to investigate the smoke back-layering length in a model tunnel with cross-passage.The heat release rate,the velocity of longitudinal air flow,and the location of the fire source were all varied.It was found that the behavior of smoke backflow for the fire source located at the upstream of bifurcation point resembles a single-hole tunnel fire.As the fire source’s position shifts downstream from the bifurcation point,the length of smoke back-layering progressively increases.A competitive interaction exists between airflow diversion and smoke diversion during smoke backflow,significantly affecting the smoke back-layering length in the main tunnel.The dimensionless smoke back-layering length model was formulated in a tunnel featuring a cross-passage,taking into account the positions of longitudinal fire sources.The dimensionless smoke back-layering length exhibits a positive correlation with the 17/18 power of total heat release rate Q and a negative correlation with the 5/2 power of longitudinal ventilation velocity V.
基金co-supported by the National Key Research and Development Program of China(2021YFC3000604)the Key Program of the National Natural Science Foundation of China(42130101)。
文摘The Sichuan-Yunnan region,located at the southeastern margin of the Qinghai-Xizang Plateau,serves as a key channel for the southeastward extrusion of plateau material.The characteristics of crustal deformation and the mechanisms of deep material flow have been central topics of interest in geoscience research.In this work,a three-dimensional viscoelastic-plastic finite element model including the upper and mid-lower crust was established,constrained by GNSS horizontal crustal velocity observations and incorporating maj or active faults and geophysical survey data to explore the contribution of mid-lower crustal flow to surface deformation and its coupling with faults.Comparison of modeling experiments shows that relying solely on boundary loading or uniform layering assumptions fails to reproduce the GNSS observed velocities.We introduce a mid-lower crustal low-velocity weak zone,derived from the latest seismic velocity structure models.The new model improves the fit to GNSS observations.Tests of different viscosity coefficients in the low-velocity zone indicate an optimal viscosity range of 7.5×10^(19)-1×10^(20)Pa·s.Vertical profiles reveal that mid-lower crustal material motion is mainly concentrated at depths of 20-40 km,forming localized channelized flow in low-velocity zone with a typical Poiseuille velocity profile which indicates a ductile,fluid-like behavior with the lowvelocity zone serving as primary pathways for deep material transport.The results further show that under the geometric constraints of upper-crustal faults,the mid-lower crustal flow contributes approximately 1-3 mm/a to surface deformation,primarily concentrated along major faults.This indicates that faults play a key role in constraining and modulating the transmission of deep-seated dynamics to shallow surface deformation.However,the contribution of mid-lower crustal flow is also significant;neglecting its influence on surface deformation would lead to an incomplete understanding of the deformation pattern and bias the interpretation of block boundaries and crustal kinematic segmentation.
基金supported by the National Natural Science Foundation of China(42304050)Major Science and Technology Projects in Anhui Province,grant number(202103a05020026)+1 种基金Open Foundation of the Key Laboratory of Universities in Anhui Province for Prevention of Mine Geological Disasters(2022-MGDP-08)University Natural Science Research Project of Anhui Province(2023AH051190)。
文摘Satellite clock bias(SCB)prediction is essential for enhancing the accuracy and reliability of real-time precise point positioning(RT-PPP)in Global Navigation Satellite Systems(GNSS).To address the nonlinearity,non-stationarity,and short-term interruptions of SCB data under complex environments,this paper proposes an enhanced SCB prediction model combining Temporal Convolutional Networks(TCN)and Transformers.Experimental results indicate that,in a 24-h prediction task,the proposed model reduces root mean square error(RMSE)and range error(RE)by 95.6%,86.0%,and 61.3%,and93.7%,86.3%,and 58.8%,respectively,compared with LSTM,Transformer,and CNN-BiGRU-Attention models,while improving computational efficiency by 48.6%over the Transformer.Moreover,although the clock bias products generated by the proposed method result in slightly higher static PPP positioning errors than the International GNSS Service(IGS)rapid clock products,the error differences are generally at the millimeter level,demonstrating the feasibility of using predicted clock bias products to replace rapid clock products in the short term.This method addresses the PPP positioning issue during short-term network service interruptions from the perspective of time series prediction and provides potential solutions for engineering applications such as landslide,earthquake,and subsidence monitoring.
基金Supported by Major Science and Technology Project of Hubei Province(2022AAA009)。
文摘By integrating self-localization,environment mapping,and dynamic object tracking into a unified framework,visual simultaneous localization and mapping with multiple object tracking(SLAMMOT)enhances decision-making and interaction capabilities in applications such as autonomous driving,robotic navigation,and augmented reality.While numerous outstanding visual SLAMMOT methods have been proposed,the majority rely only on point features,overlooking the abundant and stable planar features in artificial objects that can provide valuable constraints.To address this limitation,we propose OP(object planar)-SLAM,an RGB-D SLAMMOT system that leverages planar features to improve object pose estimation and reconstruction accuracy.Specifically,we introduce an accurate object planar feature extraction and association method using normal images,alongside a novel object bundle adjustment framework that incorporates planar constraints for enhanced optimization.The proposed system is evaluated on both synthetic and public real-world datasets,including Oxford multimotion dataset(OMD)and KITTI tracking dataset.Especially on the OMD,where planar features are prominent,our method improves object pose estimation accuracy by approximately 60%.Extensive experiments demonstrate its effectiveness in enhancing object pose estimation and reconstruction,achieving notable performance compared with existing methods.Furthermore,OP-SLAM runs in real time,making it suitable for practical robots and augmented reality applications.
文摘This study addresses the challenge of predicting zinc(Zn)recovery from carbonate ores via sodium hydroxide(NaOH)leaching.This complex process influenced by variable ore composition,surface passivation effects,and nonlinear reaction dynamics,which complicate reagent optimization and process control in hydrometallurgical operations.To tackle this,a dataset containing 422 experimental observations was compiled from previous studies,incorporating ore composition and process parameters,such as NaOH concentration,leaching time,temperature,and solid-to-liquid ratio.Four regression models(decision tree,neural network,generalized additive model,and random forest)were trained and evaluated using performance metrics,such as coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute error(MAE),mean absolute percentage error(MAPE),and symmetrical mean absolute percentage error(SMAPE).Among these,the random forest model achieved the best predictive accuracy,with R^(2)value of 0.8541 on the test set and the lowest error rates,demonstrating its effectiveness in capturing the complex relationships between input variables and Zn recovery.Explainable artificial intelligence,particularly SHapley additive exPlanations(SHAP)analysis,revealed that NaOH concentration,leaching time,and solid-to-liquid ratio had the most positive influence on Zn recovery,whereas elements such as Ca,Fe,and Pb had inhibitory effects.These findings align with known geochemical behavior and provide valuable insights for reagent optimization and process effi-ciency in leaching processes.This study demonstrates the practical potential of machine learning in mineral processing,offering a scalable framework for optimizing Zn recovery from non-sulfide ores and a data-driven approach to enhance decision-making in hydrometallurgical applications.
基金Project(42077244)supported by the National Natural Science Foundation of ChinaProject(2020-05)supported by the Open Research Fund of Guangdong Provincial Key Laboratory of Deep Earth Sciences and Geothermal Energy Exploitation and Utilization,China。
文摘Accurate prediction of rockburst intensity levels is crucial for ensuring the safety of deep hard rock engineering construction.This paper introduced an expert system for rockburst intensity level prediction that employs machine learning algorithms as the basis for its inference rules.The system comprises four modules:a database,a repository,an inference engine,and an interpreter.A database containing 1114 rockburst cases was used to construct 357 datasets that serve as the repository for the expert system.Additionally,19 types of machine learning algorithms were used to establish 6783 micro-models to construct cognitive rules within the inference engine.By integrating probability theory and marginal analysis,a fuzzy scoring method based on the SoftMax function was developed and applied to the interpreter for rockburst intensity level prediction,effectively restoring the continuity of rockburst characteristics.The research results indicate that ensemble algorithms based on decision trees are more effective in capturing the characteristics of rockburst.Key factors for accurate prediction of rockburst intensity include uniaxial compressive strength,elastic energy index,the maximum principal stress,tangential stress,and their composite indicators.The accuracy of the proposed rockburst intensity level prediction expert system was verified using 20 engineering rockburst cases,with predictions aligning closely with the actual rockburst intensity levels.
基金supported by the National Key Research and Development Program of China(Grant nos.2023YFC2809103,2024YFC2813505)the Fundamental Research Funds for the Central Universities(Grant nos.2042025kf0083,2042025gf0014)the Antarctic Zhongshan Ice and Space Environment National Observation and Research Station(Grant no.ZSNORS-20252702).
文摘The correlation between the Soil Moisture and Ocean Salinity(SMOS)L-band brightness temperature and thin sea ice thickness has been widely exploited using semi-empirical retrieval approaches based on a single-tie point(STP).However,due to pronounced spatial heterogeneity in seawater and sea ice properties across the Arctic,the use of an STP often leads to regionally biased.To address this limitation,this study proposes a multi-tie point(MTP)sea ice thickness retrieval method based on SMOS brightness temperature and sea ice concentration time series.Multiple seawater and sea ice tie-point values are identified through point-by-point time series analysis,quality control,and statistical hypothesis testing,allowing spatial variability in radiometric properties to be explicitly considered.The MTP-based retrieval is applied to Arctic freeze-up conditions.Validation against independent SMOS thin sea ice thickness products shows that the MTP approach yields significantly reduced bias and root mean square error compared with the conventional STP method,with statistically significant improvements confirmed by paired t-tests.While retrieval accuracy stabilizes beyond a certain number of tie points,the preprocessing cost associated with tie-point selection increases substantially.Considering both accuracy and efficiency,the MTP framework provides a practical and robust approach for large-scale Arctic thin sea ice thickness retrieval and enables improved characterization of regional freezing processes and maximum ice thickness.
