"Miles wide and miles deep"-Exploring the depth and breadth of geoscience during the first ten years of Geoscience Frontiers

When early explorers first crossed the Platte River in what is now Nebraska (USA), it was said the river was “a mile wide and an inch deep”(Mokler, 1923;Smith, 1971). This phrase was used to describe not only the difficulty in crossing the river but also in navigating its length. The trouble with a river being too wide is the risk that it won’t provide the depth necessary to be useful. The same thing can be said of multidisciplinary scientific journals. While a journal can claim to be multidisciplinary, there is a risk of it being so broad that its articles can only engage the reader at a superficial level. Nothing could be further from the truth with Geoscience Frontiers. Over the past ten years, this journal has successfully navigated the wide breadth of geoscience while providing a level of depth and detail that rivals discipline-specific journals.

Use of various MgO resources for high-purity Mg metal production through molten salt electrolysis and vacuum distillation

A green and effective electrolytic process was developed to produce high-purity Mg metal using primary and secondary resources containing Mg O as a feedstock. The electrolysis of various Mg O resources was conducted using a Cu cathode in MgF2– LiF – KCl molten salt at 1043 K by applying an average current of 1.44 A for 12.5 h. The electrolysis of calcined North Korean magnesite and seawater Mg O clinker yielded Mg alloys of MgCu2and(Cu) phases with current efficiencies of 89.6–92.4%. The electrolysis of oxidized Mg O-C refractory brick, aged ferronickel slag, and ferronickel slag yielded Mg alloys of MgCu2and(Cu) phases with current efficiencies of 59.3–92.3%. The vacuum distillation of Mg alloys obtained was conducted at 1300 K for 10 h to produce high-purity Mg metal. After vacuum distillation, Mg metal with a purity of above 99.994% was obtained. Therefore, this study demonstrates the feasibility of the production of high-purity Mg metal from various Mg O resources using a novel electrolytic process with a Cu cathode, followed by vacuum distillation.

Numerical study on erosion behavior of sliding sleeve ball seat for hydraulic fracturing based on experimental data

The sleeve sealing ball seat is one of the important components in the multistage fracturing process of horizontal wells.The erosion and wear of the surface will decrease the sealing performance of the fracturing ball and the ball seat.This leads to pressure leakage during the fracturing process and fracturing failure.In this paper,combined with the actual ball seat materials and working conditions during the fracturing process,the erosion tests of ductile iron and tungsten carbide materials under different erosion speeds,angles,and mortar concentrations are carried out.Then the erosion test results were analyzed by mathematical fitting,and a set of erosion models suitable for sliding sleeve setting ball seat materials were innovatively established.For the first time,this paper combines the erosion model obtained from the experiment and the computational fluid dynamics(CFD)with Fluent software to simulate the erosion of the ball seat.Based on the simulation results,the morphology of the sliding sleeve seat ball after erosion is predicted.Through analysis of the test and simulation results,it is showed that the erosion rate of tungsten carbide material is lower and the wear resistance is better under the condition of small angle erosion.This research can offer a strong basis for fracturing site selection,surface treatment methods,and prediction of failure time of ball seats.

Production of high-purity Mg metal from dolomite through novel molten salt electrolysis and vacuum distillation

In this study,a novel Mg production process for producing high-purity Mg metal from dolomite was developed.When the electrolysis of calcined dolomite was conducted using Cu cathode and C anode in MgF_(2)–LiF molten salt at 1083–1173 K by applying an average current of 1.42–1.46 A for 9.50–21.0 h,the current efficiency of 66.4–88.6%was obtained.The produced Mg alloys consisted of MgCu_(2)and Cu(Mg)or MgCu_(2)and CuMg_(2)phases,depending on the Mg concentration in the Mg alloy.When the electrolysis of calcined dolomite was conducted in MgF_(2)–LiF–CaF_(2)molten salt at 1083 K,the current efficiency was 40.9–71.4%,owing to undesired reactions such as electroreduction of Ca^(2+)or/and CO_(3)^(2−)ions.Meanwhile,the current efficiency increased from 40.9%to 63.2%by utilizing a Pt anode,because the occurrence of CO_(3)^(2−)ions in the molten salt was prevented.After vacuum distillation of the obtained Mg alloys at 1300 K for 10 h,Mg metal with a purity of 99.9996–99.9998%was produced.Therefore,the feasibility of this novel process for the production of high-purity Mg metal from dolomite was demonstrated.

Monitoring and Quantification of Carbon Dioxide Emissions and Impact of Sea Surface Temperature on Marine Ecosystems as Climate Change Indicators in the Niger Delta Using Geospatial Technology

The Niger Delta marine environment has experienced a series of environmental disasters since the inception of oil and gas exploration,which can be attributed to climate change.Carbon dioxide(CO_(2))emissions and sea surface temperature(T)ties associated with burning fossil fuels,such as gas flaring,vehicular traffic,and marine vessel movement along the sea,are increasing.Using data extracted from the NASA Giovanni satellite’s Atmospheric Infrared Sounder(AIRS)and Moderate Resolution Imaging Spectroradiometer(MODIS),this study mapped the carbon footprint and T along the coastline into the deep sea from 2003 to 2011,using ArcGIS software.The spatial distribution of CO_(2) and T concentrations determined by the inverse distance weighting(IDW)method reveals variations in the study area.The results show an increase in the quantity of the mean tropospheric CO_(2) from July 2003 to December 2011,from 374.5129 ppm to 390.7831 ppm annual CO_(2) emissions,which also reflects a continuous increase.The average Monthly sea surface temperature had a general increasing trend from 25.79℃ in July 2003 to 27.8°C in December,with the Pearson correlation coefficient between CO_(2) and T indicating 50%strongly positive,20%strongly negative,20%weakly positive,and 10%weakly negative.CO_(2) levels,like temperature,follow a seasonal cycle,with a decrease during the wet season due to precipitation dissolving and plant uptake during the growing season,and then a rise during the dry season.Carbon capture and storage technologies must be implemented to benefit the marine ecosystem and human well-being.

Strategies towards statistically robust interpretations of in situ U-Pb zircon geochronology

Zircon U-Pb geochronology has become a keystone tool across Earth science, arguably providing the gold standard in resolving deep geological time. The development of rapid in situ analysis of zircon （via laser ablation and secondary ionization mass spectrometry） has allowed for large amounts of data to be generated in a relatively short amount of time and such large volume datasets offer the ability to address a range of geological questions that would otherwise remain intractable （e.g. detrital zircons as a sedi- ment fingerprinting method）. The ease of acquisition, while bringing benefit to the Earth science com- munity, has also led to diverse interpretations of geochronological data. In this work we seek to refocus U -Pb zircon geochronology toward best practice by providing a robust statistically coherent workflow. We discuss a range of data filtering approaches and their inherent limitations （e.g. discordance and the reduced chi-squared; MSWD）. We evaluate appropriate mechanisms to calculate the most geologically appropriate age from both 238U/206pb and 207pb/206pb ratios and demonstrate the cross over position when chronometric power swaps between these ratios. As our in situ analytical techniques become progressively more precise, appropriate statistical handing of U-Pb datasets will become increasingly pertinent.

Environmental changes during the past 13500 cal. a BP deduced from lacustrine sediment records of Lake Qinghai, China

A 475-cm long sediment core (QH-2005) from Lake Qinghai was used to carry out multi-proxy analysis of δ18O and body length of ostracod valves and redness and grain size of sediments, in order to reconstruct environ-mental changes during the past 13500 cal. a BP. The age model was based on 6 14C dates for bulk orgnic carbon (BOC) and 2 14C dates for lignin. The lignin 14C dates are apparently younger than the corresponding layers' BOC 14C dates, indicating that the reservoir age varied from 728 to 1222 a since the Late Glacial and from 2390 to 2490 a immediately before the pre-bomb era. Hence, the 14C age model for Core QH-2005 was corrected by the changing reservoir age. Ostracod δ18O values were primarily related to dilution and evaporative enrichment of the lake water. The reconstructed salinity based on ostracod body length coincides well with ostracod δ18O values. High redness and mean grain size (MZ) values indicate increased riverine supply to Lake Qinghai associated with increasing monsoon rainfall. Multi-proxy results show that climate during 13500-10900 cal. a BP was relatively cold and dry with fre-quent short-term fluctuations; a warm and wet climate began at about 10900 cal. a BP and culminated around 6500 cal. a BP as a result of monsoon strengthening; the climate became cold and dry afterwards and has remained rela-tively stable since 3400 cal. a BP. Our data also reveal short-term (millennial/centennial timescales) climatic fluctua-tions including: Younger Dryas events, ice-rafting events 8 and 1 (by ～11000 cal. a BP and ～1600 cal. a BP respec-tively), 8200 cal. a BP cold event, Little Ice Age and the Medieval Warm Period.

Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses.In this study,we applied two novel deep learning algorithms,the recurrent neural network(RNN)and convolutional neural network(CNN),for national-scale landslide susceptibility mapping of Iran.We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors(altitude,slope degree,profile curvature,distance to river,aspect,plan curvature,distance to road,distance to fault,rainfall,geology and land-sue)to construct a geospatial database and divided the data into the training and the testing dataset.We then developed RNN and CNN algorithms to generate landslide susceptibility maps of Iran using the training dataset.We calculated the receiver operating characteristic(ROC)curve and used the area under the curve(AUC)for the quantitative evaluation of the landslide susceptibility maps using the testing dataset.Better performance in both the training and testing phases was provided by the RNN algorithm(AUC=0.88)than by the CNN algorithm(AUC=0.85).Finally,we calculated areas of susceptibility for each province and found that 6%and 14%of the land area of Iran is very highly and highly susceptible to future landslide events,respectively,with the highest susceptibility in Chaharmahal and Bakhtiari Province(33.8%).About 31%of cities of Iran are located in areas with high and very high landslide susceptibility.The results of the present study will be useful for the development of landslide hazard mitigation strategies.

Genesis of the Bianjiadayuan Pb-Zn polymetallic deposit, Inner Mongolia, China:Constraints from in-situ sulfur isotope and trace element geochemistry of pyrite

The Southern Great Xing’an Range (SGXR) which forms part of the eastern segment of the Central Asian Orogenic Belt (CAOB) is known as one of the most important Cu-Mo-Pb-Zn-Ag-Au metallogenic belts in China,hosting a number of porphyry Mo (Cu),skarn Fe (Sn),epithermal Au-Ag,and hydrothermal veintype Ag-Pb-Zn ore deposits.Here we investigate the Bianjiadayuan hydrothermal vein-type Ag-Pb-Zn ore deposit in the southern part of the SGXR.Porphyry Sn ±Cu ± Mo mineralization is also developed to the west of the Ag-Pb-Zn veins in the ore field.We identify a five-stage mineralization process based on field and petrologic studies including (i) the early porphyry mineralization stage,(ii) main porphyry mineralization stage,(iii) transition mineralization stage,(iv) vein-type mineralization stage and (v) late mineralization stage.Pyrite is the predominant sulfide mineral in all stages except in the late mineralization stage,and we identify corresponding four types of pyrites: Py1 is medium-grained subhedral to euhedral occurring in the early barren quartz vein;Py2 is medium- to fine-grained euhedral pyrite mainly coexisting with molybdenite,chalcopyrite,minor sphalerite and galena;Py3 is fine-grained,subhedral to irregular pyrite and displays cataclastic textures with micro-fractures;Py4 occurs as euhedral microcrystals and forms irregularly shaped aggregate with sphalerite and galena.LA-ICP-MS trace element analyses of pyrite show that Cu,Pb,Zn,Ag,Sn,Cd and Sb are partitioned into pyrite as structurally bound metals or mineral micro/nano-inclusions,whereas Co,Ni,As and Se enter the lattice via isomorphism in all types of pyrite.The Cu,Zn,Ag,Cd concentrations gradually increase from Py1 to Py4,which we correlate with cooling and mixing of ore-forming fluid with meteoric water.Py2 contains the highest contents of Co,Ni,Se,Te and Bi,suggesting high temperature conditions for the porphyry mineralization stage.Ratios of Co/Ni (0.03-10.79,average 2.13) and sulphur isotope composition of sulfide indicate typical hydrothermal origin for pyrites.The δ^34SCDT values of Py1 (0.42‰-1.61‰,average 1.16‰),Py2 (-1.23‰ to 0.82‰,average 0.35‰),Py3 (-0.36‰ to 2.47‰,average 0.97‰),Py4 (2.51‰-3.72‰,average 3.06‰),and other sulfides are consistent with those of typical porphyry deposit (-5‰ to 5‰),indicating that the Pb-Zn polymetallic mineralization in the Bianjiadayuan deposit is genetically linked to the Yanshanian (JurassiceCretaceous) magmatic-hydrothermal events.Variations of d34S values are ascribed to the changes in physical and chemical conditions during the evolution and migration of the ore-forming fluid.We propose that the high Sn content of pyrite in the Bianjiadayuan hydrothermal vein-type PbeZn polymetallic deposit can be used as a possible pathfinder to prospect for Sn mineralization in the surrounding area or deeper level of the ore field in this region.

Effect of Cristobalite and Quartz on the Properties of Gypsum Bonded Investment

Generally the gold investment material consists of cristobalite,quartz and plaster.The physical property of gold investment materials depends on its thermal expansion coefficients,compressive strength,and particles size distribution.Since the thermal expansion coefficient of cristobalite and quartz are 2.6×10^-6/℃and 2.32×10^-6/℃respectively,the composition ratio of each components influence the thermal and physical properties of gold investment materials.For the clinical applications,it is necessary to improve the properties of gold investment materials such as homogeneous size distribution and thermal expansion coefficients.In the present study,effect of inorganic fillers such as cristobalite and quartz on gold alloy investment was investigated to improve the properties of it.The compressive strength and thermal expansion coefficients of the specimens were evaluated.The results showed that cristobalite and quartz were homogeneously distributed by milling. The optimum compressive strength was obtained at the ratio of 42：22 cristobalite and quartz,respectively.

Spatial prediction of landslide susceptibility in western Serbia using hybrid support vector regression(SVR)with GWO,BAT and COA algorithms

In this study,we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models.We created a geographic information system database,and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth,aerial photographs,and other validated sources.A support vector regression(SVR)machine-learning model was used to divide the landslide inventory into training(70%)and testing(30%)datasets.The landslide susceptibility map was produced using 14 causative factors.We applied the established gray wolf optimization(GWO)algorithm,bat algorithm(BA),and cuckoo optimization algorithm(COA)to fine-tune the parameters of the SVR model to improve its predictive accuracy.The resultant hybrid models,SVR-GWO,SVR-BA,and SVR-COA,were validated in terms of the area under curve(AUC)and root mean square error(RMSE).The AUC values for the SVR-GWO(0.733),SVR-BA(0.724),and SVR-COA(0.738)models indicate their good prediction rates for landslide susceptibility modeling.SVR-COA had the greatest accuracy,with an RMSE of 0.21687,and SVR-BA had the least accuracy,with an RMSE of 0.23046.The three optimized hybrid models outperformed the SVR model(AUC=0.704,RMSE=0.26689),confirming the ability of metaheuristic algorithms to improve model performance.

Zircon U–Pb geochronology and Hf isotope geochemistry of magmatic and metamorphic rocks from the Hida Belt, southwest Japan

Zircon U–Pb and Hf isotope data integrated in this study for magmatic and metamorphic rocks fromthe Hida Belt,southwest Japan,lead to a new understanding of the evolution of the Cordilleran arc system along the ancestral margins of present-day Northeast Asia.Ion microprobe data for magmatic zircon domains from eight mafic tointermediate orthogneisses in the Tateyama and Tsunogawa areas yielded weighted mean 206Pb/238U ages spanning the entire Permian period(302–254 Ma).Under cathodoluminescence,primary magmatic growth zones in the zircon crystals were observed to be partially or completely replaced by inward-penetrating,irregularly curved featureless or weakly zoned secondary domains that mostly yielded U–Pb ages of 250–240 Ma and relatively high Th/U ratios(>0.2).These secondary domains are considered to have been formed by solid-state recrystallization during thermal overprints associated with intrusions of Hida granitoids.Available whole-rock geochemical and Sr–Nd isotope data as well as zircon age spectra corroborate that the Hida Belt comprises the Paleozoic–Mesozoic Cordilleran arc system built upon the margin of the North China Craton,together with the YeongnamMassif in southern Korea.The arcmagmatismalong this systemwas commenced in the Carboniferous and culminated in the Permian–Triassic transition period.Highly positiveεHf(t)values(>+12)of late Carboniferous to early Permian detrital zircons in the Hida paragneisses indicate that there was significant input from the depleted asthenospheric mantle and/or its crustal derivatives in the early stage of arc magmatism.On the other hand,near-chondriticεHf(t)values(+5 to−2)of magmatic zircons from late Permian Hida orthogneisses suggest a lithospheric mantle origin.Hf isotopic differences between magmatic zircon cores and the secondary rims observed in some orthogneiss samples clearly indicate that the zircons were chemically open to fluids or melts during thermal overprints.Resumed highly positive zirconεHf(t)values(>+9)shared by Early Jurassic granitoids in the Hida Belt and Yeongnam Massif may reflect reworking of the Paleozoic arc crust.

A Review of Researches on Deep Learning in Remote Sensing Application

In recent years, deep learning has been widely used in the field of image understanding and made breakthroughs research progress in image understanding. Because remote sensing application and image understanding are inseparable, researchers have carried out a lot of research on the application of deep learning in remote sensing field, and extended the deep learning method to various application fields of remote sensing. This paper summarizes the basic principles of deep learning and its research progress and typical applications in remote sensing, introduces the current main deep learning model and its development history, focuses on the analysis and elaboration of the research status of deep learning in remote sensing image classification, object detection and change detection, and on this basis, summarizes the typical applications and their application effects. Finally, according to the current application of deep learning in remote sensing, the main problems and future development directions are summarized.

Lithofacies modeling by multipoint statistics and economic evaluation by NPV volume for the early Cretaceous Wabiskaw Member in Athabasca oilsands area, Canada

The static modeling and dynamic simulation are essential and critical processes in petroleum exploration and development. In this study, lithofacies models for Wabiskaw Member in Athabasca, Canada are generated by multipoint statistics(MPS) and then compared with the models built by sequential indicator simulation(SIS). Three training images(Tls) are selected from modern depositional environments;the Orinoco River Delta estuary, Cobequid bay-Salmon River estuary, and Danube River delta environment. In order to validate lithofacies models, average and variance of similarity in lithofacies are calculated through random and zonal blind-well tests.In random six-blind-well test, similarity average of MPS models is higher than that of SIS model. The Salmon MPS model closely resembles facies pattern of Wabiskaw Member in subsurface. Zonal blind-well tests show that successful lithofacies modeling for transitional depositional setting requires additional or proper zonation information on horizontal variation, vertical proportion, and secondary data.As Wabiskaw Member is frontier oilsands lease, it is impossible to evaluate the economics from production data or dynamic simulation. In this study, a dynamic steam assisted gravity drainage(SAGD)performance indicator(SPIDER) on the basis of reservoir characteristics is calculated to build 3 D reservoir model for the evaluation of the SAGD feasibility in Wabiskaw Member. SPIDER depends on reservoir properties, economic limit of steam-oil ratio, and bitumen price. Reservoir properties like porosity,permeability, and water saturation are measured from 13 cores and calculated from 201 well-logs. Three dimensional volumes of reservoir properties are constructed mostly based on relationships among properties. Finally, net present value(NPV) volume can be built by equation relating NPV and SPIDER. The economic area exceeding criterion of US$ 10,000 is identified, and the ranges of reservoir properties are estimated. NPV-volume-generation workflow from reservoir parameter to static model provides costand time-effective method to evaluate the oilsands SAGD project.

The Effect of Grain Size on the Viscosity and Yield Stress of Fine-Grained Sediments

In debris flow modelling,the viscosity and yield stress of fine-grained sediments should be determined in order to better characterize sediment flow.In particular,it is important to understand the effect of grain size on the rheology of fine-grained sediments associated with yielding.When looking at the relationship between shear stress and shear rate before yielding,a high-viscosity zone(called pseudoNewtonian viscosity) towards the apparent yield stress exists.After yielding,plastic viscosity(called Bingham viscosity) governs the flow.To examine the effect of grain size on the rheological characteristics of fine-grained sediments,clay-rich materials(from the Adriatic Sea,Italy; Cambridge Fjord,Canada; and the Mediterranean Sea,Spain),silt-rich debris flow materials(from La Valette,France) and silt-rich materials(iron tailings from Canada) were compared.Rheological characteristics were examined using a modified Bingham model.The materials examined,including the Canadian inorganic and sensitive clays,exhibit typical shear thinning behavior and strong thixotropy.In the relationships between the liquidity index and rheological values(viscosity and apparent yield stress),the effect of grain size on viscosity and yield stress is significant at a given liquidity index.The viscosity and yield stress of debris flow materials are higher than those of low-activity clays at the same liquid state.However the viscosity and yield stress of the tailings,which are mainly composed of silt-sized particles,are slightly lower than those of low-activity clays.

Modelling of piping collapses and gully headcut landforms: Evaluating topographic variables from different types of DEM

The geomorphic studies are extremely dependent on the quality and spatial resolution of digital elevation model(DEM)data.The unique terrain characteristics of a particular landscape are derived from DEM,which are responsible for initiation and development of ephemeral gullies.As the topographic features of an area significantly influences on the erosive power of the water flow,it is an important task the extraction of terrain features from DEM to properly research gully erosion.Alongside,topography is highly correlated with other geo-environmental factors i.e.geology,climate,soil types,vegetation density and floristic composition,runoff generation,which ultimately influences on gully occurrences.Therefore,terrain morphometric attributes derived from DEM data are used in spatial prediction of gully erosion susceptibility(GES)mapping.In this study,remote sensing-Geographic information system(GIS)techniques coupled with machine learning(ML)methods has been used for GES mapping in the parts of Semnan province,Iran.Current research focuses on the comparison of predicted GES result by using three types of DEM i.e.Advanced Land Observation satellite(ALOS),ALOS World 3D-30 m(AW3D30)and Advanced Space borne Thermal Emission and Reflection Radiometer(ASTER)in different resolutions.For further progress of our research work,here we have used thirteen suitable geo-environmental gully erosion conditioning factors(GECFs)based on the multi-collinearity analysis.ML methods of conditional inference forests(Cforest),Cubist model and Elastic net model have been chosen for modelling GES accordingly.Variable’s importance of GECFs was measured through sensitivity analysis and result show that elevation is the most important factor for occurrences of gullies in the three aforementioned ML methods(Cforest=21.4,Cubist=19.65 and Elastic net=17.08),followed by lithology and slope.Validation of the model’s result was performed through area under curve(AUC)and other statistical indices.The validation result of AUC has shown that Cforest is the most appropriate model for predicting the GES assessment in three different DEMs(AUC value of Cforest in ALOS DEM is 0.994,AW3D30 DEM is 0.989 and ASTER DEM is 0.982)used in this study,followed by elastic net and cubist model.The output result of GES maps will be used by decision-makers for sustainable development of degraded land in this study area.

The role of mechanical stratigraphy in the lateral variations of thrust development along the central Alberta Foothills, Canada

Fold-thrust belts generally exhibit significant variations in structural styles such as differences in thrust geometries and frequencies in imbrication. A natural laboratory of this pattern is preserved in the central Alberta Foothills of the Canadian Rockies, where differences in thrust geometries are represented by the existence vs. non-existence of triangle zones. To seek the factors that make this difference in these regions in terms of structural geometry, stratigraphic thickness variations and mechanical stratigraphy of the sedimentary layers, structural interpretation is conducted based on admissible cross-sections and well log interpretations. In northern region, a backthrust is detached from an incompetent layer(viz.Nomad Unit of the Wapiabi Formation), which gets thinner from the Foothills to the Plains, indicating that it is developed where the shale layers are pinched out where triangle zone is developed. Backthrust is also developed in the southern region, where mechanical strengths of strata(viz. Bearpaw Formation)increase toward the foreland. In the central region, however, only forethrusts are developed along the weak continuous decollement layers(viz. Turner Valley and Brazeau formations), forming an imbricate fan without development of the triangle zone. Incompetent layers such as the top Wapiabi(Nomad),Brazeau(Bearpaw), Coalspur and Paskapoo formations are also pinched out laterally, forming fault glide horizons in different stratigraphic levels in each region. These results indicate that, along the transport direction, triangle zone is developed in relation to the stratigraphic pinch out of the Nomad Unit in the northern region, and is formed associated with the variations in strengths of the layers constituting the Bearpaw Formation in the southern region. It is notable that all the glide horizons are developed along incompetent layers. However, triangle zones are not developed in the areas of continuous stratigraphy of the Nomad Unit, which does not serve as a glide horizon in the central region. This suggests that factors such as stratigraphic thickness changes of incompetent layers and mechanical stratigraphy of the sedimentary layers play an important role in the development of lateral variations in thrust system evolution in terms of triangle zone vs. imbricate fan in the central Alberta Foothills.

Geometrical and Geotechnical Characteristics of Landslides in Korea under Various Geological Conditions

The goal of this study is to determine the geometrical and geotechnical characteristics of landslides under various geological conditions using detailed field surveys, laboratory soil tests and precipitation records. Three study areas are selected to consider different rocks, including gneiss in Jangheung, granite in Sangju and sedimentary rocks in Pohang, South Korea. Many landslides have occurred in these three areas during the rainy season.Precipitation records indicate that landslides occurring in the gneiss area of Jangheung and granite area of Sangju may be influenced by the hourly rainfall intensity rather than cumulative rainfall.However, landslides occurring in the sedimentary rock area of Pohang may be influenced by hourly rainfall intensity and cumulative rainfall. To investigate the factors that influence these types of landslides, a detailed landslide survey was performed and a series of laboratory soil tests were conducted.According to the detailed field survey, most landslides occurred on the flanks of mountain slopes, and the slope inclination where they occurred mostly ranged from 26 to 30 degrees, regardless of the geological conditions. The landslide in the gneiss area of Jangheung is larger than the landslides in the granite area of Sangju and sedimentary rock area of Pohang.Particularly, the landslide in the sedimentary rock area is shorter and shallower than the landslides in the gneiss and granite areas. Thus, the shape and size of the landslide are clearly related to the geological conditions. According to the integrated soil property and landslide occurrence analyses results, the average dry unit weight of the soils from the landslide sites is smaller than that of the soils obtained from the nonlandslide site. The average coefficient of permeability of soils obtained from the landslide sites is greater than that of soils obtained from the non-landslide sites with the same geology. These results indicate that the soils from the landslide sites are more poorly graded or looser than the soils from the non-landslide sites.

Deep learning neural networks for spatially explicit prediction of flash flood probability

Flood probability maps are essential for a range of applications,including land use planning and developing mitigation strategies and early warning systems.This study describes the potential application of two architectures of deep learning neural networks,namely convolutional neural networks(CNN)and recurrent neural networks(RNN),for spatially explicit prediction and mapping of flash flood probability.To develop and validate the predictive models,a geospatial database that contained records for the historical flood events and geo-environmental characteristics of the Golestan Province in northern Iran was constructed.The step-wise weight assessment ratio analysis(SWARA)was employed to investigate the spatial interplay between floods and different influencing factors.The CNN and RNN models were trained using the SWARA weights and validated using the receiver operating characteristics technique.The results showed that the CNN model(AUC=0.832,RMSE=0.144)performed slightly better than the RNN model(AUC=0.814,RMSE=0.181)in predicting future floods.Further,these models demonstrated an improved prediction of floods compared to previous studies that used different models in the same study area.This study showed that the spatially explicit deep learning neural network models are successful in capturing the heterogeneity of spatial patterns of flood probability in the Golestan Province,and the resulting probability maps can be used for the development of mitigation plans in response to the future floods.The general policy implication of our study suggests that design,implementation,and verification of flood early warning systems should be directed to approximately 40%of the land area characterized by high and very susceptibility to flooding.