Uncertainties of landslide susceptibility prediction:influences of different study area scales and mapping unit scales

This study aims to investigate the effects of different mapping unit scales and study area scales on the uncertainty rules of landslide susceptibility prediction(LSP).To illustrate various study area scales,Ganzhou City in China,its eastern region(Ganzhou East),and Ruijin County in Ganzhou East were chosen.Different mapping unit scales are represented by grid units with spatial resolution of 30 and 60 m,as well as slope units that were extracted by multi-scale segmentation method.The 3855 landslide locations and 21 typical environmental factors in Ganzhou City are first determined to create spatial datasets with input-outputs.Then,landslide susceptibility maps(LSMs)of Ganzhou City,Ganzhou East and Ruijin County are pro-duced using a support vector machine(SVM)and random forest(RF),respectively.The LSMs of the above three regions are then extracted by mask from the LSM of Ganzhou City,along with the LSMs of Ruijin County from Ganzhou East.Additionally,LSMs of Ruijin at various mapping unit scales are generated in accordance.Accuracy and landslide suscepti-bility indexes(LSIs)distribution are used to express LSP uncertainties.The LSP uncertainties under grid units significantly decrease as study area scales decrease from Ganzhou City,Ganzhou East to Ruijin County,whereas those under slope units are less affected by study area scales.Of course,attentions should also be paid to the broader representativeness of large study areas.The LSP accuracy of slope units increases by about 6%–10%compared with those under grid units with 30 m and 60 m resolution in the same study area's scale.The significance of environmental factors exhibits an averaging trend as study area scale increases from small to large.The importance of environmental factors varies greatly with the 60 m grid unit,but it tends to be consistent to some extent in the 30 m grid unit and the slope unit.

Spatial prediction of soil organic carbon in coal mining subsidence areas based on RBF neural network

A quantitative research on the effect of coal mining on the soil organic carbon(SOC)pool at regional scale is beneficial to the scientific management of SOC pools in coal mining areas and the realization of coal low-carbon mining.Moreover,the spatial prediction model of SOC content suitable for coal mining subsidence area is a scientific problem that must be solved.Tak-ing the Changhe River Basin of Jincheng City,Shanxi Province,China,as the study area,this paper proposed a radial basis function neural network model combined with the ordinary kriging method.The model includes topography and vegetation factors,which have large influence on soil properties in mining areas,as input parameters to predict the spatial distribution of SOC in the 0-20 and 2040 cm soil layers of the study area.And comparing the prediction effect with the direct kriging method,the results show that the mean error,the mean absolute error and the root mean square error between the predicted and measured values of SOC content predicted by the radial basis function neural network are lower than those obtained by the direct kriging method.Based on the fitting effect of the predicted and measured values,the R^(2) obtained by the radial basis artificial neural network are 0.81,0.70,respectively,higher than the value of 0.44 and 0.36 obtained by the direct kriging method.Therefore,the model combining the artificial neural network and kriging,and considering environmental factors can improve the prediction accuracy of the SOC content in mining areas.

Prediction of the flooding area of the northeastern Caspian Sea from satellite images

Studying the dynamic changes in the coastline of the northeastern Caspian Sea is significant since the level of the Caspian is unstable,and the coastline change can cause enormous damage to the ecology,economy,and population of the coastal part of Kazakhstan.In this work,we use remote sensing and Geographic Information System(GIS)technologies to study the changes in the coastline of the northeastern Caspian Sea and predict the extent of flooding with increasing water levels.The proposed methodology for creating dynamic maps can be used to monitor the coastline and forecast the extent of flooding in the area.As a result of this work,the main factors affecting changes in the coastline were identified.After analyzing the water level data from 1988 to 2019,it was revealed that the rise in water level was observed from 1980 to 1995.The maximum sea level rise was recorded at-26.04 m.After that,the sea level began to fall,and between 1996 and 2009,there were no significant changes;the water level fluctuated with an average of-27.18 m.Then,a map of the water level dynamics in the Caspian Sea from 1988 to 2019 was compiled.According to the dynamics map,water level rise and significant coastal retreat were revealed,especially in the northern part of the Caspian Sea and the northern and southern parts of Sora Kaydak.The method for predicting the estimated flooding area was described.As a result,based on a single map,the flooding area of the northeast coast was predicted.A comparative analysis of Landsat and SRTM data is presented.

The Impact of Horizontal Resolution on the CNOP and on Its Identified Sensitive Areas for Tropical Cyclone Predictions

In this study, the ilnpacts of horizontal resolution on the conditional nonlinear optimal perturbation （CNOP） and on its identified sensitive areas were investigated for tropical cyclone predictions. Three resolutions, 30 km, 60 km, and 120 kin, were studied for three tropical cyclones, TC Mindulle （2004）, TC Meari （2004）, and TC Matsa （2005）. Results show that CNOP may present different structures with different resolutions, and the major parts of CNOP become increasingly localized with increased horizontal resolution. CNOP produces spiral and baroclinic structures, which partially account for its rapid amplification. The differences in CNOP structures result in different sensitive areas, but there are common areas for the CNOP-identified sensitive areas at various resolutions, and the size of the common areas is different from case to case. Generally, the forecasts benefit more from the reduction of the initial errors in the sensitive areas identified using higher resolutions than those using lower resolutions. However, the largest improvement of the forecast can be obtained at the resolution that is not the highest for some cases. In addition, the sensitive areas identified at lower resolutions are also helpful for improving the forecast with a finer resolution, but the sensitive areas identified at the same resolution as the forecast would be the most beneficial.

A hydrocarbon enrichment model and prediction of favorable accumulation areas in complicated superimposed basins in China

The geologic conditions of superimposed basins in China are very complicated. This is mainly shown by multi-phase structural evolution, multiple sets of source-reservoir-cap rock combinations, multiple stages of hydrocarbon generation and expulsion from source rocks, multi-cycle hydrocarbon enrichment and accumulation, and multi-phase reservoir adjustment and reconstruction. The enrichment, accumulation and distribution of hydrocarbon is mainly controlled by the source rock kitchen, paleo- anticline, regional cap rock and intensity of tectonic movement. In this paper, the T-BCMS model has been developed to predict favorable areas of hydrocarbon accumulation in complicated superimposed basins according to time and spatial relationships among five key factors. The five factors include unconformity surface representing tectonic balancing （B）, regional cap rock representing hydrocarbon protection （C）, paleo-anticline representing hydrocarbon migration and accumulation （M）, source rock kitchen representing hydrocarbon generation and expulsion （S） and geological time （T）. There are three necessary conditions to form favorable areas of hydrocarbon accumulation. First, four key factors BCMS should be strictly in the order of BCMS from top to bottom. Second, superimposition of four key factors BCMS in the same area is the most favorable for hydrocarbon accumulation. Third, vertically ordered combination and superimposition in the same area of BCMS should occur at the same geological time. The model has been used to predict the most favorable exploration areas in Ordovician in the Tarim Basin in the main hydrocarbon accumulation periods. The result shows that 95% of the discovered Ordovician hydrocarbon reservoirs are located in the predicted areas, which indicates the feasibility and reliability of the key factor matching T-BCMS model for hydrocarbon accumulation and enrichment.

Ultimately exposed roof area prediction of bauxite deposit goaf based on macro joint damage

The ultimately exposed roof area(UERA)of goaf is crucial to the safety and economics of underground mining.The prediction models do not consider the mechanical weakness of rock mass and ignore the influence of the joint damage factor,causing a large predicted exposure area with a high roof falling risk.This work adopted joint damage factor to derive a new UERA prediction model.The relationships between the UERA(S)and the span ratio(m),the density(k)and the diameter of fracture(d)were analysed by the new prediction model.The results showed that the exposed area S and the span ratio m have a U-shaped curve relationship.The S decreases with the increase of m and then increases when m is beyond 2.The exposed roof area S is in an inversely proportional power-law relationship with the fracture surface density k,and the curvature of the S-k relationship curve decreases when d=0.5 and k>7,and S is close to 0.There is a negative correlation between S and the fracture surface diameter d,the curvature of the S-d curve decreases with the increase of d and k,and the variation rate increases first and then decreases with the increase of d;when k=0.5 and d>9,S is close to 0.The predicted values of the UERA prediction model are 119.3,112.8,and 114.6 m2 with different joint damage parameters,which are slightly smaller than the actual critical exposure area of a roof(S=120 m2).The case study shows that the alternative prediction model is reasonable and acceptable and provides new theoretical support for the underground mining safety of sedimentary bauxite ore.

Prediction of Hidden Granites in the Luziyuan Area of Yunnan Province and the Prospecting Direction

The Fe-Pb-Zn-Cu polymetallic deposits in the Luziyuan area, are of a sedimentary-reformed type related with magmatic hydrothermalism. Previous researches have suggested that the mineralization is closely related to the hidden granites, but little is known about these granites including their burial depth and scale, which has limited the establishment of prospecting models and the optimization of prospecting targets. Geophysical methods have a great exploration depth, and have played a unique role in the prediction of hidden granites. It is shown that granites have low density and high resistivity,

Distribution Prediction of Suitable Growth Area for Eucommia ulmoides in China under Climatic Change Background

[ Objective] The research aimed to study distribution prediction of suitable growth area for Eucommia ulmoides in China under climatic change background. [ Method] By using the maximum entropy model and many kinds of climate change scenarios, we predicted current and future distribution pattems of suitable growth area for Eucommia ulmoides in China and its change process. [ Result ] At present, highly suitable growth area of E. ulmoides mainly distributed in Sichuan, Shaanxi and Chongqing, Under climate change background, total suitable growth areas in future three decades all drastically reduced when compared with that at present. It was noteworthy that moderately and highly suitable growth areas of wild E. ulmoides all disappeared, and junction between Shaanxi and Gansu and Taibai Mountain would be stable suitable growth area of wild E. ulmoides. [ Condusioa] The research could provide useful reference data for investigation, protection and sustainable development of the wild E. ulmoides resources.

Exploring sensitive area in the tropical Indian Ocean for El Niño prediction: implication for targeted observation

Based on initial errors of sea temperature in the tropical Indian Ocean that are most likely to induce spring predictability barrier(SPB)for the El Niño prediction,the sensitive area of sea temperature in the tropical Indian Ocean for El Niño prediction starting from January is identified using the CESM1.0.3(Community Earth System Model),a fully coupled global climate model.The sensitive area locates mainly in the subsurface of eastern Indian Ocean.The effectiveness of applying targeted observation in the sensitive area is also evaluated in an attempt to improve the El Niño prediction skill.The results of sensitivity experiments indicate that if initial errors exist only in the tropical Indian Ocean,applying targeted observation in the sensitive area in the Indian Ocean can significantly improve the El Niño prediction.In particular,for SPB-related El Niño events,when initial errors of sea temperature exist both in the tropical Indian Ocean and the Pacific Ocean,which is much closer to the realistic predictions,if targeted observations are conducted in the sensitive area of tropical Pacific,the prediction skills of SPB-related El Niño events can be improved by 20.3%in general.Moreover,if targeted observations are conducted in the sensitive area of tropical Indian Ocean in addition,the improvement of prediction skill can be increased by 25.2%.Considering the volume of sensitive area in the tropical Indian Ocean is about 1/3 of that in the tropical Pacific Ocean,the prediction skill improvement per cubic kilometer in the sensitive area of tropical Indian Ocean is competitive to that of the tropical Pacific Ocean.Additional to the sensitive area of the tropical Pacific Ocean,sensitive area of the tropical Indian Ocean is also a very effective and cost-saving area for the application of targeted observations to improve El Niño forecast skills.

Prediction of Landscape Pattern of Soft Sandstone Area (SSA) after Seabuckthorn Planting

As a pioneer plant in the gully slopes in the Soft Sandstone Area (SSA) for eco-economical consideration, ten years (1999-2008) planting of seabuckthorn has made 1642.83 km2, or 9.84%, of the total area of SSA change into seabuckthorn coverage. In SSA the landscape has been divided into 9 types, such as seabuckthorn, sand, water, settlement, bush, open vegetation, forest, grassland and unused land. Seabuckthorn type is separated from the bush type for estimating the role of seabuckthron planting. By means of the Markov model, the developing trends of every landscape types can be determined to support the seabuckthorn project which influences the landscape pattern deeply in SSA. The prediction shows that the optimism ratio of seabuckthorn in the future should be 10.21%, the open vegetation 32.25%, and the forest percentage under 10%, which is a very wise tactics to avoid the serious death of various vegetations in SSA to match the local arid eco-environment.

Prediction of Potential Distribution of the Genus Cricotopus(Diptera:Chironomidae)Based on MaxEnt Model

The prediction of suitable area is a method for predicting the potential distribution by using the maximum entropy model.This study predicted the potential suitable habitats for the genus Cricotopus of Chironomidae in China.The latitude and longitude information of 98 distribution sites of Cricotopus in China and the biological environmental factors and altitude distribution in China were collected,and suitable habitats for Cricotopus were predicted,obtaining the suitable ranges and areas of Cricotopus in China,which is consistent with the known living conditions of Cricotopus.The study on the diversity of Cricotopus and the prediction of its suitable habitats provide a theoretical basis for Cricotopus in water monitoring and paddy fields,as well as basic data for the study on the genus Cricotopus.

Preliminary Study of Sensitive Areas for Several Tropical Cyclone Track Prediction Cases in 2007

Conditional （CNOP） obtained by nonlinear optimal perturbation the ensemble-based calculation method is employed to find possible sensitive areas for improving 48-h or more than 48-h tropical cyclone （TC） track predictions in several cases affecting China in 2007. These sensitive areas are examined by observing system simulation experiments （OSSEs）. Results show that these sensitive areas improve TC track predictions for 48 h or more to different extents. Further analysis is performed to determine the distribution characteristics of sensitive areas in these cases. Results show that areas south of Luzon and over surrounding oceans are significant for 48-h or more than 48-h TC track predictions, especially 60-h to 72-h track predictions. Areas over oceans north or east to Taiwan Island seem to be secondary sensitive for 48-h or more than 48-h TC track predictions.

Application of TSD tunnel seismic prediction system in karst area

TSD is one of the classical methods of tunnel seismic prediction based on higher accuracy multi-wave multi-component seismology.The working principle of the TSD and an application example of the TSD on tunnel prediction in Chongqing are introduced in this paper.This system has two ports for speed signal and acceleration signal,and the equipment is more portable and easy to use.According to the application results we can conclude that the TSD prediction system is accurate and it has the wide application prospect in tunnel seismic detection.

Residual subsidence time series model in mountain area caused by underground mining based on GNSS online monitoring

The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm,which seriously threatens the safety of people's production and life in the mining area.Therefore,it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining.Compared with traditional level survey and InSAR(Interferometric Synthetic Aperture Radar)technology,GNSS(Global Navigation Satellite System)online monitoring technology has the advantages of long-term monitoring,high precision and more flexible monitoring methods.The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation,which can not only describe the residual subsidence process from the mechanism,but also predict the residual subsidence.Therefore,based on GNSS online monitoring technology,combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics,this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area.Through the example,the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence.The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground,and the model prediction effect is good,which provides a new method for the prediction of residual subsidence in mountain mining.

Using Remote Sensing Data to Delineate the Lineaments for Hydrothermal Mineral Prediction in Heqing Area, Northwest Yunnan Province, China

Lineament extraction and analysis is one of the routine work in mapping medium and large areas using remote sensing data, most of which are satellite images. Landsat Enhanced Thematic Mapper （ETM） of 945×1 232 pixels subscene acquired on 21 March 2000 covering the northwestern part of Yunnan Province has been digitally processed using ER Mapper software. This article aims to produce lineament density map that predicts favorable zones for hydrothermal mineral occurrences and quantify spatial associations between the known hydrothermal mineral deposits. In the process of lineament extraction a number of image processing techniques were applied. The extracted lineaments were imported into MapGIS software and a suitable grid of 100 m×100 m was chosen. The Kriging method was used to create the lineament density map of the area. The results show that remote sensing data could be useful to extract the lineaments in the area. These lineaments are closely correlated with the faults obtained through other geological investigation methods. On comparing with field data the lineament-density map identifies two important high prospective zones, where large-scale deposits are already existing. In addition the map highlights unrecognized target areas that require follow up investigation.

Prediction of Farmland Protection Area in County-level Land Use Planning: A Case Study of Mizhi County of Shaanxi Province

Taking Mizhi County as an example and the year of 2005 as base period of planning,this paper made a prediction of farmland demand in 2010 and 2020 using grain security method,supply-demand balance method,and trend extrapolation method. In addition,it built a fixed weight combination model to make scientific summary of three prediction results. Finally,it predicted the farmland demand of Mizhi County in 2010 and 2020 will be 40 967 hm2 and 36 556 hm2,which can provide basis and reference for determination of farmland protection area in the land use planning.

Integrated Reservoir Prediction and Oil-Gas Evaluation in the Maoshan Area

The Maoshan area is an area with well-developed igneous rocks and complex structures. The thickness of the reservoirs is generally small. The study of the reservoirs is based on seismic data, logging data and geological data. Using techniques and software such as Voxelgeo, BCI, RM, DFM and AP, the authors have made a comprehensive analysis of the lateral variation of reservoir parameters in the Upper Shazu bed of the third member of the Palaeogene Funing Formation, and compiled the thickness map of the Shazu bed. Also, with the data from ANN, BCI and the abstracting method for seismic characteristic parameters in combination with the structural factors, the authors have tried the multi-parameter and multi-method prediction of petroleum, delineated the potential oil and gas areas and proposed two well sites. The prediction of oil and gas for Well JB2 turns out to be quite successful.

Grading evaluation and prediction of fracture-cavity reservoirs in Cambrian Longwangmiao Formation of Moxi area,Sichuan Basin,SW China

By using core, thin section, well logging, seismic, well testing and other data, the reservoir grading evaluation parameters were selected, the classification criterion considering multiple factors for carbonate reservoirs in this area were established, and the main factors affecting the development of high quality reservoir were determined. By employing Formation MicroScanner Image(FMI) logging fracture-cavity recognition technology and reservoir seismic waveform classification technology, the spatial distribution of reservoirs of all grades were predicted. On the basis of identifying four types of reservoir space developed in the study area by mercury injection experiment, a classification criterion was established using four reservoir grading evaluation parameters, median throat radius, effective porosity and effective permeability of fracture-cavity development zone, relationship between fracture and dissolution pore development and assemblage, and the reservoirs in the study area were classified into grade I high quality reservoir of fracture and cavity type, grade II average reservoir of fracture and porosity type, grade Ⅲ poor reservoir of intergranular pore type. Based on the three main factors controlling the development of high quality reservoir, structural location, sedimentary facies and epigenesis, the distribution of the 3 grades reservoirs in each well area and formation were predicted using geophysical response and percolation characteristics. Follow-up drilling has confirmed that the classification evaluation standard and prediction methods established are effective.

Prediction of Coal Seam Methane Enriched Areas Using Seismic Data

All coal mine disasters are dynamic geological phenomenon and affected by many factors. However, locating the enriched areas of CSM (coal seam methane) may be the precondition for the successful prediction of such disasters. Traditional methods of investigating CSM enriched areas use limited data and only consider a few important factors. Their success rate is low and cannot meet practical needs. In this paper, an alternative method is proposed. The proce- dure is given as follows: 1) fracture attributes derived from azimuth variations of P-wave data in coal seams and wall rocks can be extracted; 2) AVO attributes, such as the intercept P and gradient G parameters can be extracted from different azimuths from 3D seismic data; 3) seismic cubes can be inverted and the relative attributes of imped- ance cubes can be extracted; 4) using a GIS platform, multi-source information can be obtained and analyzed; these include fracture attributes of coal seams and wall rocks, the thickness of coal seams, the distribution of faults and structures, the depth of coal seams, the inclination and exposure of coal seams and the coal rank. Through this processing procedure, methane enriched areas can be systematically detected.

Metallogenic Prediction of the Peripheral Areas of the Xiyi Concealed Lead-Zinc Deposit in Baoshan City, Yunnan Province

The newly-discovered Xiyi lead-zinc deposit is a large deposit located in the north central Baoshan block of the southern Sanjiang metallogenic belt section, Southwest China.The surface of the deposit is mainly covered by eluvial-deluvial lateritic layer, without any mineralized outcrops. The main concealed orebody V3 is buffed in the depth of 300-500m. The orebodies are controlled by certain stratigraphic horizons, and most are cut by strata with a high angle, while a few occur along the strata. The direct wall rocks are calcisiltite, calclithite, bioclastic calcarenite,