Multi-resolution graph-based clustering analysis for lithofacies identifi cation from well log data: Case study of intraplatform bank gas fi elds, Amu Darya Basin

In this study, we used the multi-resolution graph-based clustering （MRGC） method for determining the electrofacies （EF） and lithofacies （LF） from well log data obtained from the intraplatform bank gas fields located in the Amu Darya Basin. The MRGC could automatically determine the optimal number of clusters without prior knowledge about the structure or cluster numbers of the analyzed data set and allowed the users to control the level of detail actually needed to define the EF. Based on the LF identification and successful EF calibration using core data, an MRGC EF partition model including five clusters and a quantitative LF interpretation chart were constructed. The EF clusters 1 to 5 were interpreted as lagoon, anhydrite flat, interbank, low-energy bank, and high-energy bank, and the coincidence rate in the cored interval could reach 85%. We concluded that the MRGC could be accurately applied to predict the LF in non-cored but logged wells. Therefore, continuous EF clusters were partitioned and corresponding LF were characteristics ＆different LF were analyzed interpreted, and the distribution and petrophysical in the framework of sequence stratigraphy.

GPR based RTM imaging technology for estimating rhizome diameters and application in the western China mining area

The rhizome functions are of great significance to the ecological protection of the western China mining area,whose ecological management can be provided with technical support via accurate calculations of the rhizome biomass.The rhizome diameter is an important index parameter of rhizome biomass.In this study,we propose an estimation of rhizome diameters based on ground penetrating radar(GPR)-based reverse time migration(RTM)imaging technology.First,the spatial distribution of shallow rhizomes is simulated using the finite difference time domain method.The simulation data are examined via RTM imaging and single-channel rhizome analysis to obtain the rhizome index parameters:Δh,the width of the maximum positive peak amplitude measured at an amplitude of zero,andΔH,the distance between the zero-amplitude position above the largest positive peak in the shallow region and the zero-amplitude position below the largest positive peak in the deeper region.The experiments of physical models verify the effectiveness of the two parameters(Δh andΔH).and indicate that the values ofΔh andΔH are independent of the rhizome burial depth;instead,they are only related to the diameter of the rhizome.For both the numerical simulations and the physical model experiment,the estimation errors ofΔh andΔH for the rhizome diameters can be constrained to less than 6%and 5%,respectively,which shows that the estimation of the rhizome diameters using GPR based RTM imaging technology is reasonable and effective and its high estimation accuracy meets the technical requirements.

Intelligent identifi cation method for near-surface ground fi ssures based on seismic data

Taking a study area in Jinzhong Basin in Qixian County,Shanxi Province,as an example,this work performs an intelligent interpretation of ground fissures.On the basis of a complete analysis of the regional geological background in the study area,dip-steering cube operation and median filtering of seismic data were performed using fast Fourier transform to improve the continuity of seismic events and eliminate random noise.A total of 200 stratigraphic continuous sample training points and 500 discontinuous training points were obtained from the processed seismic data.Thereafter,a variety of attributes(coherence,curvature,amplitude,frequency,etc.)were extracted as the input for the multilayer perceptron neural network training.During the training period,the training results were traced by normalized root mean square error(RMSE)and misclassifi cation.The training results showed a downward trend during the training period.The misclassifi cation curve was stable at 0.3,and the normalized RMSE curve was stable at 0.68.When the value of the normalized RMSE curve reached the minimum,the training was terminated,and the training results were extended to the whole data volume to obtain the attribute cube of intelligent ground fi ssure detection.The characteristics of ground fi ssures were analyzed and identifi ed from the sections and slices.A total of 11 ground fissures were finally interpreted.The interpretation results showed that the dip angles were 60°-85°,the fault throws were 0-43 m,and the extension lengths were 300-1,100 m in the whole area.The strike of 73%of the ground fi ssures was consistent with the direction of the regional tectonic settings.Specifi cally,four ground fi ssures coincided with the surface disclosed,and the verifi cation rate reached 100%.In conclusion,the intelligent ground fi ssure detection attribute based on the dip-steering cube is eff ective in predicting the spatial distribution of ground fi ssures.

Issues,Progress,and Recommendations in the Construction of Ecological Barrier on the Mongolian Plateau from the Perspective of Big Data

The Mongolian Plateau(MP),situated in the transitional zone between the Siberian taiga and the arid grasslands of Central Asia,plays a significant role as an Ecological Barrier(EB)with crucial implications for ecological and resource security in Northeast Asia.EB is a vast concept and a complex issue related to many aspects such as water,land,air,vegetation,animals,and people,et al.It is very difficult to understand the whole of EB without a comprehensive perspective,that traditional diverse studies cannot cover.Big data and artificial intelligence(AI)have enabled a shift in the research paradigm.Faced with these requirements,this study identified issues in the construction of EB on MP from a big data perspective.This includes the issues,progress,and future recommendations for EB construction-related studies using big data and AI.Current issues cover the status of theoretical studies,technical bottlenecks,and insufficient synergistic analyses related to EB construction.Research progress introduces advances in scientific research driven by big data in three key areas of MP:natural resources,the ecological environment,and sustainable development.For the future development of EB construction on MP,it is recommended to utilize big data and intelligent computing technologies,integrate extensive regional data resources,develop precise algorithms and automated tools,and construct a big data collaborative innovation platform.This study aims to call for more attention to big data and AI applications in EB studies,thereby supporting the achievement of sustainable development goals in the MP and enhancing the research paradigm transforming in the fields of resources and the environment.

Estimation of Grassland Production in Central and Eastern Mongolia from 2006 to 2015 via Remote Sensing

Mongolia is an important part of the Belt and Road Initiative"China-Mongolia-Russia Economic Corridor"and a region that has been severely affected by global climate change.Changes in grassland production have had a profound impact on the sustainable development of the region.Our study explored an optimal model for estimating grassland production in Mongolia and discovered its temporal and spatial distributions.Three estimation models were established using a statistical analysis method based on EVI,MSAVI,NDVI,and PsnNet from Moderate Resolution Imaging Spectroradiometer(MODIS)remote sensing data and measured data.A model evaluation and accuracy comparison showed that an exponential model based on MSAVI was the best simulation(model accuracy 78%).This was selected to estimate the grassland production in central and eastern Mongolia from 2006 to 2015.The results show that the grassland production in the study area had a significantly fluctuating trend for the decade study;a slight overall increasing trend was observed.For the first five years,the grassland production decreased slowly,whereas in the latter five years,significant fluctuations were observed.The grassland production(per unit yield)gradually increased from the southwest to northeast.In most provinces of the study area,the production was above 1000 kg ha with the largest production in Hentiy,at 3944.35 kg ha.The grassland production(total yield)varied greatly among the provinces,with Kent showing the highest production,2341.76x1〇4 t.Results also indicate that the trend in grassland production along the China-Mongolia railway was generally consistent with that of the six provinces studied.

Estimation of Travel Climate Comfort Degree in the Cross-border Region between China and Russia based on GIS

The duration of travel climate comfort degree is an important factor that influences the length of the tourism season and the development of a tourism destination.In this study,we used the monthly average meteorological data for the last 10 years from 46 weather stations in Heilongjiang Province(China)and Primorsky Krai(Russia)to calculate the temperature-humidity index(THI)and wind chill index(WCI)based on ArcGIS software interpolation technology.We obtained the climate comfort charts of the study area with a grid size a 1 km2 grid size,and analyzed the spatial distribution of comfort for each month.The results show the following:1)The THI and WCI of the cross-border region gradually decrease from south to north and from low altitude to high altitude.The annual comfortable climate period is longer when analyzed in terms of the WCI rather\than the THI.2)The travel climate comfortable period of the study area shows significant regional difference and the length of the comfortable period in Heilongjiang Province is 4 to 5 months.Meanwhile,the period in Primorsky Krai decreases from south to north and the length of the comfortable period length in its southern region can reach 7 months.3)The predominant length of the climate comfortable period in the cross-border area is 5 months per year,and it covers 46.6%of the total area,while areas that have a climate comfortable period of 2 months are the most limited,covering less than 0.3%of the area.The results provide a scientific basis for the utilization and development of a meteorological tourism resources and touring arrangements for tourists in the cross-border region between China and Russia.

Intelligent Identification of Building Patches and Assessment of Roof Greening Suitability in High-density Urban Areas:A Case Study of Chengdu

With the expansion of a city,the urban green space is occupied and the urban heat island effect is serious.Greening the roof surfaces of urban buildings is an effective way to increase the area of urban green space and improve the urban ecological environment.To provide effective data support for urban green space planning,this paper used high-resolution images to(1)obtain accurate building spots on the map of the study area through deep learning assisted manual correction;and(2)establish an evaluation index system of roof greening including the characteristics of the roof itself,the natural environment and the human society environment.The weight values of attributes not related to the roof itself were calculated by Analytic Hierarchy Process(AHP).The suitable green roof locations were evaluated by spatial join,weighted superposition and other spatial analysis methods.Taking the areas within the Chengdu city’s third ring road as the study area,the results show that an accurate building pattern obtained by deep learning greatly improves the efficiency of the experiment.The roof surfaces unsuitable for greening can be effectively classified by the method of feature extraction,with an accuracy of 86.58%.The roofs suitable for greening account for 48.08%,among which,the high-suitability roofs,medium-suitability roofs and low-suitability roofs represent 45.32%,38.95%and 15.73%.The high-suitability green buildings are mainly distributed in the first ring district and the western area outside the first ring district in Chengdu.This paper is useful for solving the current problem of the more saturated high-density urban area and allowing the expansion of the urban ecological environment.