Geological Environmental Factors Research Articles

Three-Dimensional Quality Assessment of Urban Underground Space Resource Based on Multiple Geological Environmental Factors

With the rapid advancement of urbanization, the development and utilization of urban underground space resource (UUSR) has become one of the dominant features. However, in certain areas, the development of UUSR may cause disasters and accidents, such as ground collapse, settlements, and tunnel water gushing. Geological environmental factors (GEFs) are recognized as the fundamental constraining factor of UUSR development. In this paper, quality based on GEFs is defined to assess the development difficulty degree of UUSR. A 3D assessment framework is proposed based on 3D geological modelling and the interval continuous mathematical model (ICMM). The subjective and objective joint weight method of analytic hierarchy process and entropy weight method (AHP–EWM) is utilized to determine the weight of each indicator. The quality index (QI) of each spatial node of the 3D geological model is calculated by the ICMM mathematical model. A case study conducted in the Jiangbei New District of Nanjing, China, serves as a demonstration of the UUSR assessment. The results clearly illustrate the 3D distribution characteristics of the quality in the study area, offering valuable insights for future 3D urban underground space planning.

An assessment on the off-road trafficability using a quantitative rule method with geographical and geological data

The assessment of off-road trafficability is an important issue in special military or disaster relief operations. It is affected by geographical and geological environmental factors, and the factor sensitivity changes with space. Previous research used a qualitative manual rule method and geographical factors to assess off-road trafficability in terms of land coverage and terrain factors. However, this caused problems because the factors were not comprehensive, the method was qualitative, and there was a great deal of noise in the assessment results. To solve these problems, we developed a quantitative rule method to comprehensively assess the obstacles caused by geographical and geological factors to off-road trafficability. First, we reviewed remote sensing images of various factors in the study area, searched for traffic rules, and quantified them using the analytic hierarchy process-weighted information content (AHP-WIC) method. Then, the corresponding relationship between rules and off-road trafficability is established by the clustering method, and it is divided into multiple types of motion (generally>2 types). Finally, we used the improved watershed method to filter off-road trafficability map noise. The results showed that the geological environment (including geological hazards, joint & structure, topographic position index, rock structure, etc.) can seriously hinder off-road trafficability but has not been considered in previous research. For complex field environments, the proposed method can adaptively establish the classification relationship between rules and off-road trafficability. Additionally, the improved watershed method can effectively filter the noise generated by the rule method.

The distribution law and coupling factors of debris flows in the G318 Linzhi–Lhasa section of the Sichuan–Tibet traffic corridor

In recent years, debris flow disasters have occurred frequently along the highway, causing river blockages and road interruptions, which seriously threaten the safety of people's lives and property. Highway G318 is an important throat project linking Sichuan and Tibet; at the same time, it is an important channel for the economic development of Sichuan and Tibet and the transportation of national defense materials. Taking the Linzhi–Lhasa Section of Highway G318 as an example, this study analyses the distribution law and characteristics of coupling factors of debris flows in the study area (under its topographical, hydrometeorological, geological, and structural conditions) using remote sensing interpretation, field surveys, and mathematical statistics. The research shows that: (1) The types and quantity of debris flows in the region show statistical laws under the factors of the slope, slope aspect, drainage area, and gradient of the gully. The vegetation coverage in the upper reaches of the Nyang River valley gradually decreases, and the average debris flow disaster density is 0.529/km, which is the most densely distributed area of debris flow. (2) The distribution density of regional debris flows in narrow valleys is greater than that in wide valleys, and those in the Nyang River basin are greater than those in the Lhasa River basin. (3) By comparing the tectonic geological map and the debris flow distribution map, it was found that the debris flow distribution is controlled by faults, and 71% of the debris flow basins have faults. (4) There is a significant positive correlation between the gradient of the material source area and the gradient of the debris flow gully, as well as a close positive correlation between the rainfall and the fault density, and a close negative correlation between the average gradient and the drainage area. Due to the unique topography and geomorphology of different regions, a difference in meteorology and hydrology occurred. This further affected the topography, geomorphology, and distribution of debris flow disasters. Based on the study of the distribution law of regional debris flow and geological environmental factors, this study provides strong support for regional debris flow prevention and related research.

Research on a Regional Landslide Early-Warning Model Based on Machine Learning—A Case Study of Fujian Province, China

China’s landslide disasters are serious, and regional landslide disaster early-warning is one of the important means of disaster prevention and mitigation. The traditional regional landslide disaster early-warning model, however, is limited by the complex landslide induction mechanism, limited data accumulation, and insufficient big data analysis methods, and has problems such as limited early-warning accuracy and insufficient refinement. In this paper, a machine learning method was introduced into the field of regional landslide disaster warning. From the model construction process of training sample-set construction, sample learning and training, model parameter optimization, model preservation, warning output, and so on, a method for constructing a regional landslide early-warning model based on machine learning was systematically proposed. In the sample learning and training, 80% of the training sample-set was used as the training set, and 20% was used as the test set for five-fold cross validation. The Bayesian Optimization algorithm was used to optimize the model parameters, and the accuracy, ROC curve, and AUC value were used to verify the model accuracy and model generalization ability. With China’s Fujian province as an example, based on nine years of geological and meteorological data (2010–2018), geological environment factors, factors of hazard-affected bodies and historical disaster situations, and rainfall-induced factors in four categories, a total of 26 indicators were used as input characteristic parameters. Six machine learning algorithms were adopted to improve model training; the results showed that the Random Forest algorithm performed the best, giving an accuracy of 92.3%, and was the model with the best generalization ability (AUC was 0.955). The second best was the Artificial Neural Network model, with an accuracy of 0.937 and an AUC of 0.935. Next were the Nearest Neighbor model, the Logistic Regression model, and the Support Vector Machine; the poorest results were from the Decision Tree model. Finally, the typical rainfall-type landslide disaster process in Fujian Province was selected as an example to verify the Random Forest algorithm model. The results showed that compared with the early-warning results of the original explicit statistical model, the hit rate of the new model was 6 times, or equal to that of the original model, and the landslide density in the early-warning area of the new model was 1.6–1.7 times that of the original model. Preliminary verification showed that the new model based on the Random Forest method has obvious advantages, a higher hit rate and a smaller warning area, and can achieve more accurate warnings. The follow-up will continue to track the new landslide disaster situation in the study area and carry out model verification and correction.

Groundwater potential mapping based on long time series remote sensing data in Penghu Islands, China

AbstractIslands are special bodies of land surrounded by seawater. Many islands are facing water shortages as the demand for groundwater increases, thus, highlighting the importance and urgency of the assessment, planning, and management of island groundwater resources. To facilitate the same, the groundwater potential of the Penghu Islands in China was assessed for the first time using remote sensing (RS), geographic information system (GIS), and the analytic hierarchy process (AHP). Long‐term series remote sensing studies were also conducted to monitor the dynamic change in groundwater potential and to identify groundwater potential areas. In this study, the geological environment factors were normalized, and their weight was allocated based on the AHP of RS and GIS. The groundwater potential assessment (GPA) index, established using the weighted comprehensive algorithm, was used to analyse groundwater potential and demarcate groundwater potential zones. Finally, the trend in the spatio‐temporal variation of groundwater potential was analysed using long‐term series remote sensing data. The results from 2015 to 2021 showed that the Penghu Islands can be divided into five groundwater potential zones of different grades, such as 6.3%–8.1% of grade I, 22.2%–24.2% of grade II, 23%–24.4% of grade III, 19.6%–21% of grade IV, and 25.1%–25.5% of grade V. The results of the GPA were verified by comparing them with the abundance of water‐content rock formation in hydrogeologic maps, which yielded correlation coefficients of 78.3%–88.9%. Overall, the groundwater potential zones of different grades in the study area tended to be stable all year‐round, including the zones of grades I and II. The results of this study highlighted the reliability of predicting groundwater potential based on long‐term series remote sensing data and its usability to local personnel in appropriate groundwater resource planning and management.

Susceptibility Analysis of Land Subsidence along the Transmission Line in the Salt Lake Area Based on Remote Sensing Interpretation

As the influence of extreme climate and human engineering activities intensifies, land subsidence frequently occurs in the Salt Lake area of Qinghai Province, China, which seriously threatens the stability of the UHV transmission line crossing the area. Current susceptibility analyses of land subsidence disasters have mostly focused on the classification of land subsidence susceptibility and have ignored the differentiation of susceptibility among different land subsidence intensities. Therefore, the land subsidence susceptibility map does not meet the operation and maintenance management needs of the UHV transmission line, let alone planning and designing of new lines in the Salt Lake area. Therefore, in this study, we proposed a susceptibility analysis of different land subsidence intensities along the transmission line in the Salt Lake area. The small baseline integrated aperture radar interferometry (SBAS-InSAR) method was used to obtain the land subsidence along the transmission line based on 67 Sentinel-1 remote sensing interpretation datasets from 2017 to 2021. Based on a combination of K-means clustering and the transmission line specifications, four annual land subsidence intensity grades were identified as 0~−2 mm/year, −2~−10 mm/year, −10~−20 mm/year, and <−20 mm/year. In addition, eight geological environmental factors were analyzed, and a multi-layer perceptron neural network (MLPNN) model was used to calculate the susceptibility of the different land subsidence intensities. The area under the curve (AUC) and practical examples were used to verify the reliability of the different land subsidence intensities susceptibility mapping. The AUC values of the four subsidence intensity grades showed that the results were accurate: the <−20 mm/year grade produced the largest AUC (0.951), with the −10~−20 mm/year, −2~−10 mm/year and 0~−2 mm/year grades producing AUCs of 0.926, 0.812, 0.879, respectively. At the same time, the susceptibility classification results of different land subsidence intensities were consistent with the interpretation and site tower deformation. The results of this study provided the distribution of land subsidence susceptibility along the transmission line, distinguished the susceptibility of different land subsidence intensities, and provided more detailed subsidence information for each transmission tower. The results provide important information for transmission line tower planning, design, protection, and operation management.

Corrosion Detection of Structural Reinforcement Based on Artificial Intelligence Technology

Due to the environmental degradation caused by soil erosion, it is of great significance to establish the relationship model between geological environmental factors and piping erosion. The method to determine the prone area of pipeline corrosion is limited. This paper introduces the mechanism of reinforcement corrosion, points out the non-destructive detection methods of common steel corrosion, and puts forward the measures to prevent and maintain the corrosion of reinforcement from the aspects of design, construction and material selection, so as to prolong the service life of concrete structure. Abrasion, capitation and chemical attack in concrete hydraulic structures can lead to deterioration of spillways, stilling basins, chutes, slabs and transverse joints, concrete blocks under sluices and any irregular surfaces affected by high flow rates. There are numerous coatings on the market that can be used to repair damaged surfaces. However, the basic data provided by the manufacturer is very limited, and if so, it is usually limited to room temperature values. The results show that the data of concrete, corrosion solution and chloride ion are 0.534, 0.673 and 0.384 respectively.

Geological environmental factors of resident’s cancer distribution in Beijing-Tianjin Corridor

The corridor region between Beijing and Tianjin in China belongs to the alluvial and proluvial sedimentary plains of different water systems, and the social economy is very developed. By personally interviewing local villagers, the author obtained data on cancer morbidity and mortality at the village level. Using GIS operating platform, the author makes contour map, its value ranges from 10/10,000 to 100/10,000 and has significant zonal distribution characteristics. The authors analyzed the relationship between cancer distribution and natural geography, soil and hydrogeochemical data in the region, the results showed that there was a significant negative correlation (n=222, γ0.05=0.163): shallow water level buried depth -0.4218, surface soil pH-0.3702, altitude elevation -0.3448, surface soil SiO2-0.3158, significant positive correlation: surface soil Pb0.5769, Cd0.4629,deep soil (-1.3m) N0.5518, Hg0.4321, surface soil Mo0.3486, Cu0.3340, As0.3234, Fe2O30.2913, Mn0.2906,U0.2882, F0.2833, shallow groundwater NH4-N0.2768, surface soil V0.2835, Ni0.2707, F0.2604, Cr0.2460, Tl0.2241, shallow groundwater As0.2199,Cd0.2109, NO30.1644. According to the characteristic pollutant frequency table of 340 key industries in Langfang area, the correlation coefficient of organic pollutants and mercury was calculated: benzopyrene 0.4723, pyridine 0.1551, formaldehyde 0.1371, dioxin 0.1138.Based on the results of this study, the authors established a mode chart of the relationship between geochemical anomalies (contamination sources) of soil waters- geotechnical hydrological conditions (migration pathways)-resident cancer distribution (sensitive receptors).

Study on urban construction land optimization based on geological environment suitability evaluation

In order to realize the optimal allocation of urban construction and geological environment resources, in this paper, the suitability evaluation index system is established, the comprehensive index method is used to evaluate, and the various evaluation factors are superimposed and analyzed. The results show that the area with good suitability accounts for 44.34%. The area with good suitability accounts for 33.86%. The area with medium suitability accounts for 19.27%, and the area with poor suitability accounts for about 2.49%. In this paper, the comprehensive environmental index method is used to evaluate the geological environment factors affecting the urban construction of Xi’an. The Xi’an urban construction geological environment suitability zoning is obtained.

Hydrochemical appraisal of groundwater quality and pollution source analysis of oil field area: a case study in Daqing City, China.

Serious groundwater pollution not only affects the development of enterprises but also threatens the life and health of residents. To explore the utilization potential of shallow groundwater and the status of water quality pollution in Daqing city, factor analysis and Kriging spatial interpolation methods were applied to analyze the spatial distribution characteristics of pollution sources. The results showed that the HCO3-Ca + Mg type water with a maximum salinity of 1.5g/L was the main chemical type of shallow groundwater in this area. Based on the Fe pollution index, the shallow underground water quality in the northeast of Daqing city can be used for drinking. Due to higher salinity, the locations of the availability of groundwater for irrigation only were in the west. Multivariate statistical analysis was carried out using a factor analysis method, and eight main impact factors were extracted in the study. The pollution sources of human activity impact factors were mainly found to be the direct discharge of organic matter from industrial wastewater in petrochemical enterprises and domestic sewage and the inappropriate or excessive application of agricultural fertilizers. The primary geological environment factors were mainly affected by the hydrogeological and runoff conditions in the study area. Pollution factors were mainly distributed in the northeast of the study area where the pollution was a serious problem, while those in the south-central area were fewer and the pollution was light. This study provides a scientific decision basis for the application of groundwater and the management of groundwater resources in this area.

Landslide hazard assessment of rainfall-induced landslide based on the CF-SINMAP model: a case study from Wuling Mountain in Hunan Province, China

The traditional Stability INdex MAPping (SINMAP) model does not perform detailed divisions of study areas and neglects differences caused by the asymmetrical spatial distribution of geotechnical parameters; thus, the accuracy of the evaluation results is insufficient. In this study, the evaluation results of the SINMAP model were improved based on a combination with the certainty factor (CF) model, and the proposed method is referred to as the CF-SINMAP model. The Wuling Mountain area in Cili County of Hunan Province (China) was selected to verify the CF-SINMAP model. First, eight geological environmental factors in the region were analyzed by the CF method, including the slope, distance from fault, slope direction, distance from water, rock and soil type, elevation, distance from road and vegetation coverage. The rock and soil type, vegetation coverage and human engineering activities were determined as the key factors underlying landslide hazards. Then, the study area was divided into six regions based on the key factors, and the physical and mechanical parameters of each region were refined by the natural environment, formation lithology and human activities. Finally, the CF-SINMAP model was used to calculate and analyze the landslide hazard assessment results under different rainfall conditions. The results show that the CF-SINMAP model is more sensitive to rainfall compared with the traditional method and the unstable areas are mainly distributed along river valleys, reservoir banks and areas with continual human engineering activities. The area under the receiver operating characteristic (ROC) curve values was 0.75 and 0.61 for the CF-SINMAP and SINMAP models, respectively. Compared with the traditional SINMAP model, the CF-SINMAP model produces more reliable results. The rainfall threshold that induced the landslide disaster in Cili County, Hunan Province, was 90 mm/d. In summary, the CF-SINMAP model provides new ideas for the prediction of regional rainfall-induced landslides.

A drilling technology guided by well-seismic information integration

Abstract The predictions by drilling-related mechanical and geological models are in some degree inaccurate due to non-unique solution of seismic velocity model. To address this problem, a new drilling technology guided by well-seismic information integration is proposed which consists of seismic velocity update of drilled formations, seismic velocity prediction of the formation ahead of drilling bit, and the prediction of geological feature and drilling geological environmental factors ahead of bit. In this technology, real information (velocity, formation and depth) behind the drilling bit and local pre-stack seismic data around the wellbore being drilled are used to correct the primitive seismic velocity field for a re-migration of seismic data and to update geological features and drilling geological environmental factors ahead of the drilling bit. Field application shows that this technology can describe and predict the geological features, drilling geological environmental factors and complex drilling problems ahead of the bit timely and improve the prediction efficiency and accuracy greatly. These new updated results are able to provide scientific basis for optimizing drilling decisions.

Geological methane emissions and wildfire risk in the degraded permafrost area of the Xiao Xing\u2019an Mountains, China

With global warming, the carbon pool in the degradation zone of permafrost around the Arctic will gradually be disturbed and may enter the atmosphere in the form of released methane gas, becoming an important factor of environmental change in permafrost areas. We selected the northwestern section of the Xiao Xing'an Mountains in China as the study area, located in the degradation zone on the southern margin of the permafrost region in Eurasia, and set up multiple study monitoring areas equipped with methane concentration sensors, air temperature sensors, pore water pressure sensors and soil temperature sensors for long-term monitoring of data changes using the high-density electrical method, ground penetrating radar and on-site drilling to survey the distribution of frozen soil and geological conditions in the study area, combined with remote sensing images of Sentinel-2 L1C and unmanned aerial vehicle photographs and three-dimensional image reconstruction, analysis of fire activities and related geological environmental factors. The results show that since 2004, the permafrost thickness of the marsh wetland in the study area has gradually reduced and the degradation rate obviously accelerated; the organic matter and methane hydrate (metastable methane hydrate and stable methane hydrate) stored in the permafrost under the marsh wetland are gradually entering the atmosphere in the form of methane gas. Methane emissions show seasonal changes, and the annual methane emissions can be divided into three main stages, including a high-concentration short-term emission stage (March to May), a higher-concentration long-term stable emission stage (June to August) and a higher-concentration short-term emission stage (September to November); there is a certain correlation between the change in atmospheric methane concentration and the change in atmospheric pressure and pore water pressure. From March to May every year (high-concentration short-term emission stage), with snow melting, the air humidity reaches an annual low value, and the surface methane concentration reaches an annual high value. The high concentration of methane gas entering the surface in this stage is expected to increase the risk of wildfire in the permafrost degradation area in two ways (increasing the regional air temperature and self-combustion), which may be an important factor that leads to a seasonal wildfire frequency difference in the permafrost zone of Northeast China and Southeast Siberia, with the peak in spring and autumn and the monthly maximum in spring. The increase in the frequency of wildfires is projected to further generate positive feedback on climate change by affecting soil microorganisms and soil structure. Southeastern Siberia and northeastern China, which are on the southern boundary of the permafrost region of Eurasia, need to be targeted to establish fire warning and management mechanisms to effectively reduce the risk of wildfires.

Landslide Susceptibility Mapping Using the Slope Unit for Southeastern Helong City, Jilin Province, China: A Comparison of ANN and SVM

The purpose of this study is to produce a landslide susceptibility map of Southeastern Helong City, Jilin Province, Northeastern China. According to the geological hazard survey (1:50,000) project of Helong city, a total of 83 landslides were mapped in the study area. The slope unit, which is classified based on the curvature watershed method, is selected as the mapping unit. Based on field investigations and previous studies, three groups of influencing Factors—Lithological factors, topographic factors, and geological environment factors (including ten influencing factors)—are selected as the influencing factors. Artificial neural networks (ANN’s) and support vector machines (SVM’s) are introduced to build the landslide susceptibility model. Five-fold cross-validation, the receiver operating characteristic curve, and statistical parameters are used to optimize model. The results show that the SVM model is the optimal model. The landslide susceptibility maps produced using the SVM model are classified into five grades—very high, high, moderate, low, and very low—and the areas of the five grades were 127.43, 151.60, 198.77, 491.19, and 506.91 km2, respectively. The very high and high susceptibility areas included 79.52% of the total landslides, demonstrating that the landslide susceptibility map produced in this paper is reasonable. Consequently, this study can serve as a guide for landslide prevention and for future land planning in the southeast of Helong city.

Spatial case revision in case-based reasoning for risk assessment of geological disasters

Case revision has always been challenging in case-based reasoning (CBR) processes. Most CBR methods used for analyzing geological disasters fail to consider the spatial relationships among geological environmental factors. Therefore, conventional case revision rules do not allow for effective case-based reasoning for geologic disaster assessment. In this study, we first establish a spatial case library of historical geological disasters. Subsequently, these spatial cases are organized, reduced, and weighted using spatial clustering, genetic algorithm and rough set hybrid algorithms, respectively. Based on these efforts, we propose a retrieval method for the spatial cases and evaluate the proposed solution for a target case. Finally, a geological disaster assessment model possessing a spatial case revision function is obtained by combining geographic information system (GIS) technology with the genetic algorithm. Experiments and applications show that our spatial revision approach is effective and that it exhibits a higher classification performance compared to traditional data mining methods with regard to geological disaster assessment. The approach also exhibits a higher accuracy and efficiency than typical spatial CBR or case revision models do. The results of this study thus facilitate rapid geological disaster assessment, making it more facile and convenient for decision makers to execute decisions efficiently and quickly.