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Article

Quaternary Depositional Framework of the Xiong’an New Area: A 3D Geological Modeling Approach Based on Vector and Grid Integration

by
Jixiang Zhu
1,2,
Xiaoyuan Zhou
1,
Guanghui Zhang
1,* and
Qian Wang
1
1
Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences, Shijiazhuang 050061, China
2
Key Laboratory of Groundwater Sciences and Engineering, Ministry of Natural Resources, Shijiazhuang 050061, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(6), 3409; https://doi.org/10.3390/su14063409
Submission received: 23 January 2022 / Revised: 3 March 2022 / Accepted: 11 March 2022 / Published: 14 March 2022
Browse Figures
Versions Notes

Abstract

:
The Quaternary stratigraphic unit is an important underground space resource for sustainable urban development. It is of great significance to understand the spatial variation characteristics of the Quaternary stratigraphic structure and its internal attributes. However, due to the openness and complexity of the Quaternary sedimentary environment, the sedimentary characteristics of Quaternary stratigraphic units are often very complex and difficult to accurately analyze. In this study, a method for analyzing Quaternary sedimentary characteristics via 3D geological modeling based on vector and grid integration is proposed. Based on this method, the Quaternary depositional framework of Xiong’an New Area is established. The results show that the study area is mainly composed of seven Quaternary strata with different sedimentary origins, and the 3D spatial variation characteristics of lithology in each stratum are unique. Taking the vector framework model as the constraint boundary, this study constructs the lithology stochastic model of each Quaternary stratigraphic unit respectively, and accurately simulates the 3D spatial variation characteristics of the inner lithology of Quaternary stratigraphic units in the study area, which is of great significance for the urban planning, construction, and environmental protection of Xiong’an New Area.

1. Introduction

The underground space resources are essential for sustainable development of cities [1,2,3,4,5]. As the Quaternary stratigraphic areas are one of the most important places for human activities [6,7], the sedimentary evolution process of them controls the spatial variation characteristics of the internal hydrogeological [8,9] and engineering geological attributes [10] of the Quaternary stratigraphic area, restricting the development and utilization of urban underground space resources, and having an important impact on urban planning [11,12], infrastructure construction [13,14], groundwater exploitation and protection [15,16], and geological disaster prevention and control [17,18], etc. Therefore, it is always one of the most important works of urban geological surveys and research to analyze the spatial variation characteristics of the Quaternary stratigraphic unit [19,20]. Most of the previous research applications usually apply the discrete and local field geological data obtained by the technical approaches such as Quaternary borehole exploration [21,22] (including well log [23,24] and granularity analysis [25,26]) and geophysical and geochemical exploration [27,28] (especially seismic exploration [29,30]) to analysis [31,32]. However, with the deepening of geological research, the research object gradually presents two extreme directions: macro and micro. The macroscopic geological research mainly aims at the temporal and spatial variation characteristics of the large-scale geological problem [33,34], and the micro geological analysis mainly focuses on the formation mechanism of geological phenomena [35,36]. Therefore, it is difficult to provide high-precision and efficient technical schemes for the variation characteristics analysis of the large-scale Quaternary stratigraphic unit based on the above technical applications [32,37].
With the rapid development of computer technology and GIS, the powerful data processing ability and spatial analysis functions provide the technical basis for the 3D geological modeling technology [38,39,40]. Based on the integration of multi-source heterogeneous geological data obtained by different technical applications, the 3D geological modeling approach utilizes the topological constraint criterions synthesized by different geological rules, such as stratigraphic sequence and phase transformation characteristics, to characterize the 3D spatial structure or the 3D spatial variation of the internal geological properties of strata. Thus, it is widely used in the study of spatial variation characteristics of Quaternary stratigraphic units [41,42,43,44,45].
Nowadays, the 3D geological modeling technology mainly includes vector structure modeling and grid interpolation modeling [46,47]. Each has its advantages and limitations. The vector structure modeling is relatively simple, the stratigraphic boundary of the model is smooth, and the model volume is small enough to manage effectively, while the biggest problem is that the internal attribute of stratum is uniform, which cannot analyze the spatial variation characteristics of the geological properties, such as lithology, porosity, water content, etc. [48,49,50], and hence it is often applied to construct the 3D spatial structure of the geological bodies with different geological origins [51,52,53]. The grid interpolation modeling performs grid division through geological research objects and conducts 3D spatial interpolation using the field geological data as the sample data [54], and it can simulate the spatial variation characteristics of the internal geological attributes, and the results of it can be used in the numerical simulation of different application topics, such as groundwater medium migration, land subsidence, and foundation bearing capacity, etc. [55,56]. Hence, it has broad application prospects in different fields such as urban geology, minerals, and oil–gas exploration [57,58], while the main issues are as follows: Firstly, the accuracy of the model is highly dependent on the field geological data. Secondly, the parameter tuning process is very complicated. Thirdly, the model volume is always huge and increases exponentially with the refinement of the modeling grid. Fourthly, the modeling efficiency is low and often requires high computational power support. Fifthly, the model does not own the geological significance of stratigraphic structure and shows serration. Based on the above factors, the grid interpolation modeling is mainly used in small-scale geological research [59,60].
Xiong’an New Area is a high-tech demonstration city planned and constructed by the Chinese government following the urban development concepts of being green, ecological, harmonious, and intelligent, and so it is very significant to make clear its geological background. As Xiong’an New Area is located at the junction of the margin of the piedmont alluvial–proluvial fan of Taihang Mountains and the catchment area of Baiyangdian Lake, there is a very complicated sedimentary evolution process of the hugely thick Quaternary stratigraphic unit, which causes the 3D spatial structure of it and the inner phase change in different strata are also distinct. In order to analyze the spatial variation characteristics of Quaternary stratigraphic units in Xiong’an New Area, an improved 3D geological modeling approach based on integrating vector structure modeling and grid interpolation modeling was proposed in this study. This method applies the vector structure modeling to construct the 3D spatial structure of Quaternary stratigraphic units, and then taking it as the constraint framework, the grid interpolation modeling is carried out to simulate the 3D spatial variation characteristics of the inner attributes of each sedimentary stratum. The purpose of this study is to use the improved 3D geological modeling approach to construct the 3D geological model of Quaternary stratigraphic units in the start-up area of Xiong’an New Area, so as to clarify the 3D spatial variation characteristics of lithology in Quaternary stratigraphic structure with different deposit geneses and provide high-precision geological information for urban planning and engineering construction of Xiong’an New Area.

2. Data and Methods

2.1. Study Area

The start-up area of Xiong’an New Area is located in the north-central part of Xiong’an New Area, covering an area of about 200 km2. As shown in Figure 1, the terrain gradually decreases from northwest to southeast where the ground slope is less than 2‰, and the ground elevation is mostly between 5 and 26 m. The study area is located at the junction of the margin of the piedmont alluvial–proluvial fan of Taihang Mountains and the catchment area of Baiyangdian Lake, and it belongs to the accumulation plain landform, which can be further divided into alluvial–pluvial plain landform, alluvial–lacustrine plain landform, and alluvial plain landform according to the genetic type and surface morphology. According to the results of field Quaternary exploration, the Quaternary stratigraphic structure in the area mainly includes the alluvial–proluvial deposits and flood plain deposits of Taihang Mountains, the lacustrine deposits of Baiyangdian Lake, and the Paleochannel deposits (Table 1). This study takes the Quaternary stratigraphic structure with different deposit geneses as the main research object to construct the 3D geological structure model and its internal lithology model of the Quaternary stratigraphic structure in the 100 m depth range of the start-up area of Xiong’an New Area.

2.2. Data

In this study, the 3D geological structure modeling and internal lithology simulation of Quaternary stratigraphic units are mainly carried out using the sedimentary stratigraphic information and lithology information of 203 geological boreholes which contain 21 boreholes with 200 m depth and 182 boreholes with 100 m depth. The Quaternary stratigraphic information in the borehole is determined by well log and granularity analysis, and the borehole is recorded once every 0.2 m. The profile based on the geological drillings shows that the spatial variation of the sedimentary sub-facies and lithologies is very complicated in the study area (Figure 2).

2.3. 3D Geological Modeling Based on Vector and Grid Integration

According to the characteristics of vector structure modeling and grid interpolation modeling, the vector–grid-integrated 3D geological modeling method utilizes the vector structure modeling to construct the 3D geological structure model of Quaternary stratigraphic structure [48,51], and then taking it as the constraint framework for integral meshing, finally applies the grid interpolation modeling to establish the simulation model of the 3D spatial variation of the internal lithology of each sedimentary stratum [55,57]. Based on a win10 operating system (memory 128 G, CPU 4.12 GHz), this study uses DeepInsight software as the modeling platform to carry out 3D geological structure modeling of Quaternary stratigraphic structure, 3D stochastic simulation of lithology, model validation, and uncertainty analysis. The specific technical scheme is shown in Figure 3.

2.3.1. 3D Geological Structure Modeling of Quaternary Stratigraphic Units

The stratigraphic sequence of Quaternary stratigraphic units is the key to constructing the 3D geological structure model of Quaternary stratigraphic structure in the study area, which can be used as a professional basis for structural modeling to determine the contact relationship and intersect mode between adjacent strata. In this study, we took the Holocene (Qh), Late Pleistocene (Qp3), Middle Pleistocene (Qp2), and Early Pleistocene (Qp1) of the Quaternary as the macro constraint framework of the Quaternary stratigraphic structure via the isotope dating technique, and then combined with the well log data and granularity analysis results, we analyzed the evolution process of the Quaternary stratigraphic structure in different geological times of the Quaternary, finally determining the stratigraphic sequence of the Quaternary stratigraphic structure in the study area, which is shown in Table 2.
Using the Quaternary stratigraphic information contained in 183 geological boreholes as sample data (the other 20 as verification boreholes), the global discrete control point set of each sedimentary stratum was obtained through interpolation and encryption, and the global constraint surface that controlled the spatial structure of each sedimentary stratum was generated as shown in Figure 4.
Taking the stratigraphic sequence as a professional basis and the global constraint surface of each layer as the modeling data to determine the contact relationship and the intersection mode of the strata, the 3D geological structure model of the Quaternary stratigraphic structure in the start-up area of Xiong’an New Area was constructed using the Deep Insight software as the 3D geological modeling platform, and the specific technical scheme is shown in Figure 2.

2.3.2. Stochastic Simulation of Spatial Variation of Lithology of Quaternary Stratigraphic

In this study, the sequential indicator simulation method was used to carry out the stochastic simulation of lithology of the Quaternary stratigraphic structure. Sequential indicator simulation is a nonparametric statistical method for uncertainty evaluation based on the indicator kriging method [61,62]. It needs to grid the study area based on pixels without the need for assuming that the original samples obey the normal distribution [63,64].
Suppose there is a set of observation data { Z ( u a ) ,   a = 1 , 2 , … , N } in the study area, and Z ( u ) represents the value at the location (the meshed node) u . Given K thresholds z 1 , z 2 , … , z K , the original variable was encoded as 0 or 1 by Equation (1) so as to obtain the indicator variable I ( u a ,   z k ) for stochastic simulation.
I ( u a ,   z k ) = { 1 Z ( u a ) ≤ z k , k = 1 , 2 , … , K 0 o t h e r s
The cumulative conditional distribution function (ccdf) of the indicator variable I ( u a ,   z k ) at the node u was obtained by the kriging method, as shown in Equation (2).
F { I ( u a ,   z k ) ; Z | Z ( u a ) , a = 1 , 2 , … , N } = p r o b { I ( u a ,   z k ) ; Z | Z ( u a ) , a = 1 , 2 , … , N }
Finally, we randomly extracted a value from ccdf of the indicator variable I ( u a ,   z k ) as the sample data of stochastic simulation of the next node. In this way, all grid nodes were traversed sequentially, obtaining the stochastic simulation value of each node via sequential traversal, which can reach the realization of stochastic simulation.
Computing units for stochastic simulation were obtained through overall meshing of the 3D geological structure model of the Quaternary stratigraphic structure. In this study, we comprehensively considered simulation accuracy, calculation load, compute performance, and model management efficiency, etc., to determine the size of the modeling grid was 20 × 20 × 0.4 m3, so that more than 182 million grids were generated for the whole geological model.
The primary data of the simulation were the lithological information of 203 boreholes, of which about 90% (183) drilling data were used as modeling data, and the other 10% (20) served as validation data. The statistics of samples of different lithology in geological boreholes are shown in Table 3, and the 3D spatial distribution of boreholes is shown in Figure 5.
Due to the differences in sedimentary environments such as provenance, hydrodynamic conditions, and paleotopographic features, the spatial variation characteristics of internal attributes of different Quaternary stratigraphic units are certainly different. Therefore, for accurate simulation of the 3D spatial variation characteristics of the lithology of the strata, it is necessary to establish the stochastic simulation model of each sedimentary stratum by utilizing the 3D geological structure model of each stratum as a simulation boundary. In this study, the GSlib was used as the stochastic simulation platform to construct the stochastic simulation models based on the lithological spatial variation characteristics of each stratum, and the specific technical scheme is shown in Figure 2.

2.4. Validation of Uncertainty Model

In this study, the sedimentary stratigraphic information and the stratigraphic lithology information contained in the 23 verification boreholes were used as the validation data to evaluate the accuracy of the geological structure model and the lithology simulation of the Quaternary stratigraphic structure. The main assessment idea was, firstly, extracting virtual boreholes from the model at the same location as the validation boreholes, then comparing their sedimentary stratigraphic information or lithology information of the virtual borehole to the verification borehole, and finally calculating the quantitative evaluation results of the model according to Equation (1).
{ a = ∑ i n M i / ∑ i n T i × 100 % T i = M i + N i
where a represents the accuracy of the model, n means the number of verification boreholes, here n = 23 , i is the serial number of verification boreholes, M i represents the number that the Quaternary stratigraphic structure or lithology in the verification borehole i consistent with its relevant virtual borehole, while N i represents the number of Quaternary stratigraphic structure or lithology in the verification borehole i inconsistent with its relevant virtual borehole, and the T i represents the total number of Quaternary stratigraphic structure or lithology involved in the verification borehole i . We made statistics on the comparison information of the Quaternary stratigraphic structure or lithology between each verification borehole and its virtual borehole, and then calculated the consistent results ∑ i 23 M i and inconsistent results ∑ i 23 N i in all 23 verification boreholes, and the total number of stratigraphic units or lithology in all 23 verification boreholes ∑ i 23 T i . Finally, the Quantitative evaluation result a can be obtained based on Equation (3).

3. Results

3.1. 3D Geological Structure Model of Quaternary Stratigraphic Units

The 3D geological structure model of Quaternary stratigraphic units in the start-up area of Xiong’an New Area consists of 33 strata of different deposit geneses, including nine types of sedimentary sub-facies. As shown in Figure 6, the spatial distribution characteristics of strata of different origins are relatively free and random and do not have strong regularity, and the main reasons include two aspects: firstly, different types of sedimentary processes are carried out at the same time, and as a result, the stratification of the Quaternary stratigraphic structure in the study area is not obvious in the vertical direction, and there are often multiple types of Quaternary stratigraphic structures coexisting at the same depth; secondly, the study area is located at the junction of the margin of the piedmont alluvial–proluvial fan of Taihang Mountains and the catchment area of Baiyangdian Lake, where the terrain is very flat and the flow rate and the transport capacity of the flood or the rivers had attenuated to the weakest. Hence, it is easy to lead to the transformation of the sedimentary process due to the differences in local topography, which result in the significant characteristics of the mutual intersection of different Quaternary stratigraphic units in space.
Since the study area is located at the margin of the piedmont alluvial–proluvial fan of Taihang Mountains, it needs large-scale floods to form a certain scale of alluvial–proluvial strata. Therefore, the alluvial–proluvial Quaternary stratigraphic structure can only be found in the north of the study area, which is relatively close to the Taihang Mountains, and the alluvial–proluvial deposition process in the Quaternary is mainly concentrated in Qp2 and Qp1 from when the floods occurred frequently. The floodplain strata are the most widely distributed in the study area, and with the factors such as the slowing down of the terrain and the termination of precipitation, the flood became relatively static after spreading to the surrounding areas with the maximum radius in the late stage of the flood, resulting in the deposition process of the floodplain. Hence, the floodplain deposition can be considered the next evolutionary stage of the piedmont alluvial–proluvial process of Taihang Mountains. The floodplain deposition process in the study area runs through the entire Quaternary era, dominating the spatial framework of the Quaternary stratigraphic structure in the study area. In fact, the floodplain strata are one of the most important Quaternary stratigraphic types in the North China Plain. Since the study area lies between Baiyangdian and Taihang Mountains, which is the direction of the floods flowing from the Taihang Mountains to Baiyangdian, the Baiyangdian lacustrine deposition process in the study area is easily disturbed by the Taihang Mountains’ piedmont floods, and as a consequence, the lacustrine Quaternary stratigraphic structure only exists in low-lying areas, mainly including shallow lake facies, shore–lacustrine facies, and limnetic facies. The fluvial strata contain the braided river, meandering river, and branch channel, etc. As it is near Baiyangdian, where the terrain is flat and easily causes the river realignment, the fluvial sedimentary type mainly depends on the hydrodynamic conditions of the river. In the wet season, the water flows over the riverbank and converges to Baiyangdian along the low-lying area, and so it is easy to form the braided river, while in the dry season, it mainly forms the meandering river. The braided rivers’ deposit strata and meandering rivers’ deposit strata in the study area are interrelated and have a unified hydraulic connection, which is the most important aquifer in the study area. The 3D geological structure models of sedimentary geology in different geological periods of the Quaternary are shown in Figure 7.

3.2. Simulation of the 3D Spatial Variation Characteristics of Internal Lithology of Quaternary Stratigraphic Structure

We took the 3D geological structure model of the Quaternary stratigraphic structure as the constraint frame to meshing and established the stochastic simulation model of each sedimentary stratum based on the sequential indicator stochastic simulation method using the Gslisb open source software as the lithological stochastic simulation platform [61,62,63,64]. We finally simulated the 3D spatial variation characteristics of internal lithology of 33 Quaternary stratigraphic units by utilizing the lithological information in 183 boreholes as the sample data. The simulation result is shown in Figure 8.
On the whole, the deposit process of the Quaternary strata in the study area was under relatively weak hydrodynamic conditions, which led to the weakness of the water transport capacity, and it cannot carry large particles of sedimentary substance. Consequently, the internal lithology of the Quaternary stratigraphic structure in the study area is mainly composed of silty clay, silt, fine sand, mealy sand, and clay with very small particle size, and the coarse sand, medium–coarse sand, and medium sand only exist in local fluvial Quaternary stratigraphic structures. As for sand–gravel with a larger particle size, it can only be found sporadically in the Quaternary stratigraphic structure in the entire study area. The simulation results of the 3D spatial variation characteristics of lithology in 33 Quaternary stratigraphic units are individually shown in Figure 9.
For a single Quaternary stratum, the 3D spatial variation of the internal lithology shows obvious sedimentary rhythmic characteristics. However, as shown in Figure 10, for all the Quaternary stratigraphic units, the spatial variation of lithology shows complex variation characteristics, and there is obvious mutation among different Quaternary stratigraphic structures, which is caused by the differences of the sedimentary environment, including hydrodynamic conditions, provenance, topography, and geomorphology, etc.
Comparing the lithological spatial variation characteristics in different periods of the Quaternary, it can be found that humans’ continuous transformation activities to the natural environment will significantly change the spatial distribution characteristics of lithology. For the Quaternary Holocene (Qh), the changes of the spatial variation characteristics of the stratigraphic lithology caused by the transformation of human activities such as plain fill, planting fill, and miscellaneous fill include two aspects. On the one hand, the transformation of Quaternary strata by human activities has increased the complexity of spatial variation characteristics of lithology in Quaternary stratigraphic structures as a whole, and on the other hand, the transformation of Quaternary strata by human activities weakens the drastic change of lithology among different Quaternary stratigraphic structures. For the Quaternary Late Pleistocene (Qp3), the stratigraphic lithology is dominated by floodplain strata, and only a certain scale of meandering river Quaternary stratigraphic structure can be found in the south. Hence, the stratigraphic lithology in Qp3 is mainly dominated by small particle sizes, such as clay, silty clay, and silt. For the Quaternary Middle Pleistocene (Qp2) and Early Pleistocene (Qp1), in addition to the floodplain deposits, there are also large-scale braided river and meandering river Quaternary stratigraphic structures. Therefore, although the spatial variation characteristics of the lithology in the strata are generally dominated by small-sized clay, silty clay, and silt, and while a certain scale of large-size lithology such as coarse sand, medium–coarse sand, and medium sand are frequently mixed, this leads to more significant heterogeneity of lithology. The 3D lithological spatial variation characteristics of the Quaternary stratigraphic structure in different periods of the Quaternary are shown in Figure 11.

3.3. Uncertainty Analysis

In this study, the inner sedimentary stratigraphic information and lithology information of the 20 geological boreholes were used as verification boreholes to evaluate the uncertainty of the 3D geological structure model of Quaternary stratigraphic structure and the stochastic simulation of stratigraphic lithology. By extracting the virtual boreholes at the same location as the verification boreholes from the model, the uncertainty analysis of the two models was carried out by comparing the consistency of strata and lithology between a verification borehole and its virtual borehole, and the quantitative accuracy evaluation results of these two models can be obtained via comprehensively analyzing the comparison results according to Equation (1) in the final analysis. The accuracy of the 3D geological structure model of Quaternary stratigraphic structure is 92.3%, and the accuracy of the 3D lithology stochastic simulation of the Quaternary stratigraphic structure is 89.6%. The uncertainty analysis results show that these two models accurately analyze the 3D spatial variation characteristics of the Quaternary sedimentary stratigraphic structure and its internal lithology.

4. Discussion

In this study, a 3D geological model with smooth geological boundaries was constructed using vector modeling (Figure 4), which can be represented by the 3D spatial distribution characteristics of each stratum (Figure 6 and Figure 7), and so it has clear geological significance. At the same time, the 3D geological structural model of Quaternary stratigraphic structure is used as the constraint boundary in the stochastic simulation, and it can alleviate the dependence of grid random modeling on geological data.
The parameter adjustment of the stochastic simulation is to fit the geological law to ensure the accuracy of simulation. The overall stochastic simulation needs to fit all the sedimentary laws of the Quaternary stratigraphic structure with different sedimentary origins at the same time, and hence the parameter adjustment process is very complex. In this study, the 3D geological model of each Quaternary stratigraphic structure was used as the constraint boundary of the stochastic simulation to establish the stochastic model of each Quaternary stratigraphic structure, respectively (Figure 9), and the parameter adjustment process was relatively simple as the internal sedimentary characteristics of each Quaternary stratigraphic unit was relatively clear. Meanwhile, the calculation load of the stochastic simulation of each stratum was relatively small, so the simulation efficiency was always higher than the overall stochastic simulation.
The scale of the model is related to the size of the grid. The smaller the grid of random simulation, the higher the accuracy of simulation in theory, but the model volume and calculation load increase exponentially. Therefore, it was necessary to comprehensively consider the factors such as simulation accuracy and simulation efficiency in order to determine the optimal scheme of 3D stochastic simulation of stratigraphical lithology.

5. Conclusions

Due to the differences in the sedimentary process, the spatial variation characteristics of internal lithology, porosity, water content, and other attributes of the Quaternary stratigraphic structure must be significantly different. Therefore, it is difficult to carry out integrated simulation analysis, and it is necessary to establish the stochastic model based on the respective spatial variation characteristics of each sedimentary stratum. The 3D geological modeling based on vector and grid integration constructs the 3D geological structure model of Quaternary stratigraphic structure via vector structure modeling and obtains the computing unit by meshing the structure model. Then, it establishes the stochastic simulation model of each sedimentary stratum by taking the 3D geological structure model of each stratum as the constraint frame, and finally simulates the 3D lithological spatial variation of each stratum by utilizing the geological field data as the sample data. This technical method can accurately analyze the spatial structure of Quaternary stratigraphic structure and the 3D spatial variation characteristics of its internal attributes.
The deposit genesis of Quaternary stratigraphic units in the start-up area of Xiong’an New Area mainly include the piedmont alluvial–proluvial deposits (middle and margin of the alluvial–proluvial fan) of Taihang Mountains, floodplain deposits, fluvial deposits (braided river, meandering river, and branch channel), and lacustrine deposits of Baiyangdian Lake (shallow lake facies, shore–lacustrine facies, and limnetic facies). The Quaternary stratigraphic structures in the study area are mainly dominated by floodplains. However, there are still some differences in the deposit process of the Quaternary stratigraphic structure in different periods of the Quaternary, and there are obvious artificial modifications in the sedimentary process of Qh; it was mainly dominated by floodplains in Qp3 due to climate and provenance, and there was only a certain scale of meandering river sediment in the local area. In Qp2 and Qp1, there were extensive braided river and meandering river deposits as well as floodplain deposits, and at the same time, there was also small-scale lacustrine deposits in local low-lying areas, which meant the Quaternary stratigraphic structures in these two periods were obviously interlaced, and the spatial structure change of strata was complicated.
The spatial variation characteristics of lithology in Quaternary stratigraphic structure in the study area are mainly controlled by hydrodynamic conditions, provenance, topography, etc. As the hydrodynamic conditions were relatively calm and the terrain was relatively flat during the Quaternary period, the lithology of the Quaternary stratigraphic structure in the study area is mainly composed of silty clay, silt, and clay with small particle sizes, and only small-scale coarse sand, medium–coarse sand, and medium sand with relatively large particle sizes can be found in certain local areas. In terms of sedimentary genesis, the lithology with small particle sizes, such as silty clay, silt, and clay, mainly exists in floodplain deposits and Baiyangdian Lake deposits. The lithology with relatively large particle sizes such as coarse sand, medium–coarse sand, and medium sand is relatively frequently mixed in fluvial deposits, especially in the braided river deposits.
The plain fill, miscellaneous fill, and planting fill generated by large-scale and continuous human activities can obviously transform the spatial structure of Quaternary stratigraphic structure and the spatial variation characteristics of internal lithology. While supporting human activities such as infrastructure construction and farmland production, the Quaternary stratigraphic structures are also undergoing the transformation of human activities. There is no large-scale soft soil layer in the Quaternary stratigraphic structure of the start-up area of Xiong’an New Area, the engineering geological condition is generally good, and the underground space resources are rich and easy for development and utilization. Therefore, it can provide support for the urban construction and sustainable development of the whole Xiong’an New Area.

Author Contributions

Conceptualization, J.Z. and G.Z.; methodology, J.Z.; software, J.Z. and Q.W.; validation, X.Z. and Q.W.; formal analysis, X.Z. and Q.W.; investigation, G.Z.; resources, X.Z.; data curation, J.Z.; writing—original draft preparation, J.Z. and G.Z.; writing—review and editing, J.Z. and G.Z.; visualization, J.Z. and Q.W. supervision, X.Z.; project administration, X.Z.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Comprehensive Monitoring of Resources and Environment Carrying Capacity of Xiong’an New Area and Construction of Digital Platform of Transparent Xiong’an (Institute of Hydrogeology and Environmental Geology, Chinese Academy of Geological Sciences) (NO.DD20189144) and 3D geological modeling of Multi-factors urban geology of Wuhan (NO. WHDYS-2020-007).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare that they have no conflict of interest.

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Figure 1. Topography and borehole location distribution map in the study area.
Figure 1. Topography and borehole location distribution map in the study area.
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Figure 2. Profile of different Quaternary ages, sub-facies, and lithologies based on the field geological drilling. Not include all the sedimentary sub-facies and lithologies in the study area.
Figure 2. Profile of different Quaternary ages, sub-facies, and lithologies based on the field geological drilling. Not include all the sedimentary sub-facies and lithologies in the study area.
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Figure 3. Technical scheme of 3D geological modeling based on vector and grid integration.
Figure 3. Technical scheme of 3D geological modeling based on vector and grid integration.
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Figure 4. Global constraint surfaces of the Quaternary stratigraphic structure obtained via interpolation and encryption based on the Quaternary geological borehole data. Fifty times magnification in vertical direction.
Figure 4. Global constraint surfaces of the Quaternary stratigraphic structure obtained via interpolation and encryption based on the Quaternary geological borehole data. Fifty times magnification in vertical direction.
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Figure 5. The 3D spatial distribution of lithology in geological boreholes. Fifty times exaggeration in vertical direction.
Figure 5. The 3D spatial distribution of lithology in geological boreholes. Fifty times exaggeration in vertical direction.
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Figure 6. The 3D geological structure model of Quaternary stratigraphic structure in the start-up area of Xiong’an New Area. Fifteen times exaggeration in vertical direction.
Figure 6. The 3D geological structure model of Quaternary stratigraphic structure in the start-up area of Xiong’an New Area. Fifteen times exaggeration in vertical direction.
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Figure 7. The 3D structural model of Quaternary stratigraphic structure in different Quaternary ages. Twenty-five times exaggeration in vertical direction.
Figure 7. The 3D structural model of Quaternary stratigraphic structure in different Quaternary ages. Twenty-five times exaggeration in vertical direction.
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Figure 8. Lithology simulation of the Quaternary stratigraphic structure in the study area. Twenty-five times exaggeration in vertical direction.
Figure 8. Lithology simulation of the Quaternary stratigraphic structure in the study area. Twenty-five times exaggeration in vertical direction.
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Figure 9. Lithology simulation of the 33 Quaternary stratigraphic units in the study area. Fifteen times exaggeration in vertical direction.
Figure 9. Lithology simulation of the 33 Quaternary stratigraphic units in the study area. Fifteen times exaggeration in vertical direction.
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Figure 10. Profile of the lithologies in different sedimentary sub-facies. Twenty-five times exaggeration in vertical direction.
Figure 10. Profile of the lithologies in different sedimentary sub-facies. Twenty-five times exaggeration in vertical direction.
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Figure 11. Lithology simulation of the Quaternary stratigraphic structure in the study area. Twenty-five times exaggeration in vertical direction.
Figure 11. Lithology simulation of the Quaternary stratigraphic structure in the study area. Twenty-five times exaggeration in vertical direction.
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Table
Table 1. All sedimentary facies and sub-facies of Quaternary stratigraphic units in the study area.
Table 1. All sedimentary facies and sub-facies of Quaternary stratigraphic units in the study area.
Sedimentary FaciesSedimentary Sub-Facies
alluvial–proluvial fan faciesmiddle of alluvial–proluvial fan,
and margin of alluvial–proluvial fan
floodplain faciesfloodplain
lacustrine faciesshallow lake facies, shore–lacustrine facies,
and limnetic facies
fluvial faciesmeandering river, braided river, and branch channel
Table
Table 2. Stratigraphic sequence of Quaternary stratigraphic structure in study area.
Table 2. Stratigraphic sequence of Quaternary stratigraphic structure in study area.
Serial IdAgeSedimentary FaciesSedimentary Sub-Facies
1Qhlacustrine faciesshallow lake facies_1
2Qhfloodplainfloodplain
3Qhlacustrine faciesshore-lacustrine
4Qhlacustrine faciesshallow lake facies_2
5Qhfluvial faciesmeandering river
6Qp3floodplainfloodplain_1
7Qp3fluvial faciesbranch channel
8Qp3fluvial faciesmeandering river_1
9Qp3floodplainfloodplain_2
10Qp3alluvial–proluvial fanmiddle of alluvial fan
11Qp3fluvial faciesmeandering river_2
12Qp3floodplainfloodplain_3
13Qp3fluvial faciesbraided river
14Qp2fluvial faciesbraided river_1
15Qp2fluvial faciesmeandering river_1
16Qp2lacustrine facieslimnetic facies _1
17Qp2floodplainfloodplain
18Qp2lacustrine faciesshore_lacustrine_2
19Qp2lacustrine faciesshallow lake facies_1
20Qp2fluvial faciesmeandering river_2
21Qp2alluvial–proluvial fanmargin of alluvial fan
22Qp2alluvial–proluvial fanmiddle of alluvial fan
23Qp2fluvial faciesbraided river_2
24Qp2lacustrine faciesshallow lake facies_2
25Qp1fluvial faciesmeandering river_1
26Qp1fluvial faciesbraided river_1
27Qp1lacustrine facieslimnetic facies
28Qp1floodplainfloodplain_1
29Qp1fluvial faciesmeandering river_2
30Qp1fluvial faciesbraided river_2
31Qp1alluvial–proluvial fanmiddle of alluvial fan
32Qp1floodplainfloodplain_2
33Qp1fluvial faciesmeandering river_3
Table
Table 3. Statistics of samples of different lithology in geological boreholes.
Table 3. Statistics of samples of different lithology in geological boreholes.
LithologySample StatisticsLithologySample Statistics
coarse sand187silty clay7589
medium–coarse sand13clay1031
medium sand806miscellaneous fill24
middle–fine sand15plain fill135
fine sand1156planting soil42
silty–fine sand109mud sand13
mealy sand1384mud silt15
silt4032mud clay6
sand–gravel8
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Zhu, J.; Zhou, X.; Zhang, G.; Wang, Q. Quaternary Depositional Framework of the Xiong’an New Area: A 3D Geological Modeling Approach Based on Vector and Grid Integration. Sustainability 2022, 14, 3409. https://doi.org/10.3390/su14063409

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Zhu J, Zhou X, Zhang G, Wang Q. Quaternary Depositional Framework of the Xiong’an New Area: A 3D Geological Modeling Approach Based on Vector and Grid Integration. Sustainability. 2022; 14(6):3409. https://doi.org/10.3390/su14063409

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Zhu, Jixiang, Xiaoyuan Zhou, Guanghui Zhang, and Qian Wang. 2022. "Quaternary Depositional Framework of the Xiong’an New Area: A 3D Geological Modeling Approach Based on Vector and Grid Integration" Sustainability 14, no. 6: 3409. https://doi.org/10.3390/su14063409

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