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Automated identification and mapping of geological folds in cross sections

  • Jian-Chu Huang , An-Bo Li EMAIL logo , Xin Wang , Chang-Zheng Shao and Yan-Gen Shen
Published/Copyright: May 11, 2023
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Abstract

Cross sections carry information on the spatial distribution of rock strata and the development of geological structures, and it is an important data source for three-dimensional (3D) geological modeling. However, the interpretation and mapping of geological structures in sections by means of manual interpretation are inefficient and costly, and the performance varies greatly with the experts’ ability and experience. The objective of this article is to develop an automatic recognition and mapping method for folds in cross sections. This method mainly includes identifying folds based on stratigraphic sequence characteristics (symmetrical and repetitive), classifying fold types based on geometric attributes of folds (interval scheduling, strike, and section morphology), optimizing strata based on the superposition principle and area conservation principle, and constructing the polygon features of folds. Based on experiments in the Parallel Fold Belt of Eastern Sichuan and the central Appalachian fold-thrust belt in the Appalachian Mountains, the method presented in this article can effectively be used for automatic recognition and high-quality mapping of folds in the cross sections. The method provides a good source of geological cross-sectional data for the 3D modeling of geologic bodies.

1 Introduction

The cross section is compiled by a two-dimensional (2D) projection according to various geological and geographical elements in a particular scale and choosing a specific direction on the geological map [1]. The cross section can clearly express the spatial distribution of a rock formation, the development of geological structures [2,3], and the particular geological relationships [4]. Furthermore, the cross section is also an important data source for three-dimensional (3D) geological modeling [5,6,7,8].

In the past, cross sections were mainly drawn by hand. The specific method was to draw the stratum and its boundary below the corresponding position of the topographic surface according to the attitude. Then, a variety of stratigraphic textures and symbols were used to represent each stratum’s lithology, time, and attitude [4]. Manual drawing of cross sections is based on a certain amount of observation data and geological knowledge. The person drawing the sections can effectively identify and express the geological structures by virtue of their experience. Nevertheless, the traditional method could be inefficient, and the quality of the drawings varies from person to person. It is difficult to manually satisfy the data needs of a large number of geological sections in 3D geological modeling.

Currently, cross sections can be extracted automatically from a 3D model or a digital map using section systems [9,10,11]. At present, mainstream cross section software mainly includes Section system [12,13], Digital Geology Survey System, 2D Move, Midland Valley software, and others [14]. These systems have the advantages of high efficiency and low cost. Cross sections generated by these systems are suitable for lateral reflection of outcrops near the surface. However, it is difficult for these systems to reflect the development of geological structures deep underground due to a lack of automatic recognition and mapping of complex geological structures [15,16]. If the generated cross sections are used to express the development of geological structures and 3D modeling of deeper geological structures, the recognition and expression of relevant geological structures must rely on further visual interpretation and expert manual drawing [17,18].

Various types of geological structures exist, including folds, faults, and intrusive structures [19]. Different types of geological structures have different genesis methods, developmental rules, spatial structures, and morphological characteristics. Therefore, automatically identifying and mapping different geological structures will have significant value in 3D modeling of geological bodies. Folds, a type of geological structure, are relatively diverse, complex, and widely distributed, and they play an essential role in cross sections [20,21,22]. The main characteristic of folds is the symmetry and repeatability of their unique stratigraphic sequence [23]. Based on this remarkable feature of folds, an automatic identification and mapping method is proposed for geological folds in cross sections.

The primary objective of this article is to develop an automatic recognition and mapping method for folded structures in cross sections using digital elevation models (DEMs) and geological maps. This article is organized as follows: Section 2 presents the methodology, Section 3 presents the platform and results of case studies, and Section 4 presents the discussion and conclusions.

2 Methodology

2.1 Basic idea

The method proposed in this article can identify and map folded structures in cross sections, which lays a sound data foundation for high-quality 3D geological modeling using cross sections [6,24]. It also provides support for the 3D modeling of folded structures.

First, according to the symmetrical repetition rule and the relative age of strata, the core strata and the fold types can be effectively identified from traditional cross sections [25]. Second, due to the influence of tectonic movement, fold types will be different. Thus, the types of folded structures can be classified by analyzing structure attributes (such as interval scheduling, strike, dip and dip direction, and section shape). The shape of the hinge zone of folds can also be deduced. The dip and dip direction shown in the resulting cross sections is partly derived from the digital map data. The strata without structure attributes can be calculated using indirect methods (three-point method, four-point method, etc.) [1,26,27], then, fitting the boundary of two limbs of corresponding strata by Bézier curves, optimizing the strata based on the principle of superposition, generating the folded strata, and optimizing the strata based on the principle of area conservation. Finally, the polygon features of folds can be constructed, and the attribute property can be perfected.

Based on the knowledge used by experts in detecting and classifying folds, this article aims to realize a fast and automatic method to identify and map folded structures in cross sections and hopes to facilitate drawing a large number of cross sections. The flow chart of the method proposed by this article is shown in Figure 1.

Figure 1 
                  Flow chart of folded structure recognition and mapping method for cross sections.
Figure 1

Flow chart of folded structure recognition and mapping method for cross sections.

2.2 Data and assumptions

Due to the complexity of geological structures, it is necessary to obtain accurate geological exploration data for the drawing cross sections. However, given the universality of the method, we hope that the method can be applied to a wider range of general geological survey areas. Therefore, this article expects to realize automatic inference and preliminary restoration of folded structures in the cross section through the limited information provided by digital geological maps and the constantly enriched knowledge inference rules.

To implement the proposed method of fold mapping in cross sections, the following input data are needed: a digital geological map; 2D vector data in shapefile (shp) format, which provides information about the spatial distribution, lithology, and chronology of strata; an XML file that indicates the regional chronostratigraphic system of the study area, which represents the age sequence of rock strata; a point attitude layer in the shp format that indicates information of dip direction/dip for the strata; DEM that provides the ground elevation information to draw the topographic line in cross sections accurately. Furthermore, considering the effects of the faults and other features superimposed on folds, the polygons representing the strata cut by faults and other features are merged according to their chronological attributes and adjacency relationship.

2.3 Identifying folds

In geological maps, artificial discrimination of folds is usually done to find a relative age stratum (i.e., the core stratum), then observe the left and right strata in accordance with the law of symmetrical distribution [19]. In geological sections, the identification of folds is based on the judgment of symmetric repeating strata first, followed by the identification of the core stratum [25].

Based on the above analysis, the specific steps to identify folds from the sections are as follows:

  1. Read cross-section’s vector data and store all strata’s codes into FD = {f i | i = 1, 2, …, n}, where f i is the strata code and n is the number of strata.

  2. Using the ASCII code table, all strata can be coded in order from new to old (Table 1). Character encoding is constructed for two purposes. On the one hand, it can convert every multi-character encoding in the set FD into a single-character encoding. On the other hand, it can generate a string Str along the starting direction of the section line from all the encoded single characters in the section.

  3. Using the palindrome pattern matching algorithm [28,29], all the symmetrically repeated palindromes s v can be searched for in Str and put it in the set GD = {p v | v = 1,2,…,N}, p v = (s v , f v , e v , c v ), where p v is a folded structure, f v is the starting index position of string s v , e v is the end index position, c v is the core index position of string s v , and N is the number of folded structures in the section (Figure 2).

Table 1

Stratum character-encoding table

Feature ID Stratum code Stratum encoding
1 Q4 0
2 Q3 1
3 Q2 2
11 D3w1 a
12 S3m b
13 S2f2 c
Figure 2 
                  Schematic diagram of identifying folds: (a) a string is formed by stratum encoding; (b) any character is read and set as the core; (c) the left and right sides of the character are read to judge whether they are the same until the left and right sides of the core characters are different; and (d) the symmetrical repeated palindrome string is extracted and stored in the set GD.
Figure 2

Schematic diagram of identifying folds: (a) a string is formed by stratum encoding; (b) any character is read and set as the core; (c) the left and right sides of the character are read to judge whether they are the same until the left and right sides of the core characters are different; and (d) the symmetrical repeated palindrome string is extracted and stored in the set GD.

2.4 Classification of fold types

The forms of folds are diverse, and the types are complex. In structural geology, for the convenience of research, there are many different methods to classify fold types [19,30]. The different classification methods of fold types highlight the different spatial structures and morphological characteristics, and then the different mapping strategies needed are determined. In order to meet the needs of fine mapping of folds in cross sections, this article mainly classifies the fold types into the following three aspects.

2.4.1 Discrimination of composite folds and secondary folds

Composite folds mainly include two types: anticlinorium and synclinorium. A composite fold is a giant anticline (or a giant syncline) composed of different levels of secondary folds, which is the main structural style of an orogenic belt. They are also known as Alpino-type folds [31]. In the process of fold recognition, if a folded structure contains the core strata of other secondary folds, it can be identified as a composite fold. However, due to the nesting characteristics of composite folds, only the folded strata of secondary folds need to be processed in fold recognition and mapping. Therefore, in order to ensure efficient and reasonable fold model construction, composite and secondary folds within them should be correctly distinguished.

The general criterion of composite folds is as follows: if the stratigraphic interval of a fold overlaps with other fold intervals and contains its core strata, it can be judged as a composite fold; otherwise, it can be identified as a secondary fold (Figure 3). In addition, based on the interval scheduling method, the discrimination steps to distinguish between composite folds and secondary folds are as follows:

  1. Read any two elements p v , p t (tv) from the set GD and judge whether the core position g t of p t is within the interval [e v , r v ]. If the result is true, then the element p v is a composite fold, g t is recorded as the attribute value of p v .

  2. According to equation (1), the position interval of p v can be calculated as follows:

    (1) p v [ e v + r v g t + 1 , g t 1 ] if g v < g t , p v [ g t + 1 , e v + r v g t 1 ] if g v > g t ,

    where g v is the core position of p v .

  3. If g t is not within [e v , r v ], then the element p v will be processed with other elements in GD, and the string s v will be updated according to the final reduced position interval.

  4. Traverse the set GD until the discrimination of the stratigraphic symbols of symmetrically repeated folds in GD is completed.

Figure 3 
                     Schematic diagram for discriminating composite folds in cross section: (a) original cross section, (b) identification of the secondary folds in the cross section, (c) identification of the composite fold containing secondary folds in the cross section, and (d) extraction of the secondary folds at the core of the composite fold using the interval scheduling method.
Figure 3

Schematic diagram for discriminating composite folds in cross section: (a) original cross section, (b) identification of the secondary folds in the cross section, (c) identification of the composite fold containing secondary folds in the cross section, and (d) extraction of the secondary folds at the core of the composite fold using the interval scheduling method.

2.4.2 Fold classification based on strike of the two limbs

According to the strike of the strata of the two limbs, the folded structure can be roughly divided into four types: isoclinal anticlinal fold, inclined anticlinal fold, isoclinal synclinal fold, and inclined synclinal fold [32] (Figure 4).

Figure 4 
                     Schematic diagram of folds by a different strike in cross section: (a) isoclinal anticlinal fold, (b) inclined anticlinal fold, (c) isoclinal synclinal fold, and (d) inclined synclinal fold.
Figure 4

Schematic diagram of folds by a different strike in cross section: (a) isoclinal anticlinal fold, (b) inclined anticlinal fold, (c) isoclinal synclinal fold, and (d) inclined synclinal fold.

According to the stratigraphic strike information in the cross section, and through the new and old comparison between the core and its adjacent strata, the fold classification can be carried out. The specific steps are as follows:

  1. Extract all stratigraphic line information in the section and store it in line set LN. Traverse all elements in the GD, query the corresponding stratigraphic line in LN, and get fold stratigraphic line set GL = {gl v |v = 1,2,…,N} (Figure 5).

  2. Read any folds p v and find the corresponding stratigraphic line set gl v . Read the stratigraphic line sl v,f/2 and sl v,f/2+1 and obtain the endpoints of the two stratigraphic lines: Q a (x a ,y a ), Q a(x a, y a), and Q b (x b ,y b ), Q b(x b, y b). Then distinguish the isoclinal fold and the inclined fold, according to the following equation:

    (2) Isoclinal fold if ( x a x a ) ( x b x b ) > 0 , Inclined fold if ( x a x a ) ( x b x b ) < 0 .

  3. Read the central core character and adjacent character of the string s v in p v , and putting their stratum encoding into Astr and Bstr, respectively. The relationship between the new and old strata can be judged by comparing the value of their stratum encoding, which can be used to distinguish anticline and syncline (equation (3)):

    (3) Anticline if Astr > Bstr , Syncline if Astr < Bstr .

  4. Traverse the set GD to judge all fold types based on strike.

Figure 5 
                     Schematic diagram of a folded structure in cross section (line aa′, bb′, cc′, dd′, ee′, ff′: the stratigraphic lines; point a, b, c, d, e, f and point a′, b′, c′, d′, e′, f′: the endpoints of the stratigraphic line; fold stratigraphic line set: contains all stratigraphic lines within this folded structure).
Figure 5

Schematic diagram of a folded structure in cross section (line aa′, bb′, cc′, dd′, ee′, ff′: the stratigraphic lines; point a, b, c, d, e, f and point a′, b′, c′, d′, e′, f′: the endpoints of the stratigraphic line; fold stratigraphic line set: contains all stratigraphic lines within this folded structure).

2.4.3 Fold classification based on section morphology

Based on the section’s morphological characteristics, folded structures can be roughly divided into the following five types: gentle fold, open fold, close fold, tight fold, and isoclinal fold [20,33,34,35]. Its corresponding characteristics are shown in Table 2.

Table 2

Fold morphology classification based on interlimb angle and fold angle [1]

Term Interlimb angle Fold angle
Gentle 120–180° 0–60°
Open 70–120° 60–110°
Close 30–70° 110–150°
Tight 0–30° 150–180°
Isoclinal Approximately 0°, and the limbs are parallel Approximately 180°

In cross sections, the shape of the hinge zone can be determined by calculating the interlimb angle and the fold angle (the angle between the normal vectors of two limbs) based on the attitudes of the stratigraphic line. According to equations (4) and (5), the fold angle α and interlimb angle ψ can be calculated, respectively. Furthermore, according to the classification standard in Table 2, the fold classification based on section morphology can be carried out (Figure 6).

(4) α = cos 1 ( sin θ a sin θ b cos ( φ b φ a ) + cos θ a cos θ b ) ,

(5) ψ = 180 α , if fold is inclined , α , if fold is isoclinal ,

where θ a , θ b are the dip angle of sl v,a , sl v,b . φ a , φ b are the inclination of sl v,a , sl v,b and sl v,a , sl v,b are any two corresponding stratigraphic lines in gl v .

Figure 6 
                     Geometry of a perfect fold in section view: (a) inclined fold
                           
                              
                              
                                 ,
                              
                              ,
                           
                         (b) isoclinal fold (i: the stratum-exposed location
                           
                              
                              
                                 ,
                              
                              ,
                           
                         
                        h: the hinge zone
                           
                              
                              
                                 ,
                              
                              ,
                           
                         
                        ψ: the interlimb angle
                           
                              
                              
                                 ,
                                  and
                              
                              ,{\rm{and}}
                           
                         
                        α: the fold angle).
Figure 6

Geometry of a perfect fold in section view: (a) inclined fold , (b) isoclinal fold (i: the stratum-exposed location , h: the hinge zone , ψ: the interlimb angle , and α: the fold angle).

2.5 Fitting and optimizing strata boundaries

2.5.1 Boundary fitting method of two limbs of corresponding strata

After the classification of folded structures, the next step is to focus on fold mapping. Many studies have analyzed the shape of folds. For example, Stabler [36] and Hudleston [37] used Fourier analysis to analyze the shape of folds quantitatively; Bastida et al. [38] suggested fitting fold shapes to power functions. In this article, a simple scheme proposed by Srivastava and Lisle [39] and Srivastava et al. [40] was used to generate a smooth simulated folded curve based on Bézier curves. This method is easy to implement and modify, and the result is consistent with the natural fold shape. Furthermore, because the stratigraphic strike of the isoclinal fold and inclined fold differs, it is necessary to adopt different stratigraphic correlation mapping methods.

  1. For an inclined synclinal fold (Table 3a(i)), the extension lines of stratigraphic lines in two limbs, which locate at point A and point B, respectively, meet at point C. Because the axial plane of the fold is also the angle bisector of the interlimb angle, the axial plane can be obtained by taking the angle bisector of ∠ACB, which intersects AB at point D. According to equations (6) and (7), the hinge zone endpoint E on CD can be found using Curve (the coefficient of the shape of the hinge zone):

    (6) Curve = DE CD ,

    (7) Curve = 0 0 . 3 if fold is gentle , 0 . 3 0 . 5 if fold is open , 0 . 5 0 . 7 if fold is close , 0.7 0 . 9 if fold is tight , 0.9 1 . 0 if fold is isoclinal ,

    where Curve is an interval range summarized according to the fold classification above, which can be used to express different fold section shapes. For different fold classifications, the parameter Curve needs to set different initial values (gentle fold: Curve = 0.2; open fold: Curve = 0.4; close fold: Curve = 0.6; tight fold: Curve = 0.8; isoclinal fold: Curve = 0.9).

    In order to ensure the smoothness and symmetry of the curve simulation of the two limbs of the fold, FG⊥CD through point E (Table 3a(iii)). Moreover, based on equation (8), Bézier curves are generated at points A, F, E and B, G, E.

    (8) x ( t ) = ( 1 t ) 2 x a + 2 ( 1 t ) t x f + t 2 x e , y ( t ) = ( 1 t ) 2 y a + 2 ( 1 t ) t y f + t 2 y e .

  2. For an isoclinal fold, the axial plane is inclined, and the two limbs have the same tendency, so this fold needs to be discussed in two cases:

  1. When the inclinations of the two limbs of the stratigraphic line are the same, but the dip is not the same, the correlation method between the two limbs is consistent with the calculation method of inclined folds (Table 3b).

  2. When the two stratigraphic lines have the same dip and the same inclination, it means that the two stratigraphic lines are parallel, so we should calculate the formation thickness h between the stratigraphic lines and get the midpoint K between the surface endpoints A and B on the stratigraphic lines of the two limbs. According to the direction of the hinge zone, extend the distance h to point C from point K (Table 3c(ii)). Then get the endpoint E of the hinge zone by using Curve, where Curve = EK/CK. Finally, use quadratic Bézier curves to correlate the strata of the two limbs (Table 3c(iii)).

  1. For a box fold or fan fold (Table 3d(i)), the dip of the two limbs is large, and the rock layer of the two limbs shows a partial overturn. The boundary fitting method of these fold types is similar to that of parallel stratigraphic lines (Table 3c) but the detail is differing. The midpoint K does not extend in the direction of the hinge zone but extends perpendicular to line AB. Moreover, the rest of the method is consistent.

Table 3

Association method based on Bézier curves

No. Situation Specific processing steps
i Ii iii
a Inclined fold
b Isoclinal fold
c Isoclinal fold with parallel stratigraphic lines
d Box fold or fan fold

2.5.2 Optimizing the strata by the principle of superposition

In the process of single stratigraphic association, when the dip of the local stratum is too different from that of the adjacent stratum, the stratigraphic lines will cross (Figure 7a); when the folds are parallel folds, a single stratigraphic correlation cannot guarantee the consistency of stratigraphic thickness (Figure 7c). In order to solve these problems, an optimization method based on the principle of superposition was adopted.

Figure 7 
                     The strategy of strata optimization treatment in cross section: (a) stratigraphic crossover due to a large difference in the dip, (b) optimized results of the anticline structure after adjusting Curve, (c) the formation thickness is not consistent in parallel folds, and (d) optimized results of parallel folds after adjusting Curve (point E
                           i
                        : the endpoints of the hinge zones, point A′, B′, C′, D′, E′, F′: the endpoints of stratigraphic lines, h
                        
                           i
                        : the thicknesses of strata).
Figure 7

The strategy of strata optimization treatment in cross section: (a) stratigraphic crossover due to a large difference in the dip, (b) optimized results of the anticline structure after adjusting Curve, (c) the formation thickness is not consistent in parallel folds, and (d) optimized results of parallel folds after adjusting Curve (point E i : the endpoints of the hinge zones, point A′, B′, C′, D′, E′, F′: the endpoints of stratigraphic lines, h i : the thicknesses of strata).

In nature, the adjacent strata have a certain similarity without being affected by tectonic activities such as faults. This similarity of adjacent strata is also called the principle of superposition [41]. The specific steps of the optimization method based on the principle of superposition are as follows:

  1. Calculate the endpoints of the hinge zone of the folded curve. Traverse the smooth folded curves set GF, according to classification by folds, and calculate the endpoints E i of all the folded curves.

  2. Adjust the Curve according to the type of folds. When the fold is isoclinal, if the endpoint E i of the outer stratum is lower than that of the inner stratum, the Curve of the outer stratum will be increased. Decrease the Curve of the inner layer successively to improve the endpoint of the hinge zone of the outer stratum (Figure 7b). When the fold is syncline, if the endpoint of the outer stratum is higher than that of the inner one, the processing procedure is the same as that of the anticline.

  3. If the folds are parallel, the stratigraphic thickness of parallel folds should be consistent. Calculate the distance between all endpoints of the hinge zone E i . According to the lithology, the distance between the hinge zone of the same strata should be kept consistent, which is consistent with the uniform thickness of the same strata in parallel folds (Figure 7d).

  4. After optimizing all the folded curves, store them in the optimized fold curve set GF′.

2.6 Strata mapping of folded structures

2.6.1 Generating polygon features for folded strata

When the fold is synclinal, if the original stratigraphic line intersects within the section (Figure 8a), the original stratigraphic line corresponding to the synclinal fold will be replaced with the folded curves (Figure 8b).

Figure 8 
                     Schematic diagram of fold formation in cross section: (a) when the original stratigraphic line intersects within the profile; (b) replacing stratigraphic lines with folded curves and generating polygon features of folded strata; (c) when the original stratigraphic line does not intersect within the profile; (d) combining stratigraphic lines with folded curves and generating folded strata (solid lines show stratigraphic lines and dashed lines show fold curves).
Figure 8

Schematic diagram of fold formation in cross section: (a) when the original stratigraphic line intersects within the profile; (b) replacing stratigraphic lines with folded curves and generating polygon features of folded strata; (c) when the original stratigraphic line does not intersect within the profile; (d) combining stratigraphic lines with folded curves and generating folded strata (solid lines show stratigraphic lines and dashed lines show fold curves).

Suppose that the original stratigraphic line does not intersect the section (Figure 8c). In that case, the original stratigraphic line set LN will be combined with the folded curve set GF′ and construct the merged stratigraphic line set LF. The new stratigraphic lines and the surface lines of the cross-sectional construct planar strata. Furthermore, according to the original stratigraphic code and the stratigraphic code of the associated strata, attributes are assigned to the ground layer from left to right along the section direction (Figure 8d).

2.6.2 Optimizing folded strata by the principle of area conservation

A balanced cross section is one on which geometric criteria can restore structural deformation and displacement. Dahlstrom first explicitly proposed balanced cross sections in 1969 [42], and they follow three basic principles: volume conservation, area conservation, and layer-length conservation in a closed system. The theory of balanced cross sections is based on volume conservation; that is, the volume of a rock formation before and after deformation does not increase or decrease and remains constant. Under ideal conditions in a closed system, the stratum only changes its structure morphology during tectonic movement, but its volume remains unchanged [42,43]. However, in natural geological environments, it is impossible to reach the ideal conditions due to tectonic action, climate, deposition, denudation, and other factors in the evolution of the stratum. Therefore, the balanced cross-sectional technique is unsuitable for every geological section; it is an approximation based on basic geometric principles.

Considering that it is difficult to calculate the stratum volume in cross sections. This article adopts the theory of area conservation in balanced cross sections to optimize the strata. The specific steps of strata optimization are as follows:

  1. According to the strata generated by the algorithm, calculate the area of any stratum S using Arc Engine API.

  2. Calculate the length of the folded line and the stratigraphic line in this stratum and obtain the total length L (the length of this stratum before deformation).

  3. According to the thickness of the same stratum, calculate the average thickness M of the stratum by using the following equation:

    (9) M = i = 0 n m i * 1 n ,

    where m i are the layer thicknesses of the same stratum at various locations (e.g., m 1, m 2, m 3, m 4, m 5, m 6, m 7 in Figure 9). m 2, m 4, m 6 are the thickness of the hinge zone, which can be calculated by endpoints of the hinge zone E (see Section 2.5.1 for details). m 1, m 3, m 5, m 7 are the thickness of the stratum at the exposed position, which can be calculated by using the distance from the end point of the left stratigraphic line to the right stratigraphic line in cross section.

    (10) R = L * M .

  4. Using the calculated total length L and average thickness M, calculate the pre-deformation stratum area R (equation (10));

  5. Compare the stratum area S generated by the algorithm with the pre-deformation stratum area R. If S < R, it indicates that the area of the stratum model is too narrow. The narrower part of the stratum at the hinge zone (m 6 in Figure 9) can be modified. The area of the stratum model can be increased by increasing the thickness of the stratum. If S > R, it indicates that the area of the stratum model is too broad. The wider part of the stratum at the hinge zone (m 2 in Figure 9) can be modified. The area of the stratum model can be reduced by decreasing the thickness of the stratum.

Figure 9 
                     Schematic diagram of a fold in cross section (L: the total length of the stratum; m
                        
                           i
                        : the layer thicknesses of the same stratum at various locations).
Figure 9

Schematic diagram of a fold in cross section (L: the total length of the stratum; m i : the layer thicknesses of the same stratum at various locations).

Of course, the area of the pre-deformation stratum obtained based on the above steps is only a rough calculation method and a simple estimation. A more reasonable optimization requires the support of more data.

The folded strata can be constructed and optimized through the above process, which effectively restores the folded structures in cross section and expresses some geological information. In the following sections, these processed cross sections are called “restored cross sections.”

2.7 Mapping of folded structures

Each folded structure contains unique elements (axial plane, limb angle, hinge line, etc.) and geometric attribute information (coordinates, area, shape, etc.), which can express or describe different morphologies and characteristics. However, in the cross section, folded structures often exist in a composite form of strata, with multiple simple folded structures (anticline, syncline, etc.) overlapping each other. Therefore, it is necessary to define the different simple fold structures’ extent within the cross section to extract relevant elements and geometric attribute information further. According to the folded structure identified above and the constructed stratigraphic boundary, in the restored cross section, the extent of folded structure can be further generated. The specific steps are as follows (Figure 10):

  1. Calculate the difference H between the highest and lowest values of the internal folded structure in the restored cross section and extract the BottomLine in the cross section.

  2. Move the BottomLine up by H/2 to get a new middle split line SplitLine (the distance of the SplitLine movement can be set artificially).

  3. Read any symmetric repeating string from the set GD (see Section 2.2.1) and find the corresponding original strata and the newly constructed folded strata according to the interval position of the string.

  4. Use SplitLine to split the strata and combine them to construct a polygon feature.

  5. Associate the determined fold types (e.g., anticline, syncline, composite folds) and the corresponding attribute information with the polygon feature.

  6. Traverse GD until all the folded structural elements are constructed.

Figure 10 
                  Schematic diagram of defining folded structural extent in cross section (BottomLine: the bottom line of the cross section; SplitLine: it was used to split the strata according to the segmentation line generated by the BottomLine).
Figure 10

Schematic diagram of defining folded structural extent in cross section (BottomLine: the bottom line of the cross section; SplitLine: it was used to split the strata according to the segmentation line generated by the BottomLine).

2.8 Method of accuracy checking

The accuracy and consistency of cross sections can be verified by the drill data of field exploration and the cross sections drawn by experts. Therefore, to objectively evaluate the rationality of the interpretation results of this article, the following scheme is designed: (1) in experimental areas with sufficient geological data, the accuracy of the automatic algorithm will be verified by comparison with drill data. By comparing the stratum thickness in the cross section with the actual thickness of the drill, the relative error of this method can be calculated, and the accuracy can be analyzed; (2) in experimental areas lacking accurate strata thickness information, the correctness of this method can be roughly judged by comparing the consistency between the section generated in this article and the section drawn by experts in the literature or geological map.

3 Case studies

3.1 Data and experimental platform

In this section, we provide two examples of how the proposed method can be used to identify and map the presence and type of folded structures. The experiments were carried out using a computer with Windows 10 Professional. All algorithms are implemented using Dotspatial 1.7 and compiled using a Microsoft Visual C# 2017 compiler. The cases use high-resolution geological map data from Dazhou in Eastern Sichuan and the southern Appalachian Mountains as the experimental data. The following subsections specifically show these research areas’ experimental process and analysis.

3.2 Case 1: The Parallel Fold Belt of Eastern Sichuan

3.2.1 Study area

The Parallel Fold Belt of Eastern Sichuan is located in southwest China, northeastern Sichuan Province, and the southern foot of Ta-pa Mountain, with a geographical range between 30°75′−32°07′ N and 106°94′−108°06′ E. Dazhou, with an area of 16,591 km2 and an altitude of 222–2,458 m, is located in the Ridge and Valley Province of Eastern Sichuan. The general altitude trend is higher in the northeast and lower in the southwest. The geological structure of this area is diverse and complex. The southeast part belongs to the Eastern Sichuan Parallel Fold Belt of the Neocathaysian system, a typical isolated folded structure. The north and northeast parts belong to the arcuate fold belt in the southern margin of Ta-pa Mountain.

3.2.2 Experimental result

Draw three parallel section lines (Figure 11) along the vertical direction of the strata. The original sections without fold mapping were generated through the self-developed automatic section-drawing system (Figure 12a, c, and e). Using the method of this article, the folded structures in the original sections were identified and modeled successfully. Automatic restoration of the parsed cross sections (Figure 12b, d, and f) effectively shows the characteristics of the Parallel Fold Belt of Eastern Sichuan. It tells that, due to the lack of drill data, only the cross sections drawn by experts in the literature or geological map can be used to analyze the consistency of the final results. Since the corresponding cross sections cannot be found in this region, only a diagram of cross sections drawn by experts can be searched (Figure 13), so the section generated by the algorithm cannot be entirely consistent with it. However, the diagram of the Eastern Sichuan cross section tells that this area includes wide-spaced folds composed of a series of parallel closed narrow anticlines and gentle open synclines: anticlines become mountains, synclines become valleys, with valleys alternate and parallel to each other, which form the landscape of the parallel ridge valley area. By comparing with a diagram of a section drawn manually in this area, it can be found that cross sections created by the method of this article are consistent with the section diagram drawn by experts.

Figure 11 
                     Parallel Fold Belt of Eastern Sichuan in the geological map.
Figure 11

Parallel Fold Belt of Eastern Sichuan in the geological map.

Figure 12 
                     Automatic drawing of the original Eastern Sichuan cross sections and the restored Eastern Sichuan cross sections: (a) an original Eastern Sichuan cross section (A–A′), (b) a restored Eastern Sichuan cross section (A–A′), (c) an original Eastern Sichuan cross section (B–B′), (d) a restored Eastern Sichuan cross section (B–B′), (e) an original Eastern Sichuan transverse cross section (C–C′), and (f) a restored Eastern Sichuan cross section (C–C′).
Figure 12

Automatic drawing of the original Eastern Sichuan cross sections and the restored Eastern Sichuan cross sections: (a) an original Eastern Sichuan cross section (A–A′), (b) a restored Eastern Sichuan cross section (A–A′), (c) an original Eastern Sichuan cross section (B–B′), (d) a restored Eastern Sichuan cross section (B–B′), (e) an original Eastern Sichuan transverse cross section (C–C′), and (f) a restored Eastern Sichuan cross section (C–C′).

Figure 13 
                     Diagram of the Eastern Sichuan cross section. (https://zhidao.baidu.com/question/2272689036589724068.html).
Figure 13

Diagram of the Eastern Sichuan cross section. (https://zhidao.baidu.com/question/2272689036589724068.html).

3.3 Case 2: The central Appalachian fold-thrust belt

3.3.1 Study area

The Appalachian Mountains, extending along the eastern side of North America from Newfoundland (Canada) to Alabama (USA), are among the most recognizable and well-studied orogenic belts. The total length of the mountain chain is nearly 3,200 km, with a northeast-southwest trend. The southern Appalachian Mountains cover parts of five physio-graphic provinces. The Piedmont physio-graphic province is an upland of rolling hills with gentle slopes. The patterns of valleys and hills rarely coincide with the underlying bedrock structure. Most of the rocks of Piedmont formed as sediments or volcanic rocks on ocean floors, islands, and continental plates; igneous rocks formed when crustal plates collided, beginning about 450 million years ago. For this experiment, we selected the central Appalachian fold-thrust belt in the southern Appalachian Mountains. The central Appalachian fold-thrust belt has been described as a blind fold-thrust belt with few emergent faults [44,45], and the geographical range is between 37°15′−37°15′ N and 80°22′−80°30′ W (Figure 14).

Figure 14 
                     Geological map of the southern Appalachian Mountains [46].
Figure 14

Geological map of the southern Appalachian Mountains [46].

3.3.2 Experimental result

The original sections without fold mapping were generated through our self-developed automatic section-drawing system (Figure 15a and c). Using the method of this article, the folded structures in the original sections were identified and modeled (Figure 15b and d). Automatic restoration of the parsed cross sections effectively shows the characteristics of the central fold belt of the southern Appalachian Mountains. It tells that this area is a mesoscopic folding composed of a gentle and open anticline and a closed and narrow syncline [44]. Due to the lack of drill data, only the cross sections drawn by experts can be used to analyze the consistency of the final results. By comparing with a measured section (Figure 16) drawn manually on the geological map, it can be found that cross sections created by the method of this article are consistent with the measured section, and the overall stratigraphic strike and folded structure are consistent with the measured section.

Figure 15 
                     Automatic drawing of the original Appalachian cross sections and the restored Appalachian cross sections: (a) an original Appalachian cross section (A–A′), (b) a restored Appalachian cross section (A–A′), (c) an original Appalachian cross section (B–B′), and (d) a restored Appalachian cross section (B–B′).
Figure 15

Automatic drawing of the original Appalachian cross sections and the restored Appalachian cross sections: (a) an original Appalachian cross section (A–A′), (b) a restored Appalachian cross section (A–A′), (c) an original Appalachian cross section (B–B′), and (d) a restored Appalachian cross section (B–B′).

Figure 16 
                     A cross section of the Appalachian Mountains drawn manually (C–C′) [46].
Figure 16

A cross section of the Appalachian Mountains drawn manually (C–C′) [46].

By analyzing the cross section of the Appalachian Mountains drawn manually (Figure 16), it can be found that the anticline is symmetrical above 300 m (the dip angle of the limbs is equal). However, below 300 m, the dip angle of the northern limb of the anticline suddenly steepens. The cross section automatically generated in this article is mainly drawn according to the attitudes of the outcrop, without considering the rapid change of strata strike below the surface. So, the modeling results are not inconsistent with the section drawn by experts. However, by comparison, it can also be found that the cross section generated by the method of this article is consistent with the measured real cross section in terms of overall stratigraphic strike and folded structure.

4 Discussion

4.1 Applicability of the algorithm

To recognize folded structures in cross sections, the most direct method is to read the stratum code of the section according to the sequence and analyze its symmetric relationship. However, when the folded structure is affected by different types of tectonic activities, it will bring great difficulty to the automatic recognition of folds. For example, (1) some strata rise or fall during the fault action, resulting in missing or repeating strata; (2) during the intrusion, magma’s ascending intrusion of results in the stratigraphic sequence’s breakdown; (3) the coverage of Quaternary period layers will affect the automatic generation of the cross section. In addition, due to the limitation of the range of section lines, the first and last two strata in the section are incomplete, so they do not participate in the identification process. Therefore, it is possible that some folds cannot be identified or cannot be identified completely.

Given the complex situation mentioned above, it is necessary to pre-process the digitized geological data and recover it according to the corresponding geological structure to restore it to the state of the folded structure. For example, when the Quaternary period layers cover the bedrock strata, the Quaternary period layers can be removed, and the covered bedrock strata can be restored with the help of the geological map of the bedrock in the area or the measured section drawn manually. When drawing section lines, an attempt should be made to cover the complete geological structure.

Moreover, the algorithm in this article has yet to consider recumbent folds. The axial surface of the recumbent fold is nearly horizontal, with one limb layer being standard and another overturned. Due to its unique morphological characteristics, the algorithm cannot identify recumbent folds in cross sections. Then, the box folds, mentioned in Section 2.5.1, should also include “the hinge zone is flat” in its morphological description. However, this method cannot identify and classify box folds at present. The strata boundary fitting scheme of box folds also has defects. The identification and mapping of box folds in the cross section will be further improved.

Based on the limited information and abundant knowledge of inference rules provided by digital geological maps, this article hopes to realize automatic inference and preliminary restoration of folded structures in cross sections. However, due to the length limitation of the thesis, the inference rules supported in this article are not perfect enough. At present, the distribution law of outcrop and attitudes of strata are mainly considered, such as the symmetrical repetition rule, attitudes of stratigraphic lines, and the theory of area conservation in balanced cross sections. The influence of rock type on the shape of geological structures and strata thickness is not considered, and the inference rules of growth strata are not considered. In the future, we will sort out and improve relevant knowledge rules based on reading literature, to deduce and recover fold strata more precisely.

4.2 Stability of the algorithm

The shape of the hinge zone of the fold is mainly controlled by the interlimb angle and fold angle, which is calculated from the attribute data of each stratum in the cross section. Therefore, on the premise that the algorithm does not modify the stratum attribute, the attribute of each stratum will significantly affect the formation of the folded morphology. In order to ensure that the strata do not intersect and overlap, this study carried out automatic optimization of the strata based on the principle of superposition. However, the algorithm only deals with the curves of the hinge zone of folded structures, which may lead to an unnatural connection between the stratigraphic line and the folded curves. The final result may deviate from the actual result. After mapping folds, experts can manually modify the model to ensure accuracy.

When optimizing folded strata by the principle of the conservation of area (Section 2.4.2), the algorithm calculates the pre-deformation stratum area and modifies the hinge zone by Curve to optimize the section. However, this method is only a rough calculation and simple estimation. After modifying the stratum area, the newly generated stratum area is not precisely equal to that pre-deformation stratum area; it is only approximate. A more reasonable optimization requires the support of more data.

The complexity of geological structure determines the mapping of measured geological cross section, which requires the detailed arrangement of geological exploration work and the premise of obtaining complete and accurate geological exploration data. However, in the general geological survey area with limited data, this article hopes to realize automatic inference and preliminary restoration of fold structures in the map section only by digital geological maps and knowledge inference rules. As described in Section 2.4, this article classified fold types from three aspects: composite and secondary folds, isoclinal and inclined folds, and folds with different section morphology. The inference rules supported in this article are not perfect, and the influence of rock type on folded formation morphology needs to be considered. Therefore, the cross section automatically generated by the method of this article can only partially be consistent with the section drawn manually. Therefore, in terms of verification results, this article adopts a relatively rough and broad verification method.

4.3 Application of algorithms

4.3.1 3D modeling of folded structures

A folded structure refers to the bending of rock strata under tectonic movement without losing its continuity and integrity. However, the strata of folded structures are incomplete due to denudation. This method of this article can not only construct the current denuded geological structures (Figure 17) but also support the reconstruction of denuded strata to restore the complete geological structures (Figure 18). After the reconstruction of the geological structure, the sections can be used to understand the spatial distribution of stratigraphic rock mass and to study and analyze the temporal and spatial variation of the stratigraphic rock mass. Based on the contrast between the 3D section model of the recovered folded structure (Figure 18) and the model of the incomplete folded structure (Figure 17), we can infer the temporal and spatial evolution process of this area or analyze the influence of the crustal movement.

Figure 17 
                     3D model of unrestored structures constructed from Eastern Sichuan cross sections.
Figure 17

3D model of unrestored structures constructed from Eastern Sichuan cross sections.

Figure 18 
                     3D model of the recovered folded structure constructed from Eastern Sichuan cross sections.
Figure 18

3D model of the recovered folded structure constructed from Eastern Sichuan cross sections.

4.3.2 Mapping of folded structures

In addition, different from the traditional cross section, this algorithm can construct folded structural elements in the section (Figure 19) and express the attribute information (e.g., fold type, fold morphology, fold scale) and specific ranges of geological structures (Table 4).

Figure 19 
                     Mapping and expression of folded structural elements in the Eastern Sichuan cross section (C-C′) (AF: anticlinal fold; SF: synclinal fold).
Figure 19

Mapping and expression of folded structural elements in the Eastern Sichuan cross section (C-C′) (AF: anticlinal fold; SF: synclinal fold).

Table 4

Attribute table of folded structures (C-C′)

Feature ID Fold Folded structure Morphology Composite fold
1 Anticlinal fold Inclined fold Open fold No
2 Synclinal fold Inclined fold Gentle fold No
3 Anticlinal fold Inclined fold Open fold No
4 Synclinal fold Inclined fold Gentle fold No
5 Anticlinal fold Inclined fold Open fold No

This article mainly infers and correlates the folded strata below the surface in the cross section according to the stratigraphic morphological characteristics of folded structures. Since the reconstruction of the eroded part of anticline structures is not the focus of this article, only a rough method is provided here. In order to reconstruct the eroded part of the anticline accurately, the mechanism of folding and the structure state of the layers must be considered. For example, folded competent layers should be thinned toward the crest of the anticline if they are as growth strata (as syn-tectonic layers). If the same layers are pre-tectonic, they should not change in thickness toward the crest of the anticline. For restoration and reconstruction of layers before deformation, examining the folding mechanism and how it has deformed the layers is necessary. In the future, more detailed inference rules can be considered to more precisely model the eroded part of the anticline.

5 Conclusions

This article presents a method for automatically recognizing and modeling folded structures in the cross section. This method enables us to (1) efficiently and correctly identify diverse and complex fold types based on symmetric repetition and nesting characteristics; (2) perform high-quality mapping of fold strata and effectively restore the denudation strata, according to fold morphological characteristics, superposition law and the principle of area conservation; and (3) construct the thematic layer of folded structures, which can intuitively express the types and scales of various folded structures and also supports the efficient query of its attribute information.

Taking the Eastern Sichuan of China and the Appalachian Mountains of the United States as examples, this article used the vector geological map of the region to automatically draw the cross section and identify and recover the folded structures in the section. Unlike the existing algorithms, this automatic method reflects the near-surface conditions in the horizontal direction and the deep strata structure in the vertical direction, which is more consistent with the rules of geoscience knowledge. Compared with the traditional manual method, this method can greatly improve work efficiency and shorten manual editing time. The method proposed in this article can provide high-quality sections for 3D geological modeling using cross sections [6,47] and a 3D modeling scheme for folded structures. The sections generated by this method cannot be entirely consistent with the measured sections. This method calculates and infers the subsurface folded structure in the sections based on the dip direction, dip, and other stratum data. The restored section may differ from the field data of the folds. With the continuous enrichment and improvement of relevant reasoning knowledge, this situation will be greatly improved.

However, geological structures are complex and diverse. In addition to folded structures, there are intrusive structures, faults, and other geological structures. Identifying and mapping these geological structures in the cross section remains an open problem. In the future, the method proposed in this article also needs to be able to identify and map different geological structures when there are different types of geological structures in the section. These are essential issues that can be explored in the future.

Acknowledgments

Thanks to anonymous reviewers and editors for their patience and detailed reviews greatly improved this work.

  1. Funding information: This study was supported by the National Key R&D Program of China (No. 2022YFB3904104 and 2022YFB3904101) and the National Natural Science Foundation of China (Project No. 41971068 and 41771431).

  2. Author contributions: Jian-Chu Huang developed the main modules of the prototype system and drafted the original manuscript. An-Bo Li conceived the original idea and offered supervision. Xin Wang and Chang-Zheng Shao supported in collecting the experimental data and conducted the algorithm test. Yan-Gen Shen developed partial modules of the prototype system.

  3. Conflict of interest: The authors declare no conflict of interest.

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Received: 2022-12-10
Revised: 2023-03-21
Accepted: 2023-04-12
Published Online: 2023-05-11

© 2023 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  24. Study on the Early Cretaceous fluvial-desert sedimentary paleogeography in the Northwest of Ordos Basin
  25. Detecting window line using an improved stacked hourglass network based on new real-world building façade dataset
  26. Automated identification and mapping of geological folds in cross sections
  27. Silicate and carbonate mixed shelf formation and its controlling factors, a case study from the Cambrian Canglangpu formation in Sichuan basin, China
  28. Ground penetrating radar and magnetic gradient distribution approach for subsurface investigation of solution pipes in post-glacial settings
  29. Research on pore structures of fine-grained carbonate reservoirs and their influence on waterflood development
  30. Risk assessment of rain-induced debris flow in the lower reaches of Yajiang River based on GIS and CF coupling models
  31. Multifractal analysis of temporal and spatial characteristics of earthquakes in Eurasian seismic belt
  32. Surface deformation and damage of 2022 (M 6.8) Luding earthquake in China and its tectonic implications
  33. Differential analysis of landscape patterns of land cover products in tropical marine climate zones – A case study in Malaysia
  34. DEM-based analysis of tectonic geomorphologic characteristics and tectonic activity intensity of the Dabanghe River Basin in South China Karst
  35. Distribution, pollution levels, and health risk assessment of heavy metals in groundwater in the main pepper production area of China
  36. Study on soil quality effect of reconstructing by Pisha sandstone and sand soil
  37. Understanding the characteristics of loess strata and quaternary climate changes in Luochuan, Shaanxi Province, China, through core analysis
  38. Dynamic variation of groundwater level and its influencing factors in typical oasis irrigated areas in Northwest China
  39. Creating digital maps for geotechnical characteristics of soil based on GIS technology and remote sensing
  40. Changes in the course of constant loading consolidation in soil with modeled granulometric composition contaminated with petroleum substances
  41. Correlation between the deformation of mineral crystal structures and fault activity: A case study of the Yingxiu-Beichuan fault and the Milin fault
  42. Cognitive characteristics of the Qiang religious culture and its influencing factors in Southwest China
  43. Spatiotemporal variation characteristics analysis of infrastructure iron stock in China based on nighttime light data
  44. Interpretation of aeromagnetic and remote sensing data of Auchi and Idah sheets of the Benin-arm Anambra basin: Implication of mineral resources
  45. Building element recognition with MTL-AINet considering view perspectives
  46. Characteristics of the present crustal deformation in the Tibetan Plateau and its relationship with strong earthquakes
  47. Influence of fractures in tight sandstone oil reservoir on hydrocarbon accumulation: A case study of Yanchang Formation in southeastern Ordos Basin
  48. Nutrient assessment and land reclamation in the Loess hills and Gulch region in the context of gully control
  49. Handling imbalanced data in supervised machine learning for lithological mapping using remote sensing and airborne geophysical data
  50. Spatial variation of soil nutrients and evaluation of cultivated land quality based on field scale
  51. Lignin analysis of sediments from around 2,000 to 1,000 years ago (Jiulong River estuary, southeast China)
  52. Assessing OpenStreetMap roads fitness-for-use for disaster risk assessment in developing countries: The case of Burundi
  53. Transforming text into knowledge graph: Extracting and structuring information from spatial development plans
  54. A symmetrical exponential model of soil temperature in temperate steppe regions of China
  55. A landslide susceptibility assessment method based on auto-encoder improved deep belief network
  56. Numerical simulation analysis of ecological monitoring of small reservoir dam based on maximum entropy algorithm
  57. Morphometry of the cold-climate Bory Stobrawskie Dune Field (SW Poland): Evidence for multi-phase Lateglacial aeolian activity within the European Sand Belt
  58. Adopting a new approach for finding missing people using GIS techniques: A case study in Saudi Arabia’s desert area
  59. Geological earthquake simulations generated by kinematic heterogeneous energy-based method: Self-arrested ruptures and asperity criterion
  60. Semi-automated classification of layered rock slopes using digital elevation model and geological map
  61. Geochemical characteristics of arc fractionated I-type granitoids of eastern Tak Batholith, Thailand
  62. Lithology classification of igneous rocks using C-band and L-band dual-polarization SAR data
  63. Analysis of artificial intelligence approaches to predict the wall deflection induced by deep excavation
  64. Evaluation of the current in situ stress in the middle Permian Maokou Formation in the Longnüsi area of the central Sichuan Basin, China
  65. Utilizing microresistivity image logs to recognize conglomeratic channel architectural elements of Baikouquan Formation in slope of Mahu Sag
  66. Resistivity cutoff of low-resistivity and low-contrast pays in sandstone reservoirs from conventional well logs: A case of Paleogene Enping Formation in A-Oilfield, Pearl River Mouth Basin, South China Sea
  67. Examining the evacuation routes of the sister village program by using the ant colony optimization algorithm
  68. Spatial objects classification using machine learning and spatial walk algorithm
  69. Study on the stabilization mechanism of aeolian sandy soil formation by adding a natural soft rock
  70. Bump feature detection of the road surface based on the Bi-LSTM
  71. The origin and evolution of the ore-forming fluids at the Manondo-Choma gold prospect, Kirk range, southern Malawi
  72. A retrieval model of surface geochemistry composition based on remotely sensed data
  73. Exploring the spatial dynamics of cultural facilities based on multi-source data: A case study of Nanjing’s art institutions
  74. Study of pore-throat structure characteristics and fluid mobility of Chang 7 tight sandstone reservoir in Jiyuan area, Ordos Basin
  75. Study of fracturing fluid re-discharge based on percolation experiments and sampling tests – An example of Fuling shale gas Jiangdong block, China
  76. Impacts of marine cloud brightening scheme on climatic extremes in the Tibetan Plateau
  77. Ecological protection on the West Coast of Taiwan Strait under economic zone construction: A case study of land use in Yueqing
  78. The time-dependent deformation and damage constitutive model of rock based on dynamic disturbance tests
  79. Evaluation of spatial form of rural ecological landscape and vulnerability of water ecological environment based on analytic hierarchy process
  80. Fingerprint of magma mixture in the leucogranites: Spectroscopic and petrochemical approach, Kalebalta-Central Anatolia, Türkiye
  81. Principles of self-calibration and visual effects for digital camera distortion
  82. UAV-based doline mapping in Brazilian karst: A cave heritage protection reconnaissance
  83. Evaluation and low carbon ecological urban–rural planning and construction based on energy planning mechanism
  84. Modified non-local means: A novel denoising approach to process gravity field data
  85. A novel travel route planning method based on an ant colony optimization algorithm
  86. Effect of time-variant NDVI on landside susceptibility: A case study in Quang Ngai province, Vietnam
  87. Regional tectonic uplift indicated by geomorphological parameters in the Bahe River Basin, central China
  88. Computer information technology-based green excavation of tunnels in complex strata and technical decision of deformation control
  89. Spatial evolution of coastal environmental enterprises: An exploration of driving factors in Jiangsu Province
  90. A comparative assessment and geospatial simulation of three hydrological models in urban basins
  91. Aquaculture industry under the blue transformation in Jiangsu, China: Structure evolution and spatial agglomeration
  92. Quantitative and qualitative interpretation of community partitions by map overlaying and calculating the distribution of related geographical features
  93. Numerical investigation of gravity-grouted soil-nail pullout capacity in sand
  94. Analysis of heavy pollution weather in Shenyang City and numerical simulation of main pollutants
  95. Road cut slope stability analysis for static and dynamic (pseudo-static analysis) loading conditions
  96. Forest biomass assessment combining field inventorying and remote sensing data
  97. Late Jurassic Haobugao granites from the southern Great Xing’an Range, NE China: Implications for postcollision extension of the Mongol–Okhotsk Ocean
  98. Petrogenesis of the Sukadana Basalt based on petrology and whole rock geochemistry, Lampung, Indonesia: Geodynamic significances
  99. Numerical study on the group wall effect of nodular diaphragm wall foundation in high-rise buildings
  100. Water resources utilization and tourism environment assessment based on water footprint
  101. Geochemical evaluation of the carbonaceous shale associated with the Permian Mikambeni Formation of the Tuli Basin for potential gas generation, South Africa
  102. Detection and characterization of lineaments using gravity data in the south-west Cameroon zone: Hydrogeological implications
  103. Study on spatial pattern of tourism landscape resources in county cities of Yangtze River Economic Belt
  104. The effect of weathering on drillability of dolomites
  105. Noise masking of near-surface scattering (heterogeneities) on subsurface seismic reflectivity
  106. Query optimization-oriented lateral expansion method of distributed geological borehole database
  107. Petrogenesis of the Morobe Granodiorite and their shoshonitic mafic microgranular enclaves in Maramuni arc, Papua New Guinea
  108. Environmental health risk assessment of urban water sources based on fuzzy set theory
  109. Spatial distribution of urban basic education resources in Shanghai: Accessibility and supply-demand matching evaluation
  110. Spatiotemporal changes in land use and residential satisfaction in the Huai River-Gaoyou Lake Rim area
  111. Walkaway vertical seismic profiling first-arrival traveltime tomography with velocity structure constraints
  112. Study on the evaluation system and risk factor traceability of receiving water body
  113. Predicting copper-polymetallic deposits in Kalatag using the weight of evidence model and novel data sources
  114. Temporal dynamics of green urban areas in Romania. A comparison between spatial and statistical data
  115. Passenger flow forecast of tourist attraction based on MACBL in LBS big data environment
  116. Varying particle size selectivity of soil erosion along a cultivated catena
  117. Relationship between annual soil erosion and surface runoff in Wadi Hanifa sub-basins
  118. Influence of nappe structure on the Carboniferous volcanic reservoir in the middle of the Hongche Fault Zone, Junggar Basin, China
  119. Dynamic analysis of MSE wall subjected to surface vibration loading
  120. Pre-collisional architecture of the European distal margin: Inferences from the high-pressure continental units of central Corsica (France)
  121. The interrelation of natural diversity with tourism in Kosovo
  122. Assessment of geosites as a basis for geotourism development: A case study of the Toplica District, Serbia
  123. IG-YOLOv5-based underwater biological recognition and detection for marine protection
  124. Monitoring drought dynamics using remote sensing-based combined drought index in Ergene Basin, Türkiye
  125. Review Articles
  126. The actual state of the geodetic and cartographic resources and legislation in Poland
  127. Evaluation studies of the new mining projects
  128. Comparison and significance of grain size parameters of the Menyuan loess calculated using different methods
  129. Scientometric analysis of flood forecasting for Asia region and discussion on machine learning methods
  130. Rainfall-induced transportation embankment failure: A review
  131. Rapid Communication
  132. Branch fault discovered in Tangshan fault zone on the Kaiping-Guye boundary, North China
  133. Technical Note
  134. Introducing an intelligent multi-level retrieval method for mineral resource potential evaluation result data
  135. Erratum
  136. Erratum to “Forest cover assessment using remote-sensing techniques in Crete Island, Greece”
  137. Addendum
  138. The relationship between heat flow and seismicity in global tectonically active zones
  139. Commentary
  140. Improved entropy weight methods and their comparisons in evaluating the high-quality development of Qinghai, China
  141. Special Issue: Geoethics 2022 - Part II
  142. Loess and geotourism potential of the Braničevo District (NE Serbia): From overexploitation to paleoclimate interpretation
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