Free iron oxides (Fed) in red soils negatively affect the accuracy of organic matter (OM) prediction using visible and near-infrared (390-1000 nm) spectroscopy. Based on a typical red soil sample set (n = 36, including both Fed-and OM-removed samples), this study investigated the spectral characteristics of Fed and OM, and applied the maximum overlapping discrete wavelet transform (MODWT) to extract wavelengths associated with Fed spectral features across different wavelet components. We evaluated whether removing the extracted Fed spectral features from the red soil spectra could improve the accuracy of OM prediction by using three datasets: (A) regional-scale, laboratory-measured dry soil spectral dataset; (B) field-scale, laboratory-measured moist soil spectral dataset; and (C) field-scale, unmanned aerial vehicle-measured soil spectral imaging dataset. Three calibration algorithms-partial least squares regression (PLSR), linear support vector machine (LSVM), and random forest (RF)-were employed for OM prediction. Compared with raw spectra as a baseline, preprocessing via Fed spectral feature removal improved the prediction accuracy of OM in red soils for all datasets and algorithms tested. The average percentage improvement across all datasets is 19.63 % in RMSEval-ori and 93.80 % (absolute values increased by 0.21 averagely) in R2val-ori. The results confirm that wavelet-decomposed Fed spectral feature removal preprocessing improves the accuracy of OM spectral prediction in red soils, and demonstrate the transferability of the feature extraction parameters across different application scenarios.

Fault dating plays an essential role in understanding deformation histories and modeling the tectonic evolution of orogenic belts. However, direct fault dating methods via different isotope geochronological techniques are expensive, and their use is often limited in many cases, making it essential to develop a fast and low-cost fault relative dating method. Therefore, on the basis of knowledge graphs and knowledge reasoning technology, this study proposes an automatic method to relatively date periods of fault activity using the principle of cross-cutting relationships between faults and strata. The method mainly involves (1) generating the knowledge graph based on a digital geological map; (2) using the knowledge reasoning algorithm to interpret the cross-cutting relationships amongst faults and generating the temporal sequence of fault activity; (3) relative dating the faults based on the cross-cutting relationships between faults and strata; and (4) according to the temporal sequence of fault activity, the relationship between faults can be revealed, and relative dating can be optimized. Results for cases in western Nevada and Qixia Hill of Nanjing illustrate the effectiveness of this method for interpreting the period of fault activity. The accuracy rates of the recognition results in the two cases were 90.24% and 80.77%, respectively, which means that the proposed method has the potential to relatively date fault activity across large areas. The algorithm is an effective supplement to the existing direct method of fault dating. The algorithm can efficiently infer the development sequence and the age of fault activity based on geological maps and geological cross-sections, which is of great significance for understanding regional tectonic history, evaluating earthquake disasters, and modeling tectonic evolution processes.

Reconstructing the stratigraphic paleosurfaces of a fold is essential for deciphering the folding mechanism, simulating landscape evolution processes, and investigating mineral resource distribution. However, standard methods for reconstructing paleosurfaces in tectonic landforms, primarily applied on large-scale sedimentary basins and orogenic belts, heavily rely on extensive geological data and generally yield low-accuracy results. This limits their applicability to small to intermediate-scale geological structural areas. Therefore, this paper introduces a stratigraphic paleosurface reconstruction method tailored for small and intermediate-scale folds, leveraging structural element features to constrain this reconstruction, which is notably helpful when dealing with sparse geological and topographic data. This method involves several steps. Firstly, define the fold units for diverse landforms. Secondly, extract fold structural elements (FSEs) with diverse geological data. Next, fit the paleo-boundary of each stratum within the two-dimensional (2D) cross-section using elemental feature constraints. Finally, the Morphing technique is applied to interpolate multiple paleo-boundaries, which are then utilized in reconstructing the stratigraphic paleosurfaces through the Contour Reconstruction Algorithm (CRA). To validate the method, tests were conducted on three representative folds in China: the eastern Sichuan comb-like fold belt, the Dayueshan Anticline on Mount Lu, and the Wulongshan Dome near the Huangling Dome. Experimental results demonstrate that utilizing structural features as constraints enables automatic, accurate, and reliable stratigraphic paleosurface reconstruction. The reconstructed paleosurfaces facilitate the analysis of geometric characteristics and structural development mechanisms of folds within the study area. Furthermore, they can be readily incorporated into landscape evolution models (i.e., TTLEM) to simulate realistic topographic evolution and tectonic paleogeographic mapping or construct three-dimensional (3D) solid models.

Groundwater level (GWL) is a significant indicator for quantifying groundwater availability. Currently, hydrologists worldwide are actively engaged in modelling and predicting GWL. In karst regions, GWL exhibit varying responses to rainfall events across different locations and the impact of rainfall events on GWL within the same location also varies. Despite incorporating rainfall as an input variable, most existing data-driven GWL prediction models inadequately account for the spatio-temporal heterogeneity of karst water areas. Therefore, this study proposes a new analysis method to investigate the response patterns of GWL to rainfall events in karst regions with typical spatio-temporal variations, known as the sensitivity analysis of rainfall-GWL response. The method introduces the rainfall response coefficient to describe the response characteristics of GWL to rainfall. Through the rainfall response coefficient, the rainfall response variable (RR) is calculated and incorporates it as an input in the RR-long short-term memory (LSTM) GWL prediction model. The effectiveness of proposed method was validated by GWL prediction in karst aquifers located in Jinan City, China, renowned for its spatial-temporal heterogeneity in karst development. Through the analysis and validation conducted by integrating geographical multi-feature, the study revealed a significant improvement in the accuracy of the RR-LSTM model after integrating RR as a variable, particularly during significant rainfall events. These findings affirm that the method proposed in this study is highly effective in karst regions characterized by anisotropic karst features. We proposed a new method to investigate the response of GWLs to rainfall to greater reveal geographical patterns in karst regions with typical spatio-temporal variation. This method extracts and corrects rainfall-GWL response sequences and develops the idea of rainfall response coefficient and rainfall response variable. By integrating the findings into LSTM model, we conducted comparative experiments to evaluate model performance by considering geographical multi-feature, demonstrating that the new method significantly improved model performance especially during significant rainfall events. image

Accurate identification and characterization of meander loops are essential for understanding river evolution, managing water resources, planning hydraulic projects, and preventing geological disasters. Traditional methods for identifying river meanders rely on detecting inflection points, where channel curvature reverses, or measuring directional changes at fixed intervals along the channel. The former approach lacks reproducibility, while the latter requires careful interval selection and intervals complicated procedures. Consequently, these methods face limitation when applied to more complex channel geometries, such as asymmetrical or compound meanders. This study aimed to develop a novel method for automatically identifying and characterizing compound meander loops using channel centerline data, and primarily through measurements of three channel planform parameters: local maximum sinuosity (LMS), maximum rotation angle (MRA), and simple subloop numbers. The key technical steps in this method include (1) detecting bends with LMS value exceeding a defined threshold using a top-down iterative search algorithm, (2) identifying meander loops based on MRA, (3) identifying compound meander loops using subloop counts and neck length, and (4) measuring the geometric parameters of compound meander loops. The proposed method was tested on the Yavari, Tarauaca, Purus, and Jurua rivers in the Amazon Basin, which are characterized by high water and sediment discharge and are among the world’s fastest-migrating meandering rivers. Results indicate that this method provides a simple yet efficient approach for identifying and characterizing compound meander loops in complex river channels. Additionally, it offers a potential solution for detecting loops in other types of linear features, such as roads, contour lines, and coastlines.

ABSTRACTAs the research and application of three‐dimensional (3D) Geographic Information Science (GIS) continue to advance, the abstract representation and symbolic modeling of geological entities in three dimensions have become one of the research focuses in the current GIS field. To address the need for the symbolic representation of complex and diverse fault structures, this paper proposes a parametric modeling method for 3D fault symbols. This method includes (1) constructing a 3D stratum model and a fault plane model based on stratum and fault plane parameters, (2) performing a 3D cutting operation based on the fault plane model to generate the fault block model, and (3) translating the strata in two faultblocks according to the parameters of fault motion to generate a fault symbol model. The experimental results show that the proposed method requires only a small number of parameters to efficiently and intuitively construct diverse 3D fault symbol models. This method breaks through the excessive dependence on geological survey data in the process of 3D geological modeling. It is suitable for 3D geological symbol modeling of folds, joints, intrusions, and other geological structures, as well as 3D modeling of typical geological structures with relatively simple spatial morphology. This paper's parametric modeling method has essential research significance and application value in various applications such as digital earth, digital city, and virtual geoscience exploration.

Cone-shaped volcanoes have important research significance and application value due to their typical cone shape and unique structural features. The existing methods for recognizing volcanoes are mainly morphological feature matching and machine learning. In general, the former has low recognition accuracy, while the latter requires a large number of training samples. The contour lines of cone-shaped volcanoes are distributed in concentric circles. Furthermore, from the center outwards, the elevation of the contour lines increases first and then decreases. Based on the morphological characteristics of cone-shaped volcanoes and the Hough transform algorithm, the main algorithm includes (1) preliminary filtering of contour lines, (2) filtering circular contour lines based on random Hough transform, (3) grouping contour lines based on contour trees, (4) recognizing cone-shaped volcanoes based on concentric-circle contour lines, and (5) automatically mapping cone-shaped volcanoes. Case studies demonstrate the effectiveness of this method for detecting cone-shaped volcanoes in the Western Galapagos shield volcanoes and the Mariana Trench submarine volcano group. The proposed algorithm has low missed and false alarm rates, which is basically consistent with the manual recognition results. This method can effectively automatically recognize cone-shaped volcanoes and cone-shaped landscapes and is a powerful means to support deep-space and deep-sea exploration.

The 3D modeling method based on parallel geological cross-sections is suitable for modeling large-scale bedrock geological bodies. However, there are usually more branching, pinch-out strata, deformation structures (such as faults and folds), and the correspondence problem of geological boundaries between adjacent cross-sections. These problems have become the most crucial obstacle in the current application of 3D geological modeling based on parallel geological cross-sections. To this end, this paper proposes a topological reasoning automatic processing method for the consistency of parallel geological cross-sections to obtain topologically consistent adjacent cross-sections efficiently. The method involves (1) Stratigraphic inference based on the distance and topological relationships, transforming strata with m:n relationships into three relatively simple cases of 1:1, 1:n, and 1:0; (2) Splitting branching strata based on the Voronoi diagram, transforming strata with 1:n relationships into 1:1 relationships; (3) Dealing pinch-out strata based on the method of filling virtual strata, transforming strata with 1:0 relationships into 1:1 relationships (4) Topologically consistent processing of stratigraphic boundaries based on automatic recognition of no-corresponding arcs and addition of virtual stratum. Based on experiments in the Longtoushan Hill area in Jiangsu Province, China, and the southeast side of Chandler Mountain, Alabama, USA, this method can effectively replace the many manual processes such as determining the location of pinch-out adding auxiliary cross-sections and performing branching strata correspondence in the current application of 3D geological modeling based on arc segments. The geological cross-sections processed based on this method have an exemplary data structure and consistent topological relationships. The 3D geological modeling algorithm based on parallel cross-sections has the advantages of low algorithm complexity, high automation, and stable modeling quality.

With the continuous expansion of the application field of 3D models and the convenience of model storage, transmission, and replication, the problems of illegal reproduction, illegal modification, and embezzlement faced by 3D models are gradually becoming severe, which has brought substantial economic losses to the producers of 3D models. Therefore, this study aims to develop a visible watermarking method for 3D model using 3D Boolean operation. First, based on TrueType font library, the 3D model of the watermark is generated through the extraction, positioning and combination of character outline. Next, the spatial registration between the watermark model and the 3D model was carried out according to the embedding position of the watermark. Finally, based on multiple 3D Boolean operations, the copyright information of the 3D watermark model is embedded into the 3D model in various modes, such as embossing, debossing, and surface mesh embedding. The experiments demonstrate that the method of this paper supports embedding watermark information in B-rep, CSG, and voxel models. This method can readily support business applications for visible watermarking of 3D models and has laid a good foundation for researching multipurpose 3D model watermarking algorithms supporting copyright notification and protection.

River capture is a surficial process that can lead to drainage reorganization and have significant impacts on sediment dispersal and biotic evolution. Discovery and study of river capture events are helpful in revealing the history of drainage basins, however, the present-day identification of river capture mainly depends on field and map observations by geomorphologists, who lack an automatic method. In the case of a large-scale drainage system with a complicated stream network, a field investigation is too time-consuming and costly. This study aims to develop a novel method for automatic river capture detection based on planform morphology and χ-plots of the stream network. The whole method can be described as a workflow including three steps: (1) searching candidate regions where river capture may have occurred; (2) iterating over each candidate region and roughly detecting the captor, captured, reversal, and beheaded rivers using the planform pattern of the stream network; (3) verifying the river capture with a χ-elevation plot in the candidate region found by rough detection. The description of the method is followed by two case studies from China and Spain, demonstrating the ability of the method to identify the location where the river capture might occur. We also discuss the parameters that must be optimized before extracting the river capture efficiently from the stream network.

With the three-dimensional (3D) geological information system development, 3D geological cross-sections (GCs) have become the primary data for geological work and scientific research. Throughout past geological surveys or research works, a lot of two-dimensional (2D) geological cross-section maps have been accumulated, which struggle to meet the scientific research and application needs of 3D visual expression, 3D geological analysis, and many other aspects. Therefore, this paper proposes an automatic generation method for 3D GCs by increasing the dimensions based on a digital elevation model (DEM) and 2D geological cross-section maps. By matching corresponding nodes, generating topographic feature lines, constructing an affine transformation matrix, and inferring the elevation value of each geometric node on the GC, the 3D transformation of the 2D GCs is realized. In this study, fourteen 2D GCs within Nanjing City, Jiangsu Province, are transformed into 3D GCs using the proposed method. The transformed results and quantitative error show that: (1) the proposed method applies to both straight and bent GCs; (2) each transformed GC can fit seamlessly with the ground and maintain minimal geometric deformation, and the geometric shape is consistent with the original GC in non-mountains area. This paper corroborated the proposed method’s effectiveness by comparing it with the other two 3D transformation strategies. In addition, the transformed GCs can be subjected to 3D geological modeling and digital Earth presentation, achieving positive effects in both 3D application and representation.

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.

Layered rock slopes are the most widely distributed slopes with the simplest structure. The classification of layered rock slopes is the basis for correctly analyzing their deformation and failure mechanisms, evaluating their stability, and adopting reasonable support methods. It is also one of the essential indicators to support the evaluation of urban and rural construction suitability and the assessment of landslide hazards. However, the present-day classification methods for layered rock slopes are not sufficiently automated. In the application process of these methods, a lot of manual intervention is still needed, and sufficient strata orientation data obtained through field surveys is required, which is not effective for large-scale applications and involves high subjectivity. Thus, this study proposes a semi-automated classification method for layered rock slopes based on digital elevation model (DEM) and geological maps, which greatly reduces human intervention. On the basis of slope unit division, the method extracts structural information of slopes using DEM and geological maps and classifies slopes according to their structural characteristics. An experiment has been carried out in the northern region of Mount Lu in Jiangxi Province, and the results demonstrate the effectiveness of this semi-automated classification method. Compared to the existing manual or semi-automated classification methods, the method proposed in this article is objective and highly automated, which can meet the requirements of classification of layered rock slopes over large areas, even in the case of sparse measured orientation data.

Geological maps have wide coverage with low acquisition difficulty. When other geological survey data are scarce, they are a valuable source of geological structure information for geological modeling. However, for structures with large deformation, geological map information has difficulty meeting the requirement of its 3D geological modeling. Therefore, this paper takes the dome structure as an example to explore a 3D modeling method based on geological maps, DEM and related geological knowledge. The method includes: (1) adaptively calculating the attitude of points on the stratigraphic boundaries; (2) inferring and generating the bottom boundary of the model from the attitude data of the boundary points; (3) generating the model interface constrained by Bézier curves based on the bottom boundary; (4) generating the top and bottom surfaces of the stratum; and (5) stitching each surface of the geological body to generate the final dome model. Case studies of the dome in Wulongshan in China and the Richat structure in Mauritania show that this method can build a solid model of the dome based only on geological maps and DEM data, whose morphological features are basically consistent with those embodied in the section view or the model generated by traditional methods.

The transverse canyon is a V-shaped, fluvial-genetic canyon, a secondary valley formed by transverse drainage crossing a tectonically uplifted mountain. Paleotopography of the transverse canyon is vital to drainage connection and river capture, offering insight into the processes that link large-scale river systems, analyzing paleodrainage patterns, and recreating headward erosion. Notably, modern paleotopographic reconstruction methods are usually limited to reconstructions of paleotopography in vast sedimentary basins and denuded hills in orogenic belts. When applied to transverse canyons, a specific secondary valley found in tiny locations, these techniques are difficult, expensive, and ineffective. This paper proposes an automated method for reconstructing the paleotopography of the transverse canyon using the digital elevation model (DEM) and river. (1) Restore the ridgeline above the transverse canyon based on the ridgelines of the mountains on both sides; (2) create a buffer zone based on the river centerline with unequal buffer distances on each side; (3) construct a mesh surface by interpolating transition curves from the morphing method, using the three-edge type; (4) apply a spatial interpolation method to the elevation points on the mesh surface to construct the DEM above the transverse canyon and stitch it to the input DEM to obtain the paleotopographic DEM; (5) calculate the spatial attributes. The objective of this study is to reconstruct the paleotopography of eight typical transverse canyons in the comb-like fold belt of northern Chongqing. As part of the paleotopographic reconstruction of the transverse canyon, we address the effects of dislocated mountains, erosion gullies, and different morphing techniques, as well as the applicability of the proposed method to reconstructing other secondary valleys. In conclusion, we reconstruct paleotopographic DEMs of transverse canyons to replicate headward erosion processes, assess paleodrainage patterns, and build three-dimensional solid models.

The river system of a region records its tectonic evolution. This study expects to infer some potential faults by effectively quantifying river morphology and identifying rivers with specific morphological and structural characteristics. Thus, this study aims to develop a novel method for automatically detecting fault-controlled rivers (FCRs) from drainage maps using spatial pattern matching and to support the scientific inference of po-tential faults. The method involves (1) constructing a scene model for the entire drainage basin in the study area based on the attributed relational graph (ARG) model; (2) defining spatial patterns for four types of FCRs using the ARG model, including straight river reaches, right-angle river reaches, barb rivers, and contra-aperture rivers, and storing all river patterns in a spatial pattern library; and (3) detecting the four types of FCRs with a spatial pattern-matching algorithm and the FCR pattern library. Case studies demonstrated the basic effectiveness of this method for detecting FCRs in the study areas of Bomi County (Parong Tsangpo River Valley and Yigong Zangbo River Valley) and southeast of the Qinghai Tibet Plateau. The goals of this study are to quantify river shape better, support the preliminary detection of potential faults, and provide a useful reference for structure-based spatial queries in GIS. Additionally, more river pattern types with specific morphological and structural characteristics can be automatically detected through dynamic maintenance of the spatial pattern library.

The automatic detection and accurate characterization of drainage patterns are of primary importance for interpreting the regional geologic origin and the features of the regional geological structure. However, there are few studies regarding the identification of cross-basin drainage patterns. In addition, the automatic level of the current approaches needs to be further improved. As a typical type of cross-basin drainage pattern, radial drainage (RD) has unique spatial morphological features: diverging from the center to the surrounding area, developing in adjacent sections of multiple basins, and having a number of source nodes that is not smaller than the number of its outlet nodes. Based on these morphological features, this study aims to develop a novel method for automatic identification of RD using a feature-matching algorithm. The experiment in Mount Lu demonstrates that the proposed method was efficient in RD identification. In this study area, the RDs were identified, and there were no false or missed judgments, which was verified through experts. The proposed method not only helps to detect cross-basin drainage patterns, RD, and centripetal drainage but also has a unique advantage in identifying geographical scenes with complex spatial structures.

Most fabrication methods for three-dimensional (3D) geological symbols are limited to two types: directly increasing the dimensionality of a 2D geological symbol or performing appropriate modeling for an actual 3D geological situation. The former can express limited vertical information and only applies to the three-dimensional symbol-making of point mineral symbols, while the latter weakens the difference between 3D symbols and 3D geological models and has several disadvantages, such as high dependence on measured data, redundant 3D symbol information, and low efficiency when displayed in a 3D scene. Generating a 3D geological symbol is represented by the process of constructing a 3D geological model. This study proposes a parametric modeling method for 3D fold symbols according to the complexity and diversity of the fold structures. The method involves: (1) obtaining the location of each cross-section in the symbol model, based on the location parameters; (2) constructing the middle cross-section, based on morphological parameters and the Bezier curve; (3) performing affine transformation according to the morphology of the hinge zone and the middle section to generate the sections at both ends of the fold; (4) generating transition sections of the 3D symbol model, based on morphing interpolation; and (5) connecting the point sets of each transition section and stitching them to obtain a 3D fold-symbol model. Case studies for different typical fold structures show that this method can eliminate excessive dependence on geological survey data in the modeling process and realize efficient, intuitive, and abstract 3D symbol modeling of fold structures based on only a few parameters. This method also applies to the 3D geological symbol modeling of faults, joints, intrusions, and other geological structures and 3D geological modeling of typical geological structures with a relatively simple spatial morphology.

Loose layers are the locus of human activities. The high-quality 3D modeling of loose layers has essential research significance and applicability in engineering geology, hydraulic and hydroelectric engineering, and urban underground space design. To address the shortcomings of traditional 3D loose-layer modeling based on borehole data, such as the lack of bedrock surface constraints, simple strata pinch-out processing, and the higher fitting error of the strata surface, a 3D loose-layer modeling method based on the stratum development law is proposed. The method mainly uses three different virtual boreholes, bedrock-boundary virtual boreholes, pinch-out virtual boreholes, and densified virtual boreholes, to control the stratigraphic distribution. Case studies demonstrate the effectiveness of this 3D loose-layer modeling method in the Qinhuai District of Nanjing and Hangkonggang District of Zhengzhou. Compared to the previous methods that interpolated stratigraphic surfaces with elevation information, the method proposed in this article interpolates the stratum thickness based on stacking, which could improve the interpolation accuracy. In the area where the loose layers and exposed bedrock are alternately distributed, stratigraphic thickness errors’ mean and standard deviation decreased by 2.11 and 2.13 m. In the pure loose-layer area, they dropped by 0.96 and 0.33 m. In addition, the proposed approach allows us to infer the different stratigraphic distribution patterns accurately and complete 3D loose-layer model construction with higher accuracy and a good visualization effect.