Multi-source Geological Data Research Articles

Metallogenic prediction based on fractal theory and machine learning in Duobaoshan Area, Heilongjiang Province

The extraction and integrated analysis of multi-source geological data are key steps in the prediction of mineralization. Current studies are focusing on the extraction and integration of the deep-level mineralization information. In the era of big data, mathematical models and computer algorithms for data mining of multi-source prospecting information have emerged as a leading research area in mineral prediction. In this study, we quantitatively analyzed the structure and remote sensing alteration information using the concentration–area (C–A) fractal model and the box-counting method for the Duobaoshan mineralization area, Heilongjiang Province, China. Results indicate that areas of high fractal dimension of remote sensing alteration correspond to abundant alteration anomalies. Fractal characterization of geological structure is consistent with the spatial distribution. Therefore, fractal characterization provides predictive factors of structure and remote sensing alteration in the development of a predictive model of mineralization. Soil geochemical data were analyzed using the component data analysis (CDA) method and the spectrum–area (S–A) fractal model. The analyses identified anomalous and background signals represented by the PC1 and PC2 principal component combinations. These combinations show a strong correlation between geochemical anomaly data and known deposits in the study area, suggesting that the S–A model effectively identifies geochemical anomalies that can be used as a predictive factor of a mineralization prediction model. The mineralization prediction model was developed using random forest (RF) and support vector machine (SVM) algorithms. The model incorporates predictive factors from multiple sources, including the ore-forming geological background, fractal-characterized geological structure, fractal-characterized remote sensing alteration, and geochemical characteristics. The models incorporated the C–A fractal model to evaluate the probability of mineral prediction. By integrating the characteristics of multi-source mineral prospecting information with the predictive results of machine-learning models, we delineated eight prospective mineralization areas. This approach validates the effectiveness of a combined method involving fractal theory and machine-learning in mineral exploration, offering new insights and theoretical guidance for further mineral prospecting in the study area.

Bibliometric analysis of research challenges and trends in urban underground space

AbstractThe utilization and development of urban underground space play a crucial role in optimizing the layout of civic architecture and enhancing the urban ecological environment, which contributes toward increasing the overall carrying capacity and promoting sustainable development in megacities. To delve into the research progress of urban underground space, knowledge maps were created using the information visualization software VOSviewer. The literature was systematically extracted from three prominent databases, namely, Web of Science, Scopus, and China National Knowledge Infrastructure. According to the bibliometric analysis of the co‐citation and core words co‐occurrence, the trends and challenges of research on urban underground space were identified. As highlighted by the results obtained, it still remains highly challenging to achieve interdisciplinary collaboration in urban underground space research; the research trends of urban underground space consist of collaborative planning and whole life cycle sustainable development, multisource geological data informatization and resource evaluation, infrastructure design optimization, and intelligent construction. The knowledge map, drawn using bibliometric methods, offers a quantitative analysis of literature retrieval across various levels. It is recognized as an essential tool for exploring and identifying challenges and trends in urban underground space.

Multifactor Prediction of the Water Richness of Coal Roof Aquifers Based on the Combination Weighting Method and TOPSIS Model: A Case Study in the Changcheng No. 1 Coal Mine.

Identifying the water richness of coal roof aquifers is an important and difficult goal of hydrogeological research to prevent and control roof water disasters. To evaluate the water richness of roof sandstone aquifers of the No. 1 coal seam in the Changcheng No. 1 coal mine, a multifactor prediction method based on the fuzzy Delphi analytic hierarchy process (FDAHP), entropy weight method (EWM), sum of squared deviations (SSD), and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was proposed. Multisource geological data, including sandstone thickness, burial depth, lithological composition index, core recovery, fault scale index, fault intersections and endpoint density, and fold fractal dimension, were chosen as the primary indicators for evaluating the water richness of roof sandstone aquifers. The FDAHP and EWM were used to scientifically determine the subjective and objective weight vectors of these seven main factors, and the SSD was used to determine the optimal combination weights based on the objective and subjective weight vectors. On this basis, the water richness index (WRI) model was developed using the TOPSIS method to rank the water richness of samples in the study area. A water richness zoning map was created using the WRI values, revealing three zones: the weak water richness zone, moderate water richness zone, and strong water richness zone. Additionally, the map was refined by incorporating hydrogeologic data collected during mining operations, including pumping tests and actual water inrushes from roadways and working faces. It is believed that the proposed WRI model is effective for predicting the water richness of the roof sandstone aquifers of the No. 1 coal seam in the Changcheng No. 1 coal mine based on the engineering practice data used to validate the WRI model.

Gold prospectivity mapping and exploration targeting in Hutti-Maski schist belt, India: Synergistic application of Weights-of-Evidence (WOE), Fuzzy Logic (FL) and hybrid (WOE-FL) models

Our study attempts to map gold prospectivity for deriving optimal exploration targets using data-driven, knowledge-driven and hybrid approaches. Prospectivity models viz. Weights of Evidence (WOE), Fuzzy Logic (FL) and a hybrid model that combines the data-driven and knowledge-driven components (WOE-FL) were applied to a part of the auriferous Hutti-Maski schist belt of 1352 km2 area with 20 known gold occurrences. With a pixel resolution of 500 m, 16 spatial evidential raster layers were created on a GIS platform encompassing viable predictive indicators, critical in gold exploration. Modelling inputs include essential ingredients and mappable criteria of the conceived orogenic gold mineral system in the study area. Multi-source geological data such as lithostratigraphic units, favourable litho-contacts, structural deformation sites, geochemical anomalies of selected gold pathfinder elements, and hydrothermal alteration zones derived from digital image processing of Landsat 8 OLI satellite imagery were integrated to generate prospectivity maps for delineation of future targets. A quantitative evaluation of the resulting three prospectivity maps was performed using concentration-area (C-A) fractal analysis, prediction–area (P–A) plot, fitting-rate curve (FRC) and area under curve (AUC). Comparative analysis indicates that the performance of the hybrid model (WOE-FL) stands out to be the most efficient, with a prediction rate of 87% and AUC of 91.40% compared to WOE and FL. A risk assessment was performed combining the outputs of prospectivity models that returned 10% of the study area as potential exploration targets out of which the low-risk exploration targets comprises merely 4.5% representing the optimal targets for gold exploration in the study area.

Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data

Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping.

Mineral prospectivity mapping by deep learning method in Yawan-Daqiao area, Gansu

Mineral prospectivity mapping is similar to probability prediction using multi-source geological data. However, the complexity of geological phenomena creates difficulties for research. In this study, a deep regression neural network was built to map the mineral prospectivity in the Daqiao Gold Mine in Gansu Province, China. The neural network was trained using multi-source data including geological, geophysical, and geochemical data for the study area. The proposed deep regression neural network reveals the complex relationships between the mineral prospectivity map and geological, geophysical, and geochemical features, improving the prediction results. Moreover, the training dataset does not require classified samples. Training samples with continuous values can help improve the fault tolerance of the training dataset and reduce the uncertainty of positive samples. The experimental results showed that the proposed neural network learned previous expert knowledge related to mineral prospectivity mapping and can be applied to deep regression neural networks to predict and evaluate mineral resources using multiple data sources. The prospectivity map obtained in this study benefits the search for gold mineralization in the study area.

3D scene and geological modeling using integrated multi-source spatial data: Methodology, challenges, and suggestions

The visualization and analysis of massive, multi-source, multi-dimensional, heterogeneous, dynamic, and complex spatial data is playing an increasingly important role in 3D scene and geological modeling. There are great differences in various research fields regarding data acquisition techniques, morphological descriptions, and application purposes of spatial objects. Therefore, based on an existing three-dimensional visualization technology and analysis program of spatial data, an integrated processing method for the effective fusion and analysis of multi-source complex spatial data is proposed. This method combines a massive point cloud model with a digital elevation model, in addition to combining the complex stratum with fault modeling technology. The structural grid and geological grid are effectively combined to realize the unified modeling and visualization of natural and artificial objects. This method was then applied to a 3D modeling scene of deeply buried mines and a geological construction model for open pit mines. The comprehensive and effective utilization of multi-source geological data is realized by using this modeling method. Finally, the challenges of spatial data visualization from the aspects of the difficulties in spatial data acquisition, the complexities of the spatial structure, the constraint relations of geological bodies, and the complexity of the 3D modeling process are discussed in this study. Three specific suggestions are proposed. This study enhances the application efficiency of multi-source spatial data modeling technologies already in practice and provides reference for future applications and developments.

Landslide hazard evaluation based on linear rupture plane method

In recent years, landslide disasters have occurred frequently, making the evaluation of the susceptibility of landslide hazards a major difficulty and hot topic in current research. The current research focuses on the use of statistical models or information models to analyze landslide hazards. However, the accuracy is not high. We first study the mechanism of landslide geological disasters. Based on this, combined with multi-source geological data, a linear rupture plane method (LRP) is employed to construct the landslide hazard evaluation system. LRP regards the failure surface of the slope as an approximate plane and the section as an approximate straight line. It uses the principle of limit equilibrium to calculate the safety factor of slope. In the paper, the Ziyang area with frequent landslide disasters is taken as the research area. Choosing landslide hazard points in Ziyang County as the sample data, we select seven factors including slope height, slope angle, soil bulk density, soil cohesion, internal friction angle, precipitation intensity and seismic intensity as influencing factors. Based on the LRP, we construct an evaluation system to divide the landslide into three grades: high-risk area, low-risk area and safe area, which provides effective technical support for the early warning and prevention planning of landslide disasters.

Collaborative 3D geological modeling analysis based on multi-source data standard

A multi-source data standard must be established to reconcile all available geological data such as borehole data, geological mapping data, and rock property data for building a reliable 3D geological model. In addition, a methodology that considers various available data must be developed to provide accurate results that are easy to interpret and convenient for post-modeling analysis. This paper presents a collaborative analysis approach for geological body modeling using multi-source geological data and interpolation theories in different stages and at different places. This approach aims to provide a detailed and comprehensive description and analysis of data, with emphasis on processing flow. The proposed approach contains three major components: establishment of geological databases based on the multi-source data standard to incorporate borehole, geological mapping and rock property data; Combined Kriging interpolation method for data processing; geological algorithms to build, visualize, and post-analyze the 3D geological model. The model integrates multi-source information and is a representative of the geological context. The proposed method is validated by applying it to the Ma-luan mountain tunnel project in Guangdong province, China.

Mapping of Fe mineral potential by spatially weighted principal component analysis in the eastern Tianshan mineral district, China

Principal component analysis (PCA) is one of the commonly used methods to integrate multi-source geological data to enhance understanding of geo-information. Each principal component (PC) obtained by PCA reflects a different aspect of the geo-information contained in a dataset. Confined by statistical significance, some PCs are not acceptable for interpretation. In this paper, the same problem in PCA occurs in mapping potential areas of Fe mineralization in eastern Tianshan mineral district, China. By spatially weighting correlation coefficient matrixes between variables, a spatially weighted principal component analysis (SWPCA) can deal with the shortcoming of PCA, thus improving the statistical acceptability of eigenvectors and eigenvalues derived by ordinary PCA. Based on the geological model in the study area, a current weighting factor is defined to enhance the geo-information possessed by the ordinary PC1. Compared with the loading of input layers on ordinary PCA, SWPC1 shows more significant physical meaning than PC1. Meanwhile, remarkable increases on the eigenvalues of SWPC2 and SWPC3 are demonstrated to exist making these spatially weighted principal components more acceptable in a statistical sense. In comparison with both loadings and scores on ordinary PCs, the improved geo-information carried by SWPCs can help with better interpretations of the geological phenomena.

Three-Dimensional Dynamic Visual Simulation of Slope Instability of Barrier Landslide

The sliding movement of barrier landslide is a complex and dynamic process, because of the great effect of seismic action and huge surge. Adopting NURBS-TIN-BRep hybrid data structure and integrating with multi-source geological data, 3D geological model of the Tangjiashan quake dam was rebuild. The instability mechanism of the landslide was analyzed. Applying visualization technology, the whole formation process of the barrier dam, the production and development process of surge were fully and realistically displayed. These results above can help the engineering personnel to understand the whole condition of the Tangjiashan barrier dam. These research can provide some technical support for comprehensive governance scheme.

Alternative 3D modeling approaches based on complex multi-source geological data interpretation

Due to the complex nature of multi-source geological data, it is difficult to rebuild every geological structure through a single 3D modeling method. The multi-source data interpretation method put forward in this analysis is based on a database-driven pattern and focuses on the discrete and irregular features of geological data. The geological data from a variety of sources covering a range of accuracy, resolution, quantity and quality are classified and integrated according to their reliability and consistency for 3D modeling. The new interpolation-approximation fitting construction algorithm of geological surfaces with the non-uniform rational B-spline (NURBS) technique is then presented. The NURBS technique can retain the balance among the requirements for accuracy, surface continuity and data storage of geological structures. Finally, four alternative 3D modeling approaches are demonstrated with reference to some examples, which are selected according to the data quantity and accuracy specification. The proposed approaches offer flexible modeling patterns for different practical engineering demands.

Coal seam surface modeling and updating with multi-source data integration using Bayesian Geostatistics

A reliable coal seam surface model needs to reconcile all available geological data such as boreholes, cross-sections, and coal seam floor contour maps. In addition, the model should be updated when local geological information such as coal seam observations is available. This paper develops a Bayesian Geostatistical approach for coal seam surface modeling using multi-source geological data in different stages and at different scales. The proposed approach contains two major components: Bayesian maximum entropy (BME) nonlinear estimation method to incorporate boreholes, cross-sections, and coal seam floor contour maps obtained in geological survey stage, and Bayesian inference (BI) method to assimilate coal seam point observations and geological sketches of tunnels obtained in mining stage. Coal seam surface elevations and its uncertainties are first estimated using BME method. The regional estimates are then used in BI method as prior knowledge, and updated when coal seam observations at a local scale are available. This provides a systematic and rigorous framework to incorporate multi-source geological data, and an effective way to improve the accuracy of coal seam surface models. The proposed approach is illustrated through a case study of a 3D subsurface modeling of the Wang-feng-gang Coal Mine, China. The coal seam surface estimates are compared with those of Ordinary kriging and Bayesian kriging methods, and compared with observed values along two tunnels in mining process.