Features Of Geological Structure Research Articles

The Geomechanics of the Dangkhar Landslide, Himachal Pradesh, India

The Dangkhar Landslide is an extremely large landslide located in the Spiti Valley of Himachal Pradesh, India. The landslide is situated in a remote high mountain desert within the Tethys Himalaya at elevations between 3400 m and 5600 m. It is amongst the five largest continental landslides on earth, covering an area of approximately 54 km2 and having an estimated volume of 15–20 km3. Geomechanical evaluations based on the block theory indicate that the Dangkhar Landslide formed as a result of unfavorable combinations of structural geological features and complex surface morphology. A massive kinematically removable block is created by a regional synclinal flexure that is crosscut and kinematically liberated by bounding side valleys. Three-dimensional block kinematics are necessary to permit the release of the giant block and its sliding along the synclinal flexure. Pseudostatic slope stability sensitivity analyses incorporating estimates of site seismicity and shear strength parameters suggest that earthquake shaking could have triggered instability if the static factor of safety was less than or in the range of about 1.5–1.9. Considering the glacial history of the region, ice debuttressing represents an additional potential triggering mechanism.

Pre-Stack Fracture Prediction in an Unconventional Carbonate Reservoir: A Case Study of the M Oilfield in Tarim Basin, NW China

The reservoir of the M oilfield in Tarim Basin is an unconventional fracture-cave carbonate rock, encompassing various reservoir types like fractured, fracture-cave, and cave, exhibiting significant spatial heterogeneity. Despite the limited pore space in fractures, they can serve as seepage pathways, complicating the connectivity between reservoirs. High-precision fracture prediction is critical for the effective development of these reservoirs. The conventional post-stack seismic attribute-based approach, however, is limited in its ability to detect small-scale fractures. To address this limitation, a novel pre-stack fracture prediction method based on azimuthal Young’s modulus ellipse fitting is introduced. Offset Vector Tile (OVT) gather is utilized, providing comprehensive information on azimuth and offset. Through analyzing azimuthal anisotropies, such as travel time, amplitude, and elastic parameters, smaller-scale fractures can be detected. First, the original OVT gather data are preprocessed to enhance the signal-to-noise ratio. Subsequently, these data are partially stacked based on different azimuths and offsets. On this basis, pre-stack inversion is carried out for each azimuth to obtain the Young’s modulus in each direction, and, finally, the ellipse fitting algorithm is used to obtain the orientation of the long axis of the ellipse and the ellipticity, indicating the fracture orientation and density, respectively. The fracture prediction results are consistent with the geological structural features and fault development patterns of the block, demonstrating good agreement with the imaging logging interpretations. Furthermore, the results align with the production dynamics observed in the production wells within the block. This alignment confirms the high accuracy of the method and underscores its significance in providing a robust foundation for reservoir connectivity studies and well deployment decisions in this region.

3D mineral prospectivity modeling using multi-scale 3D convolution neural network and spatial attention approaches

A significant proportion of recent mineral exploration has increasingly focused on the targeting of deep-seated orebodies. Mineral prospectivity modeling is one of the more important approaches that facilitates exploration targeting and the mitigation of risks associated with mineral exploration, particularly under cover. Recent advances in 3D mineral prospectivity modeling enable the effective extraction of predictive information from three-dimensional geological models, enabling more accurate exploration targeting of deep-seated orebodies. These advancements have synergized with deep learning approaches to improve the efficiency of mineral exploration based on nonlinear and multi-layer sensing attributes, effectively enabling the identification and extraction of key relationships between 3D predictive maps and mineralization. The main deep learning method used for 3D mineral prospectivity modeling is convolutional neural network (CNN) modeling. However, this research typically does not consider the multiscale features of geological structures, meaning further improvements can be made to this modeling approach. This paper introduces a multi-scale 3D convolutional neural network model (3D CNN) incorporating a spatial attention mechanism and an Inception module (MSAM-CNN) for 3D mineral prospectivity modeling. By integrating Inception modules and spatial attention mechanisms, the network's capability to identify multi-scale geological features and delineate key predictive areas is significantly enhanced compared to typical CNN approaches. This new approach provides further improvement in the accuracy and generalization capability of 3D mineral prospectivity modeling. To evaluate the effectiveness of this model, we undertook 3D mineral prospectivity modeling within the area of the Baixiangshan iron deposit, in the Ningwu Basin of the Middle-Lower Yangtze River Metallogenic Belt, China. The results show that the multi-scale 3D convolutional neural network model is remarkably robust and has good generalization capabilities. The approach can also can effectively delineate targets within the deep and peripheral areas of the deposit, providing targets for future exploration. The addition, performance indicators, ROC curve, and Capture-Efficiency curve generated during this modeling consistently demonstrate that the MSAM-CNN model outperforms Inception-enhanced CNN (M-CNN), CNN, Random Forest (RF), and Support Vector Machine (SVM) models. All of this indicates that MSAM-CNN approaches can effectively extract 3D spatial features within 3D predictive maps during 3D mineral prospectivity modeling better than other approaches that are commonly used, indicating that thius approach represents a promising tool for the accurate and precise identification of targets during future exploration for deep-seated mineralization.

Case study on long-term deformation monitoring and numerical simulation of layered rock slopes on both sides of Wudongde dam reservoir area

Layered rock slope exists widely. Because of its special slope structure, it is prone to bending deformation and toppling failure, which is a serious threat to engineering construction and safety operation. At present, the research of layered rock slope still has great innovation potential. During the construction of Wudongde Hydropower Station on Jinsha River, safety and stability problems such as slope geological structure development, face rock unloading and relaxation, and even slip and large deformation were encountered. Through field exploration, it is found that the rock and soil stratification of the slope on both sides of Wudongde Hydropower Station is highly obvious. At present, there is a lack of research on-site long-term displacement monitoring of layered rock high-steep slope, especially for layered slope in complex hydrogeology and construction environment. In order to strengthen the research on the deformation and stability of layered rock slope, this paper analyzes the measured displacement data of Wudongde hydropower station slope, and establishes three-dimensional geological finite element model with the help of numerical simulation software. The stability of the slope is calculated by combining the finite difference method and the strength reduction method. Finally, the evolution mechanism of the deformation of the layered rock slope is explained according to the geological structure characteristics. The main conclusions of this paper are as follows: the layered slope in the dam reservoir area is prone to deformation under the combined action of long-term construction disturbance and fissure water seepage, and the construction disturbance has a strong influence on the artificial excavation area below 1070 m, and the maximum rock mass deformation and surface displacement in the artificial excavation area of the slope reach 92.2 mm and 312.5 mm, respectively. However, the influence of construction disturbance on the natural mountain above 1070 m is limited, the valley deformation of the natural mountain on the left bank of the reservoir area is higher than that on the right bank, and the cumulative deformation is still less than 20 mm. The influence of seepage on the displacement of the area with higher elevation at the top of the slope is more obvious, and the influence of excavation and other disturbances on the displacement of the artificial excavation area with lower elevation is more obvious. The deformation of the river valley in the water cushion pond behind the dam increases slowly, and the change trend of the field deformation data is mostly consistent with that of the numerical calculation. The horizontal shrinkage of the mountains on both sides shows a contraction trend on the whole, and the maximum horizontal shrinkage calculated by numerical simulation is close to 20 mm, which is located at the elevation of 990 m.

Advancing Borehole Imaging: A Classification Database Developed via Adaptive Ring Segmentation

The use of in-hole imaging to investigate geological structure characteristics is one of the crucial methods for the study of rock mass stability and rock engineering design. The in-hole images are usually influenced by the lighting and imaging characteristics, resulting in the presence of interference noise regions in the images and consequently impacting the classification accuracy. To enhance the analytical efficacy of in-hole images, this paper employs the proposed optimal non-concentric ring segmentation method to establish a new database. This method establishes the transformation function based on the Ansel Adams Zone System and the fluctuation values of the grayscale mean, adjusting the gray-level distribution of images to extract two visual blind spots of different scales. Thus, the inner and outer circles are located with these blind spots to achieve the adaptive acquisition of the optimal ring. Finally, we use the optimal non-concentric ring segmentation method to traverse all original images to obtain the borehole image classification database. To validate the effectiveness of this method, we conduct experiments using various segmentation and classification evaluation metrics. The results show that the Jaccard and Dice of the optimal non-concentric ring segmentation approach are 88.43% and 98.55%, respectively, indicating superior segmentation performance compared to other methods. Furthermore, after employing four commonly used classification models to validate the performance of the new classification database, the results demonstrate a significant improvement in accuracy and macro-average compared to the original database, with the highest increase in accuracy reaching 4.2%. These results fully demonstrate the effectiveness of the proposed optimal non-concentric ring segmentation method.

Identification of geochemical anomalies in multibackground areas using the combined k-means clustering and residual contrast value method: A case study in a district in Hunan, China

Due to the complex characteristics of geological structures, identifying geochemical anomalies in valuable deposits using regional geochemical datasets of stream sediments is challenging. In this study, an effective combined method was proposed to solve the problem of anomaly identification in multibackground areas. First, samples were classified into different clusters through the k-means clustering method using major elements/minerals (such as SiO2 and Na2O) that can reflect the lithology and were chosen as classification indicators. Considering the double restriction of the contour coefficient and lithological background, each sample within the same cluster was considered to have the same background. Then, the residual value between each sample and the mean data of adjacent samples within the same cluster was calculated, and the original data of each sample were replaced with the ratio (a new parameter defined as the residual contrast value) of the residual value and the anomaly threshold obtained for the corresponding cluster. Finally, geochemical maps and anomaly maps were generated using the residual contrast values. A practical example involving a regional geochemical dataset of stream sediments in Hunan, China, was examined in detail to clarify the procedure. Moreover, a comparative analysis through success rate curves of the percentage of deposits correctly determined was performed between the traditional method, singularity method and new combined method. The results showed that the anomalies identified by the proposed method were closely associated with known deposits, and the residual contrast value, which considers the effects of the lithological background, random error, and structural anomalies, could eliminate the influence of lithology and enhance weak anomalies. Moreover, the geological significance of this method is clear, and the calculation procedure is simple. Thus, this method could be applied for identifying regional geochemical anomalies in multibackground areas and could be used as a guide for new exploration targets.

Low-sulfide platinum–metal deposits in the Kola region: A comparative analysis of the Yuzhnosopchinskoye ore occurrence and the Fedorova Tundra deposit

Research subject. Platinum–metal objects of the Kola region (contact type mineralization), including the Yuzhnosopchinskoye ore occurrence within the same-name massif belonging to the Early Proterozoic Monchetundra intrusion and the Fedorova Tundra deposit located in the western part of the Early Proterozoic Fedorovo-Pansky layered complex. Aim. To establish the formation conditions of platinum-metal ores in the Yuzhnosopchinskoye ore occurrence and the Fedorova Tundra deposit. Methods. Comparison of the geological structure and petro-geochemical features of rocks and ores from the two objects using statistical analysis. Results. Both the Yuzhnosopchinskoye ore occurrence and the Fedorova Tundra deposit belong to the contact type of platinum group element deposits. The main characteristic feature is confinement of the ore zone to the contact of two magmatic phases. In the contact zone, igneous breccia, pegmatoid, and vein bodies are widespread, with the latter being especially common in the Yuzhnosopchinskoye ore occurrence. In Yuzhnosopchinskoye ore occurrence, breccia fragments are represented by orthopyroxenites and melanocratic norites of the early magmatic phase (layered series of rocks). They were cemented by barren gabbroids of the later magmatic phase. In the Fedorova Tundra deposit, breccia zone fragments are also composed of orthopyroxenites and melanocratic norites of the early magmatic phase (layered series of rocks). They were cemented by taxitic ore-bearing gabbronorites of the late magmatic phase. Rocks of the two objects with a similar nomenclature demonstrate slight systematic differences in the composition of petrogenic components, as well as in REE and rare elements. Taxitic gabbronorites (rocks of the marginal series) from the Fedorova Tundra deposit have no rock analogues in Yuzhnosopchinskoye ore occurrence. A comparison of similar amounts of two data sets (more than 2000 samples each) of Pt, Pd, Au, Ni, and Cu sampling showed that the content of all precious metals in the Fedorova Tundra deposit was consistently higher than that in the Yuzhnosopchinskoye ore occurrence. The Fedorova Tundra deposit is homogeneous, with an average Pd/Pt ratio of 4.5. The Yuzhnosopchinskoye ore occurrence is heterogeneous and, on average, slightly more ‘platinum’ and less ‘palladium’ than the Fedorova Tundra deposit. Conclusions. The two ore objects of the contact type are similar in many respects, mainly differing in the conditions of mineralization formation. The Yuzhnosopchinskoye ore occurrence entered the contact zone from the rocks of the layered series due to the activation of the ore matter during intrusion of the late gabbroid intrusive phase. The Fedorova Tundra deposit entered the contact zone together with the late ore-bearing gabbronorite magmatic phase.

A facies‐constrained geostatistical seismic inversion method based on multi‐scale sparse representation

AbstractGeostatistical seismic inversion is an important method for establishing high‐resolution reservoir parameter models. There is no accurate representation method for reservoir structural features, and prior information about structural features cannot be incorporated into geostatistical inversion. Based on the assumption of the sparsity of stratigraphic sedimentary features, the same type of structural feature is used to represent the sedimentary pattern of reservoirs within the same facies. Different sparse representation patterns are used to represent the differences in sedimentary patterns between facies. Although changes in depositional environment might result in the multi‐scale characteristics of geological structures for varying sedimentary rhythms, this paper proposes a facies‐constrained geostatistical inversion method based on multi‐scale sparse representation to better accommodate such situation. Using the method of sparse representation combined with wavelet transform, the multi‐scale sedimentary structural features of reservoirs are learned from well‐logging data. Seismic facies and multi‐scale features are used as prior information for geostatistical inversion. Further, the likelihood function is constructed using seismic data to obtain the posterior probability distribution of reservoir parameters. Finally, the accurate inversion result is obtained by using multi‐scale sparse representation as a constraint in the posterior probability distribution of reservoir parameters. Compared with conventional geostatistical methods, this algorithm can better match the structural features of reservoir parameters with varying geological conditions. Field data tests have shown the effectiveness of this method in improving the accuracy and resolution of reservoir parameter structural features.

The Yinshan Mountains Record over 10,000 Landslides

China boasts a vast expanse of mountainous terrain, characterized by intricate geological conditions and structural features, resulting in frequent geological disasters. Among these, landslides, as prototypical geological hazards, pose significant threats to both lives and property. Consequently, conducting a comprehensive landslide inventory in mountainous regions is imperative for current research. This study concentrates on the Yinshan Mountains, an ancient fault-block mountain range spanning east–west in the central Inner Mongolia Autonomous Region, extending from Langshan Mountains in the west to Damaqun Mountains in the east, with the narrow sense Xiao–Yin Mountains District in between. Employing multi-temporal high-resolution remote sensing images from Google Earth, this study conducted visual interpretation, identifying 10,968 landslides in the Yinshan area, encompassing a total area of 308.94 km2. The largest landslide occupies 2.95 km2, while the smallest covers 84.47 m2. Specifically, the Langshan area comprises 331 landslides with a total area of 11.96 km2, the narrow sense Xiao–Yin Mountains include 3393 landslides covering 64.13 km2, and the Manhan Mountains, Damaqun Mountains, and adjacent areas account for 7244 landslides over a total area of 232.85 km2. This research not only contributes to global landslide cataloging initiatives but also serves as a robust foundation for future geohazard prevention and management efforts.

Characterization of the Fels Landslide (Alaska) Using Combined Terrestrial, Aerial, and Satellite Remote Sensing Data

The characterization of landslides located in remote areas poses significant challenges due to the costs of reaching the sites and the lack of reliable subsurface data to constrain geological interpretations. In this paper, the advantages of combining field and remote sensing techniques to investigate the deformation and stability of rock slopes are demonstrated. The characterization of the Fels landslide, a large, slowly deforming rock slope in central Alaska, is described. Historical aerial imagery is used to highlight the relationship between glacier retreat and developing instability. Airborne laser scanning (ALS) and Structure-from-Motion (SfM) datasets are used to investigate the structural geological setting of the landslide, revealing a good agreement between structural discontinuities at the outcrop and slope scales. The magnitude, plunge, and direction of slope surface displacements and their changes over time are studied using a multi-temporal synthetic aperture radar speckle-tracking (SAR ST) dataset. The analyses show an increase in displacement rates (i.e., an acceleration of the movement) between 2010 and 2020. Significant spatial variations of displacement direction and plunge are noted and correlated with the morphology of the failure surface reconstructed using the vector inclination method (VIM). In particular, steeper displacement vectors were reconstructed in the upper slope, compared to the central part, thus suggesting a change in basal surface morphology, which is largely controlled by rock mass foliation. Through this analytical approach, the Fels landslide is shown to be a slow-moving, compound rockslide, the displacement of which is controlled by structural geological features and promoted by glacier retreat.

Investigation of Landslide by Comprehensive Geophysical Exploration in Northeast Chongqing: Taking Xiawafang Landslide as Example

Aiming at the characteristics of geological structure of Xiawafang landslide in Wuxi County, northeast Chongqing, high density resistivity method and seismic image method are used to comprehensively analyze the differences between conductivity and impedance of rock stratum in this area. At the same time, the geological survey results and drilling data are taken as the basis. With the fusion of geology-geophysics analysis and interpretation, this paper shows the characteristics of geological structure and spatial distribution in the exploration area, and landslides can be analyzed according to their mode of deformation and destruction, which provides a basis of comprehensive geology-geophysics interpretation for the treatment of geological landslide.

A dual-branch fracture attribute fusion network based on prior knowledge

The Tarim Basin region harbors abundant carbonate reservoirs in which fractures throughout the structures play a crucial role in facilitating the transportation of oil and gas. The degree and distribution of fracture development play a crucial role in achieving a high yield and stable production. Seismic attribute analysis is a widely used and efficacious approach for examining fractures in reservoirs. However, different seismic attributes characterize fractures at different scales, and any single seismic attribute may be of poor quality, making it difficult to describe reservoir channel media comprehensively. In the present study, the seismic attribute volume was sliced from three perspectives to obtain complete geological spatial structural information of the seismic volume. This was accomplished using a shifted window (Swin) transformer module guided by a t-distribution before extracting and capturing the prior information and global contextual cues of the seismic attributes. Additionally, a dual-branch fusion (DBFusion) network that combined a convolutional neural network and a Swin transformer to extract the spatial geological structural features of seismic attributes was used to facilitate a comprehensive integration of local and global contextual information. Through the DBFusion network modeling, the network comprehensively extracted local information and integrated complementary global information. Taking three-dimensional seismic data from a unit in the Tarim Basin as an example, this study explores the fusion of three attributes: coherence, ant tracking, and curvature. When compared to individual attributes, the fused attributes can comprehensively leverage the advantages of all three seismic attributes in reflecting fractures. This results in a more precise representation of fractures of varying sizes in the fused output. It was discovered through comparative experiments that the DBFusion network proposed in this paper exhibits the best performance compared to other models.

Edge enhancement of potential field data using the enhanced gradient (EG) filter

Potential field data play a critical role in interpreting various geologic structural features through edge detection filters that aid in mapping subsurface structural features. For this purpose, various filters have been introduced in recent years to determine lateral boundaries. However, each of these filters has its limitations and advantages. This study presents a new edge enhancement filter named “Enhanced Gradient (EG)” based on the Richards function and applies it to potential field data for structural mapping. The EG is tested on two dimensional (2D) and three dimensional (3D) synthetic magnetic models with sources buried at different depths and variable properties. The results from the EG filter provide more accurate and higher resolution horizontal boundaries and can avoid creating the false edges in the output results. In addition, the proposed filter was examined using aeromagnetic data from the Indiana region in the USA. The primary and secondary faults and geological formations are recognizable in the EG image. The results of the EG map will allow us to improve the qualitative interpretation of potential field anomalies in studying the structural and tectonic geology of the Indiana region in the USA.

Structural geology of the Mount Meager Volcanic Complex, BC, Canada: implications for geothermal energy and geohazards

The Mount Meager Volcanic Complex ( Qwe̓ lqwe̓ lústen or Mt. Meager) coincides tectonically with the intra-arc to back-arc transition zone and exhibits the loci of strain partitioning in response to a rapid change in orientation of the Pemberton and Garibaldi Arc segments that are coeval with a shift in Pacific plate motion after 5 Ma. This strain partition is manifested through development of a transpressional deformation from 5 to 1.9 Ma at the latitude of Mt. Meager. Mt. Meager is an active volcanic system with at least two explosive eruptions in the last 25 000 years, the most recent occurring around 2360 BP. Additionally, it is the site of the largest landslide in Canadian history, which occurred during the summer of 2010, originating from the southeastern side the massif. During early exploration at Mt. Meager, geothermal boreholes drilled to 3 km reached 270 °C but did not find sufficient permeability to sustain self-flowing conditions. To understand the geological challenges in Mt. Meager’s geothermal exploration, we analyzed outcrop-scale faults and folds, incorporating structural mapping, volcanic rock paleomagnetism, and radiometric dating to establish kinematic history and kinematic compatibility of structural geology features, including faults and folds. Our findings suggest that stress partitioning during the last 5 Ma resulted in the formation of a transpressional structure exhibited as an elongate and rhomboidal structure at Mt. Meager with anomalously high topographic elevations which led to east northeast–west southwest crustal shortening and exhumation of crystalline basement. This new structural geology model improves our understanding of the geothermal reservoir and potentially significant geohazards.

Geological Structure Features and Rocks Composition of Kronotsky Volcano, the Largest Stratovolcano in the Frontal Zone of the Eastern Volcanic Belt of Kamchatka

Based on the results of 2020‒2022 field works, this paper presents a characterization of the geological structure and whole rock composition of Kronotsky volcano, one of the poorly studied eruptive centers of the Eastern Volcanic Belt (EVB) of Kamchatka. The volume of the volcanic edifice is estimated at 350 km3, that significantly exceeds the volumes of other stratovolcanoes of the frontal zone of EVB. Rocks of a volcano are presented by low-K, high-Fe tholeiitic basalts and basaltic andesites (SiO2 = 47.04–53.15 wt %; K2O = = 0.24‒0.65 wt %; FeO*/MgO = 1.2‒2.89). The basalts show extremely low contents of silica, potassium, titanium, and phosphorus in comparison with rocks of other frontal volcanoes of Kamchatka. The revealed petrochemical features were used to clarify the belonging of the objects located at the junction of Kronotsky and Krasheninnikov volcanic edifices. The obtained data will serve as the background for further petrological and geochemical studies of the volcano, and also may be used for reconstruction the sequence of volcanic events in this area, including clarification of the history of Kronotsky Lake formation.

FEATURES OF FORMATION, MATERIAL COMPOSITION AND GEOLOGICAL STRUCTURE OF RARE METAL AND PEGMATITE DEPOSITS OF THE KALBA REGION ON THE EXAMPLE OF THE YUBILEINOE DEPOSIT

Link for citation: Oitseva T.A., Dyachkov B.A., Bissatova A.Ye., Orazbekova G.B., Zinyakin S.S. Features of formation, material composition and geological structure of rare metal and pegmatite deposits of the Kalba region on the example of the Yubileinoe deposit. Bulletin of the Tomsk Polytechnic University. Geo Аssets Engineering, 2023, vol. 334, no. 7, рр. 164-176. In Rus. The relevance of the research is caused by the lack of knowledge on the behavior of rare metal in pegmatite ore formation in the Kalba region and the need to expand the rare-metal mineral resource base of the region and to establish geological and mineragenic mapping. The main aims of the research are identification of features of geotectonic development, geological structure and material composition of the rare-metal deposit Yubileynoe (Kalba region). Study area: large industrial rare metal deposit Yubileynoe and its mineral complexes. Methods: detailed geological-geochemical and mineralogical study of the region of work, use of materials from works of previous years, stock literature, as well as laboratory studies (mass spectrometric, microprobe, geochronological and other studies). The structural relationships of minerals with each other, as well as the geochemical interpretation of the results of laboratory studies, were taken into account during diagnosing mineral complexes. Results. Spatial and genetic relationship of rare-metal pegmatite deposit (Nb, Ta, Be, Cs, Li, Sn) with granites of the I phase of the Kalba complex P1 (285 Ma), is distinguished. In the location of the Yubileinoe deposit, the leading ore-controlling role is given to the system of deep latitudinal faults of ancient origin, renewed in the Hercynian cycle. A geological and genetic model of rhythmically pulsating rare-metal pegmatite formation of the Yubileinoe deposit is presented, which reflects the zonal development of mineral complexes from oligoclase-microcline (barren) to albite, greisen, spodumene-containing and pollucite-bearing (ore) with an increasing concentration of mineralization. Information was obtained on the composition of pegmatite ores with the release of typomorphic minerals (clevelandite, lepidolite, colored tourmalines, spodumene, pollucite, ixiolite, etc.) and the main geochemical elements of rare metal ore formation (Nb, Ta, Be, Cs, Li, F, P, etc.).

DEM-based analysis of tectonic geomorphologic characteristics and tectonic activity intensity of the Dabanghe River Basin in South China Karst

Abstract The Dabanghe River Basin, in the west of Guizhou Plateau, consists of numerous prominent geological structural features, such as the Shuicheng–Wangmo, Yadu–Ziyun, and Yongningzhen Faults, the Huangguoshu Waterfall, and the Guanjiao Knickpoint. The topographic conditions and structural systems are highly complex, making this a suitable area to study neotectonics. However, research on the geomorphology of the region is lacking. Combined with geomorphic parameters, quantitative exploration of tectonic geomorphic and tectonic activity in the Dabanghe River Basin is of great significance to reveal the formation of the Huangguoshu Waterfall, the development of landforms in western Guizhou, and the regularity of uplift of Guizhou Plateau. Therefore, based on a Digital Elevation Model, GIS software extracted and calculated six geomorphic parameters of the basin: Hypsometric Integral, Asymmetry Factor, Basin Shape Ratio, Stream-Gradient, and Valley Floor Width–Valley Height Ratio (VF) indexes. The tectonic geomorphic characteristics of the Dabanghe River Basin were analyzed, combined with the tectonic activity level classification method, and the Index of Relative Active Tectonics (IAT) of each sub-catchment was calculated. The geomorphic parameters of the basin were found to indicate its geomorphic characteristics well. The tectonic activity in the study area midstream is the most active, and the geomorphic evolution of some upstream and downstream reaches is affected by lithology, topography, and structure. The tectonic geomorphology of the basin is significantly affected by the fault system. Under the control of the Yadu–Ziyun Fault, the tectonic activity in the basin gradually weakens from west to east, and the differential tectonic activity (strong in the mid-reach and eastwards decrease) is consistent with observed seismic intensity. It is confirmed that our research results are consistent with the regional geological background and geomorphic characteristics through field geological survey. Therefore, this study confirms that the use of tectonic geomorphic parameters to classify active tectonics can be an important method to evaluate the stability of the crust in the region and further extends the application of geomorphic parameters in tectonics.

Inferring geological structural features from geophysical and geological mapping data using machine learning algorithms

AbstractWe present an automated approach for inferring surface geological structures from geophysical survey data. Our method employs machine learning, using mapped geological structures as labels and filtered geophysical surveys as reference maps. We compared the performance of the eight main machine learning algorithms and their 32 branches. Applied to the Geological Survey of Victoria's database for the Bendigo Zone, following an appropriate choice of geological features, the 3‐class classification model using subspace K‐nearest neighbour methods achieves a stable and validated 92% accuracy in around 1 min. The fault‐only classification model achieves a stable and validated 97% accuracy in around 6 min. This shows that geological structural features on the surface may be inferred from between one and three of the following geophysical data types: gravity, airborne total magnetic intensity and first vertical derivative of total magnetic intensity. It shows the prospect of machine learning in geological research and suggests that geophysical data combined with machine learning may be useful and efficient in determining the existence of geological structural features.

Numerical Simulation of Diversion Tunnel Excavation Using GPU-Accelerated Anisotropic Random Fields

In this study, a combined anisotropic random field and GPU-accelerated Cholesky decomposition algorithm is proposed for diversion tunnel excavation. With the MATLAB programming control be used to combine the advantages of GPU and CPU computing, the efficient method of generating large numerical model random fields is realized. Based on the geological structure characteristics of red-layered soft rocks in central Yunnan, the anisotropic rock random field and tunnel excavation with different rotation angles are simulated. The results show that the variability of rotational anisotropy random fields and mechanical parameters of the red-bedded soft rocks in central Yunnan has a large influence on the deformation and stress state of the surrounding rocks at the excavation face of the tunnel. This study can provide theoretical and technical support for the design and construction of relevant rock engineering in the red-layered soft rock area in central Yunnan.

GPU-Accelerated Anisotropic Random Field and Its Application in the Modeling of a Diversion Tunnel

In this paper, a GPU-accelerated Cholesky decomposition technique and a coupled anisotropic random field are suggested for use in the modeling of diversion tunnels. Combining the advantages of GPU and CPU processing with MATLAB programming control yields the most efficient method for creating large numerical model random fields. Based on the geological structural characteristics of red-bedded soft rocks in central Yunnan, anisotropic rock random fields and tunnel excavation with various rotation degrees are simulated. In the comparison of anisotropic random fields specifically, the relationship between the anisotropic rotation angle and the plastic zone, as well as the multiple measurements for the overall safety factor, are analyzed. The distribution of the plastic zone after excavation has a significant relationship with the random parameters of the anisotropic random field. When the stronger or weaker random parameters are located in the surrounding rock of the cavern, they will cause a change in the radius of the plastic zone. The overall safety factor of the anisotropic random field is relatively stable, with an average value of about 2, which mainly depends on the strength of the random parameter of the rock mass. Based on the random fluctuation of the suggested value in the engineering report, the simulation result is safe. This study can provide theoretical and technical support for the design and construction of relevant rock engineering in the red-bedded soft rock region of central Yunnan.