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Identification of anomalous geological structures is crucial for ensuring safe and high-efficiency mining and preventing geological disasters in iron mines. This study proposes a multi-geophysical prospecting approach that integrates the Transient Electromagnetic Method (TEM) and True Reflection Tomography (TRT) technologies for advanced exploration of anomalous geological structures, taking the Songhu Iron Mine project as an example. Initially, four typical spatial combination patterns of anomalous geological structures were proposed at the Songhu Iron Mine. Then, the interpretation characteristics of TEM for faults and karst caves were summarized from seven underground engineering projects. The interpretation characteristics of TRT for the water-rich zone, water-bearing fractured zone, fault fracture zone, and iron ore vein boundary were summarized by four underground engineering projects. Moreover, a multi-geophysical prospecting workflow utilizing TEM and TRT was developed for advanced geological forecasting in transportation and cross–ore vein tunnels. Further, the interpretation characteristics of TEM and TRT for the above four typical anomalous geological structures were summarized. Finally, a case application of advanced geological forecasting in the Songhu Iron Mine demonstrates the effectiveness of the proposed geological forecasting method. This study provides a practical and effective framework for identifying anomalous geological structures in iron mines and similar mining projects.

This study conducted a comprehensive geological background investigation of the Zhurong rover’s landing area in Utopia Planitia using 3.5 m/pixel DEM and 0.7 m/pixel DOM data and completed the compilation of a 1:250,000-scale geological map. A total of 17 geological structures were systematically identified within the landing area. Additionally, focusing on scientific questions regarding the evolution of troughs, cone units, and mesas, we theoretically designed an exploration route considering slope constraints by taking the Zhurong rover route design as a case study. This route, a conceptual design, starts from the hibernation location of the Zhurong rover and has a total length of 126 km. It can provide a reference for advancing detection strategies for volatile components (e.g., water and ice) and contribute to the design of the Tianwen-3 exploration route. Ultimately, this study aims to establish a general guideline for integrating geological mapping with rover mobility planning in future extraterrestrial exploration missions.

Slope movements are one of the most widespread geomorphic processes and can cause material damage due to their rapid and unpredictable action. The structural-geological conditions of the slope can play an important role in the resulting activity of landslide movements, and their type and mechanism. The environments in which slopes with different structural-geological structures regularly occur are cuesta. In order to define landslide activity on slopes with different structures, it is necessary to know their detailed historical behaviour. In this study, dendrogeomorphic methods were used to spatio-temporally reconstruct the activity of two landslides on slopes with contrasting structural-geological structure (bedding planes slope and escarpment slope) in the Hřebečský hřbet (Czech Republic) cuesta area. A total of 174 individuals of common spruce (Picea abies (L.) Karst.) were sampled using 348 increment cores. The internal structure and depth of the landslides on both slopes were analyzed using ERT (electrical resistivity tomography) geophysical measurements. The escarpment slope was affected by a rotational landslide that transitioned to a shallow plastic front at the base. Activity on this front was very high, which can be attributed to its heavy waterlogging, as confirmed by ERT measurements. Moreover, the chronological occurrence of landslide activity on this slope corresponded very well with the occurrence of above-average 3-day rainfall. In contrast, the slope on bedding planes was affected by rotational-translational landslide, which activity was significantly lower (four dated events in the last 80 years). Moreover, these events did not correspond to the occurrence of any pattern of above-average rainfall events. Thus, in this case, dendrogeomorphological analysis revealed distinct landslide behaviour on slopes with contrasting structural-geological conditions.

Wringinanom Mud Volcano is an interesting geological feature in Kendeng zone, East Java. Its presence is often associated with subsurface petroleum systems and active tectonic dynamics, particularly the activity of the Kendeng Fault, a major geological structure in the region. This study aims to analyze the Wringinanom mud volcano system by integrating magnetic and gravity methods to understand the subsurface geological structures that control the appearance of mud vulcanoes. Geophysical data measurements were conducted at 180 points for the magnetic method and 47 points for the gravity method using a Lacoste Romberg relative gravimeter, distributed around the mud volcano area. The results of magnetic data analysis indicate anomaly values ranging from – 1044.1 to 692.3 nT, with a low anomaly characterizing the mud volcano area, and material distribution extending to the north, west, and south. In addition, two suspected fault zones trending NE–SW and NW–SE were identified, marked by gradients between high and low magnetic anomalies. Gravity data in terms of complete Bouguer anomaly (CBA) values range from −4.1 to 0.6 mGal, showing local anomalies that indicate lower subsurface density beneath the mud volcano compared to surrounding areas. Further analysis using the First Horizontal Derivative (FHD) filter identified a possible NW–SE trending fault crossing the mud volcano area, consistent with the findings from the magnetic method. The integration of both geophysical methods emphasizes the significant role of geological structures, especially fault systems, in controlling the appearance of the Wringinanom mud volcano. This study contributes to the limited knowledge of mud volcano systems in Java and highlights the need for further investigation, particularly through detailed subsurface modeling and fault pattern identification.

On February 26, 2021, the ancient Moli landslide in Guoye town, Zhouqu County, Gansu Province, China, was reactivated. The volume of the reactivated landslide was approximately 2120 × 104 m3, which was classified as a remote accumulation layer landslide in a deep superlarge fault fracture zone. No casualties occurred due to timely warnings. The deformation characteristics and failure mechanisms of the landslide were systematically studied by means of field investigations and engineering mapping, field exploration, deep displacement monitoring, and high-density electrical methods. We found that the geological structure and landform of Zhouqu County are extremely complex. The sliding mass of this landslide was mainly gravelly soil with poor soil mechanical properties. The gravelly clay and phyllite clastic layers were prone to sliding. The landslide as a whole presented the comprehensive characteristics of front edge erosion traction and rear load displacement and was associated with multiple secondary landslides. The Heisongping–Sanjiaoping fault zone of the Bailong River passes through the rear edge of the landslide, and a variety of inducing factors worked together to make the landslide failure mechanism particularly complex. The monitoring and comprehensive control of this kind of landslide should be strengthened.

The uncertainty of structural interpretation complicates the practical production and application of data-driven complex geological structure modeling technology. Intelligent structural modeling excavates and extracts structural knowledge from structural interpretation through human–machine collaboration and combines structural interpretation to form a new model of complex structural modeling guided by knowledge. Specifically, we focus on utilizing knowledge rule reasoning technology to extract topological semantic knowledge from interpretive data and employ knowledge inference to derive structural constraint information from complex geological structure models, thus effectively constraining the 3D geological structure modeling process. To achieve this, we develop a rule-based knowledge inference system that derives theoretical models consistent with expert cognition from interpretive data and prior knowledge. Additionally, we represent the extracted knowledge as a topological semantic knowledge graph, which facilitates computer recognition and allows estimation of intersection lines during 3D geological modeling, resulting in the creation of accurate models. The applicability of our proposed method to various complex geological structures is validated through application tests using real-world data. Furthermore, our method effectively supports the realization of intelligent structure modeling in real working area.

The seismic failure of rock slopes is commonly a cumulative damage process; in particular, local slope damage usually occurs before the occurrence of landslides. The local damage in rock slopes is often caused by the high-frequency components of the waves. Special attention should be paid to the relationship between the local damage and the dynamic failure of landslides, which is of great significance to the study of the cumulative failure evolution of landslides. Based on a numerical modal analysis and dynamic characteristics determined using shaking-table tests, the relationship between the local damage and the dynamic failure of a rock slope with discontinuous joints and its failure mechanism is investigated in the frequency domain. The modal analysis clarifies the natural frequencies and vibration modes of the slope. The tests investigate the effects of the natural frequencies on the slope dynamic characteristics. The numerical and test results show that the high- and low-frequency components mainly induce local and overall deformation of the surface slope, respectively. The analyses of the peak Fourier spectrum amplitude (PFSA) suggest that the dynamic failure process of the slope includes a local damage stage (< 0.297 g) and an overall failure stage (> 0.297 g). The high-frequency components play a major role in the slope cumulative deformation process, and the low-frequency components determine the failure mode of the landslide. The local damage induced by high-frequency components first occurs and progressively develops; then, when the damage is accumulated to a certain extent, the surface slope fails because of the low-frequency components.

As a knowledge representation method, knowledge graph is widely used in intelligent question answering systems and recommendation systems. At present, the research on knowledge graph mainly focuses on information query and retrieval based on knowledge graph. In some domain knowledge graphs, specific subgraph structures (patterns) have specific physical meanings. Aiming at this problem, this paper proposes a method and framework of knowledge graph pattern mining based on gat. Firstly, the patterns with specific physical meaning were transformed into subgraph structures containing topological structures and entity attributes. Secondly, the subgraph structure of the pattern is regarded as the query graph, and the knowledge graph is regarded as the data graph, so that the problem is transformed into an approximate subgraph matching problem. Then, the improved relational graph attention network is used to fuse the adaptive edge deletion mechanism to realize the approximate subgraph matching of subgraph structure and attribute, so as to obtain the best matching subgraph. The proposed method is trained in an end-to-end manner. The approximate subgraph matching is realized on the existing data set, and the research work of key pattern mining of complex geological structure knowledge graph is carried out.

The Pingdingshan mining region in China has witnessed severe coal and gas outbursts. A total of 153 coal and gas outburst accidents have occurred in this mining region. As the mining depth progressively increase, mining conditions in the region have become more complex, and gas outburst disasters have become more severe. This research statistically analyzed the 153 outburst accidents in the Pingdingshan mining region. Additionally, based on historical data and typical outburst cases, the characteristics and primary factors affecting coal and gas outbursts within the mining region were obtained. The results indicate that the outburst accidents in this mining region were primary influenced by the following factors: geological structure, mining depth, seam thickness, the lithology of roof and floor, and the mode of operation. As mining depth increased, the geotechnical environment and the mechanical properties of coal varied. Moreover, under the combined action of high stresses and considerable amounts of high-pressure gas accumulated within the coal and rock mass, the structures of both coal seam and rock strata in the mining faces or roadways were destroyed instantly. Therefore, coal and gas outbursts occurred. The outburst intensity increased significantly when mining depth is greater than 500 m. Coal and gas outburst accidents happened most frequently and severely in regions with geological structures containing such features as faults and folds due to the increasing gas pressure and sharply rising ground stress. Moreover, variation of seam thickness and the lithology of the roof and floor of the seam have significant control effect on coal and gas outburst. In addition, engineering disturbance was an external factor inducing coal and gas outbursts. For example, blasting and coal cutting caused changes in the stress state in the coal at the end of excavation roadways and mining faces.

Muography is a novel imaging method using natural cosmic-ray radiation for characterising and monitoring variation in average material density in a diverse range of objects that cannot be imaged by conventional imaging techniques. Muography includes muon radiography and muon tomography. Cosmic-ray-induced muons were discovered in the 1930’s, but rapid development of both muographic techniques has only occurred in the last two decades. With this rapid development, muography has been applied or tested in many fields such as volcano imaging, archaeology, underground structure and tunnel detection, rock mass density measurements, cargo scanning, imaging of nuclear waste and reactors, and monitoring of historical buildings and the inside of blast furnaces. Although applications of muography have already touched mining and rock engineering, such applications are still rare and they are just beginning to enter the market. Based on this background, this paper aims to introduce muography into the fields of mining and rock engineering. First, the basic properties of muons are summarized briefly. Second, potential applications of muography to mining and rock engineering are described. These applications include (1) monitoring temporal changes in the average material density of fracturing and deforming rock mass; (2) detecting geological structures and isolated ore bodies or weak zones in mines; (3) detecting a reservoir or boulders during tunnelling or drifting; (4) monitoring caving bodies to search remaining ore; (5) evaluating and classifying rock masses; (6) exploring new mineral deposits in operating underground mines and their surrounding brownfields. Finally, some issues such as maximum depth muons can reach are discussed.