Geological Prediction Research Articles

A Study on the Controlling Effect of Geological Structures on Coalbed Methane Occurrence in the Northeast Margin of Qinshui Basin, North China

This study investigated the patterns of gas occurrence in the coal seam structural zones of the northeastern margin of the Qinshui Basin, with a focus on the Sijiazhuang Coal Mine. Using laboratory experiments, theoretical analysis, and field exploration, we examined how geological structures influence gas distribution. The results show that gas content and pressure near normal faults are generally higher than those in reverse fault areas. However, fault-induced gas occurrence is complex, with stress superposition potentially reversing this trend. When a normal fault intersects modern tectonic stress at a perpendicular or large angle, the fault zone may transition to a compressional state, enhancing gas preservation. Fold structures were found to play a significant role in gas distribution, with anticline zones exhibiting the highest gas content, followed by syncline and normal zones. Collapse columns were shown to affect gas occurrence within a range of 15 to 180 m, with the impact depending on factors such as surrounding rock properties, hydrogeological conditions, and fault activity during collapse formation. Additionally, mirror-like sliding surfaces, formed by multiple factors, are prevalent in the coal seam structures of this region. These sliding surfaces are closely linked to structural zones and serve as valuable indicators for geological predictions in coal seam development.

Numerical simulation method for generating seismic waves by a simplified point source based on seismic-while-excavation in coal mine roadways

The advanced geological prediction of seismic-while-excavation (SWE) in coal mine roadways employs the noise vibrations generated during the construction process as the signal source. The excitation process of seismic waves derived from this noise source significantly differs from that associated with conventional pulse sources. Exploration of the characteristics and process involved in exciting seismic waves through excavation noise sources is undertaken. Initially, challenges posed by complex vibration sources during excavation are addressed, with analysis of signal characteristics derived from field data and consideration of the excavation environment. Investigation of the seismic wave excitation forms of these complex noise sources leads to a summary of their characteristics. The simplification of various excavation machinery into distinct forms of point sources facilitates theoretical analysis. Coupling the excitation processes of each point source and leading into virtual source theory of seismic record interferometry reconstruction results in a simplified point source time function of excavation noise sources. Analysis of the types and magnitudes of forces exerted by the cutting head during coal seam excavation, in conjunction with the operating mode of a boom-type roadheader, is conducted. A spatial loading method for the noise source is proposed, grounded in the components of the excited wavefield. The staggered grid finite difference algorithm is employed for numerical simulations. Comparison of synthetic seismic records with field-measured data validate the rationality and effectiveness of the simulated source term. A theoretical foundation for future investigations into the imaging and inversion of excavation noise sources is established.

WITHDRAWN: GeoPredict-LLM: Intelligent Tunnel Advanced Geological Prediction by Reprogramming Large Language Models

WITHDRAWN: GeoPredict-LLM: Intelligent Tunnel Advanced Geological Prediction by Reprogramming Large Language Models

GeoPredict-LLM: Intelligent tunnel advanced geological prediction by reprogramming large language models

With the improvement of multisource information sensing and data acquisition capabilities inside tunnels, the availability of multimodal data in tunnel engineering has significantly increased. However, due to structural differences in multimodal data, traditional intelligent advanced geological prediction models have limited capacity for data fusion. Furthermore, the lack of pre-trained models makes it difficult for neural networks trained from scratch to deeply explore the features of multimodal data. To address these challenges, we utilize the fusion capability of knowledge graph for multimodal data and the pre-trained knowledge of large language models (LLMs) to establish an intelligent advanced geological prediction model (GeoPredict-LLM). First, we develop an advanced geological prediction ontology model, forming a knowledge graph database. Using knowledge graph embeddings, multisource and multimodal data are transformed into low-dimensional vectors with a unified structure. Secondly, pre-trained LLMs, through reprogramming, reconstruct these low-dimensional vectors, imparting linguistic characteristics to the data. This transformation effectively reframes the complex task of advanced geological prediction as a "language-based" problem, enabling the model to approach the task from a linguistic perspective. Moreover, we propose the prompt-as-prefix method, which enables output generation, while freezing the core of the LLM, thereby significantly reduces the number of training parameters. Finally, evaluations show that compared to neural network models without pre-trained models, GeoPredict-LLM significantly improves prediction accuracy. It is worth noting that as long as a knowledge graph database can be established, GeoPredict-LLM can be adapted to multimodal data mining tasks with minimal modifications.

Application of Geo-Radar in the Detection of Various Adverse Geology in Tunnel Over-advance Geological Forecasting

Abstract Tunnel excavation process will encounter a variety of adverse geological problems, different adverse geologic bodies on the safety of tunneling projects will have more or less impact. This paper summarizes and analyzes several kinds of common adverse geological bodies based on previous engineering examples, and compares and analyzes the prediction results of geo-radar with the actual excavation characteristics in the preliminary over-advanced geological prediction, which shows that different adverse geological bodies such as fracture zones (faults), water-bearing bodies, cavities, joints and fissures development zones, etc., will have an impact on the frequency and amplitude (energy) of radar waves, which will confirm the frequency and amplitude changes of radar waves in the face of the palm. By analyzing the frequency and amplitude changes of radar waves, we can confirm the change of surrounding rocks in front of the palm face. The application of geo-radar in over-advanced geological forecasting and detection of adverse geology has solved the problems encountered in the process of tunnel construction and excavation, and has provided reliable technical support for safe tunnel construction.

Reliable simulation analysis for high-temperature inrush water hazard based on the digital twin model of tunnel geological environment

In complex mountainous terrains, tunnel construction faces unique challenges from high-temperature water inrush hazards, a systemic risk arising from the interplay of stress, seepage, and temperature fields. Traditional simulation methods, focusing on isolated disaster scenarios, fall short in addressing the multifaceted nature of these risks due to geological ambiguity and data incompleteness. Digital twin technology presents an effective solution to these challenges; however, its core challenge lies in how to utilize digital twin technology for data-model-co-driven simulation analysis of coupled multi-physical fields in situations of incomplete data. This paper introduces a digital twin paradigm for the simulation analysis of water inrush, which significantly enhances efficiency and accuracy through the integration of advanced machine learning and finite element analysis techniques. Specifically, this is achieved by combining a high-precision geological modeling method based on Gaussian Processes (GP) with a parameter calibration method through Gaussian Process-Differential Evolution (GP-DE) back-analysis. Firstly, a voxel structure is utilized to integrate the multi-field attribute features of the tunnel environment. Secondly, through the integration of multi-source advanced geological prediction data, we construct a dynamic digital twin model of the tunnel environment leveraging machine learning techniques. To overcome the issue of low modeling accuracy, the GP is employed, enhancing the exploitation of latent information within multi-source geophysical data. Lastly, we utilize the GP-DE back-analysis method to calibrate the parameters of the tunnel environment, thereby enhancing the precision and reliability of water inrush simulations. The method has been validated through application to a section of an ultra-high-temperature water inrush tunnel in China, featuring a burial depth of 230 meters. The accuracy of the method is corroborated by the monitoring data from the tunnel, supporting dynamic optimization design and safety prevention measures during construction.

Diffraction Separation for the Ground Penetrating Radar Data by Masking Filters

Ground penetrating radar is a high-resolution, efficient, non-destructive geophysical detection method. It is widely used in various application scenarios such as tunnel geological prediction and road maintenance. Ground penetrating radar data contains a variety of valid signals as well as noise. The diffracted waves of ground penetrating radar contain high-resolution small target imaging information. A critical challenge in GPR applications is how to extract diffracted waves from the wave fields. We provide a strategy to achieve this goal by applying the masking filters. Considering the complexity of the ground penetrating radar wave field and the weak energy of the diffracted waves, the median filter is first employed to suppress the linear reflections and then the f-k filter and filter are implemented to further increase the proportion of diffractions in the wave fields. Three numerical experiments are employed to test the diffraction-separation method.

Sandstone Porosity Evolution and Reservoir Formation Models of the Paleogene Huagang Formation in Yuquan Structure of West Lake Sag, East China Sea Basin

The West Lake Sag is abundant in oil and gas reserves, primarily in the Huagang Formation reservoir which serves as the primary source of production. The level of exploration is rather high, but there are still some unresolved issues, such as an unclear understanding of pore evolution features and reservoir growth mode. To tackle the aforementioned problems, this study employs optical microscopic examination, scanning electron microscope analysis, inclusion analysis, isotope analysis, X-ray diffraction analysis, and other techniques to elucidate the primary factors governing reservoir development and establish an analytical model regarding the cause of the sandstone reservoir. The results are as follows: (1) The sandstone reservoirs of the Huagang Formation of the Yuquan (abbreviated to YQ) Structure are now in the mesomorphic A stage as a whole, and minerals such as 4-phase authigenic quartz, 2-phase illite, 2-phase chlorite, 1-phase kaolinite, 1-phase ammonite mixing layer and 2-phase carbonate were formed during the diagenesis. (2) Feldspar and carbonate solution pores make up the majority of the reservoir space. About 10% of the porosity is made up of carbonate solution pores, which are the most prevalent reservoir space. Carbonate solution pores are primarily made up of metasomatic carbonate solution pores and cemented carbonate solution pores. Feldspar solution pores come next, contributing roughly 6.2% of the porosity. At 1.8%, residual intergranular holes are the least common. (3) The four main processes listed below are responsible for the creation of pores in the sandstone of the Huagang creation. The early carbonate cements resist the destruction of mechanical compaction and effectively preserve intergranular volume. The high content of feldspar provided a material basis for later dissolution. Early chlorite surrounding the edges of particles reduced the damage of authigenic minerals to porosity. The faults and cracks formed by the later structural inversion connected to the acidic water in the atmosphere, causing the dissolution of carbonate minerals and feldspar in the sandstone of the Huagang Formation. (4) Carbonate dissolution + feldspar dissolution type, carbonate dissolution type, and feldspar dissolution type are the three main types of reservoir formation in the Huagang Formation; the first two types mainly develop in the Upper Huagang Formation, while the latter mainly develops in the lower part of the Huagang Formation. The research results are conducive to the establishment of a geological prediction model for high-quality reservoirs of different geneses in the Huagang Formation and promote the exploration process of deep-seated hydrocarbons in the West Lake Sag.

Study on InSAR deformation information extraction and stress state assessment in a railway tunnel in a plateau area

Introduction: The study proposes a method for evaluating stress distribution in high-altitude Tibetan Plateau railway tunnels using high-precision radar satellite time-series interferometric synthetic aperture radar technology.Methods: To effectively monitor and prevent geological hazards during the construction process, this method i employed, as it serves as a component of advanced geological prediction and surrounding rock deformation monitoring technology for high-altitude tunnels, particularly in the Dongelu Tunnel of the China–Tibet Railway. The study utilizes time-series interferometric synthetic aperture radar to obtain deformation information for Dongelu Tunnel area between 2022 and 2023 from Sentinel- 1A orbit images. This quantitatively investigates the upper mountain body and line-of-sight direction along the tunnel. The deformation characteristics are correlated with high-frequency and high-precision automated vertical displacement monitoring results, determining the spatiotemporal distribution of tunnel deformation. In this study, a model that determines the vertical stress state of the Dongelu Tunnel under loading near the entrance and evaluates its health status was established.Results: The results show that the surface deformation of the mountain above the tunnel axis develops slowly and is relatively small, with a maximum vertical deformation rate of 1–3 mm/year. The average stress on the tunnel arch is 5.54 MPa, with a fluctuation range of 0.01 MPa. Temporal Q9 changes in various parts of the tunnel are periodic, with maximum fluctuations observed in December 2022. The study reveals inconsistent surface settlement of the tunnel arch and mountain above it, causing minor vertical stress changes. As the tunnel construction progresses, vertical stress variation shows periodicity because of an initial imbalance in internal stress within the mountain. Stress fluctuations near the tunnel entrance occur during the initial excavation phase, gradually diminishing as the project progresses and internal stress stabilizes.Discussion: The proposed tunnel monitoring and stability assessment method can reduce its impact on engineering construction and provide guidance for advanced geological prediction.

Research and application of comprehensive detection methods for geological structural planes in drill and blast tunnels

At present, the detection of geological structural plane that give rise to rockburst is a complex problem. A large number of rockburst cases show that the geological structural plane influence the boundary of rockburst, especially the slip-type rockburst of structural plane. However, there is no set of comprehensive geological prediction methods at home and abroad for the detection of geological structural plane that give rise to rockburst, or low accuracy and high workload. Therefore, a method that can effectively detect geological structural plane is needed. Based on the analysis of regional engineering geological characteristics and geostress, this paper puts forward that based on the characteristics of rockburst cases in high geostress area and the rockburst prone to occur in the position with large geostress difference, the method of “combination of long and short geophysical prospecting method first, drilling and engineering geological analysis combination” is adopted to detect geological structural plane finely. The results show that: (1) Under the background of regional engineering geological survey and geostress inversion, the comprehensive geological prediction is carried out to detect the geological structural plane. The TSP has the detection accuracy of 10 m to meters, the ground penetrating radar has the detection accuracy of meters to decimeters, and the borehole imaging has the observation accuracy of centimeters. The combination of the three can multi-scale clearly reflect the distribution of geological structural plane compared with the previous single geological prediction. (2) Due to the complex distribution of surrounding rock mass interface and geological structural plane, the collapse phenomenon will also appear in the rockburst area on site, and the collapse causes a number of structural plane, which means that these structural plane becomes an important factor in slip-type rockburst of structural plane. In short, accurate detection of geological structural plane has certain reference significance for predicting the boundary of slip-type rockburst of structural plane and collapse.

Seismic resolution improving by a sequential convolutional neural network.

Thin-bed soft rock is one of the main factors causing large deformations of tunnels. In addition to relying on some innovative construction techniques, detecting thin beds early during surface geological exploration and advanced geological prediction can provide a basis for planning and implementing effective coping measures. The commonly used seismic methods cannot meet the requirement for thin beds detection accuracy. A high-resolution (HR) seismic signal processing method is proposed by introducing a sequential convolutional neural network (SCNN). The deep learning dataset including low-resolution (LR) and HR seismic is firstly prepared through forward modeling. Then, a one-dimension (1D) SCNN architecture is proposed to establish the mapping relationship between LR and HR sequences. Training on the prepared dataset, the HR seismic processing model with high accuracy is achieved and applied to some practical seismic data. The applications on both poststack and prestack seismic data demonstrate that the trained HR processing model can effectively improve the seismic resolution and restore the high-frequency seismic energy so that to recognize the thin-bed rocks.

Thermodynamic descriptions of the CaO–Al2O3 and CaO–Al2O3–SiO2 systems over the whole composition and temperature ranges

AbstractThe CaO–Al2O3–SiO2 system is a fundamental system in aluminosilicate materials, and its thermodynamic description provides key information for ceramic design, cement production, slag tapping, and geological prediction. However, the existing thermodynamic calculations for the CaO–Al2O3 system show inaccurate heats of formation for the compounds and consequently a decomposition of the CA2 (CaO⋅2Al2O3) phase at a low temperature. Besides, the solid solutions in anorthite and cristobalite have never been considered in the previous thermodynamic assessments for the CaO–Al2O3–SiO2 system. Therefore, thermodynamic reassessments of the CaO–Al2O3 and CaO–Al2O3–SiO2 systems are carried out in the present work using the CALculation of PHAse Diagram method, considering the solid solution in anorthite and cristobalite for the first time. The anorthite and cristobalite phases are modeled based on the individual mechanism of solid solution, with the anorthite described by (Ca+2, Va)1(Al+3, Si+4)2(Si+4)2(O−2)8 and the cristobalite described by (Va, Ca+2)1(Si+4, Al+3)2(O−2)4. The present assessment solves the above issues and provides consistent thermodynamic descriptions for the liquid and solid phases. Based on the established thermodynamic database, a Scheil solidification simulation is carried out for a cement clinker and the variations of mass fractions of the phases as well as the composition of liquid during Scheil cooling are calculated. The present work guides the design of relevant materials and lays an essential foundation for the establishment of the thermodynamic database of multicomponent aluminosilicate materials.

Ground Prediction by Markov-ANN Hybrid Analytics Using Baseline Geotechnical Data and Observed Field Data

ABSTRACT Geology along the tunnel length usually differs from the anticipated data as per Geotechnical Baseline Report (GBR), sometimes quite significantly. This has always led to the disruption of the planning, resource mobilisation, cost and duration of the project. One way to minimise this uncertainty is by exhaustive investigation like probe holes and geophysical prospecting. Forecasting or prediction of Geology can also be done by soft computing and analytical methods. Using the analytical tool of the Markov Model and the learning powers of Artificial Neural Networks (ANN) has been attempted in this study. The idea is to build a hybrid model that would combine both capabilities and do a probabilistic prediction of the ground condition ahead of the tunnel face in real time to better suit the site, accommodate complexities and can capture the associated uncertainties. The Geotechnical Baseline Report (GBR) having the initial survey details of the tunnel geology is used to build the Markov Model while the deterministic borehole data in the GBR is used to build the ANN model. Bayesian joint probability theorem is used to update the model with the face observed geological parameters. This hybrid model demonstrated to be a good complement to the physical forecasting methods and the geological uncertainty of a complex and challenging Himalayan region was well captured by the present approach. The window of the prediction was for a region of approximately 250m where it showed a very good prediction of ground class. The model can help in systematic planning and resource mobilisation in a better way and subsequent key decisions for the upcoming excavation method and corresponding support measures.

A method to construct a main hole structural plane model based on advanced parallel adit geological body

The advanced parallel adit of tunnel refers to the adit parallel to the direction of the main hole. Generally it is spaced tens of meters from the axis of the positive hole. During the tunnel construction, the parallel adit is driven before the main hole. Main function of it is to provide geological reference for the subsequent excavation of the main hole. At the same time, it can also help improve the long-term construction and operation and maintenance efficiency of the main tunnel. In terms of providing geological prediction information for the main tunnel, the surrounding rock information disclosed in the advanced parallel adit excavation is mainly used for surrounding rock classification and geological sketch, so as to qualitative analysis the possible geological conditions when the positive tunnel is excavated near the distance where the advanced parallel adit is located. However, the above methods cannot accurately and quantitatively analysis the geological conditions that the main tunnel will face. The lack of research on the correlation between the main tunnel and the advanced parallel adit geological conditions results in the waste of the key factor of the horizontal geological information.In this paper, the improved DBSCAN clustering algorithm is used to group the dominant structural planes according to the attitude of rock and other information. Some non-primary structural planes are screened out. The average distance between the same group of structural planes is calculated according to the average attitude of the dominant structural plane. The structural planes that are close enough are fused. Then, the axial and radial projection was carried out to form a larger range of surrounding rock fracture models. When the main tunnel is excavated to the relevant area, the situation of the surrounding rock in front can be predicted according to the model, so that the countermeasures can be made in advance. The method in this paper is applied to a tunnel project with bidirectional single line and advanced parallel adit. The prediction results after tunnel excavated show that the method in this paper is reasonable and effective.

Research on Intelligent Identification and Analysis Algorithm of Tunnel Engineering Geological Information

When tunneling, detecting abrupt changes in geological circumstances can be difficult. In recent years, the proliferation of tunneling characteristics has been strongly related to the surrounding geology. These parameters offer significant potential for using data-driven artificial intelligence (AI) approaches to infer patterns from information without reference to known consequences. This research introduces the Simulated Fire Hawk Optimizer-based Deep Action Selection Network (SFHO-DASN) model, which uses a Simulated Fire Hawk Optimizer to anticipate the geological conditions needed for tunneling. The optimized hybrid neural network technique can anticipate geological conditions effectively, as demonstrated by a case study of the constructed model. It is especially significant for rock types, water intrusion, karst caverns, and surface subsidence, for which the predicted accuracy is greater than 95%. These findings imply that the geological circumstances behind the tunnel face could be reliably and correctly predicted by a DASN that has received the proper training. This procedure's most significant advantage is its ability to adjust every scored parameter's weighting in response to variations in geological circumstances. The accuracy performance of the proposed neural network outperforms the conventional neural network, as indicated by the area under the curve (AUC) and performance analysis. A proposed model for geology prediction can attain predictive accuracy with a small number of tunneling parameters, according to an analysis of the feature relevance of each tunneling parameter. The suggested approach ought to be more practical for proposals about tunnel support architecture in the East Asian geological region and for future tunnel building.

Investigation on the Mechanical Characteristics of the Excavation of a Double-Line Highway Tunnel Underpass Existing Railway Tunnel under the Influence of Dynamic and Static Load

Research on the excavation mechanical properties of underpass tunnels has already had certain results, but only a few of them consider the effects of dynamic and static loads on the excavation mechanical properties of underground tunnels at the same time; particularly, there is a lack of research investigating double-line highway tunnels with angled underpasses of existing railway tunnels. In this paper, based on the tunnel project of the new double-line Shiqian Highway Tunnel passing under the Hurong Railway with an oblique angle, based on the method of over-advance geological prediction and investigations into the palm face surrounding the rock, the rock degradation caused by dynamic and static loads is quantified using the perturbation system. Additionally, the mechanical parameters of the rock under the influence of dynamic and static load coupling in the influence area of the cross-tunneling project are determined using the Hoek–Brown criterion, and the mechanical characteristics of the excavation of a tunnel under the double-lane highway tunnel passing under the existing railroad are constructed with the mechanical characteristics of the double-lane highway tunnel, taking into consideration the influence of the dynamic and static load coupling in a three-dimensional model. The results show that, in line with the new tunnel rock movement law for the top of the arch sinking, the bottom plate bulging, the side wall outward movement, the height and width of the arch, and the bottom plate arch show an increase with the tunnel excavation, while the side wall rock displacement effect is smaller; the left and right line tunnel disturbed area of the rule of change is similar; the existing tunnel bottom plate displacement is larger than the top plate and the left and right side wall, under the influence of the excavation time step. Typical profile point displacement is mainly determined by the distance from the excavation surface; von Mises stress extremes are observed in the top plate and side walls of the existing tunnel, which occur in the tunnel structure, and there are unloading and pressure-bearing zones in the bottom plate; the new tunnel has the same rock disturbance angle under the four calculation conditions and, based on the displacement control criterion, the excavation method is preferred and the upper and lower step blasting excavation method is recommended.

Fractal Analysis of Polarizability in Graphite Deposits: Methodological Integration for Geological Prediction and Exploration Efficiency

Most geophysical and geochemical data are commonly acknowledged to exhibit fractal and multifractal properties, but the fractal characteristics of polarizability have received limited attention from the literature. The present study demonstrates that the polarizability data of the graphite deposits have fractal characteristics and introduces the fractal method for its quantitative analysis to indicate and predict the properties of graphite deposits. The results show that the concentration-area (C-A) method is superior to classical interpolation in anomaly extraction but inferior to the spectrum-area (S-A) method in the coverage region. Because the type of graphite ore is sedimentary-metamorphic in this area, the graphite ore-bodies can be regarded as a special stratum, which is different from most metal deposits, and the anomaly of graphite ore are shown in the background mode of the S-A method. The high values of the background mode effectively indicate the potential areas where the graphite-bearing strata occur, while observing a decrease in the power-law exponent (β) of the background mode as the width of ore-bodies increases. The validity of this conclusion was confirmed based on the vertical profiles of the predicted area, and the uncharted ore vein was thereby identified. Furthermore, it was found that the anomaly mode can serve as a grade indicator of graphite ore rather than delineating the fault. By integrating the background and anomaly modes of the S-A method, we can quantitatively predict and effectively identify high-grade targets from sedimentary deposits containing minerals in future exploration.

Exploration of Advanced Geological Prediction for the Double-arch Highway Tunnel in Karst Area

Combined with the construction method of a highway double-arch tunnel in the karst area, a comprehensive advanced geological prediction method combining geological radar and advanced horizontal drilling is proposed, and the comprehensive advanced geological pre- diction method is successfully applied to the construction of a highway double-arch tunnel in a karst area of Guangxi. The result of site con- struction proves that the method has a satisfactory forecasting effect.

A review of data analytics techniques in enhancing environmental risk assessments in the U.S. Geology Sector

In an era where environmental risks pose significant challenges to the U.S. geology sector, this paper meticulously explores the integration of data analytics techniques to enhance risk assessments. The study delves into the intricate relationship between geological processes and human activities, underscoring the necessity for advanced analytical methodologies in mitigating environmental risks. The background sets the stage, highlighting the evolving perception of risk and sustainability in geological activities, and the critical role of reliable construction practices and engineering investigations. The aim of this paper is to synthesize and critically evaluate the current methodologies in data analytics, particularly their impact on reducing environmental risks associated with geological activities. The scope encompasses a detailed examination of the evolution from traditional to modern analytical methods, emphasizing the integration of predictive analytics, machine learning, big data, and Geographic Information Systems (GIS) in geological predictions and risk management. The main findings reveal a significant advancement in data analytics, marked by the integration of AI and machine learning with traditional geological methods. This fusion enhances the accuracy, efficiency, and comprehensiveness of risk assessments. The study concludes with recommendations for continued integration of advanced data analytics in geological studies, advocating for sustainable and responsible practices. It emphasizes the importance of international collaboration and harmonization of regulatory standards to enhance environmental risk assessments in geology. This paper provides valuable insights for researchers, policymakers, and practitioners in the field, offering a roadmap for future advancements in geological data analytics and environmental risk management.

Comprehensive Application of Borehole Fine Detection Methods: A Case Study in Shantou Bay Subsea Tunnel

Water inrush disaster is one of the most severe problems during the construction of sea tunnels, primarily due to faults, karst, and weathered zones. Once a water inrush disaster occurs, it can lead to construction delays, traffic disruptions, and major economic losses, as well as potential consequences such as seawater intrusion, casualties, project suspension, and tunnel closure. Thus, advanced geological prediction is indispensable. During the construction of the Shantou Bay subsea tunnel, a sudden water inrush accident occurred in the sea–land transition section. To prevent such incidents and ensure safety, an integrated approach was employed. Firstly, the cross‐hole resistivity method was used to predict water content in front of the tunnel, as it is highly sensitive to water. Subsequently, borehole ground‐penetrating radar was applied to finely characterize the geological structure. By combining these two methods, the size, scale, location, water content, and spatial distribution of water‐bearing structures in front of the tunnel were identified. With the above measures, the Shantou Bay subsea tunnel passed through the detection section successfully. Herein, we present a case study and offer a valuable reference for similar projects concerning subsea tunnel construction.