This study focus on debris flows in Huangniba gully, Muli County, Liangshan Prefecture, Sichuan Province, utilizing a mass flow numerical simulation platform. Through field investigations and the construction of numerical models, we analyze the mechanisms driving debris flow formation and evolution, aiming to invert these mechanisms.Based on this foundation, we assess debris flow hazards, develop a vulnerability model for masonry structures under different damage modes, and establish a dynamic process-based debris flow risk assessment method.

The risk assessment indicates that, for a 20-year return period, very high- and high-risk zones for debris flow encompass 0.15×10⁴ m² and 1.68×10⁴ m², affecting 10 and 13 buildings, respectively. For a 50-year return period, the areas of very high- and high-risk zones expand by 40% and 70.8%, with 2 and 4 additional buildings affected. Moreover, for a 100-year return period, these zones increase by 113.3% and 132.1%, respectively, affecting 11 and 5 more buildings compared to the 20-year scenario.

Furthermore, the erosion-incorporating debris flow dynamics model developed in this study accurately represents the debris flow events in Huangniba gully. Additionally, the vulnerability assessment model for masonry structures was validated against other debris flow events, confirming its enhanced feasibility. These findings provide a foundation for quantitative risk prediction in Huangniba gully.

In high geostress or complex geological conditions, tunnel convergence frequently exceeds the threshold, resulting in damage to support structures and, in extreme cases, tunnel collapse. Accurately predicting the deformation trend and convergence of surrounding rock during tunnel construction is crucial to ensuring the safety of workers and improving construction efficiency. Traditional single prediction models struggle to adapt to the dynamic nature of tunnel convergence, limiting their predictive accuracy.

To address this, this study introduces a dynamic prediction model for tunnel convergence based on continuous Bayesian updating and an optimal model selection strategy. Utilizing real-time monitoring data of tunnel convergence deformation, the parameters in three empirical models are continuously updated and refined. The optimal model is then selected to predict the final convergence deformation of the surrounding rock and quantify its associated uncertainty.

The model was tested on 16 measurement points across 9 sections of the Baima Tunnel, achieving a mean relative error of only 3.22% between the predicted and monitored final convergence rates.

Additionally, with just 10 days of observed data, the model can forecast the final convergence deformation for up to 40 days post-excavation, offering valuable technical support for preventing squeezing disasters in the full-section tunnel excavation.

Landslide disasters are widely distributed in China. Effective monitoring, early warning, and risk management measures are key to disaster prevention and mitigation.

Compared with conventional techniques, distributed fiber optic sensing (DFOS) technology has made significant progress in landslide monitoring in recent decades, owing to its strengths in distributed, long-distance, large-range, and multiparameter monitoring. This paper first introduces several representative fiber optic sensing technologies, then proposes the concept of a fiber optic neural sensing system for landslides, and last elaborates the working principles of various fiber optic sensors and their deployment methods. Two typical landslide monitoring cases using ultra-weak fiber Bragg grating (UWFBG) monitoring technology are introduced, and the current technical bottlenecks are discussed.

The case studies show that the fiber optic neural sensing system can achieve remote, real-time, high-precision underground multiparameter data acquisition, accurately detect potential slip surfaces and other key interfaces. Additionally, multiphysical changes at these interfaces provide important data support for understanding the underground evolution of landslides, which offers new insights into landslide prediction and early warning.

To address current engineering challenges such as poor stability and accuracy, limited coverage, and high costs associated with soil landslide displacement monitoring, a novel method based on microelectromechanical system (MEMS) sensor technology is proposed.

By considering the kinematic characteristics of slope deformation, a time-domain acceleration integration algorithm is designed to correct random and systematic errors in the acceleration data collected by MEMS sensor. To verify the effectiveness and practicality of this displacement monitoring method, two indoor landslide model tests and corresponding finite element simulation calculations were designed and conducted. MEMS technology was used to monitor the internal displacement of the soil for the test slopes, and the results were compared with the the finite element simulation outputs to evaluate the accuracy and reliability of the algorithm.

The findings indicate that the MEMS-based soil landslide displacement monitoring achieved a minimum average relative error of 0.09% in the horizontal direction and 0.50% in the vertical direction, demonstrating high accuracy and suitability for practical engineering applications.

The research results provide a novel approach to soil landslide monitoring and provides a theoretical foundation for the integrating MEMS sensors in landslide and slope safety engineering.

To accurately and efficiently extract the water boundaries of mountainous barrier lakes, this paper focuses on the Attabad barrier lake along the Karakoram Highway, proposing a water extraction method based on decision tree classification. This method incorporates slope information into six conventional water extraction methods for decision tree classification. The effectiveness of these six methods was compared for extracting water from barrier lakes in the experimental area. The best-performing methods suitable for barrier lakes in high-altitude areas were applied to extract the water body range of the Attabad barrier lake.Accuracy was assessed using a confusion matrix, and classification post-processing was performed to refine the water boundary extraction.

The research results indicate that (1) among the six models, the CWI model demonstrates the best performance, effectively distinguishing between slope, water, and shadow water, leading to a highly accurate outline of the barrier lake. However, a limitation of this model is the presence of a few mountain shadows in the middle of the slope. (2) The decision tree classification method based on slope achieved an overall accuracy of 89.31% and a kappa coefficient of 0.84. It effectively extracts the actual water range, excluding slope shoreline and mountain shadows, and provides a clearer lake boundary. Nevertheless, black fragments observed in the lower area of the barrier lake, likely due to landslides and mountain shadows, remained challenging to classify. Overall, the decision tree classification-based method proved effective in identifying water bodies, particularly in areas with rugged terrain and numerous shadows.

This paper proposes a method for extracting water bodies from barrier lakes in high mountain areas using decision tree classification. By incorporating slope information into conventional water body extraction methods, this approach accurately extracts the water boundary, effectively eliminates shadows from steep slopes, retain shadowed water on gentler slopes, and improves extraction efficiently. The simplicity and high extraction efficiency of this method make it a practical solution for widespread application.

Mountain restoration is currently one of the major projects in environmental engineering. The backfill formed in artificial slopes is relatively loose and highly susceptible to the impact of rainfall intensity, leading to slope instability.

In this study, a combination of numerical simulation method and onsite monitoring technology was used to analyze the stability of artificial slopes formed during the restoration of Dingguanfeng Mountain. By establishing precise geological models, defining material parameters, and setting boundary conditions, the stability coefficients of the slope under the four different rainfall working conditions set were obtained, and the distribution characteristics of the seepage field and deformation field of the slope under different conditions were simulated. A real-time monitoring cloud platform was established on the site to monitor the surface horizontal displacement and deep horizontal displacement of the fill slope on site. The monitoring results were compared with those obtained from numerical simulation to quantitatively assess the slope stability under different working conditions.

The results indicate that the stability coefficients of the fill slope under different rainfall conditions are greater than the critical factor for interface sliding. Under various working conditions, the pore water pressure at the slope toe and the portion close to the slope surface increases considerably. Seepage channels are mostly developed at the front edge of the slope body and the steep areas of the slope surface, and the stability of these areas is relatively more affected by rainfall. With the increase in rainfall intensity, the maximum horizontal displacement in the middle and lower parts of the slope gradually enlarges. The greater the amount of rainfall infiltrating into the slope within the same time, the more significant the reduction in shear strength, the larger the area with large horizontal displacement, and it gradually extends towards the front and rear edges of the slope. By comparing the numerical simulation results with the data obtained from on-site monitoring, it is discovered that there is a good consistency between them, and the slope is basically in a stable state.

Henceforth, for the data generated from numerical simulation analyses, they should be combined with on-site monitoring data to conduct a more comprehensive assessment of the engineering stability.

The RM hydropower station is proposed to be constructed in the upper reaches of the Lancang River. Its impoundment and operation might result in deformation and instability of the RS giant accumulation on the left bank near the dam, thereby triggering landslide-induced wave disasters and endangering the safety of the key hydraulic structures and the downstream residents.

This study combines extensive geological surveys and physical mechanics experiments to investigate the potential instability and failure modes of RS accumulation under reservoir filling. On this basis, a three-dimensional numerical model of the entire river channel from the RS accumlation to the dam section was established. An analysis of the dynamic evolution of landslide-wave chain disasters caused by RS accumulation was conducted, and parameters such as the initial wave height, wave height along the opposite bank, propagation characteristics, wave height at the dam front, and wave height climbing along the dam were predicted.

The results indicate that as the reservoir water level is gradually elevated to an altitude of 2 800 meters, the RS accumulation is most likely to undergo a large-scale instability failure. The rear tensile fracture boundary is the crushed stone and soil layer of the accumulation within a certain range above the reservoir water level, and the front shear boundary is the fine-grained layer in the middle and lower parts of the accumulation. After the instability failure of the accumulation, it induces a landslide-induced wave. The height of the first wave peaks near the water entry point, approximately 31.5 meters, and lasts for about 15 seconds. As the wave propagates downstream, the wave height decreases by 39.5% at the No. 1 river bay, reaching the dam in approximately 147 s and continuing to climb along the dam slope. The climbing wave height is approximately 2.6 m and lasts for 180 s, with no risk of overtopping by the initial or subsequent smaller waves. After being impeded by the dam, the initial wave propagates upstream, creating a backflow phenomenon, which, combined with subsequent waves, forms a locally high wave area. At monitoring point P5, the maximum backflow wave height reaches approximately 4.56 m and lasts for 219 s.

During the wave propagation process, the topography of the river bay and backflow phenomena significantly accelerate the energy dissipation of the waves, effectively reducing the risk of wave impact and secondary disasters.

With the rapid development of China's transportation industry, the geological conditions encountered in highway construction are becoming increasingly complex. Tunnels, owing to their ability to traverse mountainous terrain, are widely used in highway projects through challenging geological environments. However, as the number of tunnel projects has increased, the frequency of rock collapses and landslides during highway tunnel construction has also risen, resulting in significant economic losses and casualties. Therefore, accurate risk assessments have become crucial in tunnel engineering.

To address this, 40 cases of instability engineering were summarized and analyzed, refining 14 secondary indicators and establishing a comprehensive risk assessment index system. Risk was then assessed in terms of disaster probability and its consequences. The interpretive structural modeling (ISM) method was employed to construct a hierarchical topology diagram, and a Bayesian network model was established and refined using a causality graph method. The model was trained on 80% of the case data and validated with the remaining 20%. Based on this, the highway tunnel instability risk assessment Bayesian network evaluation system (RIAS) was independently developed, offering both engineering applicability and user-friendly functionality, and enabling accurate and rapid assessments of surrounding rock instability during highway tunnel construction.

The system was applied to sections such as ZK5+937~ZK5+917 of the Beigushan Tunnel, predicting an 18.2% probability of tunnel instability with a "None(no risk)" magnitude of instability and a risk level of "Low Ⅰ" -consistent with the actual excavation results.

This study introduces and innovative approach by constructing a Bayesian network model tailored for highway tunnel risk assessments, overcoming the limitations of single-risk-level models and the challenge of insufficient engineering datasets. The model is successfully tested in the Beigushan Tunnel and holds significant potential for application in other highway tunnel projects, enhancingsafety and risk prediction capabilities.

The soil solidification technique is widely used in soft foundation treatment. To exploit spatial plasticity of this technique, composite slab wall soil solidification foundations have gradually been applied in engineering projects. However, a reliable method for calculating the bearing capacity of composite slab wall soil solidification foundations is lack, and the mechanical parameters of both solidified and soft soil remain uncertain. These factors complicate the optimization of the composite slab wall soil solidification foundation designs. Therefore, it is crucial to propose a method for calculating the bearing capacity and optimizing the design of such foundations.

This study focuses on the 110 kV Jingwei coastal substation in Taizhou, Zhejiang Province. A numerical model is established based on the mechanical parameters of solidified and soft soil to calculate the bearing capacity of composite slab wall soil solidification foundations. The results of these calculations are used to train a neural network, enabling predictions of the bearing capacity for various design parameters, thus facilitating engineering applications. Uncertainties of the mechanical parameters are addressed through Monte Carlo simulations, and their impact on design is estimated using the robustness evaluation index standard deviation. The design cost is approximately estimated by the cross-sectional area of the foundation. Robust design theory is introduced to optimize the design while balancing cost-effectiveness and robustness.

This method is implemented in an engineering project, resulting in an optimal design with solidified plate thickness P=2 m, solidified wall depth W=3 m, solidified wall thickness D=1.5 m, solidified wall net spacing S=1 m, and upper foundation width B=4 m, providing a reference for engineering designs.

The proposed methods for calculating bearing capacity and optimizing the design of composite slab wall soil solidification foundations offer new concepts and approaches for similar projects.

To investigate the differences in anti-pull bearing characteristics between single anchor and group anchors foundations of transmission lines,

this study employs a combination of theoretical analysis and field experiments. First, based on the structural characteristics, the bearing mechanisms of singleand group anchors were analyzed in terms of force and deformation. The full-length bonded anchors, commonly used in transmission line projects, were selected as the research object, with the granite ground in Quanzhou selected as the test site. On-site full-scale tests were conducted on three single anchors and four group anchors. Displacement sensors were used to monitor foundation and ground deformation, while optical frequency domain reflectometry (OFDR) recorded the strain in the anchor rods. The load-displacement curve of the test foundation and the internal force distribution along the anchor interface were obtained. Finally, a comparative analysis of the deformation and failure mechanisms for both anchor types was performed.

The results show that the load-displacement curve of a single anchor differs from that of group anchors, with plastic deformation being more pronounced in group anchors. In the initial stages of testing, anchor system displacement is primarily governed by the tension in the anchor bars, whereas at the end of the test, displacement is more influenced by slippage at the anchor-rock interface. The axial tension stress of the anchor rod decreases gradually with depth, reaching near-zero at depths of 2 to 3 m. The failure mode of a single anchor under tensile load is related to the saturated uniaxial compressive strength of rock, while the failure mode for group anchors is influenced by the number of single anchor. It is recommended that group anchors foundation tests be used to determine the bond strength at the anchor interface for design purposes in engineering applications.

The research findings can provide references for the selection and design of rock anchor foundations for transmission lines.

As socioeconomic development advances, challenges associated with coal mining beneath structures such as buildings, water bodies, and railways have intensified markedly. It is increasingly imperative to extract underground minerals within acceptable boundaries while diligently monitoring the environmental impacts of such activities. Previous research showed that mining-induced subsidence was a primary contributor to environmental geological disasters in mining areas, particularly when the integrity of the overlying strata is breached. Therefore, it is crucial to develop methodologies for the early detection of surface subsidence, which requires in-depth research into monitoring the fracture signals from overburden rock. Existing methods, including acoustic emission and microseismic monitoring systems, face significant challenges in achieving widespread, comprehensive, and distributed monitoring. In response to these limitations, distributed acoustic sensing (DAS), a state-of-the-art optoelectronic sensing technique, has recently gained prominence and been extensively employed across geophysical exploration fields such as oil and gas exploration and seismic monitoring. We explore applying DAS technology to enhance the monitoring and identification of fracture signals in the overburden of mined-out areas, aiming to improve both safety and sustainability in mining operations.

This research selects a coal mine in Ningdong town, Lingwu city, Ningxia Hui Autonomous Region, China. DAS technology is used to continuously monitor the fracture signals of the overburden in underground voids. A fibre optic cable was installed at the bottom of a trench stretching parallel to the coal mining face with dimensions of approximately 1 km in length, 15 cm in width, and 30 cm in depth. Additionally, several triaxial node seismometers were deployed along the route for comparison validation. Given the low signal-to-noise ratio of DAS data, comparative experiments were conducted using five denoising techniques: high-pass filtering, empirical mode decomposition, Fourier transform, F-X deconvolution, and synchronous compression wavelet transform. The DAS signals were preprocessed through detrending, mean removal, and denoising, followed by time-frequency analysis to extract overburden fracture signals. The event signals collected by the DAS system which formed a dataset were converted into recurrence plots. This dataset was used to train an intelligent recognition model for overburden fracture signals based on the convolutional neural network VGG-16.

The results demonstrate that synchronous compression wavelet transform effectively eliminates noise from DAS data. The overburden fracture signals detected by DAS were consistent with those by seismometers. Recurrence plots of DAS-collected fracture signals differed from nonfracture signals, which can be distinguished by the trained VGG-16 model with an accuracy of 85%.

Monitoring overburden fractures via DAS technology is feasible. The proposed deep learning approach, based on recurrence plots and the VGG-16 convolutional neural network, can effectively recognize fracture signals. This research provides significant technical support for developing an intelligent early warning system for mining subsidence based on DAS.

Classifying the swelling-shrinkage degree of expansive soil is a critical issue in their management of expansive soil. To prevent and mitigate engineering disasters, accurate evaluation of the swelling-shrinkage for expansive soil is essential. This study applies the efficacy coefficient method to classify the swelling-shrinkage grade of expansive soil.

Five factors(the liquid limit, total swell-shrink ratio, plastic index, water content, and free swelling ratio) are selected as evaluation indices to comprehensively assess the swelling-shrinkage characteristics. The weight coefficients of these indices are determined using both objective and subjective weighting method, namely the Delphi method and information entropy theory. The final classification is obtained by calculating the total efficacy coefficient of the sample. A new model for evaluating the swelling-shrinkage grade of expansive soil is established using the efficacy coefficient method in combination with the subjective and objective weighting approach. The model is tested on 19 sets of field test data from relevant literature, with 15 groups of data compared to other methods and 4 groups of data compared to actual situation.

The accuracy of the model, when compared with extension theory, the variable weight and the unascertained measurement method, reached 93.3% for the 15 groups of data. For the 4 groups of data compared with the real-world conditions, the accuracy reached 100%.

The proposed model is both reasonable and effective in classifying the swelling-shrinkage grade of expansive soil, offering valuable guidance for the safe construction of expansive soil projects.

Infrasound is an effective approach for debris flow warning. Traditional threshold-based warning methods focus solely on individual infrasound characteristics, which can lead to false alarms or missed detections. Thus, incorporating multiple time-frequency characteristics is essential to improve warning accuracy.

Infrasound data from Caodianshui Village, Beijing, were analyzed to differentiate the infrasound characteristics of debris flows from environmental background. A random forest algorithm was employed to establish an infrasound warning model for debris flows.

The effective pressure of debris flow infrasound ranges from 0.4 to 1.0 Pa, while environmental infrasound typical remains below 0.1 Pa, though noise can raise it above 0.4 Pa.Noise infrasound energy is primarily concentrated below 6 Hz, whereas debris flow infrasound exhibits significantly higher energy in the 6-15 Hz. Therefore, comprehensive time-frequency characteristics, especially the energy in the range of 6-15 Hz, should be considered when identifying debris flow infrasound. Using effective infrasound pressure, infrasound pressure within 6-15 Hz, zero crossing rate, dominant frequency, and its amplitude as characteristic variables, a debris flow warning model was constructed based on a random forest algorithm. The model achieved an AUC of 0.99, with a 90% recognition accuracy for test data, a 15% improvement over threshold methods.

The random forest-based infrasound warning model substantially improves warning accuracy for debris flows and is applicable to typical basins in the Beijing mountainous areas. This approach offers a valuable reference for infrasound-based debris flow warning research in other areas.

Precise monitoring of settlement and convergence deformation in urban subway tunnels is crucial for ensuring operational safety and the stability of the surrounding environment. Traditional methods, such as manual inspections and fixed sensor monitoring, suffer from poor real-time performance and limited data availability.

To address the limitations of traditional approaches, an innovative wireless sensor network (WSN) monitoring system is introduced in this study by taking the East Extension Section of Shanghai Metro Line 2, a large-scale urban underground tunnel constructed using shield technology in soft soil as the example. Additionally, a missing value imputation algorithm tailored to WSN characteristics is proposed to address the potential data gaps in WSN monitoring which may arise in the following 8-year monitoring period.

The implementation of this monitoring network and algorithm provides full data and characteristic indicators for the tunnel monitoring, which aids in revealing the influencing factors of lateral convergence deformation in shield tunnels constructed in soft soil.

By ensuring the effectiveness and completeness of the monitoring data, this research offers technical support and data assurance for the safety of shield tunnels in soft soil and the operational safety of subways.

This study aims to scientifically and rationally assess the impact of the spatial variability of soil parameters on the stability of soil-rock mixture slopes.

Based on the random field theory, the effective shear strength parameters cohesion c and internal friction angle φ are selected as random variables. The local averaging method is used to simulate the random field, with random field parameter generation conducted in MATLAB. Python scripts are employed to map the random field parameters to the soil-rock mixture slope via finite element software, accounting for the actual shape and content of block rocks in the soil-rock mixture. The strength reduction method is then applied to calculate the slope stability safety factor.

The results reveal that the stability safety factor of soil-rock mixture slopes follows a normal distribution. As the block stone content increases, the mean value of the stability safety factor rises from 1.005 to 1.095, reflecting a transition from shallow to deep failure. For block stone content of 35%, the stability safety factor reaches 1.334 for larger block stones and 1.064 for smaller ones. Compared to deterministic calculation results, incorporating the spatial variability of the soil parameters yields higher stability safety factor.

Therefore, in soil-rock mixture slope stability analyses, the spatial variability of effective shear strength parameters must be fully considered to prevent overly conservative designs.

Differential deformation of urban land surfaces can threaten or damage surface infrastructure, leading to fractures and distortions. Monitoring spatial differential deformation and assessing associated risk levels are crucial for urban safety management.

This study employs Sentinel-1 satellite data and the time series InSAR techniques to analyze surface deformation over time, enabling the derivation of spatial-temporal deformation gradients. Hazard and vulnerability assessment factors are calculated using an analytic hierarchy process, integrating data such as nighttime light remote sensing, land use, and Chinese building height datasets.A macroscopic risk assessment is conducted, with supplementary microscopic-levelanalysis to assess building risks and identify potential high-risk areas. Comparison experiments verify the effectiveness of the research.

Significant deformation disparities are identified between the eastern Chaoyang District and the northwestern Tongzhou District. In addition, high-risk areas are observed around the Capital International Airport region and the vicinity of Anding South Street. Therefore, the study highlights the importance of multisource data for effectively monitoring differential deformation to ensureurban safe.

To investigate the spatial-temporal variations in landslide susceptibility due to human engineering activities in resettled urban areas.

This study focuses on the new urban area of Yunyang County in the Three Gorges Reservoir region. Landslide susceptibility time-varying index factors were introduced to map spatial-temporal susceptibility differences and explore the spatial-temporal evolution of landslide disasters during urbanization in resettled urban areas. First, the stacking ensemble model was selected as the static susceptibility evaluation model. Then, the InSAR deformation rates and land use types over three distinct time spans (namely, January 16, 2017, to August 27, 2018 (T1), September 20, 2018, to July 30, 2021 (T2), and August 23, 2021, to November 17, 2023 (T3)) were selected as time-varying factors. Last, the time-varying factors were combined with the static evaluation results to create susceptibility difference distribution maps for the different periods.

The study revealed that introducing time-varying factors in the analysis of spatial-temporal susceptibility differences effectively reflects the impact of urbanization on landslide disasters. When the land type in the study area changed from non-engineering land to engineering land, the landslide susceptibility level generally increased, with grid shares of 61.3% and 67.1% in the two change stages, respectively. The temporal trends of the InSAR displacement time series curves for selected typical landslides in urban areas showed high spatial-temporal correlations with land type changes, further validating the reliability of this method.

The proposed research approach provides the basis for disaster prevention, mitigation, and regional planning during the urbanization process in resettled urban areas of the Three Gorges Reservoir region.

In the aftermath of the "5.12" Wenchuan earthquake, the frequency of chain disasters, precipitated by debris flow blockages and subsequent breaching floods, significantly increased in the Bailong River basin. The region's distinctive geological conditions, characterized by numerous towns situated on canyon terraces and debris flow accumulation fans, further exacerbate its susceptibility to such chain disasters. This study aims to investigate the potential risks associated with debris flow blockages and flood chain disasters in the Bailong River basin, with a specific focus on the Zhaizi gully debris flow in Bailong River basin, Zhouqu County, Gansu Province. The objective is to elucidate the disaster-breeding characteristics, disaster-inducing conditions, and evolution patterns of the Zhaizi gully debris flow chain disaster, while also delineating the threat range posed by these chain disasters.

Through remote sensing interpretation and field surveys, a comprehensive database encompassing the topography, geomorphology, and material sources of the Zhaizi gully debris flow was established. This facilitated the identification of the development characteristics and various physical and mechanical parameters of the debris flow. Using the FLO-2D and HEC-RAS models, numerical simulations were performed under different rainfall frequencies (P=1%, 2%), yielding key parameters such as depth, flow velocity, and the threat range of the debris flow and breaching floods. These parameters facilitated the analysis of hazard intensity and potential risks associated with debris flows and breaching floods.

Under a 100-year rainfall frequency, the maximum flow velocity of the Zhaizi gully debris flow can reach 11.96 m/s. The average thickness of the debris dam formed is approximately 10 m, leading to complete blockage of the Bailong River and the formation of a dammed lake with a capacity of 6.26 km³. The evolution of the breaching flood lasts approximately 12 hours, with the peak flow occurring about 30 minutes after breach. The impact range of the breaching flood extends from the downstream area of Fengdie Town in Zhouqu County, Gannan, along the main stream of the Bailong River, to the upstream section of Jigan Township in Wudu District, Longnan City, covering an area of 56.36 km² and spanning a distance of approximately 97.4 km. Based on the simulation results, a preliminary discussion was conducted on a comprehensive risk prevention and control model for basin-wide debris flow disaster chains, integrating monitoring and mitigation measures.

This study highlights the limitations of traditional models in flood disaster assessment and enhances the understanding of cascading hazards induced by debris flow blockages. The findings provide valuable insights for the risk assessment and engineering design of mitigation projects for similar debris flow disaster chains in the middle and lower reaches of the Bailong River basin.

This study aims to investigate the focal mechanism of the Menyuan earthquake.

D-InSAR technology was employed to process ascending and descending SAR data from Sentinel-1A, producing a coseismic deformation field. Using InSAR LOS deformation as a constraint, a two-step inversion method was applied to determine the geometric parameters of the earthquake fault and the detailed coseismic slip distribution. Additionally, the coseismic static Coulomb stress changes were calculated, and the seismogenic structure, along with the regional seismic hazard, was further analyzed.

The findings reveal that the long axis of the InSAR coseismic deformation field is oriented WNW-ESE, indicating left-lateral strike-slip movement. The refined double-fault slip distribution shows that both the Lenlongling and Tuolaishan rupture segments exhibit high-inclination left-lateral strike-slip motion. To better understand the seismic deformation patterns, this study employs anelastic dislocation model and a viscoelastic half-space layered medium model to simulate the three-dimensional coseismic surface deformation, incorporating more accurate three-dimensional deformation from crustal layered models. The coseismic Coulomb stress changes suggest an earthquake risk at the western end of the Tuolaishan fault, the eastern end of the Lenlongling fault, and near the Daliang tunnel of the Lanzhou-Xinjiang high-speed railway, indicating a heightened potential for future rupture.

The research results can provide a reference for enhanced understanding of the three-dimensional crustal deformations associated with the Menyuan earthquake and the related seismic research.

In the southwest region of China, where canyons are deeply incised and water flows are turbulent, disasters frequently occur. The accumulated masses are widely distributed, and understanding the mechanisms and evolutionary processes of riverbank slopes composed of these masses under dynamic water scour, such as dam collapses and reservoir flood discharge, is of significant practical importance for hydropower, road construction, and urban development.

Building upon previous research, this study qualitatively analyzes the progressive deterioration of riverbank slopes under flood conditions. The development mechanisms of erosion grooves on both straight and concave riverbank slopes under dynamic water scour are theoretically derived. Furthermore, the multistage sliding process of the Ganhaizi landslide in Danba County, triggered by rising water levels, was simulated using Geo-studio software.

A function describing the extent of erosion in straight riverbank slopes over time, considering factors such as water flow shear stress, slope shear strength, and initial shear stress was established. Both qualitative and quantitative analyses of the sliding process under dynamic water scour conditions show that erosion begins near the water surface and progresses inward, leading to traction landslides at the rear edge of the erosion groove. This is followed by erosion at the slope foot, resulting in continuously changes in slope morphology and multistage traction landslides. The Ganhaizi landslide experienced multiple traction stages due to a 15-meter rise in water levels and extended erosion time. Even currently stable bank slopes of accumulated masses remain vulnerable to large-scale sliding disasters under extreme hydraulic conditions.

This study offers a novel theoretical framework for analyzing riverbank collapse and provides guidance for preventing downstream disasters in water conservancy projects, such as reservoirs.

This study aims to investigate the causes of typical soil landslides in eastern Jiangxi and assess the influence of rainfall on slope stability in the region.

Using the Ziwu landslide in Guangfeng District, Shangrao City, Jiangxi Province as a case study, this paper conducted a comprehensive analysis of rainfall data and surface displacement monitoring. A two-dimensional (2D) mechanical model was established using GeoStudio finite element software to simulate the deformation of small residual slope soil landslides under different rainfall conditions. This study analyzes the local rainfall characteristics, modeled four typical rainfall patterns across five scenarios, applied multiple linear regression to fit the data, and developed an I-D-Fs evaluation model.

The results show that (1) the peak values of earth pressure and soil moisture content are 16.8 kPa and 16.3%, respectively, with a 3 to 5 days lag between rainfall onset and the increase in these values during the early stages of rainfall; (2) rainfall is the primary trigger for landslides, which progress through three distinct phases: Creeping of the front slope, pulling of the rear slope, and sudden sliding of the whole slope; and (3) rainfall patterns significantly impact slope stability, necessitating seasonal monitoring and early warning systems. During periods of low rainfall, uniform rainfall is the most detrimental, decreasing the stability coefficients by 2% compared to other patterns. During heave rainfall, frontal rainfall is the most hazardous, decreasing the stability coefficient by 8% compared to other patterns.

These results provide a scientific basis for the monitoring and early warning of shallow soil landslides.

In recent years, notable discoveries in deep-water oil and gas exploration have emerged in the Rakhine Basin, located in the northern Bay of Bengal.

In this study, high-resolution 3D seismic data from the Rakhine Basin were used to identify deep-water sedimentary architectural elements, including channels, levees, lobate fans (such as crevasse-splay lobes and distributary channel-lobe complexes), hemipelagic mud, and mass-transport deposits. Alongside drilling logs and sampling results, the reservoir characteristics and exploration potential of channels, levees, and lobes in the wells were further analyzed, and the reservoir hierarchy and spatial distribution were determined.

Notably, the Rakhine Basin is characterized by relatively shallow burial depths, weak compaction, and minimal diagnostic alteration since the Pliocene, positioning channels, levees, and lobate fansas potential exploration targets for shallow biogenic gas. In particular, meandering stacked channels emerge as a high-quality reservoir type due to their thick sediment accumulation, strong connectivity, widespread distribution, and elevated high sand content and porosity.

The research findings can provide important guidance and insights for deep-water oil and gas exploration.

To betterclarify the specific impact of coal seam development in the PB area on reservoir identification and effective identification techniques,

this paper focuses on analyzing seismic and logging data to evaluate the prestack and poststack seismic response characteristics of the reservoirs. We then investigate the seismic response characteristics of the reflection coefficient after the coal seam is removed and perform poststack seismic forward modeling based on the wave equation. This analysis elucidates the influence of coal seams on seismic responses. Finally, rock physics multiparameter intersection analysis is employed to identify the sensitive parameters and establish the thresh values for reservoir lithology identification within the study area.

When the reservoir thickness is less than the tuning thickness, the seismic data can be rotated by 90°, which reduces the impact of the coal seam on the reservoir diminishes.

For thin reservoirs with associated thin coal seams in the study area, we adopt a three-step high-quality reservoir prediction process that involves broadband spectrum inversion, specifically prestack V_p/V_s and prestack AVOG. This approach allows amore effective description of the distribution range of aerated sand bodies in the study area.

Addresses issues related to evaluating and preventing interference between wells,

based on an integrated geological engineering workflow, 3D DDM and EDFM technologies have been comprehensively used to establish an integrated geological engineering simulation model for horizontal well groups. Then, the operating range of single wells and well groups following fracturing stimulation was evaluated, and factors affecting the degree of interference between wells were analysed.

The findings indicate that (1) when the matrix permeability exceeds 0.3×10^-3 μm², the half-length of fractures is greater than 100 m, and the spacing between fractures is less than 40 m, a largerrange of fracturing transformation correlates with an increased degree of interwell interference. (2) As the well spacing increases, the degree of interwell interference diminishes. At a spacing of 400 m, the impact of interwell interference on a single well's EUR can be ignored. (3) It is essential to balance the relationship between the block recovery rate and cumulative production of a single well by optimizing and determining appropriate well spacing.

The above research results can provide valuable insights for optimizing well spacing and enhancing the effectiveness of repeated fracturing technology.

This study aims to establish a scientific framework for managing pollution in the Anjiang underground river system in central Guizhou and maintaining the ecological integrity of the Wujiang River basin.

The approach involves comprehensive hydrogeological surveys and analytical testing of water samples. We analyze hydrogeological conditions, apply the Shukarev classification system, utilize Piper's trilinear diagrams, conduct normality and Grubbs' tests, and calculate pollution indices. This investigation methodically examines the hydrogeological context, hydrochemical profiles, sources of major ions, background concentrations, and current pollution levels, and identifies the factors driving pollution in the Anjiang underground river system.

The Anjiang underground river system covers approximately 18.91 square kilometers. The hydrochemical composition is classified into four types: HCO₃·SO₄-Ca, HCO₃-Ca·Mg, HCO₃·SO₄-Ca·Mg, and SO₄-Ca, each constituting roughly equal proportions. The predominant ions mainly originate from the dissolution of carbonate rocks, specifically dolomite from the Loushanguan Formation and limestone from the Qixia Formation-Maokou Formation, as well as sulfur-bearing minerals from the Longtan Formation. The pollution levels are significant, with total phosphorus, fluoride, and sulfate being the most crucial contaminants. Notably, the limestone aquifer of the Qixia Formation-Maokou Formation has higher pollution level than the dolomite aquifer of the Loushanguan Formation. This study confirms that the karst conduit network in the Anjiang underground river system has developed primarily within the Qixia Formation-Maokou Formation, with substantial secondary development in the Loushanguan Formation. The groundwater, enriched with inorganic pollutants such as total phosphorus, fluoride, and sulfate ions, flows northeast through karst conduits, contaminating the Anjiang underground river system. This polluted water eventually discharges into the Wujiang River via the S50 underground river outlet.

Our findings provide crucial theoretical support for the management and mitigation of pollution in karstic underground river systems.

In this study, the hydrochemical method and self-organized neural network (SOM-KM) coupling method were employed to identify recharge sources and reveal the water-rich mechanism in the karst groundwater-rich area of the Changxiao karst water system in Jinan City. The contribution ratio of karst groundwater recharge sources in the karst groundwater-rich area was quantitatively calculated using the end-element mixed model. The enrichment mechanism of karst groundwater is explored by combining with topography, geological structure, stratigraphic lithology, and catchment conditions.

The results showed that the karst groundwater in the catchment drainage area had similar water chemistry to that in the southern recharge area, the karst groundwater in the lateral runoff area, and the Yellow River, indicating a close hydraulic connection. This implies that the karst groundwater in the catchment drainage area is recharged by three sources: The southern mountain area, the karst groundwater in the lateral runoff area, and the Yellow River. The contribution ratios of the three components are 75.09%, 21.02%, and 3.89%, respectively. Carbonate rocks are widely distributed, and fissured karst is well developed in the accumulation and discharge areas, especially in the Maji-Xiaoli-Guide area. Moreover, there are abundant karst groundwater recharge sources in this area. During the runoff process of karst groundwater from southeast to northwest, it is impeded by sandstone and mudstone in the north. As a result, it accumulates in the contact zone between soluble rock and insoluble rock, thus forming impeded-type karst groundwater-rich structures.

Revealing the enrichment mechanism of karst groundwater in the Changxiao karst water system can provide scientific support for accurate calculations of recoverable resources and the protection of the springs in Jinan.

To analyze the research trends and frontiers in the field of groundwater-lake interaction, we conducted a comprehensive review the relevant papers from the Web of Science (WOS) database. Using VOSviewer software, we mapped the developmental trajectory of research topics in the field. Core papers from both WOS and the China National Knowledge Infrastructure (CNKI) were analyzed to systematically summarize prominent topics, research tools, and existing gaps. Based on the historical development of the field, future trends were also predicted.

Our analysis identified three successive developmental stages in this field, including the individualism stage, the reductionism stage, and the holism stage. Current hot research topics focus on water exchange, solute transport, and ecosystem mutual feedback mechanisms. Several key challenges remain, such as the spatiotemporal heterogeneity of groundwater-lake interactions, biogeochemical processes at the groundwater-lake interface, and the delayed impact of aquifers on lake ecological restoration. The primary research methods are stable isotopes, radioisotopes, temperature tracing, remote sensing, and numerical modelling. However, variations in data accuracy and spatial coverage continue to pose challenges for the practical application of these techniques.

In the future, this field will enter a fourth stage characterized by big data. At this stage, it is essential to integrate diverse technological approaches, with an emphasis on using big data for high-precision monitoring to improve the characterization of dynamic groundwater-lake interactions. Additionally, multidimensional inversion models of element migration should be developed, and enhanced data mining techniques should be applied at the interface to more accurately quantify solute transport flux across the groundwater-lake interface. Finally, fostering interdisciplinary collaboration and establishing a digital ecological framework will be essential to support research on the reciprocal interactions between groundwater and lake ecosystems, promoting sustainable development and environmental protection.

The pore system of low-permeability sandstone reservoirs is intricate, and the distribution of pore-throat sizes is highly variable. The microscopic pore structure significantly influences the reservoir′s petrophysical properties and plays a critical role in controlling fluid flow within sandstone reservoirs. Traditional approaches for evaluating pore structures primarily rely on morphological analyses of pore throat size distributions or regression analyses of pore structure parameters. These methods are significantly affected by human bias and often lack precise evaluation frameworks.

Poroperm analysis, mercury injection capillary pressure, nuclear magnetic resonance (NMR) measurements, and X-ray computed tomography (X-ray CT) scanning experiments were performed to characterize the pore structures of the Es₄^s low-permeability sandstones in the G oilfield, Bohai Bay Basin. On this basis, 15 parameters that reflect the microscopic features of low-permeability sandstones were selected, and four types of pore structures were classified by applying an unsupervised self-organizing-map neural network algorithm.

The findings reveal that the Type Ⅰ pore structure predominantly features large pore throats, with a median throat radius (r₅₀) ranging from 0.38 to 2.35 μm. This type exhibits excellent pore connectivity, contributing significantly to permeability. The petrophysical properties and pore connectivity of Type Ⅱ pore structures are second only to those of Type Ⅰ pore structures. The movable fluid porosity ranges from 2.76% to 5.61%, and the median throat radius (r₅₀) is primarily distributed in the range of 0.01 to 0.23 μm. Type Ⅲ pore structures display good pore connectivity along with considerable microscopic heterogeneity. The petrophysical properties and seepage properties of Type Ⅲ pore structures are comparable to those of Type Ⅰ and Type Ⅱ pore structures. The Type Ⅳ pore structures are characterized by small pore throats and poor microscopic connectivity, which hinders fluid movement within the sandstones.

The self-organizing map neural network algorithm effectively classifies pore structure types in cases involving multiple parameters. The classification results are not affected by inaccurate user-defined information, and there is no limitation on the number of parameters involved in the training process, making the application effect in pore structure classification remarkable. The established pore structure evaluation scheme, which is based on a self-organizing feature map neural network algorithm, is vital for investigating the microscopic seepage behavior and reservoir quality of low-permeability sandstones.