Geohazard Risk Research Articles

Large-scale geohazards risk of submarine landslides considering the subsea cables vulnerability: A case study from the northern continental slopes of South China Sea

Submarine landslides pose significant threats to subsea cables distributed on the global seabed. However, regional scale risk assessment of landslide geohazards is rarely reported. This study introduces a methodology for regional-scale geohazard risk prediction of submarine landslides, focusing on the northern continental slopes of the South China Sea. Initially, the study employed the infinite-slope method to calculate safety factors for typical submarine slopes. Addressing uncertainties in geotechnical and seismic parameters, Monte Carlo simulations determine slope failure probabilities. Using kriging interpolation, localized failure probabilities are extrapolated to regional scales, establishing a spatiotemporal distribution method for large-scale geohazard susceptibility. Hazard levels are subsequently determined considering the volumes of potential landslides. The density of subsea cables is used as a vulnerability factor to guide the regional-scale assessment of cable vulnerability. Finally, integrating vulnerability with hazard levels provides a comprehensive assessment of landslide-induced large-scale geohazard risks. The findings highlight elevated geohazard risks in the Taiwan Bank slope segment, moderate risks in the Zhujiang Valley and Shenhu slope segments, with lower risks in other areas.

Spatiotemporal characteristics of non-seismically fatal landslides in Northwest China: A case study from Shaanxi province

Landslides are one of the devastating geo-hazards that result in severe casualties in Northwest China (NW China) every year. However, a comprehensive landslide database is yet to be available for quantitatively assessing the distribution of fatal landslides in NW China. To investigate the spatiotemporal characteristics of non-seismically fatal landslides in NW China, we carried out a study using fatal landslides that occurred in Shaanxi province as an example to construct a new database by incorporating data of fatal landslides that occurred in the period between 1996 and 2018. A total of 332 non-seismically fatal landslides that claimed 1132 lives were compiled in this database. Additionally, a thorough study of spatial and temporal variations of fatal landslides reveals that anthropogenic landslides occur approximately evenly throughout the year. Natural fatal landslides, however, rise noticeably in July, August, and September. The spatial distribution analysis showed that fatal landslides in Shaanxi province of China were mainly distributed in the An’ kang, Yan’an, and Shangluo regions. Additionally, areas with relatively high relief (46.1 to 123 m), steep–slope topography (19.2 to 26.1°), and intense precipitation (465 to 937 mm) are more vulnerable to naturally-triggered landslides. The spatiotemporal analysis of the fatal landslides revealed increasing trends in zones such as Yan’an and Yulin cities (in northern part), and in An’kang city (in southern part). When combined with other information, the Fatal Landslide Database of Shaanxi Province can be used to provide a guide for risk assessment and spatial planning studies to mitigate geo-hazard risks.

Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm

Flint Hall Farm in Godstone, Surrey, UK, is situated adjacent to the London Orbital Motorway, or M25, and contains several landslide systems which pose a significant geohazard risk to this critical infrastructure. The site has been routinely monitored by geotechnical engineers following a landslide that encroached onto the hard shoulder in December 2000; current in situ instrumentation includes inclinometers and piezoelectric sensors. Interferometric Synthetic Aperture Radar (InSAR) is an active remote sensing technique that can quantify millimetric rates of Earth surface and structural deformation, typically utilising satellite data, and is ideal for monitoring landslide movements. We have developed the hardware and software for an Unmanned Aerial Vehicle (UAV), or drone radar system, for improved operational flexibility and spatial–temporal resolutions in the InSAR data. The hardware payload includes an industrial-grade DJI drone, a high-performance Ettus Software Defined Radar (SDR), and custom Copper Clad Laminate (CCL) radar horn antennas. The software utilises Frequency Modulated Continuous Wave (FMCW) radar at 5.4 GHz for raw data collection and a Range Migration Algorithm (RMA) for focusing the data into a Single Look Complex (SLC) Synthetic Aperture Radar (SAR) image. We present the first SAR image acquired using the drone radar system at Flint Hall Farm, which provides an improved spatial resolution compared to satellite SAR. Discrete targets on the landslide slope, such as corner reflectors and the in situ instrumentation, are visible as bright pixels, with their size and positioning as expected; the surrounding grass and vegetation appear as natural speckles. Drone SAR imaging is an emerging field of research, given the necessary and recent technological advancements in drones and SDR processing power; as such, this is a novel achievement, with few authors demonstrating similar systems. Ongoing and future work includes repeat-pass SAR data collection and developing the InSAR processing chain for drone SAR data to provide meaningful deformation outputs for the landslides and other geotechnical hazards and infrastructure.

A Novel Strategy Coupling Optimised Sampling with Heterogeneous Ensemble Machine-Learning to Predict Landslide Susceptibility

The accuracy of data-driven landslide susceptibility prediction depends heavily on the quality of non-landslide samples and the selection of machine-learning algorithms. Current methods rely on artificial prior knowledge to obtain negative samples from landslide-free regions or outside the landslide buffer zones randomly and quickly but often ignore the reliability of non-landslide samples, which will pose a serious risk of including potential landslides and lead to erroneous outcomes in training data. Furthermore, diverse machine-learning models exhibit distinct classification capabilities, and applying a single model can readily result in over-fitting of the dataset and introduce potential uncertainties in predictions. To address these problems, taking Chenxi County, a hilly and mountainous area in southern China, as an example, this research proposes a strategy-coupling optimised sampling with heterogeneous ensemble machine learning to enhance the accuracy of landslide susceptibility prediction. Initially, 21 landslide impact factors were derived from five aspects: geology, hydrology, topography, meteorology, human activities, and geographical environment. Then, these factors were screened through a correlation analysis and collinearity diagnosis. Afterwards, an optimised sampling (OS) method was utilised to select negative samples by fusing the reliability of non-landslide samples and certainty factor values on the basis of the environmental similarity and statistical model. Subsequently, the adopted non-landslide samples and historical landslides were combined to create machine-learning datasets. Finally, baseline models (support vector machine, random forest, and back propagation neural network) and the stacking ensemble model were employed to predict susceptibility. The findings indicated that the OS method, considering the reliability of non-landslide samples, achieved higher-quality negative samples than currently widely used sampling methods. The stacking ensemble machine-learning model outperformed those three baseline models. Notably, the accuracy of the hybrid OS–Stacking model is most promising, up to 97.1%. The integrated strategy significantly improves the prediction of landslide susceptibility and makes it reliable and effective for assessing regional geohazard risk.

Late Cenozoic mass transport deposits in the offshore Tanzania continental margin

Sediment mass movements and their associated Mass Transport Deposits (MTDs) have been widely studied due to their economic and geohazard potentials. This study combines 2D and 3D seismic reflection data with a seismic facies approach and attribute analysis to reveal the presence and distribution of different MTDs in the southernmost region offshore Tanzania. Results of seismic facies analysis show that the study area contains different Late Cenozoic MTDs. The MTDs have limited petroleum reservoir potential and include slides, slumps, debris flow deposits and occasional turbidites. The formation of these MTDs was caused by tectonic events associated with the development of the East African Rift System. Seismic attribute maps have shown the locations of channels, remnant blocks, headwall scars, and grooves confirming downslope sediment mass mobilization. The seabed attribute maps have shown areas where recent mass mobilizations were initiated. Some of these areas coincide with faults which have dissected the seabed, forming potential future gravity failure sites. Other future gravity failure sites include channel banks and slope edges, which may be present over the whole Tanzania continental margin. Sediment mass movements may be catastrophic, and therefore future infrastructure installations in the area must involve detailed mapping of the seabed to assess geohazard risks and act accordingly.

Evaluation of geo-hazard risks in the pearl river delta based on geographic information system and weighted informativeness approach

Evaluation of geo-hazard risks in the pearl river delta based on geographic information system and weighted informativeness approach

Migration characteristics and geohazards risk analysis of swelling clay in clayey silt hydrate sediments

The safe and long-term hydrate production in loose clayey silt sediments with high clay content is one of the research priorities, in which the fines migration is one of the key factors. The characteristics of montmorillonite swelling and dispersion make its migration behavior more challenging, which causes the evolution of reservoir seepage properties and potential geohazards risk cannot be predict. The migration behavior of montmorillonite induced by pore water was investigated, and discusses its potential impact on the geomechanical properties of the reservoir. The results show that the combined effects of montmorillonite swelling, which creates favorable conditions for blockage, and migration, which exacerbates permeability decline, make montmorillonite have a much greater effect on seepage characteristics in clayey silt sediments than illite. Higher montmorillonite content and low salinity increase the particle concentration in the pore water, which creates a source of particles for blockage. The low reynolds number indicates that the particles mainly detach from the pore wall with rolling in laminar flow and the lifting force can be neglected. As a result of this combined effect, the montmorillonite is more likely to readsorb and block the pore throat with bridging. This work will help to understand the migration behavior of montmorillonite in clayey silt formations with low permeability, which needs to be paid attention to when formulating gas production schemes and safety control strategies.

Forecasting and early warning of shield tunnelling-induced ground collapse in rock-soil interface mixed ground using multivariate data fusion and Catastrophe Theory

The high spatial variability of the rock-soil interface (RSI) in complex geological conditions introduces strong uncertainties in both subsurface stratigraphy and geotechnical properties. Inaccurate interpretation of such uncertainties during engineering geology investigations increases the geohazard risk of excessive surface settlement or even severe catastrophic ground collapse when shield machines are excavating in RSI mixed ground. Prediction and early warning of excessive surface settlement are necessary measures to address such a risk; however, unavoidable drawbacks such as overfitting, insufficient accuracy, and ineffectiveness remain in existing prediction models and early warning algorithms and have posed significant challenges. In this study, a novel framework using both a multivariate data fusion prediction model and a dynamic early warning algorithm was developed for forecasting and early warning of ground collapse during shield tunnelling in RSI mixed ground. The prediction model is the Differential Evolutionary Optimized Quadratic Taylor Series Extended Kalman Filter (DEQT-EKF); the early warning algorithm is based on Catastrophe Theory and uses the Gradient Ratio (GR) criterion to identify catastrophic singularities. The practicality and accuracy of the framework are well verified by a subway shield tunnelling-induced ground collapse incident in East China with complex RSI mixed ground conditions. The prediction results are compared with the surface settlement measurements and good agreement is obtained, indicating that the DEQT-EKF model can achieve satisfactory accuracy in predicting excessive settlement. The use of the GR criterion can trigger the early warning one time step before the ground collapse event, indicating that it is a competent and practical early warning strategy for shield tunnelling-induced ground collapse. The framework has the potential to significantly reduce the risk of ground collapse caused by geological uncertainties when constructing shield tunnels through complex ground conditions.

Multi-model combination in key steps for landslide susceptibility modeling and uncertainty analysis: a case study in Baoji City, China

Reliable landslide susceptibility maps are essential for geohazard risk management. However, the selection of models and methods in the landslide susceptibility modeling (LSM) process is subjective and can lead to uncertainties in susceptibility outcomes. This paper introduces a framework for LSM and uncertainty analysis that leverages multiple models in key steps. The framework dynamically operates and combines models at each LSM step to generate susceptibility evaluation results from various method combinations. The uncertainty of different model combinations is then analyzed by comparing these results. The framework’s effectiveness is validated using Baoji City as a case study, examining the impact of different attribute interval numbers (AIN), non-landslide negative sample selection methods, and prediction models on susceptibility prediction outcomes. The results of each group show that the highest AUC value is 0.963 (AIN = 12, buffer-controlled sampling, Random forest) is about 0.26 higher than the lowest (no AIN, low-slope controlled sampling, Support vector machine). The findings suggest that the combinations involving larger AIN values, buffer-controlled sampling, XGBoost, or Random forest model yield relatively high AUC accuracy and relatively low uncertainty in the susceptibility index.

Learning Ground Displacement Signals Directly from InSAR-Wrapped Interferograms.

Monitoring ground displacements identifies potential geohazard risks early before they cause critical damage. Interferometric synthetic aperture radar (InSAR) is one of the techniques that can monitor these displacements with sub-millimeter accuracy. However, using the InSAR technique is challenging due to the need for high expertise, large data volumes, and other complexities. Accordingly, the development of an automated system to indicate ground displacements directly from the wrapped interferograms and coherence maps could be highly advantageous. Here, we compare different machine learning algorithms to evaluate the feasibility of achieving this objective. The inputs for the implemented machine learning models were pixels selected from the filtered-wrapped interferograms of Sentinel-1, using a coherence threshold. The outputs were the same pixels labeled as fast positive, positive, fast negative, negative, and undefined movements. These labels were assigned based on the velocity values of the measurement points located within the pixels. We used the Parallel Small Baseline Subset service of the European Space Agency's GeoHazards Exploitation Platform to create the necessary interferograms, coherence, and deformation velocity maps. Subsequently, we applied a high-pass filter to the wrapped interferograms to separate the displacement signal from the atmospheric errors. We successfully identified the patterns associated with slow and fast movements by discerning the unique distributions within the matrices representing each movement class. The experiments included three case studies (from Italy, Portugal, and the United States), noted for their high sensitivity to landslides. We found that the Cosine K-nearest neighbor model achieved the best test accuracy. It is important to note that the test sets were not merely hidden parts of the training set within the same region but also included adjacent areas. We further improved the performance with pseudo-labeling, an approach aimed at evaluating the generalizability and robustness of the trained model beyond its immediate training environment. The lowest test accuracy achieved by the implemented algorithm was 80.1%. Furthermore, we used ArcGIS Pro 3.3 to compare the ground truth with the predictions to visualize the results better. The comparison aimed to explore indications of displacements affecting the main roads in the studied area.

Active Deformation Areas of Potential Landslide Mapping with a Generalized Convolutional Neural Network

Early discovery and monitoring of the active deformation areas of potential landslides are important for geohazard risk prevention. The objective of the study is to propose a one-step strategy for automatically mapping the active deformation areas of potential landslides from a Sentinel-1 SAR dataset. First, we built a generalized convolutional neural network (CNN) based on activity and topographic characteristics. Second, we conducted a comparative analysis of the performance of various multi-channel combiners for detecting the active deformation areas of the potential landslides. Third, we verified the transferability of the pretrained CNN model for an unknown region. We found that by incorporating topographic characteristics into a generalized convolutional neural network, we were able to enhance the accuracy of identifying the active deformation areas of potential landslides, rapidly mapping these areas. The methodology is robust and efficient, and it has the capability to automatically detect the active deformation areas of potential landslides, even in unknown or unfamiliar regions. This product can facilitate automated pipelines, updating and mapping active deformation areas for final users who are not InSAR experts. This implementation can be used for providing support to risk management activities.

The 2015 earthquake swarm in the Fentale volcanic complex (FVC): A geohazard risk for Ethiopia's commercial route to the Djibouti port.

We investigate rare seismicity in the neighborhood of the Fentale volcano, main Ethiopian rift that occurred in spring 2015 using data recorded by the Ethiopian Seismic Station Network. Over 1350 earthquakes are located and a seismic swarm is observed to the northeast of Fentale volcano, with the largest earthquake having a magnitude of 4.3 ML. Several VT (volcano-tectonic) and LP (long-period) events are observed in the swarm, indicating that it is induced by magmatic intrusion, consistent with dike-induced deformation in the same area inferred from satellite geodesy. The seismic activity commenced at about 40 km depth and migrated southwestward as it shallowed, towards Fentale volcano, suggesting that the dike feeding system might be influenced by the magma rich Afar Depression further to the northeast rather than the adjacent Fentale volcano. An earlier cluster of seismicity in the same area was recorded by a temporary deployment of seismic stations that ran from November 2001 to January 2003; extensive clusters of less well recorded seismic activity in the region occurred in June 1989, January–February 1981, October–November 2003, and 2004. Such frequent seismicity at the Fentale Volcanic Complex coupled with the adjacent fast-growing Lake Basaka poses an eminent and significant risk to Ethiopia's access to Djibouti port, its most important commercial route. This should be a wakeup call to concerned stakeholders to take precautionary measures by developing risk management and rapid response strategies.

Measurement and Modeling of Thermo-Hydro-Mechanical Behaviors of Frozen Clays: Frost Susceptibility and Compressibility

The risk of geohazards associated with frozen subgrades is well recognized, but a comprehensive framework to evaluate frost susceptibility from microstructural characteristics to macroscopic thermo-hydro-mechanical (THM) behaviors has not been established. This study aims to propose a simple framework for quantitatively assessing frost susceptibility and compressibility in frozen soils. A systematic THM model was devised to predict heat transfer, soil freezing characteristics, and stress states in frozen soils. Constant freezing experiments and oedometer compression tests were performed on bentonite clays under varying temperatures (−5°C, −10°C, and −20°C) and stress levels to validate the proposed model. Additionally, soil electrical conductivity measurements were employed to assess the temperature- and stress-dependent volumetric and mechanical properties of frozen soils. The model used Fourier’s law to compute the transient soil temperature profile and estimated the volume change and stress states based on the soil freezing characteristic curve. Experimental results showed that frost heave of bentonite reached between 9.0% and 26.6% of axial strain, which was largely predicted by the proposed model. It also demonstrated that the frost heave was mainly attributed to the fusion of the porewater. Additionally, the preconsolidation pressure of frozen soils exhibited a rapid increasing trend with decreasing temperature, which was explained by the temperature-dependent ice morphology in the soil interpore. Furthermore, the findings also demonstrated a remarkable sensitivity in the electrical conductivity in response to the soil temperature during the frost heave process and the stress state under the loading or unloading path.

Data-augmented landslide displacement prediction using generative adversarial network

Landslides are destructive natural disasters that cause catastrophic damage and loss of life worldwide. Accurately predicting landslide displacement enables effective early warning and risk management. However, the limited availability of on-site measurement data has been a substantial obstacle in developing data-driven models, such as state-of-the-art machine learning (ML) models. To address these challenges, this study proposes a data augmentation framework that uses generative adversarial networks (GANs), a recent advance in generative artificial intelligence (AI), to improve the accuracy of landslide displacement prediction. The framework provides effective data augmentation to enhance limited datasets. A recurrent GAN model, RGAN-LS, is proposed, specifically designed to generate realistic synthetic multivariate time series that mimics the characteristics of real landslide on-site measurement data. A customized moment-matching loss is incorporated in addition to the adversarial loss in GAN during the training of RGAN-LS to capture the temporal dynamics and correlations in real time series data. Then, the synthetic data generated by RGAN-LS is used to enhance the training of long short-term memory (LSTM) networks and particle swarm optimization-support vector machine (PSO-SVM) models for landslide displacement prediction tasks. Results on two landslides in the Three Gorges Reservoir (TGR) region show a significant improvement in LSTM model prediction performance when trained on augmented data. For instance, in the case of the Baishuihe landslide, the average root mean square error (RMSE) increases by 16.11%, and the mean absolute error (MAE) by 17.59%. More importantly, the model's responsiveness during mutational stages is enhanced for early warning purposes. However, the results have shown that the static PSO-SVM model only sees marginal gains compared to recurrent models such as LSTM. Further analysis indicates that an optimal synthetic-to-real data ratio (50% on the illustration cases) maximizes the improvements. This also demonstrates the robustness and effectiveness of supplementing training data for dynamic models to obtain better results. By using the powerful generative AI approach, RGAN-LS can generate high-fidelity synthetic landslide data. This is critical for improving the performance of advanced ML models in predicting landslide displacement, particularly when there are limited training data. Additionally, this approach has the potential to expand the use of generative AI in geohazard risk management and other research areas.

Fine-resolution mapping and assessment of artificial surfaces in the northern hemisphere permafrost environments

ABSTRACT Permafrost degradation has strong and long-lasting effects on anthropogenic land-use activities, contradicting the goal of sustainable development in polar and high-elevation regions. The artificial surface (AS) plays a central role in determining human-environment relationships in permafrost environments. Despite recent progress in monitoring land surfaces, attempts to map permafrost AS with satellite remote sensing have been limited. In this study, we propose an operational framework for fine-resolution mapping and assessment of permafrost AS across the entire Northern Hemisphere landmass. The proposed framework was designed to take advantage of prior knowledge obtained from existing global-scale land-cover products. As a result, a 10 m resolution permafrost AS map for 2016–2017 was created using a locally adaptive classification strategy. We found that the created map exhibited an overall accuracy of 91.7 ± 2.1% with minimum accuracies > 70%. We estimated that the total area of permafrost AS in the Northern Hemisphere was approximately 9,000 km2, most notably in the Russian Arctic. Future projections indicate that there will be over one-seventh of the permafrost AS area at high geohazard risk by the end of the twenty-first century. Our study provides new perspectives on the ‘permafrost-human-climate’ nexus, which can advance our understanding of the terrestrial system.

Analysis of the application of BIM combined with GIS in engineering digital management

Abstract This study centers on the application of digital intelligence technology in project management, takes the geohazard project in Z area as the research object, and evaluates and predicts the grade of the danger of slope disaster and mudslide disaster in this area. The study begins with an in-depth analysis of the integrated fusion of BIM and GIS technologies. The integrated assessment and prediction model of geologic hazards was constructed using fuzzy comprehensive evaluation and support vector machine algorithm. The study unfolded the analysis of the hazard class and risk prediction of geohazards. The results showed that most of the hazard classes in slope hazards were assessed as low danger, accounting for 33.33%. Meanwhile, about 70% of the mudslide hazards were assessed to have a low to medium hazard rating. Regarding risk prediction, the prediction accuracy of slope and mudslide hazards reached 89% and 90%, respectively. In addition, the detection rate of all geohazards in the ROC (receiver operating characteristic) curve was above 85%, while the false alarm rate remained between 14% and 22%. This result verifies the accuracy of the model in geohazard risk prediction. The comprehensive assessment and prediction model proposed in this paper will help the risk assessment and prediction of geohazards, provide adequate technical support for managing disaster information, and enhance the science and rationality of disaster prevention and mitigation.

Research on Coupling Model of Foundation Treatment and Geological Hazard Risk Assessment

Abstract High-risk areas of geologic disasters need to select appropriate foundation treatment techniques according to different risk assessment levels to ensure the overall quality and safety of housing construction projects. In this paper, for the three foundation treatment technologies for different geologies, the geologic condition sensing measurement algorithm is designed according to the principle of InSAR technology, and the informativeness-logistic regression algorithm is proposed for vulnerability risk evaluation. The TRIGRS algorithm that has been improved is used to construct the geologic hazard risk assessment model. Then, the data preparation work is carried out in the practical application, and input data are connected through a series of preprocessing operations. Taking the landslide geohazards occurring in a certain place as an example, the geologic deformation curve generated by the system was used to analyze them, and the risk assessment of geohazard susceptibility was also carried out in a certain area. It was found that the cumulative deformation curve from the 20th week to the 30th week of 2020 decreased significantly by more than 100mm. The actual major landslide disaster occurred at the beginning of June 2020, and the results of the system operation are in line with the time of the actual disaster. For all 11 factors, logistic regression analysis yielded significant results. The proportion of 5-level susceptibility areas of geologic hazards is 31.5%, 24.6%, 20.2%, 14.7%, and 9%, respectively, and the distribution of assessment results is reasonable. This study provides new ideas and effective methods for defining the level of geohazard risk assessment and selecting foundation treatment technology.

From theoretical to sustainable potential for run-of-river hydropower development in the upper Indus basin

A comprehensive assessment of hydropower resource potential considering factors beyond technical and financial parameters is missing for the upper Indus basin (UIB). Our framework takes a systems approach to quantify the theoretical to sustainable hydropower potential by successively considering natural, technical, financial, anthropogenic, environmental, and geo-hazard risk constraints on hydropower at individual sites as well as at the basin-scale. Theoretical potential of the UIB is 1564 TWh/yr at 500-m resolution. Across three energy focus and three geo-hazard risk scenarios, our cost-minimization model finds that technical (12%–19%), financial (6%–17%) and sustainable (2%–10%) potential are a small portion of the theoretical value. Mixed development combining plants of various size, cost and configuration provides the highest potential with the best spatial coverage. Alongside, our review of 20 datasets reveals a visualized potential exceeding 300 TWh/yr from 447 hydropower plants across the UIB, with only a quarter of the potential materialized by mostly large plants in the mainstreams. Hydropower cost curves show that Swat and Kabul sub-basins have a larger proportion of cost-effective and sustainable potential untapped by the visualized potential. Water use for other sectors represents the strongest constraints, reducing a third of the technical potential when evaluating sustainable potential. Ultimately, human decisions regarding scale, configuration and sustainability have a larger influence on hydropower potential than model parameter assumptions. In quantifying hydropower potential under many policy scenarios, we demonstrate the need for defining hydropower sustainability from a basin-scale perspective towards energy justice and balanced fulfilment of Sustainable Development Goals for water and energy across the Indus.

Risk Assessment and Analysis of Geological Disaster of Hydraulic Engineering Environmen

The formation of hydraulic engineering environmental geohazards is mostly caused by human factors, rough investigation, illegal operation, and improper countermeasures can trigger or even magnify the hazardous degree and scope of geohazards. Timely, scientific and accurate assessment of the risk of hydraulic engineering environmental geohazards is an important means to prevent disasters and reduce losses. However, the risk assessment needs to refer to a variety of information, and in many cases, it rely on the work experience of the assessors, so the actual work is carried out in a mixed situation, resulting in a huge disaster hidden danger. It is necessary to sort out the recent assessment work, sum up the experience and analyze the interests in order to point out the direction for future work.

Seismic expression of paleokarst morphologies and associated Siderolithic infill in the Geneva Basin, Switzerland: Implications for geothermal exploration

The Geneva Basin is witnessing intense geothermal exploration campaigns arising from the Cantonal and Swiss federal energy transition strategies towards an ultimate net-zero future by 2050. However, a major challenge to successful geothermal exploration in the basin are uncertainties regarding the tectonostratigraphic framework of the basin, especially in relation to subsurface morphologies of significance to geothermal prospectivity. This was demonstrated when the newly drilled GEo-02 geothermal exploration borehole surprisingly penetrated a c. 158 m Siderolithic reservoir unit known to infill paleokarst structures in the Molasse Basin. Therefore, we aim to understand the seismic character diagnostic of these paleokarst features and their infill in subsurface geophysical dataset. This was achieved by interpreting industrial 2D seismic reflection and borehole dataset integrated with satellite imagery and outcrop analogues. The seismic dataset reveals these paleokarsts as depressions along the Cretaceous-Paleogene transition (K-Pg) and is characterized by three distinct end-member morphologies: (1) fault-controlled type, (2) doline type associated with dissolution/collapse and (3) complex type, atop the Lower Cretaceous carbonates. However, the karst infill is characterized by different seismic facies exhibiting either a higher or lower seismic reflectivity depending on the karst-end member type. This is related to differences in the acoustic properties of the karst infill, overlying lower molasse and the Lower Cretaceous carbonates hosting the karst. The GEo-02 borehole reveals that the Siderolithic infill consists of a variety of lithologies including quartz-rich sandstone, polygenic breccias and conglomerates, terra rossa and lateritic deposits. The evolution of the paleokarst features in the study area is influenced by the structural re-organization of the Molasse Basin in relation to the Alpine orogeny. This resulted in its emergence and change in base-level promoting the dissolution of the carbonate substratum by meteoritic water subsequently generating the paleokarst morphologies. Considering the regional geothermal gradient, the foredeep of the Geneva Basin that exhibit subsurface manifestations of this Siderolithic reservoirs may present new opportunities for an underground thermal energy storage. However, the presence of karstified intervals may pose geohazard risks to geothermal exploration during drilling operations targeting deeper structures in the basin.