Because of their social, economic and political contexts, and their intrinsic multi-hazard nature, volcanic islands are one of the most vulnerable environments, where natural hazards (volcanic and ...non-volcanic) tend to occur in a simultaneous way causing cascading effects. To date, most of the scientific knowledge, as well as hazard assessment and risk management protocols focus on individual hazards and risks, while it remains a challenge to correctly predict the outcomes and impacts of a multi-hazard scenario where several hazardous phenomena may interact in simultaneous or consecutive ways. The multi-hazard concept originated in the 1990s in the international political context precisely to respond to this need. After its first appearance, different–and often, contradictory–usage perspectives of the multi-hazard concept have been increasingly put forward, thus making it difficult for this new approach to be fully implemented into disaster reduction policies. The present study assesses the current status of the application of the multi-hazard approach in existing risk management systems, and proposes future improvements to disaster risk reduction. It also presents the multi-hazards to which volcanic islands are exposed and analyses their potential impacts, taking the Canary Islands as a case study. In doing so, it emphasizes the need to establish a cross-sectoral, climate change-oriented, socially-inclusive, multi-risk management system, based on scientific knowledge and linked to critical societal demands and solutions.
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•Volcanic islands are environments prone to the impact of multiple geological hazards.•The impact of multi-hazard scenarios could be aggravated by Climate Change processes.•Multi-hazard approach should be fully implemented in disaster risk reduction policies.•Canary Islands' emergency plans don't consider concatenation of hazards.•Multi-risk management systems must be framed by research, conservation and education.
Monitoring surface deformation associated with geohazards is a prerequisite for geological disaster prevention. Interferometric synthetic aperture radar (InSAR) has the ability to capture ground ...deformation of landslides with high precision over a large area. However, in mountainous regions this capability is often limited by decorrelation noise and atmospheric phase artifacts. Over Eldorado National Forest, California, where many landslides need to be monitored and investigated, InSAR images are severely affected by atmospheric noise and the coherence is highly variable throughout the year, challenging InSAR techniques to effectively detect movement of active landslides. In order to obtain reliable measurements, we have designed an interferogram selection method and an InSAR segment processing (SP) technique to improve the deformation measurement. Compared with the traditional non-segment processing (NSP), the SP technique has demonstrated advantages in reducing the impact of atmospheric noise. Our results from both the ascending and descending InSAR datasets based on SP indicate that many landslides along the Highway 50 corridor were creeping at a rate of less than 10 cm/year during the investigation period. We have found that landslide movements in the study region present obvious seasonal patterns. The precipitation and pore-water measurements and our hydrogeological diffusion models suggest that the seasonal movements of these landslides are primarily driven by the pore-water pressures, and the peak deformation of the landslides may occur in the dry season (May to October) due to the time lag of precipitation infiltration. In addition, we have observed subtle upward movement of the landslides after the precipitation begins, which is likely caused by the swelling of clay-rich landslide body due to an increase in the pore pressure. Furthermore, several other localized unstable regions which may contain potential landslide hazards were also detected and mapped in the study area, and their dynamics need further investigation. We conclude that InSAR is capable of detecting slow landslide motions over difficult terrains if associated artifacts in the interferograms are suppressed. InSAR time-series measurements along with hydrogeological models enable us to characterize the time delay between peaks of landslide motions and precipitation.
•InSAR segment processing is used to mitigate atmospheric noise in mountainous areas.•Slow-creeping landslides in Eldorado National Forest in California are mapped.•Hydrogeological diffusion models suggest landslides are driven by pore-water pressure.•Peak deformation occurs in dry season due to time lag of precipitation infiltration.•Subtle upward movement is likely caused by swelling of clay-rich landslide body.
•We proposed a deep learning network (DeforNet) to identify geohazards from InSAR obtained deformation results.•Our network incorporating an attention mechanism that can mitigate the effects of ...atmospheric delays and noises in deformation areas identification.•We obtained the deformation time series of the Shanxi province, China, from January 2019 to December 2021 using InSAR.•The proposed network identified 1,553 geohazards with the minimum area of 0.21 km2.•The locations of the identified deformation areas are strongly correlated with the mining areas.
The development of InSAR satellite hardware and data processing technology enables us to rapidly obtain massive high spatial and temporal resolution surface deformation results. The abundant information helps geohazard detection, but also brings challenges for geohazard interpretation. InSAR geohazards intelligent detection methods based on deep learning can greatly improve the efficiency and precision of geohazards interpretation. However, these methods are usually limited by quality of the InSAR result that have various errors, such as decoherence error, topography residue and atmospheric delay. This study proposes a convolutional neural network incorporated an attention mechanism, referred to as DeforNet, which can effectively reduce the influences of the atmospheric delay and topography noises by introducing convolutional block attention module and depth-wise separable convolution. The new method can efficiently and accurately detect small and medium-scale geohazards from InSAR results. The comparison between the DeforNet with FCN, U-net and SegNet, using both synthetic and real samples, show that DeforNet has significant superiority in noise suppression and deformation identification. In the application of the whole Shanxi province, China, the DeforNet detected 1,553 geohazards with the minimum area of 0.21 km2. Our result shows that a strong spatial correlation between the location of geohazards and coal mining in this region. Within the coalfield, the number of identified geohazard accounts for 64.6 % of the total number in Shanxi. We also found that the identification accuracy of DeforNet is affected by the quality of the InSAR results, the scale of the geohazard and the wrap interval. DeforNet can serve to refine the detailed investigation of geohazards and promote the application of InSAR technology in geohazards prevention and mitigation.
•A framework is proposed for the wide area automated detection of active geohazards.•The first inventory of active geohazards (AGs) in the Hexi Corridor is established.•Elevation, temperature and ...precipitation are the primary conditioning factors of AGs.•Faults have more control on very slow-moving landslides than on slow-moving ones.•Dual conditioning factor interaction contributes to a bivariate enhancement of AGs.
With the escalation of global climate change and human activity, geohazards become increasingly frequent which cause severe casualties and property losses to local communities. To alleviate this situation and provide scientific guidance for risk reduction, it is imperative to address some of the basic questions related to geohazards, including: i) how to detect active geohazards (AGs) rapidly and automatically over a wide area; ii) how to determine the region with a high level of hazard activity; iii) what are the primary conditioning factors (CFs) of AGs; and iv) do factors operate independently or are they interconnected. To tackle these issues, we propose a universally applicable framework for wide area automated detection of AGs. The framework is based on multi-source Earth observations which capture surface deformation ranging from millimeters to meters. Our study has focused on the Hexi Corridor (HXC) in Gansu Province, China, covering an area of 210,000 km2 with a length of 1100 km. First, we construct an AGs database for the HXC with high automatic and rapid update capabilities, including a total of 4492 AGs (3652 active landslides and 840 land subsidence areas). Second, using the Geographic Detectors method, we determine the primary CFs including elevation, land surface temperature, and precipitation. We find that faults exert greater control over very slow-moving landslides, but are less effective over slow-moving landslides. Third, we analyzed the interactive effects of dual CFs on geohazard actives. Any interaction effect of dual CFs contributes to the bivariate enhancement of geohazard activity. This study significantly enhances the capabilities of the wide area automated detection of AGs, and provides a crucial dataset for hazard prediction and mitigation along the HXC.
Geohazard prediction is one of the most important and challenging tasks in underground mining. It still remains difficult to improve the prediction accuracy and make it compatible with the ...ever-increasing data in mining, especially when the data are sparsely allocated in a large-scale mining environment. This study introduces an innovative multimodal data fusion approach for geohazard prediction in underground mining to address this challenge. By incorporating visual model data as a novel modality and using interpolated rock mass rating data as a cross-complementary factor, the framework enhances the effectiveness of data fusion. Specific machine learning models were used and validated (e.g., neural networks, SVM, KNN, etc.) for proposed multimodal data fusion, addressing challenges posed by sparsely scattered multidimensional data, which generally have weak spatial connections across diverse datasets. In detail, to enhance spatial connection among diverse datasets, this paper leverage digitalised and gridded CAD file-based visual model data as a foundational carrier, the new modality, to facilitate the establishment of robust internal connections with routine data. Additionally, rock mass rating data is interpolated and aligned with visual model data to enhance spatial connections, improving spatial information-orientated data fusion. Then, to validate the accuracy and efficiency of the novel multimodal data fusion framework, we process and integrate two different routine data from a case study mine. Performance is tested by nine different data combinations, originating from two routine datasets, visual model data, and rock mass rating data. Finally, through comprehensive cross-validation, the proposed multimodal data fusion framework significantly improves the stability of prediction models at a comprehensive mine site scale, with high accuracy and low False-Negative rate.
•Visual model data digitalisation and visual analytics in mining engineering.•Data fusion with visual model data and routine data.•Data interpolation-based ML model fine-tuning regarding underground mining application.•Multimodal data fusion for geohazard prediction.
Rock mechanical properties are of key importance in coal mining exploration, coal bed methane production and CO2 storage in deep unmineable coal seams; accurate data is required so that geohazards ...(e.g. layer collapse or methane/CO2 leakage) can be avoided. In this context it is well established that coal matrix swelling due to water adsorption significantly changes the coal microstructure. However, how water adsorption and the associated with microstructural changes affect the mechanical properties is only poorly understood, despite the fact that micro-scale mechanical properties determine the overall geo-mechanical response as failure initiates at the weakest point. Thus, we measured nanoscale rock mechanical properties via nanoindentation tests and compared the results with traditional acoustic methods on heterogeneous medium rank coal samples in both dry and brine saturated conditions. The microscale heterogeneity of the rock mechanical properties was mapped and compared with the morphology of the sample (measured by SEM and microCT). While the nanoindentation tests measured decreasing indentation moduli after water adsorption (−60% to −66%), the traditional acoustic tests measured an increase (+17%). We concluded that acoustic tests failed to capture the accurate rock mechanical properties changes for the heterogeneous coal during water adsorption. It is thus necessary to measure the coal rock mechanical properties at the microscale to obtain more accurate data and reduce the risk of geohazards.
Geohazard recognition and inventory mapping are absolutely the keys to the establishment of reliable susceptibility and hazard maps. However, it has been challenging to implement geohazards ...recognition and inventory mapping in mountainous areas with complex topography and vegetation cover. Progress in the light detection and ranging (LiDAR) technology provides a new possibility for geohazard recognition in such areas. Specifically, this study aims to evaluate the performances of the LiDAR technology in recognizing geohazard in the mountainous areas of Southwest China through visually analyzing airborne LiDAR DEM derivatives. Quasi-3D relief image maps are generated based on the sky-view factor (SVF), which makes it feasible to interpret precisely the features of geohazard. A total of 146 geohazards are remotely mapped in the entire 135 km
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study area in Danba County, Southwest China, and classified as landslide, rock fall, debris flow based on morphologic characteristics interpreted from SVF visualization maps. Field validation indicate the success rate of LiDAR-derived DEM in recognition and mapping geohazard with higher precision and accuracy. These mapped geohazards lie along both sides of the river, and their spatial distributions are related highly to human engineering activities, such as road excavation and slope cutting. The minimum geohazard that can be recognized in the 0.5 m resolution DEM is about 900 m
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. Meanwhile, the SVF visualization method is demonstrated to be a great alternative to the classical hillshaded DEM method when it comes to the determination of geomorphological properties of geohazard. Results of this study highlight the importance of LiDAR data for creating complete and accurate geohazard inventories, which can then be used for the production of reliable susceptibility and hazard maps and thus contribute to a better understanding of the movement processes and reducing related losses.
Geohazards, such as landslides, rock avalanches, debris flow, ground fissures, and ground subsidence, pose a significant threat to people’s lives and property. Recently, machine learning (ML) has ...become the predominant approach in geohazard modeling, offering advantages such as an excellent generalization ability and accurately describing complex and nonlinear behaviors. However, the utilization of advanced algorithms in deep learning remains poorly understood in this field. Additionally, there are fundamental challenges associated with ML modeling, including input variable selection, uncertainty quantification, and hyperparameter tuning. This reprint presents original research exploring new advances and challenges in the application of ML in the spatial–temporal modeling of geohazards. The contributions cover the susceptibility analysis of glacier debris flow and landslides, the displacement prediction of reservoir landslides, slope stability prediction and classification, building resilience evaluation, and the prediction of rainfall-induced landslide warning signals.
Rivers and turbidity currents are the two most important sediment transport processes by volume on Earth. Various hypotheses have been proposed for triggering of turbidity currents offshore from ...river mouths, including direct plunging of river discharge, delta mouth bar flushing or slope failure caused by low tides and gas expansion, earthquakes and rapid sedimentation. During 2011, 106 turbidity currents were monitored at Squamish Delta, British Columbia. This enables statistical analysis of timing, frequency and triggers. The largest peaks in river discharge did not create hyperpycnal flows. Instead, delayed delta-lip failures occurred 8–11 h after flood peaks, due to cumulative delta top sedimentation and tidally-induced pore pressure changes. Elevated river discharge is thus a significant control on the timing and rate of turbidity currents but not directly due to plunging river water. Elevated river discharge and focusing of river discharge at low tides cause increased sediment transport across the delta-lip, which is the most significant of all controls on flow timing in this setting.
•Detailed monitoring of landslides and turbidity currents at fjord-head delta.•106 mass movements recorded enabling statistical analysis for the first time.•Elevated river discharge leads to delayed slope failure, not hyperpycnal flow.•Most significant control on turbidity current timing is delta-top bed shear stress.•River discharge and low tides increased flux of bedload driven over the delta lip.
Structurally-Controlled Differential Subsidence (SCDS) is the gradual sinking of the ground, characterized by the development of a damage band, terrain discontinuities and collapses, aligned ...according to the strike of a controlling geological structure. SCDS has been reported since the 1980s in several cities settled on tectonic valleys in central Mexico. Although groundwater abstraction is the main trigger, recent research efforts also point-out a tectonic component as a driving force. The monitoring and quantification of SCDS has been done through a variety of techniques, such as extensometry, GPS and InSAR. Furthermore, the associated hazards endangering the population are floods, aquifer pollution, cracking and housing collapse. This paper presents a comprehensive review of the current state of SCDS, allowing, for the first time, the standardization of its definition, mechanisms and triggering factors. Additionally, this helps to avoid misinterpretation in the cases of sinking produced by the Mexico City Subsidence Type (MCST) and thus, provides the elements for proper methodological study of SCDS. Finally, the review includes future research directions that need to be improved in order to reduce the impact of the phenomenon.
•Mexico subsidence is divided in Mexico City Subsidence Type (MCST) and Structurally-Controlled Differential Subsidence (SCDS)•The monitoring and quantification of SCDS has been done through a variety of techniques, such as extensometry, GPS and InSAR•The associated hazards to SCDS endangering the population are floods, aquifer pollution, cracking and housing collapse•Standardization of SCDS concepts allows differentiation with MCST, providing the proper basis for methodological planning•SCDS review allows the development of civil and geological engineering technologies that can reduce the impact
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