Explore the vast range of titles available.

Landslide monitoring is a mandatory step in landslide risk assessment. It requires collecting data on landslide conditions (e.g., areal extent, landslide kinematics, surface topography, hydrogeometeorological parameters, and failure surfaces) from different time periods and at different scales, from site-specific to local, regional, and national, to assess landslide activity. In this analysis, we collected information on landslide monitoring techniques from 17 members of the Earth Observation and Geohazards Expert Group (from EuroGeoSurveys) deployed between 2005 and 2021. We examined the types of the 75 recorded landslides, the landslide techniques, spatial resolution, temporal resolution, status of the technique (operational, non-operational), time of using (before the event, during the event, after the event), and the applicability of the technique in early warning systems. The research does not indicate the accuracy of each technique but, rather, the extent to which Geological Surveys conduct landslide monitoring and the predominant techniques used. Among the types of landslides, earth slides predominate and are mostly monitored by geological and engineering geological mapping. The results showed that Geological Surveys mostly utilized more traditional monitoring techniques since they have a broad mandate to collect geological data. In addition, this paper provides new insights into the role of the Geological Surveys on landslide monitoring in Europe and contributes to landslide risk reduction initiatives and commitments (e.g., the Kyoto Landslide Commitment 2020).

The literature about landslide susceptibility mapping is rich of works focusing on improving or comparing the algorithms used for the modeling, but to our knowledge, a sensitivity analysis on the use of geological information has never been performed, and a standard method to input geological maps into susceptibility assessments has never been established. This point is crucial, especially when working on wide and complex areas, in which a detailed geological map needs to be reclassified according to more general criteria. In a study area in Italy, we tested different configurations of a random forest–based landslide susceptibility model, accounting for geological information with the use of lithologic, chronologic, structural, paleogeographic, and genetic units. Different susceptibility maps were obtained, and a validation procedure based on AUC (area under receiver-operator characteristic curve) and OOBE (out of bag error) allowed us to get to some conclusions that could be of help for in future landslide susceptibility assessments. Different parameters can be derived from a detailed geological map by aggregating the mapped elements into broader units, and the results of the susceptibility assessment are very sensitive to these geology-derived parameters; thus, it is of paramount importance to understand properly the nature and the meaning of the information provided by geology-related maps before using them in susceptibility assessment. Regarding the model configurations making use of only one parameter, the best results were obtained using the genetic approach, while lithology, which is commonly used in the current literature, was ranked only second. However, in our case study, the best prediction was obtained when all the geological parameters were used together. Geological maps provide a very complex and multifaceted information; in wide and complex area, this information cannot be represented by a single parameter: more geology-based parameters can perform better than one, because each of them can account for specific features connected to landslide predisposition.

In gravity data processing, selecting the correct filter results in accurate interpretation. In this study, upward continuation and moving average filters were applied to delineate the existence of the Probolinggo Fault. The data used is satellite gravity data from the Global Gravity Model Plus (GGMplus) 2013, after which corrections were made to the data to obtain a complete Bouguer anomaly map. The next step is filtering to obtain residual anomalies. In the moving average filter, a window width of 47 was used, and in the upward continuation filter, the best anomaly was obtained at an elevation of 1500 m. The residual anomaly resulting from the moving average filter ranged from -19.3 to 8.4 mgal. Meanwhile, the residual anomaly resulting from the upward continuation filter ranged from - 6.8 to 5.8 mgal. The comparison results of the two filters show that the upward continuation filter has more representative results than the moving average filter, where the RMS error of the residual anomaly of the upward continuation filter is 2.76%, whereas the RMS error of the residual anomaly of the moving average filter is 8.04%. The qualitative interpretation also shows that the upward continuation filter is able to delineate the existence of the Probolinggo Fault, which is in accordance with the geological map, which is trending West-Northeast.

Underground monument preservation is tightly linked to geological risk. The geological risk management of underground structures typically relies on a preliminary site investigation phase. Engineering geological mapping—as a key site investigation element—is largely based on manual in situ work, often in harsh and dangerous environments. However, although new technologies can, in many cases, decrease the on-field time as well as eliminate inaccessibility issues, the example presented in this study demonstrates a special challenge that had to be addressed. The ancient underground marble quarries of Paros Island in Greece constitute a gallery complex of a total length of 7 km and only two portals, resulting in total darkness throughout almost the full length of the unsurveyed galleries. As such, the entire survey and engineering geological mapping solely relied on a virtual reality application that was developed based on a digital replica of the quarries using laser scanning. The study identifies several critical locations with potentially unstable geologic structures and computes their geometrical properties. Further numerical analyses based on data extracted directly from the digital replica of the rock mass led to the definition of appropriate risk mitigation measures along the underground marble quarries.

Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.

Large-sample hydrology datasets have advanced hydrological research, yet the impact of landscape map details on identifying dominant streamflow generation processes remains underexplored. This study investigates the role of geology using maps of increasing detail – global, continental, and regional – each reclassified into four permeability classes. These geological attributes were used along with topography, soil, land use, and climate attributes to identify dominant controls on streamflow signatures across 4469 European catchments. To distinguish landscape influences from the otherwise dominant influence of climate, we conducted separate analyses on nested basins. Three scales were considered to assess scale-dependent patterns: large (63 nested basins), intermediate (the Moselle nested basin), and small (five nested catchments within the Moselle). The large-scale study used geology information from global and continental maps, while the others also incorporated regional maps. At the large scale, dominant controls varied widely between nested basins, but landscape generally outweighed climate, highlighting the value of our nested basin design. At this scale, continental and global geology maps produced different correlation patterns, with neither consistently superior. At the intermediate scale, increased geological detail led geology to shift from the least to the most correlated variable for certain streamflow signatures. The small-scale experiment reinforced these findings, as the regional map highlighted controls more consistent with process understanding. This study underscores the benefit of integrating detailed, region-specific geological data into large sample hydrology studies, and demonstrates the utility of a nested basins design. These findings have important implications for hydrological regionalization and streamflow prediction in ungauged basins.

Multi-spectral remote sensing has already played an important role in mapping surface mineralogy. However, vegetation – even when relatively sparse – either covers the underlying substrate or modifies its spectral response, making it difficult to resolve diagnostic mineral spectral features. Here we take advantage of the petabyte-scale Landsat datasets covering the same areas for periods exceeding 30 years combined with a novel high-dimensional statistical technique to extract a noise-reduced, cloud-free, and robust estimate of the spectral response of the barest state (i.e. least vegetated) across the whole continent of Australia at 25 m 2 resolution. Importantly, our method preserves the spectral relationships between different wavelengths of the spectra. This means that our freely available continental-scale product can be combined with machine learning for enhanced geological mapping, mineral exploration, digital soil mapping, and establishing environmental baselines for understanding and responding to food security, climate change, environmental degradation, water scarcity, and threatened biodiversity. In this study, the authors combine Landsat images spanning 30 years with a new statistical estimator to produce a soil and mineral spectra map of the Australian continent largely unobscured by vegetation or clouds.

The crater and proximal effects of the largest known young meteorite impact on Earth have eluded discovery for nearly a century. We present 4 lines of evidence that the 0.79-Ma impact crater of the Australasian tektites lies buried beneath lavas of a long-lived, 910-km³ volcanic field in Southern Laos: 1) Tektite geochemistry implies the presence of young, weathered basalts at the site at the time of the impact. 2) Geologic mapping and 40Ar-39Ar dates confirm that both pre- and postimpact basaltic lavas exist at the proposed impact site and that postimpact basalts wholly cover it. 3) A gravity anomaly there may also reflect the presence of a buried ∼17 × 13-km crater. 4) The nature of an outcrop of thick, crudely layered, bouldery sandstone and mudstone breccia 10–20 km from the center of the impact and fractured quartz grains within its boulder clasts support its being part of the proximal ejecta blanket.

Identifying flood risk-prone areas in the regions of extreme aridity conditions is essential for mitigating flood risk and rainwater harvesting. Accordingly, the present work is addressed to the assessment of the flood risk depending on spatial analytic hierarchy process of the integration between both Remote Sensing Techniques (RST) and Geographic Information Systems (GIS). This integration results in enhancing the analysis with the savings of time and efforts. There are several remote sensing-based data used in conducting this research, including a digital elevation model with an accuracy of 30 m, spatial soil and geologic maps, historical daily rainfall records, and data on rainwater drainage systems. Five return periods (REPs) (2, 5, 10, 25, 50, 100, and 200 years) corresponding to flood hazards and vulnerability developments maps were applied via the weighted overlay technique. Although the results indicate lower rates of annual rainfall (53–71 mm from the southeast to the northwest), the city has been exposed to destructive flash floods. The flood risk categories for a 100-year REP were very high, high, medium, low, and very low with 17%, 41%, 33%, 8%, and 1% of total area, respectively. These classes correspond to residential zones and principal roads, which lead to catastrophic flash floods. These floods have caused socioeconomic losses, soil erosion, infrastructure damage, land degradation, vegetation loss, and submergence of cities, as well life loss. The results prove the GIS and RST effectiveness in mitigating flood risks and in helping decision makers in flood risk mitigation and rainwater harvesting.

The geological map of Piemonte Region (Italy) is a graphic representation of the geology of the region, grounded on a large geodatabase, that can be also browsed as an interactive scalable map (GeoPiemonte Map) using a WebGIS application. The Map, produced at 1:250,000 scale, is the first original release of the 'GeoPiemonte Map' project. The geological data represented on the map derive from a thorough revision of available geological maps and literature, integrated with unpublished original data. The revision and harmonisation of existing and new data have been based on explicit criteria used for the classification of geologic units and their representation on the Map. These criteria firstly aimed at providing a lithostratigraphic, hierarchic subdivision of Piemonte geologic units and describing them using shared concepts and vocabularies, consistent with IUGS Descriptive Standards for the Geosciences.