High-resolution population maps play a critical role in addressing the growing risks of urban disasters. This study develops a transferable, building-scale population spatialization framework for residential areas, entirely using freely accessible open data. The framework avoids dependence on costly or sensitive fine-grained demographic datasets and overcomes the limitations of census data, which are updated infrequently and available only at coarse spatial scales. Using 10-meter SDGSAT-1 NTL data, we applied a statistical modeling approach directly at the community level within residential areas, effectively resolving the scale inconsistency that often arises when coarse-scale models are downscaled to finer resolutions. We further introduced a Building Residential Weight index that integrates building capacity, occupancy rate, and functional attributes. This factor enables the population of each community to be proportionally allocated to its buildings, producing a detailed and realistic building-level population distribution. Model evaluation experiments demonstrate that the Random Forest algorithm achieved the highest modeling accuracy in this study, with an R2 of 0.779, representing an improvement of more than 0.55 compared with widely used global population datasets such as WorldPop, LandScan, and GHS-Pop. The generated building-level population distribution maps provide a high-resolution spatial foundation for megacity disaster risk management, resource allocation, and urban planning.
This study investigates the factors associated with failure in each of the four thematic units of a General Statistics course offered at a private university in Colombia. Unlike traditional analyses that treat performance as a single outcome, this research disaggregates results by unit: Exploratory Data Analysis, Probability and Random Variables, Statistical Inference, and Linear Regression -- highlighting distinct challenges across content areas. Based on a sample of 186 undergraduate students from Engineering, Geology, and Interactive Design programs, the study combines exam performance data with self-perceived preparedness surveys to develop unit-specific logistic regression models. The findings reveal consistent structural disadvantages for students from non-engineering programs, especially in concept-heavy units such as Inference and Regression. Academic stage and perception of competence also emerged as important predictors, though their effects varied across units. The results align with prior research on statistical thinking and self-efficacy, and support the need for targeted pedagogical interventions and curricular alignment. This disaggregated approach offers a more nuanced understanding of academic vulnerability in statistics education and contributes to the design of evidence-based, context-sensitive strategies to reduce failure and improve learning outcomes.
Co-production of geothermal energy and lithium is an emerging opportunity with the potential to enhance the economic potential of geothermal operations. The economic reward of extracting lithium from geothermal brine is determined by how the lithium concentration evolves during brine production. In the initial stage, production will target lithium contained in the brine resident close to the production well. While lithium recharge, in the form of rock dissolution and inflow from other parts of the reservoir, is possible, the efficiency of such recharge depends on the geology of the reservoir. In this work, we study how structural heterogeneities in the form of fractures impact the flow of lithium-carrying brine. Using a numerical simulation tool that gives high resolution of flow and transport in fractures and the host rock, we study how the presence of fractures influences energy and lithium production. Our simulations show that, due to heat conduction and the lack of mineral recharge from the rock, differences in fracture network geometries have a much larger impact on lithium production than energy production. The simulations thus confirm that in addition to the geochemical characterisation of lithium in geothermal brines, understanding fracture characterisation and its impact on production is highly important for lithium production.
Christofer Valencia, Alexis Llumigusín, Silvia Alvarez
et al.
Geotechnical reports are crucial for assessing the stability of rock formations and ensuring safety in modern engineering. Traditionally, these reports are prepared manually based on field observations using compasses, magnifying glasses, and notebooks. This method is slow, prone to errors, and subjective in its interpretations. To overcome these limitations, the use of artificial intelligence techniques is proposed for the automatic generation of reports through the processing of images and field data. The methodology was based on the collection of photographs of rock outcrops and manual samples with their respective descriptions, as well as on the reports prepared during the Geotechnical Studies course. These resources were used to define the report outline, prompt engineering, and validate the responses of a multimodal large language model (MLLM). The iterative refinement of prompts until structured and specific instructions were obtained for each section of the report proved to be an effective alternative to the costly process of fine-tuning the MLLM. The system evaluation establishes values of 0.455 and 0.653 for the BLEU and ROUGE-L metrics, respectively, suggesting that automatic descriptions are comparable to those made by experts. This tool, accessible via the web, with an intuitive interface and the ability to export to standardized formats, represents an innovation and an important contribution for professionals and students of field geology.
Hiroki Senshu, Takahide Mizuno, Toru Nakura
et al.
Abstract The spacecraft for the Japanese Martian Moon eXploration mission is equipped with a LIDAR laser altimeter. The slant range continuously measured by the LIDAR is used for the correction of the local topography and the orbit and attitude of the spacecraft. The channel and gain setting of the LIDAR in the proximity phase will be automatically controlled based on the received energy. This paper reports the result of ground-based calibration tests. The calibration function is obtained for each channel and gain settings. Then, the performance test of the auto gain control function is carried out by changing the received energy gradually. This test demonstrates that the automatic gain control system of the LIDAR works well and the obtained slant range and received energy change smoothly.
In environmental management, monitoring transitions toward regenerative agriculture (RA) supports carbon offset initiatives aligned with Regulation (EU) 2018/841. Current Land Use, Land Use Change, and Forestry (LULUCF) platforms primarily analyze macro-scale Earth Observation (EO) vegetation trends, yet are increasingly enhancing ground-based data collection. This study integrates these approaches through a methodological workflow comprising: (1) a survey segment with a 30 × 30 m pixel sampling grid for landscape-scale trend assessment and sub-hectare Survey Validation Areas delineating specific RA management practices; and (2) an EO monitoring segment using Landsat 5, 7, and 8 time series, processed in R and Google Earth Engine (GEE) to model 30 m phenological dynamics, alongside 10 m Sentinel-2 NDVI 15-day Maximum Value Composites published via a GEE application (RegenAPP). Applied to an experimental RA site, La Junquera – Camp Altiplano (Murcia, Spain), the workflow enabled fine-scale analyses, identifying greening trends in no-till RA plots in contrast to browning in adjacent tilled organic fields. Sub-hectare analyses further detailed phenological patterns linked to specific RA practices. This integrated EO–Survey approach complements LULUCF assessments by coupling EO-derived vegetation analytics with targeted field validation, capturing spatial and temporal RA transition dynamics.
The study focuses on the hydrogeological characterization of the aquifer system in the central sector of Cartago, Costa Rica. This area was selected due to its significant urbanization and agricultural activities, both of which heavily depend on groundwater resources. The conceptual hydrogeological model was developed using well records, field hydrogeological observations along rivers and material extraction pits, macroscopic sample collection for thin-section analysis, spring and well inventories, and piezometric level analysis. A series of hydrogeological profiles were modeled to visualize the subsurface configuration of hydrogeological units and their relationships with existing geological materials. In areas with sufficient well density and ad¬equate geographic distribution, the groundwater flow dynamics within the hydrogeological units were also analyzed. The results revealed that the aquifer system consists of a variety of materials, predominantly alluvial and laharic deposits, which function as aquifer hydrogeological units. These materials contain interspersed clay lenses, fine sands, and coarse sands, which collectively influence the formation of saturated zones, aquitards, and aquicludes. Additionally, these characteristics determine the degree of confinement of the aquifer units. In some sectors, this confinement results in water upwelling, creating artesian conditions. Flow directions were predominantly oriented from north to south, following the surface gradient, although variations in flow direction highlighted the complexity and interconnectivity of the units. For the first time, the hydrogeological model of the Cartago aquifer system was defined. It comprises the Taras, La Chinchilla, Cartago, El Bosque, Tejar, and Dulce Nombre hydrogeological units. Each of these units corresponds to a specific portion of the study area within the central sector of Cartago, which lies atop the Cartago aquifer system.
Comparing spherical probability distributions is of great interest in various fields, including geology, medical domains, computer vision, and deep representation learning. The utility of optimal transport-based distances, such as the Wasserstein distance, for comparing probability measures has spurred active research in developing computationally efficient variations of these distances for spherical probability measures. This paper introduces a high-speed and highly parallelizable distance for comparing spherical measures using the stereographic projection and the generalized Radon transform, which we refer to as the Stereographic Spherical Sliced Wasserstein (S3W) distance. We carefully address the distance distortion caused by the stereographic projection and provide an extensive theoretical analysis of our proposed metric and its rotationally invariant variation. Finally, we evaluate the performance of the proposed metrics and compare them with recent baselines in terms of both speed and accuracy through a wide range of numerical studies, including gradient flows and self-supervised learning. Our code is available at https://github.com/mint-vu/s3wd.
Water exhibits complex behaviors as a result of hydrogen bonding, and low-dimensional confined water plays a key role in material science, geology, and biology science. Conventional techniques like STM, TEM, and AFM enable atomic-scale observations but face limitations under ambient conditions and surface topographies. NV center magnetic resonance technology provides an opportunity to overcome these limitations, offering non-contact atomic-scale measurements with chemical resolution capability. In this study, a nanoscale layer dissection method was developed utilizing NV center technology to analyze water layers with diverse physicochemical properties. It unveiled the presence of a non-evaporating sub-nanometer water layer on a diamond surface under ambient conditions. This layer demonstrated impervious to atmospheric water vapor and exhibited unique electronic transport mediated via hydrogen bonding. These findings provide new perspectives and a platform for studying the structure and behavior of low-dimensional water, as well as the surface properties influenced by adsorbed water under native conditions.
In recent years, muon tomography has turned into a powerful and innovative technique for non-invasive imaging of large and small structures with applications in different areas like geology, archaeology, security, etc. We present the design and simulation of a transportable and easy to construct detector based on plastic scintillator and Silicon photomultipliers current technology. From a flux of cosmic rays reaching the atmosphere we simulated atmospheric muons at ground using CORSIKA. The detector and the object to analyze are simulated with GEANT4, where the previously obtained muon flux is transported. We use two methods for muon tomography to differentiate objects made of different materials: absorption and scattering. The statistical differences for several object sizes and materials are quantified. Using a threshold of 3 $σ$ in the first method, we conclude that materials made of lead can be differentiated from objects made of other materials. The observation time needed to differentiate an object made of lead from one of aluminum was 4.9 and 9.9 days using the first and second method, respectively. In general, the absorption method gives the best results.
Seismic geobody interpretation is crucial for structural geology studies and various engineering applications. Existing deep learning methods show promise but lack support for multi-modal inputs and struggle to generalize to different geobody types or surveys. We introduce a promptable foundation model for interpreting any geobodies across seismic surveys. This model integrates a pre-trained vision foundation model (VFM) with a sophisticated multi-modal prompt engine. The VFM, pre-trained on massive natural images and fine-tuned on seismic data, provides robust feature extraction for cross-survey generalization. The prompt engine incorporates multi-modal prior information to iteratively refine geobody delineation. Extensive experiments demonstrate the model's superior accuracy, scalability from 2D to 3D, and generalizability to various geobody types, including those unseen during training. To our knowledge, this is the first highly scalable and versatile multi-modal foundation model capable of interpreting any geobodies across surveys while supporting real-time interactions. Our approach establishes a new paradigm for geoscientific data interpretation, with broad potential for transfer to other tasks.
Abstract Monitoring and predicting fault slip behaviors in subduction zones is essential for understanding earthquake cycles and assessing future earthquake potential. We developed a data assimilation method for fault slip monitoring and the short-term prediction of slow slip events, and applied to the 2010 Bungo Channel slow slip event in southwest Japan. The observed geodetic data were quantitatively explained using a physics-based model with data assimilation. We investigated short-term predictability by assimilating observation data within limited periods. Without prior constraints on fault slip style, observations solely during slip acceleration predicted the occurrence of a fast slip; however, the inclusion of slip deceleration data successfully predicted a slow transient slip. With prior constraints to exclude unstable slip, the assimilation of data after slow slip event occurrence also predicted a slow transient slip. This study provides a tool using data assimilation for fault slip monitoring and prediction based on real observation data. Graphical Abstract
Mohamed Mahmoud Sebbab, Abdelhadi El Ouahidi, Mehdi Ousbih
et al.
The purpose of this paper is to identify, quantify and delineate the areas with suitable aggregate resources in the Precambrian massif of Ifni and the limestone plateau of Lakhssas (southwest Morocco). To fulfill this objective, a study was undertaken on the geotechnical parameters of the various geological outcrops of the region based on the analysis of 42 rock samples (carbonate, magmatic, detritic and volcano-detritic). Initially, we subjected these samples to a series of laboratory tests (impact resistance (L.A), wear resistance (MDE), density, porosity, absorption), to classify them according to geotechnical standards. Then, a geospatial database was created, to exploit these geotechnical data, from a geographical information system (GIS) to produce various thematic maps. Based on the results of this study, all geotechnical classes according to the standards (A to E for the European standard and 1A to 6D for the Moroccan standard) are present with good to very good geomechanical properties (L.A between 12% and 35%, MDE between 5% and 30%). This classification allowed us to use GIS to identify and quantify potential areas for exploitation by assigning five categories of geotechnical suitability levels (high (4), medium (3), low (2), very low (1) and others (0)) and to show that approximately 72% of the study area belongs to the categories high, medium and low. The combination of laboratory results and GIS has allowed us to carry out geotechnical mapping that will be used by regional authorities and actors for good management of the field of quarrying to rationalize the national natural heritage.