Search Results (79)

The Santa Elena Peninsula has experienced local subduction earthquakes in 1901 (7.7 Mw) and 1933 (6.9 Mw), during which local ground conditions, including deposits of longshore-current sediments, paleo-lagoon or marsh, sandspit, and ancient tidal channel sediments, exhibited various coseismic deformation behaviors in Quaternary soils of inferior geotechnical quality. This study shows that geophysical profiles from seismic refraction and shear-wave velocities are correlated with stratigraphic data from sedimentary sequences obtained from slope cutting and geotechnical drilling. This database is used to create a comprehensive map to describe the lithological units of Salinas’ urban geology. The thickness of the Tertiary–Quaternary sedimentary sequences and the depth to the bedrock of the Piñon and Cayo geological formations determine the periods of sites in these stratigraphic sequences, which range from 0.3 to 1.5 s. This study provides the first geotechnical zoning map for the city of Salinas at a scale of 1:25,000, which is a technical requirement of the Ecuadorian construction standard. This geotechnical zoning information is essential for appropriate land management in Salinas and its neighboring cities, La Libertad and Santa Elena, as well as for outlining municipal restrictions on future construction. Full article

This study presents the digitization and integration of Kazakhstan’s legacy gravimetric maps at a scale of 1:200,000 into a modern geospatial database using ArcGIS. The primary objective was to convert analog gravity data into a structured, queryable, and spatially analyzable digital format to support contemporary geoscientific applications, including geoid modeling and regional geophysical analysis. The project addresses critical gaps in national gravity coverage, particularly in underrepresented regions such as the Caspian Sea basin and the northeastern frontier, thereby enhancing the accessibility and utility of gravity data for multidisciplinary research. The methodology involved a systematic workflow: assessment and selection of gravimetric maps, raster image enhancement, georeferencing, and digitization of observation points and anomaly values. Elevation data and terrain corrections were incorporated where available, and metadata fields were populated with information on the methods and accuracy of elevation determination. Gravity anomalies were recalculated, including Bouguer anomalies (with densities of 2.67 g/cm³ and 2.30 g/cm³), normal gravity, and free-air anomalies. A unified ArcGIS geodatabase was developed, containing spatial and attribute data for all digitized surveys. The final deliverables include a 1:1,000,000-scale gravimetric map of free-air gravity anomalies for the entire territory of Kazakhstan, a comprehensive technical report, and supporting cartographic products. The project adhered to national and international geophysical mapping standards and utilized validated interpolation and error estimation techniques to ensure data quality. The validation process by the modern gravimetric surveys also confirmed the validity and reliability of the digitized historical data. This digitization effort significantly modernizes Kazakhstan’s gravimetric infrastructure, providing a robust foundation for geoid modeling, tectonic studies, and resource exploration. Full article

Today’s rapidly increasing number and performance of Remotely Piloted Aircraft Systems (RPASs) and sensors allows for an innovative approach in monitoring, mitigating, and responding to natural disasters and risks. At present, there are 100s of different RPAS platforms and smaller and more affordable payload sensors. As natural disasters pose ever increasing risks to society and the environment, it is imperative that these RPASs are utilized effectively. In order to exploit these advances, this study presents the development and validation of a Natural Disaster Information System (NDIS), a geospatial decision-support framework for RPAS-based natural hazard missions. The system integrates a global geohazard database with specifications of geophysical sensors and RPAS platforms to automate mission planning in a generalized form. NDIS v1.0 uses decision tree algorithms to select suitable sensors and platforms based on hazard type, distance to infrastructure, and survey feasibility. NDIS v2.0 introduces a Random Forest method and a Critical Path Method (CPM) to further optimize task sequencing and mission timing. The latest version, NDIS v3.8.3, implements a staggered decision workflow that sequentially maps hazard type and disaster stage to appropriate survey methods, sensor payloads, and compatible RPAS using rule-based and threshold-based filtering. RPAS selection considers payload capacity and range thresholds, adjusted dynamically by proximity, and ranks candidate platforms using hazard- and sensor-specific endurance criteria. The system is implemented using ArcGIS Pro 3.4.0, ArcGIS Experience Builder (2025 cloud release), and Azure Web App Services (Python 3.10 runtime). NDIS supports both batch processing and interactive real-time queries through a web-based user interface. Additional features include a statistical overview dashboard to help users interpret dataset distribution, and a crowdsourced input module that enables community-contributed hazard data via ArcGIS Survey123. NDIS is presented and validated in, for example, applications related to volcanic hazards in Indonesia. These capabilities make NDIS a scalable, adaptable, and operationally meaningful tool for multi-hazard monitoring and remote sensing mission planning. Full article

This study presents the results of preliminary documentation and radon tracer investigations conducted at the “Aurelia” Mine in Złotoryja. Measurements of ²²²Rn activity concentrations were carried out between 17 March and 26 August 2023, while terrestrial laser scanning (TLS) for mapping purposes was performed on 16 November 2024. The radon data exhibited a consistently right-skewed distribution, with skewness coefficients ranging from 0.9 to 8.2 and substantial standard deviations, indicating significant data dispersion. Outliers and extreme outliers were identified as key factors influencing average radon activity concentrations from April through August, whereas data from March displayed homogeneity, with no detected anomalies. The average ²²²Rn activity concentrations recorded from March to July ranged from 51.4 Bq/m³ to 65.9 Bq/m³. In contrast, July and August showed elevated average values (75.8 Bq/m³ and 5784.8 Bq/m³, respectively) due to the presence of outliers and extreme values. Upon removal of these anomalies, the adjusted means were 73.8 Bq/m³ and 1003.6 Bq/m³, respectively, resulting in reduced skewness and improved representativeness. These findings suggest that the annual average radon concentrations at the “Aurelia” Mine remain compliant with the regulatory threshold of 300 Bq/m³ set by the Atomic Law Act, with exceedances likely related to atypical or rare geophysical phenomena requiring further statistical validation. August exhibited a significant occurrence of outliers and extreme outliers in ²²²Rn activity concentration data, particularly concentrated between the 13th and 17th days of the month. This anomaly is hypothesized to be associated with geological processes, notably mining-induced seismic events within the LGOM (Legnica–Głogów Copper District) region. It is proposed that periodic transitions between tensional and compressional phases within the rock mass, triggered by mining activity, may lead to abrupt increases in radon exhalation, potentially occurring before or after seismic events with a magnitude exceeding 2.5. Although the presented data provide preliminary evidence supporting the influence of tectonic kinematic changes on subsurface radon dynamics, further systematic observations are required to confirm this relationship. At the current stage, the hypothesis remains speculative but may contribute to the broader understanding of radon behavior in geologically active underground environments. Complementing the geochemical analysis, TLS enabled detailed geological mapping and 3D spatial modeling of the mine’s underground tourist infrastructure. The resulting simplified linked data model—integrating radon activity concentrations, geological structures, and spatial parameters—provides a foundational framework for developing a comprehensive GIS database. This integrative approach highlights the feasibility of combining tracer studies with spatial and cartographic data to improve radon risk assessment models and ensure regulatory compliance in underground occupational settings. Full article

Impact cratering determined by collisions with meteorites and asteroids is considered one of the main natural processes in the Solar System, modifying the planets and their satellites surface during time. The Earth includes in its impact record a small number of such events due to active plate tectonics, sedimentation, and volcanism, with these geological processes destroying and burying their impact geomorphological signatures. To enlarge the Earth’s impacts database, new concepts and research methods are necessary, as well as the reinterpretation of old geological and geophysical models. Geomorphological, Geological, and Geophysical (3G) signatures in concealed impacted areas are discussed in this paper; the first offers the target characteristics, while the others give means for detecting their unseen remnants. The 3G signatures have been applied to the Transylvanian Depression, a fascinating geological structure, with difficulties in explaining the direct overlapping of regionally developed thick tuff and thick salt layers, and undecided interpretation of the regional magnetic anomaly. Large and deep sedimentary basins, such as the Precaspian, Alexandria and Transylvanian depressions, are interpreted to have started as impacted areas during the Permian or the Lower Neogene. Geophysical and geological existing information have been reinterpreted, offering a new way in understanding deeply located geological structures. Full article

The quantitative characterization of near-surface heterogeneity using ground-penetrating radar (GPR) is an important but challenging task. The estimation of subsurface geostatistical parameters from surface-based common-offset GPR reflection data has so far relied upon a Monte-Carlo-type inversion approach. This allows for a comprehensive exploration of the parameter space and provides some measure of uncertainty with regard to the inferred results. However, the associated computational costs are inherently high. To alleviate this problem, we present an alternative deep-learning-based technique, that, once trained in a supervised context, allows us to perform the same task in a highly efficient manner. The proposed approach uses a convolutional neural network (CNN), which is trained on a vast database of autocorrelations obtained from synthetic GPR images for a comprehensive range of stochastic subsurface models. An important aspect of the training process is that the synthetic GPR data are generated using a computationally efficient approximate solution of the underlying physical problem. This strategy effectively addresses the notorious challenge of insufficient training data, which frequently impedes the application of deep-learning-based methods in applied geophysics. Tests on a wide range of realistic synthetic GPR data generated using a finite-difference time-domain (FDTD) solution of Maxwell’s equations, as well as a comparison with the results of the traditional Monte Carlo approach on a pertinent field dataset, confirm the viability of the proposed method, even in the presence of significant levels of data noise. Our results also demonstrate that typical mismatches between the dominant frequencies of the analyzed and training data can be readily alleviated through simple spectral shifting. Full article

The 2012 Emilia earthquake highlighted the vulnerability of fortified architecture. Based on the observed seismic behaviors, this research proposes a GIS geodatabase, designed with a proactive approach, for the prediction and prevention—at a territorial scale—of the most frequent damage mechanisms of the investigated typology. The designed geo-database allows for the identification of possible correlations between constructive features and the occurrence of damage, through statistical and geo-referenced analysis. Moreover, the designed geodatabase, by enabling the comparison of the damage level data with the seismic action of the site, through INGV (National Institute of Geophysics and Volcanology) shakemaps, allowed the definition of experimental fragility curves, for three of the most common damage mechanisms. By applying these functions to castles in the province of Parma, it was possible to define future seismic risk scenarios for the mechanisms considered, thanks to the use of the seismic hazard map. Therefore, the described methodology could be functional to identify the most urgent and high-priority interventions in order to optimize the management of economic resources. The final aim is to promote the application of the concept of minimum intervention, and more in general to preserve the architectural heritage, avoiding emergency interventions and aiming instead to apply planned conservation strategies. Full article

Soil degradation is a critical challenge to global agricultural sustainability, driven by intensive land use, unsustainable farming practices, and climate change. Conventional soil monitoring techniques often rely on invasive sampling methods, which can be labor-intensive, disruptive, and limited in spatial coverage. In contrast, non-invasive geophysical techniques, particularly ground-penetrating radar, have gained attention as tools for assessing soil properties. However, an assessment of ground-penetrating radar’s applications in agricultural soil research—particularly for detecting soil structural changes related to degradation—remains undetermined. To address this issue, a systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines. A search was conducted across Scopus and Web of Science databases, as well as relevant review articles and study reference lists, up to 31 December 2024. This process resulted in 86 potentially relevant studies, of which 24 met the eligibility criteria and were included in the final review. The analysis revealed that the ground-penetrating radar allows the detection of structural changes associated with tillage practices and heavy machinery traffic in agricultural lands, namely topsoil disintegration and soil compaction, both of which are important indicators of soil degradation. These variations are reflected in changes in electrical permittivity and reflectivity, particularly above the tillage horizon. These shifts are associated with lower soil water content, increased soil homogeneity, and heightened wave reflectivity at the upper boundary of compacted soil. The latter is linked to density contrasts and waterlogging above this layer. Additionally, ground-penetrating radar has demonstrated its potential in mapping alterations in electrical permittivity related to preferential water flow pathways, detecting shifts in soil organic carbon distribution, identifying disruptions in root systems due to tillage, and assessing soil conditions potentially affected by excessive fertilization in iron oxide-rich soils. Future research should focus on refining methodologies to improve the ground-penetrating radar’s ability to quantify soil degradation processes with greater accuracy. In particular, there is a need for standardized experimental protocols to evaluate the effects of monocultures on soil fertility, assess the impact of excessive fertilization effects on soil acidity, and integrate ground-penetrating radar with complementary geophysical and remote sensing techniques for a holistic approach to soil health monitoring. Full article

The magnetic field penetration depth poses challenges in communications, geophysical exploration, biomedical therapies, and superconducting device design. The objective of this systematic review was to identify, analyze, and understand the depth of magnetic field penetration in conductors, semiconductors, superconductors, insulators and natural materials, modelling applications in medicine, engineering, and basic sciences. An exhaustive search was carried out in databases such as Scopus, IEEE Xplore, ScienceDirect, and IOPSCIENCE using keywords such as depth and penetration and magnetic and field and materials. PRISMA was used as a method to define inclusion and exclusion criteria, ensuring quality, transparency, and methodological rigor, followed by a detailed statistical analysis. The results showed that the penetration depth varies according to the material, frequency, and application conditions. In human tissues, infrastructure, and agricultural applications, the penetration range is between 0.01 and 0.2 m, with the possibility of a range greater than one meter in specific applications. The higher the frequency, the lower the penetration depth is confirmed. In conclusion, the penetration of the magnetic field depends on material properties such as conductivity and magnetic permeability, frequency, and temperature. The developed models offer relevant information for future research and technological applications. Full article

In the context of seismic hazard assessment and engineering design, a comprehensive understanding of local geological and geophysical factors is essential. However, previous studies have lacked crucial components such as local soil condition, ground response analysis, topographic influences, active fault characteristics, slip rates, groundwater behaviour, and slope considerations. To ensure the accuracy of the seismic hazard map of a country for the safe and cost-effective design of engineering structures in urban areas, a detailed analysis of these factors is imperative. Moreover, multidisciplinary investigations, such as logic-tree considerations, are needed to enhance seismic hazard maps. As a result, adopting a performance-based approach in structural design has become an essential priority. A performance-based approach allows engineers to design buildings to specified performance levels (IO, LS, CP) even without a reliable seismic hazard map. This approach is akin to a miracle for countries that do not have a reliable seismic hazard map. This study presents a systematic and comprehensive bibliometric analysis of the academic literature pertaining to performance-based design (PBD). By fostering collaborative efforts and expanding research networks, we aim to facilitate the development of coordinated initiatives within the field. Preferred journals, leading countries, leading organisations, and international institutions were identified utilizing the Scopus database. This study examined 3456 PBD-related publications spanning from 1969 to 2023 using VOSviewer version 1.6.19, a bibliometric mapping and visualization software tool. The analysis of co-citations revealed that performance-based design serves as the primary theoretical foundation for structural design and analysis. Furthermore, through a co-word analysis, we tracked the evolution of research topics within the PBD domain over time. This investigation uncovered noteworthy trends, including the steady growth of research output, the increasing prominence of the term “PBD”, and a focus on various types of performance-based analyses. Full article

Modern satellite positioning and navigation technologies are not applicable in specific areas such as the exploration of oil and gas deposits by means of directional drilling techniques. Here, we can rely solely on natural geophysical fields, such as the Earth’s magnetic field. The precise underground navigation of borehole drilling instruments requires a seamless, near-real-time access to operational geomagnetic data. This paper describes the MAGNUS BD hardware-software system, deployed at the Geophysical Center of the Russian Academy of Sciences, that provides the efficient accumulation, storage, and processing of geomagnetic data. This system, based on the Big Data (BD) technology, is a modern successor of the MAGNUS processing software complex developed in 2016. MAGNUS BD represents one of the first cases of the BD technology’s application to geomagnetic data. Its implementation provided a significant increase in the speed of information processing and allowed for the use of high-frequency geomagnetic satellite data and expanding the overall functionality of the system. During the MAGNUS BD system’s deployment on a physically separate dedicated cluster, the existing classical database (DB) was migrated to the Arenadata database with full preservation of its functionality. This paper gives a brief analysis of the current problems of directional drilling geomagnetic support and outlines the possible solutions using the MAGNUS BD system. Full article

Melothria pendula L., a wild relative of cucurbit crops, is also used for food and as a medicinal plant in Mexico. The objective of this study was to ecogeographically characterize the known populations of M. pendula in Mexico, determining its adaptive range and possible sites for in situ and ex situ conservation. To achieve this goal, we compiled a dataset of 1270 occurrences of M. pendula from herbarium and botanical databases and individual observations. Adaptive scenarios were generated through the development of an ecogeographic land characterization (ELC) map, preceded by the identification of abiotic variables influencing the species’ distribution. Eleven bioclimatic, edaphic, and geophysical variables were found to be important for the species’ distribution. The ELC map obtained contained 21 ecogeographic categories, with 14 exhibiting the presence of M. pendula. By analyzing ecogeographic representativeness, 111 sites of high interest were selected for the efficient collection of M. pendula in Mexico. Eight high-priority hotspots for future in situ conservation of M. pendula were also identified based on their high ecogeographic diversity, with only three of these hotspots located within protected natural areas. In this study, ecogeographic approaches show their potential utility in conservation prioritization when genetic data are scarce, a very common condition in crop wild relatives. Full article

Research on rainfall is critically important due to its significant impact on climate change and natural disasters in Indonesia. Various factors influence rainfall variability. Consequently, when examining spatial aspects, it is likely that spatial dependency exists not only in the response variable but also in the exogenous variables. Hence, a model that accounts for spatial dependencies between these variables is required. The integration of the Spatial Durbin Model (SDM) with Casetti’s expansion approach can be utilized to predict spatial patterns of rainfall influenced by exogenous variables. By incorporating spatial effects and relevant independent variables, this model can provide more precise estimates of rainfall distribution across different regions. This modeling technique is particularly effective for accurate rainfall prediction, considering exogenous factors such as air temperature, humidity, solar irradiation, and surface pressure. The SDM with Casetti’s expansion approach was employed to predict rainfall patterns in the Java Island region, utilizing data from the National Aeronautics and Space Administration’s Prediction of Worldwide Energy Resources (NASA POWER) big data website. The application of this model in the context of rainfall prediction highlights its importance in enhancing the understanding of weather dynamics and aiding disaster risk mitigation in Java Island, a highly populated region characterized by a Monsoon rainfall pattern. The rainfall prediction follows a Knowledge Discovery in Databases (KDD) methodology. The results of this study are expected to be valuable to relevant agencies, such as the Meteorology, Climatology, and Geophysics Agency (BMKG), and agribusiness companies, improving agricultural planning and planting seasons. Additionally, the general public can benefit from more accurate climate information, particularly regarding rainfall. The computational framework is developed within an RShiny web application, and the performance of the proposed technique is measured by the Mean Absolute Percentage Error (MAPE), achieving a very accurate prediction rate of 2.78%. Full article

Application of lineament analysis in structural geology gained renewed interest when remote sensing data and technology became available through dedicated Earth observation satellites like Landsat in 1972. Lineament data have since been widely used in general structural investigations and resource and geohazard studies. The present contribution argues that lineament analysis remains a useful tool in structural geology research both at the regional and local scales. However, the traditional “lineament study” is only one of several methods. It is argued here that structural and lineament remote sensing studies can be separated into four distinct strategies or approaches. The general analyzing approach includes general structural analysis and identification of foliation patterns and composite structural units (mega-units). The general approach is routinely used by most geologists in preparation for field work, and it is argued that at least parts of this should be performed manually by staff who will participate in the field activity. We argue that this approach should be a cyclic process so that the lineament database is continuously revised by the integration of data acquired by field data and supplementary data sets, like geophysical geochronological data. To ensure that general geological (field) knowledge is not neglected, it is our experience that at least a part of this type of analysis should be performed manually. The statistical approach conforms with what most geologists would regard as “lineament analysis” and is based on statistical scrutiny of the available lineament data with the aim of identifying zones of an enhanced (or subdued) lineament density. It would commonly predict the general geometric characteristics and classification of individual lineaments or groups of lineaments. Due to efficiency, capacity, consistency of interpretation methods, interpretation and statistical handling, this interpretative approach may most conveniently be performed through the use of automatized methods, namely by applying algorithms for pattern recognition and machine learning. The focused and dynamic approaches focus on specified lineaments or faults and commonly include a full structural geological analysis and data acquired from field work. It is emphasized that geophysical (potential field) data should be utilized in lineament analysis wherever available in all approaches. Furthermore, great care should be taken in the construction of the database, which should be tailored for this kind of study. The database should have a 3D or even 4D capacity and be object-oriented and designed to absorb different (and even unforeseen) data types on all scales. It should also be designed to interface with shifting modeling tools and other databases. Studies of the Norwegian mainland have utilized most of these strategies in lineament studies on different scales. It is concluded that lineament studies have revealed fracture and fault systems and the geometric relations between them, which would have remained unknown without application of remote sensing data and lineament analysis. Full article

The World Heritage Committee has been meeting to discuss the arrangements of existing World Heritage Sites, and, on 22–26 October, the area from Giza to the Dahshur was included in the list of World Heritage Sites. According to the Egyptian Antiquities Authority (EAA), the groundwater levels at the Pyramids Plateau are too shallow, which threatens the ancient Sphinx and Pyramids in Giza, Egypt. In addition, many geophysical studies have been carried out in the archaeological area of Giza, which prove that the area is facing the risk of a high level of groundwater, specifically threatening the Sphinx. Recent developments in Earth observation have helped in the field of land monitoring such as land use changes, risk observation, and the creation of models for protecting cultural heritage sites. This study aimed to examine the impact of land use changes on on the archaeological sites of the Giza Necropolis area by integrating various data sources including optical satellite imagery and SRTM data during the period of 1965–2019. A historical database of Corona 1965 and Landsat 2009 data was investigated along with the new acquisitions of Sentinel-2 2016 and Sentinel-1 2016 and 2019. In addition, the radar Sentinel-1 SLC data were collected and analyzed for calculating the land subsidence value in the area of interest through two periods between 6–30 July 2016 and 30 July–15 December 2016. Various methods were implemented, including cluster outliers, the Moran index, and spatial autocorrelation to examine the changes in urban masses. Additionally, the relationship between groundwater leakage and land subsidence in the region was investigated. The analysis was carried out using Envi5.3, ArcMap10.6.1, and SNAP6.0 software to extract spatial data from the raw data. The results from our investigation highlighted rapid changes in urban areas between 1965 and 2019. The data obtained and analyzed from optical and radar satellite imagery showed that changes in land use can cause changes in the topographic situation by decreasing the level of groundwater, which adversely affects Egyptian monumental pyramids and the Sphinx. Land use analysis showed that the urban area represented 7.63% of the total area of the study area in 1965, however it reached 32.72% in 2009, approximately half of the total area in 2016, and in 2019, the urban mass area increased to nearly two-thirds of the total area. The annual growth rate between 1965 and 2019 was estimated by nearly 0.642 km²/year. These land-use changes possibly affected the land subsidence value (−0.0138 m), causing the rising groundwater level close to the Sphinx. Using the information obtained from our RS- and GIS-based analysis, mitigation strategies have also been identified to support archaeological area preservation. Full article