The growing volume of high-resolution time-series data in Earth system science requires the implementation of standardised and reproducible quality control workflows to ensure compliance with the FAIR data standards. Automated tools such as SaQC¹ address this need, but lack the capacity for manual data review and flagging. We are therefore planning to develop a Python-based tool with an intuitive graphical user interface (GUI) for local machines, thereby enhancing the functionality of SaQC. It is anticipated that the tool will be user-friendly, even for those with limited experience of Python. The GUI will be capable of interactively visualising the time-series data, highlighting the data that has already been automatically flagged. The selection of data points may be accomplished by clicking on them or via box-selection, and a flag may be assigned via a dropdown menu. An optional comment field can be used to record supplementary information, such as details of pollution events. Moreover, the option to unflag data that has failed the automated quality control process, but which is considered valid by the scientist, will be available.

The manual flagging tool will be based on SaQC, thereby facilitating a future integration into this software package. Consequently, integration into an existing SaQC workflow will be straightforward. It should be noted, however, that this is not exclusive to SaQC users; it can be easily applied to data created by another tool for automatic quality control. A simple conversion of the data via the pandas library will be sufficient for utilisation of the manual flagging tool. The flagging schemes can either be adopted from SaQC or user-specific schemes can be integrated. Once the flagging process is completed, the user is able to decide how to export the data set.

The manual flagging tool represents a valuable addition to existing toolkits for all scientists handling time-series datasets, effectively completing the data quality control process. From a scientific perspective, the benefits of this tool include increased efficiency and traceability in the data flow, as well as improved data quality through the fine-tuning of automatic controls based on experience and contextual knowledge.

¹ Schäfer, David, Palm, Bert, Lünenschloß, Peter, Schmidt, Lennart, & Bumberger, Jan. (2023). System for automated Quality Control - SaQC (2.3.0). Zenodo. https://doi.org/10.5281/zenodo.5888547

How to cite: Büttner, N., Louisot, B., Lorenz, C., Schäfer, D., and Fösig, R.: Manual data review and quality control – An add-on to SaQC, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16190, https://doi.org/10.5194/egusphere-egu25-16190, 2025.

Communicating marine geohazards to stakeholders can be challenging, and traditional media may not be sufficient to convey the full range of processes involved. In addition, news images of devastating events such as tsunamis and volcanic eruptions can be associated with prejudice and fear, hindering fact-based awareness-raising. With recent advances in computer graphics and virtual reality headset hardware, immersive visualization methods are becoming accessible to a wider scientific community. Virtual presentations of different local scenarios provide an opportunity to discuss with experts, policy makers and the general public, overcoming abstraction and prejudice and transforming scenarios into realistic and spatially explicit experiences.

The MULTI-MAREX collaborative project is establishing a living lab in the Aegean Sea to study extreme marine geological events and associated hazards, with the aim of developing the knowledge needed to manage geohazards at different scales. Digital reconstruction of real, physical study sites leads to and enhances situational awareness, resulting in a personalized, in-depth understanding of local scenarios. Accessibility of the communication format is important to reach the target user.

We are exploring a wide range of hardware options, from the portability and convenience of smartphones, to the highly immersive experiences offered by head-mounted displays, through immersive simulators such as video walls and dome theatres. These diverse platforms ensure that immersive visualizations are adaptable to different user needs and environments, facilitating greater accessibility and engagement across stakeholder groups. We focus on developing workflows for geoscientists to enable semi-automated, asset-enhanced, immersive visualization that synthesize collected remote sensing data, such as terrestrial and marine digital outcrop models, hydroacoustic and numerical simulations within popular game engines. The use of popular game engines to seamlessly integrate different data types enables dynamic and interactive environments where users can explore scenarios in real time, enhancing both scientific analysis and stakeholder engagement to bridge the gap between complex geohazard science and effective stakeholder understanding, enabling informed decision making and risk management.

How to cite: Eisermann, J. O., Gross, F., Vollert, J., Wolf, J., Petersen, L., Kopp, H., Berndt, C., Reicherter, K., Krastel, S., Hübscher, C., Wagner-Ahlfs, C., Kwasnitschka, T., and Bernstetter, A.: Immersive Visualizations for Marine Geohazards in the Aegean Sea: Bridging Science and Stakeholder Engagement., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5968, https://doi.org/10.5194/egusphere-egu25-5968, 2025.

The Korea Meteorological Administration has developed a tool that can visualize various numerical model data in three dimensions by combining them with spatial information to support forecasters' weather analysis work.
The 3D visualization tool is a precise analysis tool that can compare and analyze weather data according to time, space, and altitude.
An open-source 3D visualization engine (CesiumJS) was applied to display numerical model data on 3D spatial information.
Spatial information displays major spatial information (national and administrative boundaries, major roads, rivers, etc.) and a three-dimensional numerical elevation model.
The numerical model data was developed to express the precursor model (ECMWF) and the local model (LDAPS), but it was flexibly implemented to express other numerical model data.
It is expected that more three-dimensional and precise analysis will be possible by combining numerical model data with spatial information and analyzing it in three dimensions.

How to cite: Kim, S.: Development of 3D visualization tool for KMA (Korea Meteorological Administration) numerical model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2209, https://doi.org/10.5194/egusphere-egu25-2209, 2025.

Cloud MICAPS Engine is a next-generation cloud and component-based application development framework for Meteorological Information Comprehensive Analysis and Processing System(MICAPS)  in China, which has the following features:

I.Microservice Architecture
It utilizes a hybrid microservices management framework composed of K8s (Kubernetes) and Spring Cloud. The primary computational services are based on the underlying K8s + container cloud to implement microservices, while the upper-layer business applications integrate business-oriented microservices functions through Spring Cloud.

II.Real-time Visualization and Analysis
Developed using B/S technology, all weather-related business data is encapsulated as meteorological data layers through methods such as OGC, resumable transmission, and streaming services for loading by front-end business applications. It includes professional analysis components for common meteorological business operations such as points, lines, and surfaces, as well as interactive analysis of time curves, vertical soundings, and arbitrary sections.

III.Real-time Visualization Rendering
Based on real-time drawing technologies such as WebGL and WebGPU, it supports real-time product map services for products with high access volumes through pre-processing and pre-service methods, forming a highly consistent map service data environment with an integrated approach.

IV.Collaborative Editing and Forecast Service
Based on a 2D/3D map engine, the high-resolution real-time data visualization rendering technology is supported by CogTiff data storage and service technology. Real-time synchronization of interactive editing operations is achieved through WebSocket to realize real-time collaboration among multi-terminals. The back-end data editing algorithm realizes the consistency of updated data in FIFO.

V.LLM-driven Interaction and Processing
Large language models technology is used, with aggregating algorithms in the whole process of intelligent digital forecast service business, including observation perception, analysis diagnosis, interactive judgment, processing and generation, inspection and evaluation. "AI Agent" is the core to drive human-computer intelligent interaction and information recommendation.

Cloud MICAPS Engine  will be released later in 2025.

How to cite: Xue, F.: Progress in Cloud MICAPS Engine, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1941, https://doi.org/10.5194/egusphere-egu25-1941, 2025.

Standardisation of geological maps visualisation is crucial for improving data legibility and comparison across different scales and regions. In Poland, overview geological maps ranging from scales of 1:2,500,000 to 1:500,000 have been traditionally prepared using distinct graphical styles, each tailored to the particular characteristics of the mapped geological units. These maps used to employ individual patterns and colour palettes to enhance the visibility and readability of geological features, prioritising the requirements of printed editions. However, differences in the number and types of geological units across various maps led to inconsistent visual representations, limiting the ease of comparison between them.

To address the above, the Polish Geological Institute’s team embraced the idea of creating a unified, semantically harmonised graphical style for overview geological maps. The main objective was to develop a common style for all stratigraphic units, which could be applied across various maps of Poland, particularly those prepared for online publication in the frame of the Polish Geological Cartograhy Platform. This experiment aimed to standardise the colour and pattern schemes, building upon the stratigraphic classification system provided by the International Commission on Stratigraphy (ICS), but with necessary extensions to accommodate mixed stratigraphy.

While this approach slightly reduced the visibility of details in certain areas, it significantly enhanced the comparability of geological data across maps. By adopting a consistent visual language, the maps delivered a clearer cartographic message, particularly when zooming in and out in map viewers. The harmonisation of the graphical style also enhanced data visualisation across various scales, making it easier for geologists to interpret and compare geological units.

This initiative was inspired by earlier efforts, such as the OneGeology initiative and the INSPIRE Directive, both of which sought to standardise the visualisation of lithological and stratigraphic data. However, these frameworks primarily focused on older geological units and their principal lithologies, which could lead to potential misinterpretations of the data. For example, geological units spanning from the Cambrian to the Devonian period were often represented using a single colour, which could obscure their true geological diversity. To address this, PGI team proposed the use of distinct colours for each geological period, drawing inspiration from the Commission for the Geological Map of the World (CGMW) colour codes.

The results of this experiment demonstrate that a semantically harmonised approach to geological map visualisation not only enhances the clarity of individual maps but also makes data more comparable across different scales and regions. By providing a consistent and intuitive visual representation of geological units, this method helps to improve the overall understanding of geological data and facilitates its use in various scientific, educational, and practical contexts.

How to cite: Jóźwik, K., Urszula, S., and Joanna, P.: Zoom in - zoom out challenge: Semantically and visually coherent overview geological maps of Poland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10206, https://doi.org/10.5194/egusphere-egu25-10206, 2025.

Although digitized, geological maps have poorly evolved since their inception and still retain the constraints imposed by printing: heterogeneity between map sheets, limited point data, and challenging update procedure that remains essentially nonexistent.

The heterogeneity of geological maps also complicates their integration to Information System. Maps, based on rules and concepts that evolved over time, are no longer suited to the precision required by database structures. As concepts and models evolve in geology together with the scientific knowledge, the choice of the representation on the map also depends on the authors’ interpretations and the period of production. As a result, the mapped geological units reflect arbitrarily chosen characteristics of the rocks concerning either their protolith nature, their metamorphic characteristics or alteration transformations, considered as the most representative at the date of publication.

In France, 1:50,000 scale geological maps provide a full territory coverage but do not escape these heterogeneity issues. To address this problem, the BRGM (French Geological Survey) has implemented the RGF program (Référentiel Géologique de la France), which aims to develop a methodology to overcome these limitations and propose an innovative approach to represent the geological knowledge. In this research program new data are acquired by PhD students allowing to improve geological knowledge and to produce updated and harmonized information on geological maps and boreholes.

The tools developed and implemented over the past decade, based on the establishment of an Information System structured as a knowledge database called the Geological Reference System, now make it possible to offer a standardized representation of geological knowledge derived from traditional geological maps. This knowledge can be represented through different reference systems: lithostratigraphic unit, event (and thus the geological history), or domain and zone (e.g. lithotectonic units).

Here, we present some of the results obtained from works conducted in the Alps, the Pyrenees, and the Montagne Noire, illustrating how this structuring of geological knowledge into reference system enables the creation of new maps tailored to the geological information one wishes to represent and, consequently, to the scientific and societal challenges at hand.

How to cite: Padel, M., Le Bayon, B., Bernachot, I., Cagnard, F., Plunder, A., Issautier, B., Baudin, T., Tissoux, H., Lacquement, F., Grataloup, S., and Ricodel, C.: The geological reference system: from the concept of an integrated geological knowledge management to cartographic representation., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11034, https://doi.org/10.5194/egusphere-egu25-11034, 2025.

The RGF research program (French Geological Reference platform) aims to establish a continuous and coherent geological knowledge base covering the entire French territory. To achieve this, the program relies on the development and implementation of comprehensive geological reference systems designed to structure, harmonize, and manage geological data, including:

• The lithostratigraphic reference system, which provides a hierarchical classification of geological units (super-group, group, sub-group, formation, member),
• The geological events reference system, which organizes and ranks geological events to document and reconstruct the geological history of geological units,
• The lithotectonic reference system, encompassing structural zones, hierarchically organized tectono-stratigraphic units, and paleogeographic domains,
• The geological boundaries reference system, which catalogs structural and geologic contact information.

These reference systems are being conceived and structured in alignment with international standards such as GeoSciML and INSPIRE. Built as PostgreSQL databases, they are under development through close collaboration between geologists, computer scientists, and GIS experts to address scientific requirements and serve as a repository of geological knowledge.

At the same time, tools and applications are being developed to utilize these reference systems to constrain the attribution of geological elements across various datasets (e.g. boreholes, geological maps, 3D models), thus facilitating the harmonization, enrichment, and updating of legacy data. This includes, for example, the modernization of historical French geological maps at a 1:50,000 scale. The process involves linking map geometries to the reference systems through GIS tools and custom QGIS plugins developed by BRGM. This approach supports the transition from static maps to dynamic, multi-scale digital representations, and enables the creation of maps tailored to various scientific objectives and practical applications.

This presentation gives an overview of the lithotectonic and geological event reference systems, the underlying database model and the QGIS plugins developed for their application. Initially developed within the RGF and now within the Digital Earth project of the PEPR research program, these tools are also applicable to other projects, such as the ongoing development of the European Lithotectonic Map by the GSEU. The presentation will also give an overview of the work carried out in the Pyrenees region, demonstrating the dissemination of updated maps of lithostratigraphic units and the ability to query geological events associated with specific formations, all accessible through a dedicated ArcGIS mapping viewer.

How to cite: Bernachot, I., Le Bayon, B., Padel, M., Cagnard, F., Plunder, A., and Dechambenoit, G.: Development of geological reference systems: integrating databases and tools to improve geological knowledge and data harmonization, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11428, https://doi.org/10.5194/egusphere-egu25-11428, 2025.

Field observations and measurements are essential for reconstructing the geological history of a region and for producing accurate maps and models. For national geological surveys, these raw data are critical resources that need to be efficiently managed, stored and disseminated for effective reuse in research. At BRGM, GeoField¹, an application developed within the framework of the Référentiel Géologique de la France (RGF)² and subsequently extended to support other geological projects, allows field data to be managed and capitalised according to the geological reference system, ensuring compatibility with internal and international standards. This system facilitates the compilation, sharing and reuse of data in accordance with the FAIR principles (Findable, Accessible, Interoperable, Reusable).

Recently, a data acquisition process was implemented to streamline field data collection and enable direct integration into GeoField. This workflow relies on QField³, an open-source mobile application built on the QGIS engine, leveraging key features such as seamless integration with QGIS projects, customizable data forms, the ability to use proper vocabularies, and offline mapping capabilities. A dedicated master project was designed to meet the specific needs of field geologists, enabling them to capture essential information through tailored forms, complete with dropdown lists to ensure consistency in terminology. Adapted symbology allows for real-time visualization of structural measurements on the map. The master project is made available to users through a QGIS plugin, which loads the project template, including a predefined database structure and up-to-date BRGM lexicons. Users can then customize their project by adding relevant layers, such as map backgrounds and other vector or raster data (e.g., DEM, geochemical analysis, geophysical data), and load the project onto their mobile devices for field acquisition.

Upon returning to the office, the plugin facilitates the automatic transfer of field data into GeoField, ensuring seamless integration into the BRGM central database. This workflow provides a robust, efficient, and standardized approach to geological data collection, capitalizing on the synergies between QGIS/QField and GeoField to enhance data management, sharing, and reuse within the geoscientific community.

¹GeoField page: https://rgf.brgm.fr/page/geofield

²RGF page: https://rgf.brgm.fr/

³QField - Efficient field work built for QGIS, url: https://qfield.org/

How to cite: Stephan-Perrey, J., Bernachot, I., Plunder, A., Padel, M., Le Bayon, B., and Bezard, M.: Streamlining geological field data collection, management, and integration with QField, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17590, https://doi.org/10.5194/egusphere-egu25-17590, 2025.

The island of Pantelleria is an active volcanic complex, rising 800 m from sea level, situated on a continental rift within the NW sector of the Sicily Channel (Italy).

The island is characterized by a bimodal magmatism, mafic and felsic, with this latter -by far more abundant- including metaluminous trachyte and pantellerite (peralkaline rhyolites) magmas, erupted either as lava flows, pumice falls or pyroclastic currents.

This scenario, in addition to discontinuous field exposures and closely spaced (in space and time) explosive events produced by a multitude of eruptive centers producing compositionally similar deposits, makes challenging the detailed stratigraphic reconstruction of the volcanological evolution of the island.

In the last 40 years many studies have focused on specific volcanological, geochronological geochemical and petrological aspects of the island, unravelling peculiar eruptive dynamics and petrographic aspects of peralkaline magmatism. These efforts produced great steps forward in the knowledge of the volcanological evolution of the island, strictly tied to the peculiarities of peralkaline magmas. Nevertheless, a detailed geological map comprehensive the entire volcanological evolution (pre- and post -Green Tuff ignimbrite eruption) is still missing.

In the mid 70’s and 80’s unofficial schematic geological maps have been realized, mostly focused on lithological aspects of the erupted products and their areal distribution, summarized in the seminal paper of Mahood & Hildreth (1986) the first to define a comprehensive of pre- and post-Green Tuff stratigraphy. These studies generated discordant stratigraphic subdivisions and a not univocal stratigraphic nomenclature, until the work of Jordan et al. (2018) who defined an accurate stratigraphy and nomenclature of the pre-Green Tuff ignimbrite eruptions, adjuvated by progresses made by slightly earlier geochronological and paleomagnetic studies.  The stratigraphy of post-Green Tuff volcanism, mildly explosive to effusive, though much improved by recent Ar/Ar, field and petrographic studies, still has some doubtful points.

To summarize, harmonize and integrate all the existing data into a single detailed product, the Geological Survey of Italy (in collaboration with Palermo University) performed, between 2022 and 2024, a field survey at scale 1:10.000 in the framework of the Italian Geological Mapping Project 1:50.000 scale (CARG project).

Here, we present the preliminary geological field map of Pantelleria Island (according to CARG Project guidelines) corroborated by existent and new geochronological, geochemical and petrographic analyses on hundreds of samples collected from the different volcanic structures of the island.

Such detailed field mapping of the entire island, together with the geological survey of the offshore area (still in progress), updating the findings of previous studies, allows us to obtain the first official geological map of such an active volcanic island. This represents the first fundamental step for the development of future studies of volcanic hazard assessment.

References:

Mahood, G.A., Hildreth, W. Geology of the peralkaline volcano at Pantelleria, Strait of Sicily. Bull Volcanol 48, 143–172 (1986).

Jordan, N. J., Rotolo, S. G., Williams R., Speranza, F., McIntosh, W. C., Branney, M. J., Scaillet S. Explosive eruptive history of Pantelleria, Italy: Repeated caldera collapse and ignimbrite emplacement at a peralkaline volcano. JVGR, 349, 47-73, 2018

How to cite: Pensa, A., Bonomo, R., Cinquegrani, A., Ricci, V., Rotolo, S. G., Urbani, S., and Vita, L.: Unravelling the complex volcanological evolution of Pantelleria Island (Channel of Sicily, Italy): new insights from the Italian Geological Mapping Project (CARG Project), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10643, https://doi.org/10.5194/egusphere-egu25-10643, 2025.

This study introduces a novel methodology to enhance the accuracy and efficiency of ⁸⁷Sr/⁸⁶Sr isoscape mapping by integrating deep learning (DL) techniques with geostatistical methods. Utilizing kriging-based data augmentation, the proposed framework addresses data scarcity by generating high-quality synthetic training data while incorporating spatial geological factors and geochemical elements as input variables to improve model performance. The study developed an isotopic basemap for South Korea using 409 soil samples and a feedforward deep neural network (FDNN) model. The FDNN model demonstrated superior accuracy (91.67%) compared to traditional kriging (76.8%) and convolutional neural network (CNN)-based models (86.14%). The robustness of the FDNN model was significantly enhanced by kriging-based data augmentation, which not only captured geological anisotropies but also incorporated uncertainty analysis to improve reliability. The resulting ⁸⁷Sr/⁸⁶Sr isoscape map revealed distinct isotopic distributions across South Korea, with higher ratios associated with metamorphic and granitic rocks, reflecting geological history and topographical influences. Notably, the predicted isotopic distributions closely aligned with the boundaries of tectonic provinces, underscoring the geospatial accuracy of the developed model. Validation using bone samples additionally confirmed the efficacy of the proposed method in accurately estimating isotopic levels. These findings highlight the potential of combining geostatistical and DL approaches to overcome traditional challenges in isotopic mapping, offering scalable solutions for applications in environmental monitoring, archaeology, and provenance studies.

How to cite: Lee, H. and Jeong, J.: Enhancing Deep Learning-based Strontium Isotopic Landscape Estimation Using Geostatistical Method: A Case Study in South Korea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5423, https://doi.org/10.5194/egusphere-egu25-5423, 2025.

The application of complementary geochemical analysis alongside deep learning techniques serves as a powerful tool in identifying geographical origin of environmental samples on a national scale. This research presents methodologies aimed at enhancing the accuracy of origin determination for soil samples across South Korea, leveraging geochemical and geological data. It addresses challenges associated with integrating Sr isotopes and multivariate geochemical variables and preserving geological interpretability by incorporating Autoencoder deep learning algorithms,which facilitate efficient feature engineering for comprehensive data analysis. Through the analysis of 412 soil samples collected nationwide, a geographic origin distribution and classification model was developed, establishing a novel framework for environmental sample analysis. The analysis identified six origins within South Korea, each distinguished by its geological tectonic units, bedrock age, and bedrock type. Extensively wide areas with granite bedrock nationwide were mostly classified into the same origin, irrespective of their geological tectonic configurations. The findings highlight the efficacy of integrating isotopic with geochemical data through advanced analytical techniques, significantly improving origin tracing accuracy and efficiency. Such advancements have significant implications for disciplines including agriculture, forensics, and archaeology, showcasing the potential of these methodological innovations.

How to cite: Lee, S. and Jeong, J.: Utilizing deep learning-based feature engineering for effective geographical origin subdivision and classification of environmental soil samples in South Korea, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5488, https://doi.org/10.5194/egusphere-egu25-5488, 2025.

The segmentation of ice in X-ray Computed Tomography (CT) scans of permafrost samples has traditionally relied on the Hounsfield Unit (HU) thresholding approach while their accuracy is often limited by overlapping density ranges in complex and heterogeneous samples. Recent advances, including automated thresholding algorithms and machine learning techniques, offer improved precision by leveraging texture, contrast, and morphological features in CT images. This study investigates the evolution of ice segmentation methodologies by applying multiple approaches to a 164 cm long permafrost core drilled from a Yedoma upland in north-eastern Siberia. The core was analyzed using traditional HU thresholding, automated thresholding methods (e.g., Otsu and adaptive histogram-based segmentation), and machine learning models (e.g., random forests and convolutional neural networks). The results from CT scans and segmentation methods were validated and compared against laboratory measurements of ice content and density, ensuring a robust evaluation of each technique's accuracy and reliability.

The results provide critical insights into the strengths, weaknesses, and suitability of different segmentation methods for permafrost cores. These findings contribute to the development of standardized, high-precision methodologies for non-destructive characterization of ice-rich soils, supporting geotechnical and climate change studies in permafrost regions.

How to cite: Roustaei, M., Nitzbon, J., Harvey, J., Francis, E., Schlueter, S., Boike, J., and Froese, D.: Improving Ice Segmentation in Permafrost Cores using Computed Tomography, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20681, https://doi.org/10.5194/egusphere-egu25-20681, 2025.