PICO
We welcome contributions that introduce software tools/toolboxes and their real-world applications, showcasing how they have advanced the field, providing practical insights into the development/application process. Additionally, we seek presentations that discuss methodologies for software testing, continuous integration in software projects, upgrades and deployment. Moreover, we are looking for case studies demonstrating the successful implementation of these tools in various seismological/geophysical problems and how these can bring value to the community.
Sharing of resources, toolboxes, and knowledge is encouraged to improve the overall quality and (re)usability of research software. We encourage the inclusion of demonstrations to showcase usability and functionality examples, as well as videos to illustrate proposed workflows. Videos and other resources can be added as supplementary material and will be available after the conference. Depending on the technical setup and the time available, we will also support live demonstrations for the on-site participants.
We warmly invite seismologists, geophysicists, software developers, and researchers to participate in this session and share their insights, experiences, and solutions to elevate software development standards and practices in our field. Join us to contribute to and learn from discussions that will drive innovation and excellence in seismological and geophysical research.
Focal mechanism and moment tensor computation based on regional and local waveforms has become routine task in seismology. These tools are essential for understanding seismotectonic stress regimes and are among the most widely used data for stress inversion, playing a crucial role in identifying deformation zones and tectonically active structures at both local and regional scales (e.g., Totaro et al., GRL 2016; Martínez-Garzón et al., JGR SE 2016).
Many different and similar approaches are available to perform inversion for the double-couple, deviatoric or full moment tensor. However, a key aspect often not fully addressed is the estimation of moment tensor uncertainty. It can be mostly caused by measurement (e.g., data contamination by noise) and theory errors (e.g., mathematical simplifications), and can affect the accuracy of results limiting their interpretation. Over the past decades, considerable efforts have been made in this context, and Bayesian inference is increasingly being applied in moment tensor inversion problems due to the advantage of quantifying parameter uncertainties (Vasyura-Bathke et al., SRL 2020). The Bayesian approach allows for a thorough exploration of the solution space by using appropriate samplers (e.g., Del Moral et al., JRSS 2006) and generates an ensemble of solutions rather than a single optimal one, providing a measure of uncertainty based on the solution distribution.
In this study, we focused on testing the stability of double-couple solutions obtained using two recently developed open-source software packages: BEAT (Bayesian Earthquake Analysis Tool; Vasyura-Bathke et al., SRL 2020) and MCMTpy (Yin and Wang, SRL 2022). These moment tensor inversion algorithms are extremely useful for estimating source parameter uncertainties and the range of acceptable solutions. We applied them to the 2016 Mw 6.0 Amatrice mainshock and a Mw 3.2 earthquake from the same sequence occurred in Central Italy, in order to check the performance of the algorithms at different magnitude levels. We selected this region due to several reasons: it is characterized by active tectonics, it benefits from good azimuthal coverage of seismic stations, and it offers plenty of moment tensor solutions obtained using different approaches (e.g., Scognamiglio et al., BSSA 2009; Artale Harris et al., JGR SE 2022).
For these two earthquakes we compared the results obtained by BEAT and MCMTpy with solutions available in the main seismic catalogs to evaluate the overall coherence of the results and the possible improvements in resolution and robustness. Then, we focused on the performance evaluations by proposing a series of methodological tests which simulate different data setup as not-optimal network geometry, epicentral location errors, biases in the velocity model. By applying these tests on the selected algorithms, we (i) explored their stability, (ii) identified their limitations in resolving double-couple moment tensors and (iii) evaluated the related uncertainty estimates. By doing so, we provide a comprehensive understanding of how these algorithms perform in real-world scenarios and we also suggest an approach useful to verify and eventually compare the performance of moment tensor inversion algorithms also taking into account the uncertainty estimates.
How to cite: Mancuso, T., Totaro, C., and Orecchio, B.: Comparison of moment tensor inversion methods in a Bayesian framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-399, https://doi.org/10.5194/egusphere-egu25-399, 2025.
The 2D Bayesian transdimensional inversion methodology is a data-driven methodology that allows for multiple solutions and does not need regularization. This is because Bayesian transdimensional inversion allows the retrieval of the parameters and the number of parameters needed to explain the data simultaneously. It also has intrinsic parsimony, meaning simple solutions will be chosen over complex ones. For all these reasons, it is a perfect tool to retrieve the spatial b-value variation.
The b-value corresponds to the slope of the Gutenberg–Richter law, which relates the number of earthquakes with their magnitude. Several authors agree that the changepoints of the b-value (i.e., the places where the b-value varies) show more valuable information than the value by itself. In particular, the spatial changes in the b-value in seismicity catalogs have been associated with different stresses, fluid processes, geological structures, and earthquake hazard estimation.
Given this parameter's importance, robustly retrieving and characterizing b-values and their changepoints is essential. In general, most of the methodologies to retrieve the b-value fix the spatial window of the seismic catalog (i.e., binning) and/or use optimization methods to obtain the values, introducing methodological bias in the solutions. For this reason, we use the Bayesian transdimensional approach to objectively estimate b-value variations along two arbitrary dimensions. This implementation allows a self-defined seismic domain according to the seismic catalog information, where it is unnecessary to prescribe the location and extent of domains, as other methodologies do.
This study focuses on obtaining 2D spatial b-values changes across the seismic region. To explore the possible changes in the b-value along the space, we use the TransTessellate2D algorithm that allows us to implement the trans-dimensional inference methodology for 2D cartesian problems with Voronoi cells. The synthetic tests were performed to analyze the spatial resolution of the methodology and the smallest b-value variation that the method can retrieve. This methodology has been successfully implemented in central-northern Chile and California, allowing us to characterize the mechanical behavior on the plate interface of subduction and cortical zones, obtaining a similar solution to previous studies, evidencing the reliability of the Bayesian transdimensional method for capturing robust b-value variations. Our future work includes extending the approach to other 2D dimensions (e.g., time, latitude, longitude, depth).
How to cite: Morales-Yáñez, C., Benavente, R., Cummins, P., Sambridge, M., and Hawkins, R.: 2D Bayesian transdimensional inversion for b-value variations, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14527, https://doi.org/10.5194/egusphere-egu25-14527, 2025.
The Seismic Hazard Parameters Evaluation (SHAPE) toolbox (Leptokaropoulos and Lasocki, SRL, 2020, https://doi.org/10.1785/0220190319) has evolved into SHAppE, an interactive MATLAB app. SHAppE facilitates probabilistic assessment of seismic hazard parameters, including the mean return period (MRP) and exceedance probability (EP) of earthquake magnitudes, along with confidence intervals. Its interactive features support real-time analysis and visualization, making it suitable for researchers and practitioners analyzing time-dependent seismicity, such as aftershocks, stress triggering, and seismicity induced by human activities.
SHAppE offers a graphical user interface (GUI) that simplifies parameter selection and data filtering, making it more accessible to users with limited programming experience. It supports four magnitude distribution models, the Unbounded and Truncated versions of the Gutenberg-Richter law and non-parametric Kernel density estimation. The app is demonstrated through case studies from regional datasets (e.g., Song Tranh 2 reservoir in Vietnam) and global catalogues (ISC), showcasing its utility in monitoring seismic responses and evaluating hazard mitigation measures. All input parameters, output data, and results are systematically archived to ensure thorough experiment tracking and facilitate reproducibility. SHAppE provides an intuitive platform, suitable for research and teaching time-dependent, probabilistic seismic hazard analysis.
How to cite: Redfearn, A. and Leptokaropoulos, K.: Interactive Time-Dependent Seismic Hazard Assessment with SHAppE, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8491, https://doi.org/10.5194/egusphere-egu25-8491, 2025.
Confirming the prompt and accurate notification of earthquakes is vital for mitigating their potential impacts. To achieve this, statistical approaches, including Machine Learning (ML), have become indispensable tools across various scientific fields, particularly in Seismology and seismic data. This research explores the utilization of ML techniques to improve earthquake real time alerts. The case study is Greece and the surrounding region, an area with highest seismic activity throughout the Mediterranean.
This work is focused on the real time collection and processing of an extensive earthquake dataset to generate earthquake alerts by making phone calls and providing details about the time, magnitude, and epicenter of each seismic event. Previous efforts aimed to extend these alerts beyond the notifications (emails and messages) that analysts at the Seismological Center of AUTH (Aristotle University of Thessaloniki) received during their duty. The goal was to make these alerts accessible to all citizens, communities, civil protection agencies and various authorities (e.g. municipalities, schools, police, etc.). The island of Kefalonia served as a pilot region where this functionality was initially implemented. We then chose to extend the application to all Ionian islands to encompass the entire region.
The new insight here is the development of a mobile application that allows users to define a specific geographical region for receiving notifications-alerts. The AI Service will combine the real time earthquake information in conjunction with the geometry defined by each user in order to classify whether a notification should be sent to that specific user.
As training input data used in the application, we first require a catalog of earthquakes spanning from 2011 to 2025 with M≥3.0, along with demographic data for Greece region provided by the Hellenic Statistical Authority. A radius around each epicenter is calculated by considering the earthquake’s macroseismic Intensity (I), the earthquake’s magnitude (M), earthquake depth, total population and number of households within the calculated radius. The labeled dataset is then used to train a classification model via Azure AutoML. This model identifies significant earthquakes and determines which areas to call in order to provide earthquake alert. Notification messages could be to any subscribed mobile number with the calling voice available in Greek, English, or French.
How to cite: Paradisopoulou, P., Spyrou, G., Karagianni, I., Adamaki, A., and Leptokaropoulos, K.: Applying Statistical and Machine Learning Methods for rapid earthquake alert system in Greece with a new mobile application , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19627, https://doi.org/10.5194/egusphere-egu25-19627, 2025.
Recently the automatic processing of seismological data recorded in areas with anthropogenic seismicity has become an important issue, as the number of available recordings has increased significantly over the past few years. The problem is also addressed within the DT-Geo Project WP8: Anthropogenic Geophysical Extremes, where a specific workflow is being developed for the automatic processing of induced seismicity-related waveforms. The workflow is designed as a set of independent applications implemented inside the interactive, publicly available Episodes Platform (https://episodesplatform.eu/). The applications created for the workflow include:
- A tool for picking the first arrivals of seismic waves using neural network-based solutions available within the SeisBench library,
- A phase association tool that employs the PyOcto algorithm,
- Location procedures already existing within the Episodes Platform.
- An application for calculating spectral parameters using the spectral fitting method,
Ultimately, the workflow will enable fully automated processing of raw and continuous seismic data, including event detection, localization, and spectral parameter calculation. The workflow can be used on the Episodes Platform either with data collected from various Episodes, which are geophysical datasets related to regions where induced seismicity has been observed, or with datasets uploaded by the user.
This work is supported by Horizon Europe grant DT-Geo 101058129 and a project co-financed by the Minister of Science Republic of Poland under contract no. 2024/WK/05. We gratefully acknowledge Polish high-performance computing infrastructure PLGrid (HPC Centers: ACK Cyfronet AGH) for providing computer facilities and support within computational grant no. PLG/2024/017279.
How to cite: Rudzinski, L., Kokowski, J., Kocot, J., and Siejkowski, H.: Automatic workflow for detection, localization, and calculation of spectral parameters for induced seismicity on the Episodes Platform, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10682, https://doi.org/10.5194/egusphere-egu25-10682, 2025.
The use of open-source processing tools represents a strategic resource for the scientific community. The Open Science philosophy (https://www.unesco.org/en/open-science) promotes transparency, reproducibility and accessibility to data and source codes. This not only ensures continuous and collaborative development, but also increases the quality of proposed solutions.
Characterizing the near surface based on geophysical methods is of considerable interest for many disciplines, and the reliability and quality of the provided results is tied to the available processing resources. The surface wave analysis (SWA) of active seismic data is widely used to determine the shear wave velocities of a site. Several efforts have been made to create open-source tools for SWA, starting with the precursor Geopsy (Wathelet, 2005), continuing with the more recent SWIP (Pasquet and Bodet, 2017), MASWaves (Olafsdottir et al., 2018), and SWprocess (Vantassel and Cox, 2022). The classical procedure they propose is limited to a local 1D analysis on (moving) spatial windows, where homogeneous conditions are assumed. Although this is a robust approach, it does not highlight small-scale lateral variations.
In this talk, we introduce a new open-source tool under continuous development for processing surface wave data. The Python-based library incorporates, in addition to the classical 1D analysis on moving windows, more advanced techniques such as the Multi-Offset Phase Analysis (MOPA; Strobbia and Foti, 2006) and the Tomography-like approach (Barone et al., 2021), which perform high-resolution 2D SWA for a more accurate identification of lateral velocity variations. The ultimate intent of our Python library is to contribute to further developing standards for processing and inversion of surface wave data in a proper 2D sense.
References
Barone I., Boaga J., Carrera A., Flores Orozco A. and Cassiani G., 2021. Tackling Lateral Variability Using Surface Waves: A Tomography-Like Approach. Surveys in Geophysics 42, no. 2, 317–38. https://doi.org/10.1007/s10712-021-09631-x
Olafsdottir E. A., Erlingsson S., and Bessason B, 2018. Tool for Analysis of Multichannel Analysis of Surface Waves (MASW) Field Data and Evaluation of Shear Wave Velocity Profiles of Soils. Canadian Geotechnical Journal 55, no. 2, 217–233. https://doi.org/10.1139/cgj-2016-0302
Pasquet S., and Bodet L., 2017. SWIP: An Integrated Workflow for Surface-Wave Dispersion Inversion and Profiling. GEOPHYSICS 82, no. 6, WB47–61. https://doi.org/10.1190/geo2016-0625.1
Strobbia C., and Foti S., 2006. Multi-Offset Phase Analysis of Surface Wave Data (MOPA). Journal of Applied Geophysics 59, no. 4, 300–313. https://doi.org/10.1016/j.jappgeo.2005.10.009
Vantassel J. P., and Cox B.R., 2022. SWprocess: A Workflow for Developing Robust Estimates of Surface Wave Dispersion Uncertainty. Journal of Seismology 26, no. 4, 731–56. https://doi.org/10.1007/s10950-021-10035-y
Wathelet M., 2005. Array recordings of ambient vibrations: surface-wave inversion. Ph.D. Thesis, University of Liège (Belgium)
How to cite: Barone, I., Roser, N., Carrera, A., and Flores Orozco, A.: A new open-source Python toolbox for processing seismic surface wave data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6731, https://doi.org/10.5194/egusphere-egu25-6731, 2025.
The Matlab toolbox B3AM (B3AMpy for Python) for three-component beamforming of ambient noise data provides a means to characterise the seismic (noise) wavefield and image near-surface seismic properties quickly and cheaply. Provided with three-component array data, B3AM outputs dispersion curves for pro-/retrograde Rayleigh and Love waves, estimates of wavefield composition and propagation direction as a function of frequency, and can be extended for surface wave anisotropy analysis. We present recent results from seismic array data gathered at geothermal sites in the Netherlands, the UK, and Switzerland using B3AM or B3AMpy.
For the geothermal site Kwintsheul (NL), we derive a shear-velocity profile for the first 500 meters, updating an existing profile based on P velocities and regional vp/vs estimates. Comparing dispersion curves from beamforming to those from cross-correlation interferometry, we find that the Rayleigh first higher mode seems to provide most of the energy in the considered frequency range and that the fundamental mode can only be recovered using the beamforming scheme but not from interferometry.
Using a nodal seismic data set collected at the Eden geothermal project (Cornwall, UK), we investigate the anisotropy of the ambient noise wavefield and relate it to faults and fractures in the area. With the additional module AssessArray we estimate the effect array geometry and source distribution have on observed anisotropy. AssesArray synthesises a data set by computing (vertical component) phase shifts at each station location corresponding to a wavefield excited by a single source or multiple sources distributed randomly around the array. We then beamform the data set as we do for real data (although for 1 component only) and analyse the variation in velocity and number of detections as a function of azimuth and frequency. We find that the array design introduces frequency dependent anisotropy as well as apparent dominant directions of wave energy that align with the maximum aperture of the array. Further, we find that the number of sources used in creating the synthetic wavefield affects the observed anisotropy. In general, we observe a larger magnitude of anisotropy for a larger number of sources, i.e., for a more complex wavefield, whereas apparent anisotropy is small or not detectable for fewer sources or a single source, respectively.
For the GeoHEAT project, which explores a joint analysis of passive seismic and borehole geo-radar data for characterising and monitoring fractured geothermal systems, we implemented and tested the beamforming workflow for a novel nodal data set from the Kanton of Thurgau (CH). Besides dispersion analysis and source directionality, we consider wavefield composition and classify time windows with respect to their dominant wave type to inform and improve Green’s function recovery for ambient noise cross-correlation tomography.
How to cite: Löer, K., Simonet, G., Kennedy, H., Finger, C., and Hudson, T.: Near-surface characterisation with B3AM: case studies of 3C ambient noise beamforming from geothermal sites across Europe, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9026, https://doi.org/10.5194/egusphere-egu25-9026, 2025.
The International Seismological Centre (ISC) combines seismic observations from ~150 agencies in ~100 counties to produce the definitive global earthquake catalogue by combining seismic phase arrivals. As well as seismic phase data, hypocentres and magnitudes the ISC Bulletin includes other earthquake parameters such as moment tensors that are reported by many agencies. This data is freely accessible, searchable and downloadable through the ISC website (www.isc.ac.uk/iscbulletin). The ISC Earthquake Toolbox for MATLAB provides access to this parametric earthquake data via a graphical user interface (GUI) within the MATLAB environment. The GUI replicates the search options of the ISC website and reads this data into MATLAB. Several live scripts are included to demonstrate how to interrogate the ISC Bulletin data. Examples include plotting earthquake aftershock sequences, comparing different magnitude and hypocentre types and authors, as well as plotting moment tensors reported in the ISC Bulletin. The toolbox also enables 3D visualisation of earthquake distributions, 2D and 3D moment tensor plotting, as well as introducing new functionality to plot moment tensors within MATLAB mapping toolbox figures. It is hoped that the ISC Earthquake Toolbox for MATLAB will be used as a teaching tool to explore the wealth of earthquake data available at the ISC, as well as a tool for researchers to build more complex applications upon. The toolbox is publicly available to download via GitHub (github.com/tomgarth/ISC_Earthquake_Toolbox) and MathWorks file exchange (https://uk.mathworks.com/matlabcentral/fileexchange/167786-isc-earthquake-toolbox).
How to cite: Garth, T., Gallacher, R., and Leptokaropoulos, K.: The International Seismological Centre (ISC) Earthquake Toolbox for MATLAB: Interactive Access to Earthquake Observations & Parameters, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20943, https://doi.org/10.5194/egusphere-egu25-20943, 2025.
The study area is Tbilisi, the capital of Georgia, which is the most densely populated part of the region and is undergoing rapid urbanization. Tbilisi is situated in a tectonically active and stressed region, characterized by significant seismic activity. Additionally, the area features various geological rock structures and complex topography; thus, the seismic effects of an earthquake will vary across different geological zones.
Given these factors, assessing the impact of natural hazards on building sites is a critical prerequisite for construction projects. To achieve this, it is necessary to analyze soil categories and physical-mechanical properties in accordance with building codes.
In the initial phase, our objective was to collect all available materials from geophysical and geological surveys conducted in Tbilisi. We created an online database that facilitated the selection of new research locations based on an engineering-geological map. Subsequently, we performed a geophysical survey at over 100 locations and generated a map of Vs30 points across Tbilisi.
Calculating the average shear wave velocity Vs for a specific depth range (top 30 meters) can be performed using various methods. We used the seismic refraction method and multi-channel analysis of surface waves (MASW), tailored to the geological area. Field data collection, processing, and interpretation were conducted according to ASTM standards. Seismic data were processed using the SeisImager and ParkSEIS software packages.
Following the guidelines outlined in the Georgian building code and Eurocode 8, we classified the ground category at each surveyed point.
How to cite: Akubardia, D., Godoladze, T., Javakhishvili, Z., Jorjiashvili, N., Tserodze, M., Tsiklauri, D., and Tatunashvili, G.: Geophysical Analysis Of Soil Properties For Engineering-Geological Studies And Urban Planning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1838, https://doi.org/10.5194/egusphere-egu25-1838, 2025.
Fracture surface morphology influences important rock joint behavior, such as shear strength, fluid flow, contaminant transport, and heat transfer. Digitizing a fracture surface in the laboratory or from a drill core is state of the art – its quantitative assessment is not. There are many suggestions and comparisons of roughness parameters, each highlighting a different morphological feature. However, models and experiments dealing with surface roughness often present only a single quantity – if any at all. This makes those experiments and simulations difficult to reproduce and compare.
On the other hand, temperature profiles along boreholes, fractures, or mine shafts can provide a tremendous amount of information. For example, the determination and monitoring of water and heat fluxes, as well as heat generation mechanisms, are possible through the analysis of such temperature-depth profiles. Hence, understanding complex hydraulic systems using temperature as a tracer is possible with comparatively simple measurement devices. However, the analysis and processing of such profiles are so far primarily based on experience and individual data perception.
This work presents two toolboxes developed to standardize data-driven analysis of geophysical data: (1) FSAT – A fracture surface analysis toolbox; (2) TDprof – Algorithm-based segmentation of temperature-depth profiles. Both toolboxes provide easy access to common methods of data analysis in their field. This includes well-documented open-source code, maintenance of the code base, videos, guides, and manuals.
Building on the experience with these two toolboxes for geophysical data analysis, this contribution highlights the differences, additional efforts needed, and potential benefits of going the extra mile in delivering (re-)usability to the scientific community, while being “low-key” on continuous maintenance.
How to cite: Heinze, T.: A fracture surface analysis toolbox and a temperature-depth profiler – toolboxes for standardized geophysical data analysis, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12479, https://doi.org/10.5194/egusphere-egu25-12479, 2025.
TerraceM is an open-source software for mapping and analysing marine terraces. One of the primary challenges in accurately mapping marine terraces is the limited availability of digital elevation data with the resolution necessary to capture the subtle and ephemeral morphology of these geomorphic features. Recent advancements in remote sensing, such as NASA's ICESat-2 satellite mission, offer new opportunities to address this limitation. The ICESat-2 was designed to study Earth's polar ice, land canopy, and bare-earth topography using its Advanced Topographic Laser Altimeter System (ATLAS), a laser-based instrument similar to a LiDAR sensor, providing highly accurate surface elevation measurements in the form of geolocated photons along profiles. While the data are not continuous, the mission has completed thousands of orbits, densely covering most of the world's coastal areas with photon profiles, making it possible to achieve highly accurate mapping of marine terraces.
The latest version of TerraceM introduces new scripts and graphical user interfaces (GUIs) to efficiently interact with ICESat-2 photon data. These features enable users to select, download, preprocess, and map marine terraces interactively. Preprocessing capabilities include filtering canopy signals and reconstructing nearshore bathymetry, allowing the analysis of both subaerial and submarine terraces. Additionally, the new version of TerraceM supports MATLAB and Python, broadening its accessibility to a wider range of users. TerraceM-3 delivers advanced modelling and mapping functionalities, empowering researchers and students involved in marine terrace studies. By leveraging ICESat-2 data, TerraceM significantly extends our ability to analyse past sea-level changes and understand the interplay between tectonics and climate processes in coastal environments.
How to cite: Jara-Muñoz, J., Weiß, M., Mey, J., Pedoja, K., and Melnick, D.: TerraceM 3.0: Advancing marine terrace mapping using worldwide open satellite altimetry of the ICESat-2 mission., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15670, https://doi.org/10.5194/egusphere-egu25-15670, 2025.
This study is about the pilot development of a satellite image-based spatial analysis tool to support rural spatial planning. For the sustainable development and systematic use of space in rural areas, accurate, integrated, and data-driven decision-making is essential. However, the dispersed management of rural-related data, non-standardized formats, and the absence of periodic monitoring systems are acting as obstacles to establishing effective plans. To address this issue, this study applied the following methodology. First, rural-related data stored separately by the central government and local governments were collected and processed into standardized spatial data based on Geographic Information System (GIS). Second, a satellite image-based facility detection and classification tool was developed for periodic and efficient monitoring of rural facilities. The target facilities were selected as livestock, factories, and solar panels, and the latest deep learning model based on HRNet-OCR architecture was implemented and optimized for the rural environment. For the training and validation data, the mosaic image of the Korean Peninsula (2019~2020) produced by KOMPSAT satellite images was used, which provided a high-resolution spatial resolution of 1m and multiple spectral bands to enable the analysis of various indicator characteristics. Third, to verify the effectiveness of the developed tool, Seosan City, Anseong City, Naju City, and Geochang County in the Republic of Korea were selected as pilot areas. These regions were deemed to represent diverse rural characteristics and facility distributions. Finally, a user-friendly web-based information support tool was developed by integrating processed rural data and satellite image analysis results. The results of this study are expected to be utilized as foundational data for establishing rural spatial plans to support rural spatial restructuring and regeneration, and the developed spatial analysis tool is deemed capable of contributing to the formulation of more efficient and sustainable rural development strategies by providing a data-driven decision support system to rural policymakers.
How to cite: Oh, K.-Y., Lee, K.-J., Chang, J.-Y., Lee, M.-J., Chae, G.-Y., and Park, N.-J.: Pilot development of a satellite image-based spatial analysis tool to support rural spatial planning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17793, https://doi.org/10.5194/egusphere-egu25-17793, 2025.
Thermochronology is one of the most versatile tools available to geoscientists to constrain the colling and exhumation history of rocks. In tectonically active mountain belts around the world, it is not unusual to have many hundreds, if not thousands of published ages available from various studies. Although several well-established thermal models allow for a detailed exploration of how cooling or exhumation rates evolved in a limited area or along a transect, integrating large, regional datasets into such models remains a major challenge. Here, we present age2exhume, a thermal model in the form of a Matlab or Python script, which can be used to rapidly obtain a synoptic overview of exhumation rates from large, regional thermochronometric datasets. The model incorporates surface temperature based on a defined lapse rate and a local topographic relief correction that is dependent on the thermochronometric system of interest. Other inputs include sample cooling age, uncertainty, and an initial (unperturbed) geothermal gradient. The model is simplified in that it assumes steady, vertical rock-uplift and unchanging topography when calculating exhumation rates. For this reason, it does not replace more powerful and versatile thermal-kinematic models, but it has the advantage of simple implementation and rapidly calculated results. In our example datasets, we show exhumation rates calculated from 1785 cooling ages from the Himalaya, 1587 cooling ages from New Zealand, and 916 cooling ages from Central Asia (Tian Shan and Pamir). Despite the synoptic nature of the results, they reflect known segmentation patterns and changing exhumation rates in areas that have undergone structural reorganization. These regionally estimated exhumation rates have been used in combination with other datasets to assess regional climatic versus tectonic controls on key aspects of the landscape, including river valley width and modern erosion patterns.
How to cite: Schildgen, T. and van der Beek, P.: Age2exhume - A Matlab/Python script to calculate exhumation rates from thermochronometric ages, with application to the Himalaya, New Zealand, and Central Asia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20472, https://doi.org/10.5194/egusphere-egu25-20472, 2025.
