Geoscience Data Journal最新文献

The outer Apulian Foreland ramp, i.e., the outer slope of the Taranto Trench is affected by submarine landslides, which may represent a geological hazard for the Ionian coastal area of Apulia. One of the major landslides is reported in the offshore Taranto, with evidence detectable in the vintage seismic reflection lines available for free. These are unmigrated and staked seismic reflection profiles as low-resolution PDF raster images, making challenging their interpretation. The main goal of the present paper is the building of a dataset of these seismic reflection profiles, processed and improved, useful to whom interested in future investigation of this landslide area. Therefore, F75-85, F75-83, F75-44, F75-42, MT-457-85 and D-482 seismic reflection profiles were transformed to SEG-Y file. We first converted the PDF files in TIFF ones; these files, accompanied by related files in TXT format consisting of code rows, were transformed by the use of MATLAB program IMAGE2SEGY. Subsequently, the obtained SEG-Y seismic images were enhanced by a light processing consisting in the removing the low frequency noise in DELPH Seismic software ambient. To complete the propaedeutic dataset to investigate this submarine landslide, the digitalisation of the PDF raster image of the sonic log belonging to the exploration well Sansone-1 was performed. A CSV file was obtained after manual picking using WebPlotDigitizer. These data will allow to calculate the average velocity of the seismic P-wave related to the lithostratigraphic units in the exploration well and, finally, to carry out the correlation between these units and the seismostratigraphic facies within the SEG-Y reflection seismic sections.

High-quality, long-term time series of photovoltaic (PV) output measurements are scarce at high latitudes, limiting both academic research and commercial applications. Here, we describe and publish high-resolution (1 min) PV output data—together with ancillary measurements—from three high-latitude sites in Finland covering 26 August 2015 to 31 December 2021. The PV data, comprising averaged power readings, were retrieved from inverter registries. Ancillary measurements from the PV field—plane-of-array irradiance, air temperature, module temperature, and photographs of the modules—were collected using dedicated instrumentation. Additional meteorological variables, including solar radiation components and snow depth, were obtained from nearby Finnish Meteorological Institute (FMI) weather stations. Daily snow cover classification of the modules was performed manually from daily plots of PV, ancillary and meteorological data and partially validated with photographs. Beyond visual inspection, the PV data underwent the quality control routine as described in a recent paper by Visser and colleagues; however, we found the routine exhibits several shortcomings under high latitude conditions. Snow coverage on the PV modules varied significantly with site location and system design. Subsets of the dataset have previously been used for PV output-model validation. The complete dataset offers further opportunities, including PV model development, refinement of performance metrics and quality control methods for high-latitude installations, and investigations of snow-related losses and gains. The data is freely available from the FMI METIS data repository.

This study aims to establish a regional model for predicting seismic landslide areas. Using the 2013 Minxian earthquake-induced landslide database as the research foundation, mathematical statistics and GIS techniques were applied to predict landslide areas through the Generalised Additive Model (GAM). The study area was divided into slope units using r.slopeunits, with these units serving as the basis for landslide area prediction. The influencing factors such as elevation, slope angle, profile curvature, distance to seismogenic fault (Dis2fault), distance to epicentre (Dis2epicenter), peak ground acceleration (PGA), distance to rivers (Dis2rivers) and lithology were selected for analysis. The predicted landslide areas for different slope units were calculated using the GAM and then compared with actual landslide distribution. The results show that slope angle and Dis2fault have a more significant impact on the spatial distribution of landslide areas compared with other influencing factors. Slope angle shows a positive correlation with landslide occurrence; the landslide area increases with the rise of slope angle. For the Dis2fault, the actual distribution of landslides shows that most landslides primarily occur on both sides of the seismogenic fault, indicating a significant effect of the fault on landslide distribution. Otherwise, our modelling result indicates that the predicted landslide areas align well with the actual distribution. However, a notable tailing effect was observed in regions with either very small or large landslide areas. Specifically, in slope units with less developed landslide areas, the model tended to overestimate the size, whereas in areas with more extensive landslides, the model tended to underestimate the actual area.

An updated dataset of air/sea turbulent fluxes has been created for 2005–2024 using three bulk flux algorithms and input data from a collection of research vessels contributing to the Shipboard Automated Meteorological and Oceanographic Systems (SAMOS) initiative. Based on requests from the marine surface flux community, the dataset includes the input data (e.g., air and sea temperature, wind speed and direction, humidity, pressure), several adjustments of these data, latent and sensible heat flux, wind stress (momentum flux), transfer coefficients, a measure of boundary stability, metadata, and flags quality assessing the input and output data. The fluxes span the globe from the Southern to the Arctic Oceans with the highest concentration in the oceans surrounding North America. The differences in the flux algorithms are described, and several of the differences are demonstrated. A brief overview of each flux product is provided along with information on how to access the data from the National Science Foundation National Center for Atmospheric Research and via the MarineFlux ERDDAP service. The minute-by-minute data are ideal for validation of satellite observations and for studying sub-mesoscale air-sea interaction. Differences in assumptions and physics considered in the flux parameterizations result in different turbulent fluxes, and can be used to assess the impacts of these different considerations.

Global copper supply faces systemic challenges, mainly from geographically concentrated reserves, aging mines and declining ore grades. One way to help overcome these issues is with technology. In this study, we present a new, open-source global copper deposit dataset (GCDD), facilitating artificial intelligence-driven data analysis for exploration targeting and improving our understanding of copper mineralizing systems and their mappable expressions. The newly developed GCDD hosts information about 1483 copper deposits worldwide, capturing key deposit attributes such as location, genetic type, metallogenic age, mineral assemblage, grade and tonnage. Rigorous manual validation procedures ensured data accuracy and consistency. The GCDD, intended as a standardised, comprehensive resource for copper deposit exploration and geoscientific research, was established by systematically integrating copper deposits information from three authoritative open-source databases: Mindat, MRDS, and the Mineral Evolution Database. The data extracted from these sources were supplemented with information sourced from peer-reviewed literature. Whilst Mindat, MRDS and the Mineral Evolution Database each contain important copper deposit data, they lack standardised genetic classifications and quantitative mineralogical records and, therefore, do not lend themselves well to exploration targeting or advanced modelling. The GCDD, on the other hand, supports both (i) traditional metallogenic studies and resource assessments, and (ii) advanced analyses such as network-based mapping of mineral co-occurrence patterns and association rule mining to uncover intrinsic links between mineral assemblages and copper deposit types. As such, it can facilitate critical mineral assessment, spatiotemporal metallogenic analysis, and data-driven exploration targeting of sustainable future copper supply.

On January 23, 2024, an Ms7.1 (Mw7.0) earthquake struck Wushi County in the Aksu Prefecture of Xinjiang Uygur Autonomous Region, China. The Wushi earthquake triggered various types of landslides, including rockfalls and rolling stones. This study utilised satellite images to establish an inventory of landslides triggered by the Wushi earthquake and conducted a preliminary analysis of their development patterns. The results indicate that the Wushi earthquake induced at least 1273 landslides within an area of 36,395 km². The landslides ranged in size from as small as 9 m² to as large as 13,418 m², with a total affected area of 0.42 km². The overall landslide number density and area density in the study area were 0.03 km⁻² and 0.001%, respectively. The majority of landslides occurred in Yamansukeerkezizu Town of Wushi County and were predominantly small in scale. Nearly 95% of the landslides were smaller than 1000 m² and exhibited high mobility. The spatial distribution of landslides was significantly influenced by the positions of the hanging wall and footwall, with the number of landslides on the hanging wall being nearly 5 times that of the footwall. The Wushi earthquake displayed a relatively weak landslide-triggering capacity, with both the number and scale of landslides being lower than those typically observed in strike-slip earthquakes of similar magnitude. These findings provide a detailed inventory for analysing the distribution patterns and hazard assessment of landslides triggered by the Wushi earthquake and offer a crucial basis for studying the mechanisms of earthquake-induced landslides in the Tianshan region.

Bulk turbulent heat and momentum fluxes are derived from individual marine reports from ships and moored buoys. The source dataset is the International Comprehensive Ocean–Atmosphere Data Set (ICOADS), specifically release 3.1.0 (1990–2014) and release 3.0.2 (2015–2020). Prior to flux calculation, the ICOADS data undergo extensive quality control to remove suspect observations. Fluxes are calculated using three bulk algorithms well known to the air-sea interaction community. The ships and moorings used to create the fluxes are globally distributed, with a higher concentration along primary shipping lanes and within the tropical oceans. A brief overview of each flux product is provided along with information on how to access the data from the National Science Foundation National Center for Atmospheric Research and via the MarineFlux ERDDAP service. Applications of the ICOADS MarineFlux potentially include validating fluxes from numerical models and satellite-based wind and flux products. The flux dataset could be used in developing new gridded analyses and has the potential to be used to assess variations in air-sea energy exchange between 1990 and 2020. All MarineFlux products are freely available for use and reuse, with no restrictions other than a request to cite the source.

Sub-Antarctic Marion Island provides a critical habitat for pelagic species, yet its terrestrial ecosystem faces increasing threats from climate change. Despite being situated in one of the windiest regions globally, the impact of changing wind patterns at the intra-island scale remains poorly understood. Existing datasets lack the spatial resolution necessary to capture fine-scale wind dynamics across the island. This study aimed to address this gap by presenting high-resolution wind speed and direction data to investigate the effects of wind on terrestrial systems. We present two complementary datasets: (1) wind measurements collected from 17 stations distributed across the island between May 2018 and March 2021, and (2) computational fluid dynamics (CFD) simulations providing wind vectors and associated properties at a 30 × 30 m resolution for heights up to 200 m above ground level. The data reveal significant differences in wind speed and direction across different geographical sectors of Marion Island. Notably, anemometers situated in the south recorded more frequent gale-force winds, while the western stations experienced calmer conditions. By using the observed wind direction frequencies, a weighted average vector plot was generated from the CFD simulations, providing an island-scale representation of spatial wind patterns across the island. These datasets offer valuable insights into variations in wind patterns, including upstream and downstream effects, and serve as a crucial resource for studying wind-driven processes affecting the landscape and ecosystem, such as seed dispersal.

Artificial intelligence (AI) is being increasingly applied in the geosciences, particularly in fields like mineralogy, where it supports tasks such as mineral classification, automated thin-section image analysis, or mineral exploration targeting. Such tasks require large structured and standardized data sets, which are currently not available. We build two databases to fill this gap: (i) MRMinerals contains a list of the 400 most common and geologically significant minerals, including major rock-forming minerals, key accessory minerals, and economically important ore minerals with machine-readable formulas as the key feature. (ii) MineralTD contains a large training data set with 10,000+ compositions for each of the 400 minerals in MRMinerals. MineralTD is split into two subdatasets: MineralTDMeasured and MineralTDSynthetic. MineralTDMeasured contains approximately 140,000 mineral compositions from the open-access geochemical databases and repositories GEOROC, Pangaea, PetDB, RRUFF, and ESMD. MineralTDSynthetic contains synthetic mineral compositions, generated using machine-readable formulas from MRMinerals, with at least 10,000 compositions per mineral. MineralTD is annotated with metadata, such as mineral frequency, rock classification, data source, and methods used to provide a full understanding of the individual data set. The MRMinerals and MineralTD are ready-to-use open access data sets that enable scalable, data-driven research in mineralogy, e.g., ML applications.

Integrated Global Radiosonde Archive Toolkit (IGRAT) is a software that allows users to process data from the Integrated Global Radiosonde Archive. The archive provides global radiosonde observations in a text-based format that requires additional manipulation to make it suitable for analysis. IGRAT provides an easy-to-use set of tools to streamline this preprocessing step, allowing users to readily visualise temporal and spatial patterns, plot atmospheric profiles, and export processed data sets in the more standard formats. IGRAT is accessible through a Python library and web interface, and users can adopt it to their preferred workflow. IGRAT significantly reduces preprocessing time before analysis, making it suitable for applications in climate research, meteorology and atmospheric sciences. IGRAT is fully open-source, allowing the community to make contributions as well as modify IGRAT for personal use.