Interference from very low frequency (VLF, 3–30 kHz) and low frequency (LF, 30–300 kHz) radio stations is a ubiquitous and challenging noise source in transient electromagnetic (TEM) data. It can be difficult to suppress interfering radio signals with the commonly applied methods of gating and stacking. However, the characteristics of VLF and LF radio signals encoded with minimum-shift keying methods allow for a better solution where the noise is modeled and subtracted. This approach has previously been shown to give good results for continuous streams of TEM data. Recently proposed new use cases for TEM instrumentation, such as time-lapse measurements of fluctuating groundwater levels and dynamic groundwater-saltwater interfaces produce discontinuous streams of TEM data with regular gaps between individual transients. We show that under mild constraints of data availability, radio signals can still be modeled in this case. We further show that the addition of an adaptive filter can fine-tune the radio model and improve the signal-to-noise ratio. The performance is analyzed on a synthetic noise data set and on a real field noise data set. For this field noise data set, we find that the standard errors of early time TEM data are reduced by about a factor of two.
This paper introduces a modular research data framework designed for geoscientific research across disciplinary boundaries. It is specifically designed to support small research projects, providing a bottom-up solution that empowers individual teams that need to adhere to strict data management requirements from funding bodies, but often lack the financial and human resources to do so. The framework supports the transformation of raw research data into scientific knowledge. It addresses critical challenges, such as the rapid increase in the volume, variety and complexity of geoscientific datasets, data heterogeneity, spatial complexity, and the need to comply with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. The framework uses a dual-component architecture. First, an Online Transaction Processing (OLTP) system features a user interface and a persistent relational database, ensuring accurate and consistent data storage when capturing and managing diverse geoscientific research data. Complementing this, an orchestration layer manages automated data pipelines to process the stored data and generate dynamic in-memory Online Analytical Processing (OLAP) databases that allow flexible, high-performance analysis. It is adaptable to evolving research requirements and supports various data types and methodological approaches, such as machine learning and deep learning, that place high demands on the data and their formats. A case study in Western Romania demonstrates the application of the data framework in an interdisciplinary geoarchaeological research project by processing and storing heterogeneous datasets, thereby reducing data management efforts, improving findability, replicability, and reproducibility, and streamlining the integration of high-dimensional data for small, interdisciplinary teams.
Taylor bubbles, or gas slugs, are elongated gas pockets that drive discrete and cyclic Strombolian explosions. To understand the surface dynamics of such eruptions, it is essential to first characterize the subsurface flow behaviour within the shallow (< 1 km) volcanic plumbing system. This can be achieved experimentally by simulating Taylor bubble flow under conditions that are mathematically scalable to volcanic conduit settings. This paper presents a novel application of kymography – an existing visual analysis technique – for measuring isolated or continuous Taylor bubble flow experimentally in vertical cylindrical pipes. Kymographs condense thousands of frames of experimental footage into a single space-time image, enabling efficient analysis of flow dynamics. The method utilises open-source software (ImageJ), affordable experimental equipment, and straightforward calibration, making it both cost-effective and widely accessible. Here, we illustrate the value of incorporating kymography to simplify and enhance data retrieval from complex two-phase fluid problems which provide a rigorous first-order understanding of the flow processes governing surface eruption dynamics exhibited by open-vent basaltic volcanoes. We show that kymography serves as a valuable and effective visual analysis tool for the experimental measurement of gas volume fraction, gas and liquid slug velocities, bubble length and diameter, falling film thickness, bubble and coalescence event counts, and to indicate steady-state ascent. In a volcanic conduit, these parameters have important implications for flow stability, interaction dynamics, overpressure development, and the volume of gas released at burst, which ultimately aids our ability to understand and predict eruption style, periodicity, repose, and explosivity level.
We present a method for improved calibration of multi-point electron density measurements from incoherent scatter radars (ISR). It is based on the well-established Flatfield correction method used in imaging and photography, where we exploit the similarity between independent measurements in separate pixels in an image sensor and multi-beam radar measurements. Applying this correction method adds to the current efforts of estimating the magic constantsystem constant
Forest ecosystems in Central Europe are increasingly affected by climate change, with rising temperatures and more frequent drought events posing substantial challenges to dominant tree species such as beech and oak. Understanding how site-specific factors interact with climatic stressors is therefore essential for assessing forest resilience and future viability. In particular, soil properties such as texture (sand, silt, clay) influence water retention capacity and root penetration, thereby directly affecting tree growth and vitality.To quantify tree water balance and physiological condition, trunk circumference and sap flow are continuously monitored at multiple sites. These in situ measurements are complemented by remote sensing data derived from drone-based and satellite imagery, as well as by meteorological observations. This integrative monitoring framework enables a comprehensive assessment of drought stress susceptibility in relation to site conditions.This article introduces the research design and first findings of the DryTrees
In recent years, the field of geodetic monitoring is undergoing a profound transformation driven by the transition from GPS-only positioning to a fully multi-GNSS environment. With Galileo, BeiDou, and modernized GPS & GLONASS constellations now operational, a wealth of new signals and frequencies provides enhanced opportunities for high-precision positioning and real-time monitoring. However, these advances present challenges: the integration of heterogeneous receivers across local and campaign-based networks, the continued reliance on outdated RINEX 2 workflows, and the discontinuation of the teqcInternational GNSS ServicerinexmodRINEX Modification), is a lightweight utility for editing RINEX headers, renaming files, and enriching metadata. It replaces critical teqcrinexmod, it provides a unified pipeline capable of near real-time operation (down to 5-minute intervals). Together, these tools modernize GNSS workflows across networks that are both technically diverse and geographically remote, ensuring interoperability with IGS standards while preserving operational robustness in challenging field conditions.We illustrate their deployment at the Institut de physique du globe de Paris’s volcanological observatories and monitoring networks in Guadeloupe, Martinique, La Réunion, and Mayotte, where they enable continuous monitoring of volcanic and tectonic processes. Beyond local applications, these tools contribute to bridging the gap between global GNSS standards and regional network realities, supporting the long-term sustainability of GNSS-based geo-hazard monitoring.
Scientific research in remote environments has traditionally relied on manual data retrieval from data loggers, requiring multiple field visits that are costly, logistically challenging, and sometimes hazardous. While satellite telemetry solutions exist, their integration with widely used research-grade data loggers in extreme environments remains poorly documented, limiting reproducibility and adoption. This study presents the design and implementation of a data logging and telemetry system deployed in the Western Cwm of Mount Everest/Sagarmatha, Nepal, to transmit several meteorological parameters from an automatic weather station and firn layer temperatures obtained from a suite of borehole thermistors. Drawing on recent advances in satellite Internet of Things (IoT) connectivity, we present the integration and deployment of Campbell Scientific data loggers with Ground Control's compact satellite-enabled RockREMOTE Mini, which uses the Iridium Certus 100 networks and is powered by Iridium's 9770 modem. This work represents the first documented integration of research-grade Campbell Scientific loggers with Iridium Certus 100 satellite IoT technology, providing validated protocols, performance metrics, and automated workflows for extreme environments. The system, which operated at 6660 m a.s.l., in an extremely cold climate with a limited sky-view factor due to steep surrounding terrain, provided continual monitoring of ice temperatures and meteorological conditions transmitted every 24 h, from 4 May to 10 August 2025. Data integrity and transmission reliability were consistently maintained, with 100 % data retrieval, despite the challenging environmental conditions and limited power availability. The system established a robust methodological framework for other researchers working in remote locations, demonstrating the potential for sustained and high temporal resolution measurements of environmental conditions in locations where traditional communication infrastructure is unavailable.
The seismometer synchronous observation and zero crossing methods are applied to laser interferometer absolute gravimeter to suppress the vibration interference. However, during the synchronous observation of the seismometer and the gravimeter, the observation point of the seismometer does not coincide with the reference corner cube in space, resulting in spatial dislocation and impossibility to accurately reflect the vibration state of the reference corner cube. So it can be considered to accurately measure the vibration acceleration of the reference corner cube by inertial suspension. In this paper, an open-loop reference corner cube inertial suspension device (RCCISD) hanging the reference corner cube was developed based on the principle of seismometer, which is used to measure the vibration acceleration of the reference corner cube of the laser interferometer absolute gravimeter. Experimental test results show that the power spectrum of gravitational acceleration calculated by an interference fringe observed jointly by the RCCISD is about 40 dB lower than that of the reference corner cube directly placed on the ground. The RCCISD can restrain the vibration interference to a certain extent. At the same time, it can not only measure the vibration of the reference corner cube more accurately than the seismograph synchronous observation method, but also the volume is about
To gain context on the ambient sea ice field during the 2022 NASA Salinity and Stratification at the Sea Ice Edge (SASSIE) expedition we developed a machine learning model to predict sea ice cover classification from screen captures of a ship-board S-bandkmd, during which time screen captures from the shipboard S-band radar were collected. Our goal was to analyze these images to determine when the ship was approaching sea ice, in the ice, or in open water. Here we report on the development of a machine learning method built on the PyTorch software packages to classify the amount of sea ice observed in individual radar images on a scale from C0–C3. C0 indicates open water and C3 is assigned to images taken when the ship was navigating through thick sea ice in the marginal ice zone. The method described here is directly applicable to any radar images of sea ice and allows for the classification and validation of sea ice presence or absence. Furthermore, this method uses a standard marine navigation radar that is not generally used to measure sea ice and thus opens the opportunity to categorize sea ice concentration using the type of navigation radar installed on most vessels.
Digital Elevation Models (DEMs) derived from Synthetic Aperture Radar (SAR) interferometry are a key data source for numerous geospatial applications, from hydrological modelling to environmental monitoring. The launch of Sentinel-1C in late 2025 introduces a new sensor into the Sentinel-1 constellation. This study evaluates the vertical accuracy of DEMs generated from interferometric image pairs acquired during the satellite's calibration phase. The analysis uses a set of image pairs with temporal baselines of 1, 6, and 12 d, over a test site in Angola, validated against ICESat-2 elevation measurements. The workflow includes interferometric processing, coherence assessment, and statistical error evaluation. Results indicate high accuracy for the 1 d pair (RMSE≈14.7 m) and moderate degradation for the 6 d pair (RMSE≈16.4 m), but a pronounced loss of accuracy for the 12 d pair (RMSE≈49.4 m), primarily linked to coherence loss in vegetated areas. Coherence and elevation error distributions reveal clear land cover and slope dependencies, with lower performance in forested and steep terrain. These findings should be regarded as indicative due to the limited number of suitable image pairs for the calibration phase. However, this early assessment provides an important reference point for future Sentinel-1A/C DEM generation studies, informing both methodological refinement and application planning in SAR-based topographic mapping.
Statical analyses were conducted in sequence on 12 sets of geomagnetic instrument comparison data from the Chinese Geomagnetic Network (GNC) between 2010 and 2024. First, by examining these comparison data, it was found that when their cumulative probabilities at the same level, the instrument differences for declination (D) are significantly higher than those for inclination (I). For the same set of instruments, as the frequency of observer changes increases, the instrument differences for DI. This indicates that inter-observer differences have a notable impact on D, primarily due to the complexity of aligning the azimuth marks and levelling instruments. Second, though a multi-source error uncertainty analysis, including instrument error, operator related error, pillar correction error and so on, the systematic differences between the reference fluxgate theodolite and the test instruments were quantified. The operator related errors of DI
Doppler wind measurements in the middle atmosphere by ground-based lidar are challenging and benefit from precise spectral characterization of the laser source. We present a system for frequency control and monitoring of pulsed commercial high-power Nd:YAG lasers, which is entirely software controlled, automated, and works in real-time. It basically consists of an embedded controller handling the cavity control of the injection-seeded power laser and an embedded controller based spectrometer performing the spectral analysis of each individual power laser pulse using a Fabry-Pérot etalon. The power laser cavity length is optimized by pulse build-up time minimization, yielding a stable long-term single-mode operation. The spectrometer is able to analyze continuous-wave as well as pulsed lasers with repetition rates of 100 Hz and resolves frequency changes of less than 300 kHz, corresponding to a resolution of ∼ 5 × 10−10
For the azimuth observation to be made at its magnetic observatories routinely, Japan Meteorological Agency (JMA) has adopted a traditional method based on Polaris sighting. Due to its difficulty to implement under overcast weather conditions and to its demand on observers, for overtime work into the evening, we are motivated to seek for an alternative method based on the Global Navigation Satellite Systems (GNSS) observation that might potentially relieve those two disadvantages. An experiment is made at Kakioka to assess the eligibility and effectiveness of the GNSS method for JMA's unmanned observatories, Memambetsu and Kanoya. The GNSS observations themselves achieve as high a precision as approximately 1 arcsec, as far as they are analyzed with Static mode. Derived from the results of GNSS observation and some supplementary horizontal angle measurements, the azimuth of the azimuth mark for the absolute measurement is determined with a precision of a few arcsecond, which is comparable to the azimuth precision achieved by the Polaris sighting. However, we end up with their significant difference by about 10 arcsec. We discuss this discrepancy to be possibly due to a local geoid gradient. The Polaris observation is made with a theodolite tilted in the gravitational direction, also known as the vertical line deviation, whereas the GNSS observations are based on the azimuth of the compliant ellipsoid plane.
Pituffik is a newly established geomagnetic observatory located in the high Arctic – a region with sparse geomagnetic observational coverage. Positioned within the polar cap at Quasi-Dipole geomagnetic coordinates 83.19° latitude and 25.25° longitude, the observatory provides valuable data from a geophysical significant area. The observatory was carefully designed and constructed using non-magnetic materials with special considerations for the Arctic conditions. All building components were pre-assembled and tested in Denmark before being disassembled, shipped, and reassembled at the remote site in northwest Greenland. This paper presents the observatory design, and an analysis of over one year of operational data, including baseline stability, temperature control, and calibrated vector and scalar magnetic field measurements. The results demonstrate stable baselines and high data quality, confirming the observatory's readiness for data-distribution to the international geomagnetic community.
Humidity variables are important for monitoring climate. Unlike, for instance, temperature, they require data transformation to derive water vapour variables from observations. Hygrometer technologies have changed over the years and, in some cases, have been prone to sensor drift due to aging, condensation or contamination in service, requiring replacement. Analysis of these variables may provide rich insight into both instrumental and climate dynamics. We apply tipping point analysis to dew point and relative humidity values from hygrometers at 55 observing stations in the UK. Our results demonstrate these techniques, which are usually used for studying geophysical phenomena, are also potentially useful for identifying historic instrumental changes that may be undocumented or lack metadata.
INTERMAGNET, a global network of geomagnetic observatories, publishes so-called “definitive” data products, which are subjected to an international peer-review system. Currently, geomagnetic data is submitted by about 100 observatories worldwide. Besides the mandatory one-minute data products, INTERMAGNET has also accepted one-second (1 Hz, 1 s) data products for the past decade. The amount of data to be reviewed has significantly increased, making traditional manual data reviews increasingly challenging. The INTERMAGNET ROBOT (short IMBOT) has been developed to perform automated routines to convert and evaluate INTERMAGNET (IM) data submissions. The primary objectives of IMBOT are to (1) simplify one-second and one-minute data submissions for providers, (2) speed up the evaluation process significantly, (3) consider current IM archive formats and meta information, (4) simplify and speed up the peer-review process and finally, (5) reduce the workload of human data checkers. IMBOT automatically generates detailed reports and notifies submitting institutes and human referees. It provides templates for corrections and also triggers re-evaluations automatically when data or any information in the submission directory is updated. This automated system makes data review faster and more reliable, providing high-quality data for the geomagnetic community.
Flash temperatures induced by flash heating can lead to thermal softening or decomposition of fault-zone materials at microscopic grain contacts and, consequently, cause a rapid reduction in fault strength during seismic slip. To quantify the efficiency of short-term frictional heating at the contact scales and its impact on the mechanical fault strength, we conducted rotary-shear friction experiments on Ottawa quartz sand “gouges” with variable grain sizes of 250–710 µm−1µm∼ 220 and ∼ 100 °C, respectively. In addition, the flash temperature increases with increasing slip velocity and grain size, while decreasing at higher normal stress, which is likely associated with enhanced grain size reduction. In our study, we showed that flash temperatures in shearing fault gouges can be constrained using a fast thermal camera. Although difficulties remain in the experimental set-up related to the need to confine the gouge layer and to the evolution of contact size due grain size reductions, the trends in maximum temperatures we observed agree with those predicted from theory.
Autonomous instruments, powered using solar panels and batteries, are a vital tool for long-term scientific observation of the polar regions. However, winter conditions, with low temperatures and prolonged lack of sunlight, make power system design for these regions challenging. Minimising winter power consumption is vital to successful operation, but power consumption data supplied by equipment manufacturers can be confusing or misleading. We measured the night consumption (power consumption in the absence of sunlight) of 16 commercially available solar regulators and compared the results to the manufacturers' reported values. We developed a simple model to predict the maximum depth of discharge of a battery bank, for given values of regulator and instrument power consumption, solar panel size, location, and battery capacity. We use this model to suggest the minimum battery capacity required to continuously power a typical scientific installation in a polar environment, consisting of a single data logger (12 mW power consumption) powered by a 12 V battery bank and 20 W solar panel, for eight different types of solar regulator. Most of the tested solar regulators consumed power at or below the manufacturer's reported values, although two significantly exceeded them. For our modelled scenario, our results suggest that current consumption may be reduced by two orders of magnitude (from 23 to 0.1 mA) through careful choice of solar regulator, and the mass of the battery required for year-round operation may thus be reduced from 45 to 1.5 kg, a factor of 26×. These results demonstrate that choice of solar regulator can significantly increase the chances of successful year-round data collection from a polar environment, eases deployment and reduces costs.
Earth's magnetic field, a dynamic shield influenced by internal and external forces, holds critical insights into space weather forecasting and the planet's core dynamics. The Choutuppal (CPL) and Hyderabad (HYB) magnetic observatories in India are pioneering this field by delivering high-resolution geomagnetic data to INTERMAGNET with unprecedented speed and precision. Utilizing a novel, low-cost protocol, CPL transmits 1 s resolution data and HYB provides 1 min data, both achieving a latency of less than 300 s, making CPL one of the first Indian observatories to send 1 s real-time data to GIN (Geomagnetic Information Node). This rapid data transmission enhances global collaboration in space weather prediction, safeguarding critical infrastructure like satellites and power grids from solar storms.To further elevate data utility, we developed Python-based software for real-time visualization and quality control at both observatories. This tool generates plots, performs initial quality checks, and computes first differences at 1 s and 1 min intervals, with a latency under 300 s. By enabling daily evaluation of data quality, the software facilitates the identification of anomalies and noise, supporting the preparation of quasi-definitive data essential for geomagnetic research. Our Python server and web applications are designed with the future in mind, integrating artificial intelligence (AI) and machine learning (ML) capabilities. These advancements at CPL and HYB are set to transform the processing, forecasting, and visualization of geomagnetic data. By improving both the accuracy and accessibility of these data, we aim to revolutionize geomagnetic research, making it more precise, accessible, and actionable.
This paper seeks to demonstrate how the implementation of a quality management system facilitated the organization and enhancement of the operations of the Teoloyucan Magnetic Observatory, the only one in Mexico. This document describes the operation, functionality, and deployment of the Earth's magnetic field data recorded at this observatory, as well as the procedure for acquiring ISO 9001:2015 certification.
Geomagnetic observatories are essential for the study of the Earth's magnetic phenomena; they allow the precise and continuous measurement of the geomagnetic field. They are built and operated according to rigorous international standards to ensure the acquisition of high-quality geomagnetic data. Given the nature of Quality Management Systems (QMS) based on ISO 9001:2015, we consider that their implementation in a geomagnetic observatory can be a valuable tool that allows monitoring the follow-up of international standards and ensuring their proper operation, thus guaranteeing high-quality geomagnetic data.Some of the main advantages of implementing a quality management system and obtaining an ISO 9001:2015 certification include setting clear objectives, systematically analyzing risks that could affect both functionality and data quality, fostering a culture of continuous improvement, promoting context analysis through a strengths, weaknesses, opportunities, and threatsThis study presents the registered experience in the implementation of a QMS in the only magnetic observatory in Mexico: Teoloyucan. It outlines the operation of the observatory, including data acquisition platforms, transmission, reduction, management, and data publication. It also describes the process for implementing the quality management system in the data deployment procedures, highlighting its advantages, disadvantages, and challenges during its adoption.