Satellite System Research Articles

Volcanic arc rigidity variations illuminated by coseismic deformation of the 2011 Tohoku-oki M9.

Rock strength has long been linked to lithospheric deformation and seismicity. However, independent constraints on the related elastic heterogeneity are missing, yet could provide key information for solid Earth dynamics. Using coseismic Global Navigation Satellite Systems (GNSS) data for the 2011 M9 Tohoku-oki earthquake in Japan, we apply an inverse method to infer elastic structure and fault slip simultaneously. We find compliant material beneath the volcanic arc and in the mantle wedge within the partial melt generation zone inferred to lie above ~100 km slab depth. We also identify low-rigidity material closer to the trench matching seismicity patterns, likely associated with accretionary wedge structure. Along with traditional seismic and electromagnetic methods, our approach opens up avenues for multiphysics inversions. Those have the potential to advance earthquake and volcano science, and in particular once expanded to InSAR type constraints, may lead to a better understanding of transient lithospheric deformation across scales.

An adaptive frequency-based C/N0-constrained multipath extraction and mitigation method for GNSS pseudorange measurements

Abstract The multipath effect poses an inevitable challenge for Global Navigation Satellite System (GNSS) receivers, particularly pronounced in smart devices, which are nevertheless the most common means through which the general public accesses GNSS positioning. Therefore, we propose a novel adaptive method for extracting multipath errors, which fully exploits the multipath frequency characteristics, multipath repeatability, and the correlation between multipath errors and carrier-to-noise ratio (C/N0). This approach aims to enhance the susceptibility of smart devices to multipath influences. Specifically, this method uses the code minus carrier method to calculate the noisy multipath error, exploits the correlation between C/N0 and multipath to constrain the frequency range, employs the ensemble empirical mode decomposition method to decompose the error signal, and utilizes the Hilbert–Huang transform method to obtain the frequency information of the signal component. In multipath repeatability, the proposed method can effectively improve the correlation of multipath signals by up to 0.79. By applying the extraction method, the positioning performance of smart devices can be improved by 13.9%. Therefore, the proposed method extracts multipath error more accurately and is highly usable in low-cost devices.

Landau Tidal Damping and Major-Body Clustering in Solar and Extrasolar Subsystems

Major (exo)planetary and satellite bodies seem to concentrate at intermediate areas of the radial distributions of all the objects orbiting in each (sub)system. We show that angular-momentum transport during secular evolution of (exo)planets and satellites necessarily results in the observed intermediate accumulation of the massive objects. We quantify the ‘middle’ as the mean of mean motions (orbital angular velocities) when three or more massive objects are involved. Radial evolution of the orbits is expected to be halted when the survivors settle near mean-motion resonances and angular-momentum transfer between them ceases (gravitational Landau damping). This dynamical behavior is opposite in direction to what has been theorized for viscous and magnetized accretion disks, in which gas spreads out and away from either side of any conceivable intermediate area. We present angular momentum transfer calculations in few-body systems, and we also calculate the tidal dissipation timescales and the physical properties of the mean tidal field in planetary and satellite (sub)systems.

LiDAR-based SLAM pose estimation via GNSS graph optimization algorithm

LiDAR simultaneous localization and mapping (SLAM) is widely used in positioning and navigation. By illuminating a series of light spots on the surface of an object, orientation and pose information is obtained. However, improving the accuracy of the pose optimization algorithm without affecting the position information is difficult. Therefore, this study combines the graph optimization algorithm and the Global Navigation Satellite System (GNSS) to optimize the coordinates of the target object in the LiDAR SLAM pose. A GNSS pose estimation algorithm is proposed to show the relationship between positioning algorithms with and without GNSS pose optimization by analyzing the deviation of the distance, level, and height laser point cloud coordinates. The results show that with GNSS pose optimization, the deviations in distance, level, and height are 99% smaller than those without GNSS pose optimization. Furthermore, we demonstrate the effectiveness of the proposed graph optimization algorithm and GNSS optimization of LiDAR SLAM. Finally, this study highlights the directions for the application of wireless communication technology in the field of LiDAR SLAM.

3D Printing of Composite Radiation Shielding for Broad Spectrum Protection of Electronic Systems.

The miniaturization of satellite systems has compounded the need to protect microelectronic components from damaging radiation. Current approaches to mitigate this damage, such as indiscriminate mass shielding, built-in redundancies, and radiation-hardened electronics, introduce high size, weight, power, and cost penalties that impact the overall performance of the satellite or launch opportunities. Additive manufacturing provides an appealing strategy to deposit radiation shielding only on susceptible components within an electronic assembly. Here, a versatile material platform and process to conformally print customized composite inks at room temperature directly and selectively onto commercial-off-the-shelf electronics is described. The suite of inks uses a flexible styrene-isoprene-styrene block copolymer binder that can be filled with particles of different atomic densities for diverging radiation shielding capabilities. Additionally, the system enables the combination of multiple distinct particle species within the same printed structure. The method can produce graded shielding that offers improved radiation attenuation by tailoring both shield geometry and composition to provide comprehensive protection from a broad range of radiation species. The authors anticipate this alternative to traditional shielding methods will enable the rapid proliferation of the next generation of compact satellite designs.

Amplitude scintillation detection with geodetic GNSS receivers leveraging machine learning decision tree

The amplitude scintillation detection is typically achieved by using the scintillation index generated by dedicated and costly ionospheric scintillation monitoring receivers (ISMRs). Considering the large volume of common Global Navigation Satellite System (GNSS) receivers, this paper presents a strategy to accurately identify the ionospheric amplitude scintillation events utilizing the measurements collected with geodetic GNSS receivers. The proposed detection method relies on a pre-trained machine learning decision tree algorithm, leveraging the scintillation index computed from the carrier-to-noise data and elevation angles collected at 1-Hz. The experimental results using real data demonstrate a 99% accuracy in scintillation detection can be achieved. By combining advanced machine learning techniques with geodetic GNSS receivers, this approach is feasible to effectively detect ionospheric scintillation using non-scintillation GNSS receivers.

Elevating performance in BDS-3 Precise Point Positioning Ambiguity Resolution through LEO augmented constellations

With the help of constellations combined BDS-3 (BeiDou Navigation Satellite System) and LEO (Low Earth Orbit), we aim to improve the performances of PPP (Precise Point Positioning) and PPP Ambiguity Resolution (PPP-AR). First, LEO constellations with 66, 96, 156, 192, 270, and 288 satellites are designed to establish six LEO augmented constellations. Based on four challenge scenarios, PPP of six LEO augmented constellations are performed. Even in the strip-shaped obstruction area losing more than 60% of observations, the positioning accuracies of static PPP are improved by (1.0%, 5.7%, 0.2%) for BDS+66LEO, and (14.9%, 33.7%, 7.5%) for BDS+288LEO in the east, north, and up components. The positioning accuracy and convergence time improve quickly as the number of LEO satellites increased from 66 to 156, and slowdown from 192 to 288. Two constellations, BDS+156LEO and BDS+192LEO, are considered to strike the balance of acceptable PPP performance and constellation load. Then, the performance of PPP-AR is realized with the estimated UPD (Uncalibrated Phase Delay). The correlations among geodetic parameters and ambiguities deriving from the post-fit variance–covariance matrix are analyzed to illustrate the enhancement of LEO augmented constellations. Under the condition of 20°elevation angle occlusion with azimuth ranging from 0 to 360°, BDS+156LEO can achieve an ambiguity fix rate of up to 98.37% with a faster mean TTFF (Time To First Fix) of 12.76 min, and the positioning accuracies in the east, north, and up components are improved by 28.2%, 21.4%, and 15.1% respectively, compared with BDS-3. BDS+192LEO realized an 98.42% ambiguity fixing rate, and improved BDS-3 by 31.4%, 26.7% and 11.5% with its TTFF been 12.25 min.

Lake Environmental Data Harvester (LED) for Alpine Lake Monitoring with Autonomous Surface Vehicles (ASVs)

This article introduces the Lake Environmental Data Harvester (LED) System, a robotic platform designed for the development of an innovative solution for monitoring remote alpine lakes. LED is intended as the first step in creating portable robotic tools that are lightweight, cost-effective, and highly reliable for monitoring remote water bodies. The LED system is based on the Shallow-Water Autonomous Multipurpose Platform (SWAMP), a groundbreaking Autonomous Surface Vehicle (ASV) originally designed for monitoring wetlands. The objective of LED is to achieve the comprehensive monitoring of remote lakes by outfitting the SWAMP with a suite of sensors, integrating an IoT infrastructure, and adhering to FAIR principles for structured data management. SWAMP’s modular design and open architecture facilitate the easy integration of payloads, while its compact size and construction with a reduced weight ensure portability. Equipped with four azimuth thrusters and a flexible hull structure, SWAMP offers a high degree of maneuverability and position-keeping ability for precise surveys in the shallow waters that are typical of remote lakes. In this project, SWAMP was equipped with a suite of sensors, including a single-beam dual-frequency echosounder, water-quality sensors, a winch for sensor deployment, and AirQino, a low-cost air quality analysis system, along with an RTK-GNSS (Global Navigation Satellite System) receiver for precise positioning. Utilizing commercial off-the-shelf (COTS) components, a Multipurpose Data-Acquisition System forms the basis for an Internet of Things (IoT) infrastructure, enabling data acquisition, storage, and long-range communication. This data-centric system design ensures that acquired variables from both sensors and the robotic platform are structured and managed according to the FAIR principles.

Variations of Kelvin Waves around the Tropical Tropopause Inversion Layer from GNSS RO Measurements

Abstract In the present study, the tropical tropopause inversion layer (TIL) Kelvin waves are extracted from the Global Navigation Satellite System (GNSS) radio occultation (RO) temperature data of multiple missions from January 2007 to December 2020. We focus on the variations of TIL Kelvin waves in two longitude regions, the Maritime Continent (MC; 90°–150°E) and the Pacific Ocean (PO; 170°–230°E). The results show that over both regions, ENSO leads to the opposite variations of TIL Kelvin wave temperature amplitude during different ENSO phases. Specifically, during La Niña, the strong (weak) deep convection over MC (PO) leads to strengthened (weakened) static stability. With enhanced easterly (westerly) winds and strengthened (weakened) static stability, the TIL Kelvin wave temperature amplitudes are stronger (weaker) over MC (PO). The opposite phenomenon occurs during El Niño. The zonal-mean zonal winds affect TIL Kelvin wave temperature amplitudes by two mechanisms. First, the prevalence of easterlies (westerlies) in the upper troposphere affects the upward propagation of Kelvin waves, resulting in stronger (weaker) TIL Kelvin wave temperature amplitudes over MC (PO). Second, the TIL Kelvin wave temperature amplitude peaks about 2 months before the zero-wind line of the descending westerly QBO phase occurs, due to dissipation on the critical line. Additionally, the rapid increase of zonal-mean static stability significantly affects the annual variation of TIL Kelvin wave temperature amplitudes. They both reach maxima during DJF and minima during JJA, which should be related to the annual cycles of temperature and ozone mixing ratio in the TIL. Significance Statement Recent studies indicate that the Kelvin wave temperature amplitudes in the tropical tropopause inversion layer (TIL) exhibit distinct characteristics compared with those in other height levels, while the modulation mechanisms of the TIL Kelvin waves need further investigation. The present study aims to study the differences in the variabilities and the modulation factors of TIL Kelvin waves over two longitude regions. Our findings suggest that the different responses of background conditions during ENSO phases influence the spatiotemporal distribution of the TIL Kelvin waves. Besides, the zonal winds and the static stability significantly affect the temporal variations of TIL Kelvin waves. Our work fills the research gap of TIL Kelvin waves and contributes to understanding the dynamics of tropical tropopause variations.

Performance enhancement of PPP/SINS tightly coupled navigation based on improved robust maximum correntropy kalman filtering

Extended kalman filter (EKF) is widely used to integrated navigation system of global navigation satellite system (GNSS) and strapdown inertial navigation system (SINS). However, non-Gaussian noise and the uncertainty of measurement noise can seriously reduce the performance of EKF, so it is difficult to obtain an optimal GNSS/INS integration solution. At present, non-Gaussian noise processing is still a difficult issue in filter research. In this paper, an adaptive and robust maximum correntropy extended kalman filter (MCEKF) method based on Cauchy kernel function is proposed to solve the above problem. Thanks to the excellent properties of Cauchy kernel function, the proposed method can effectively avoid filter faults and has better stability. However, the performance of MCEKF depends on the select of kernel bandwidth parameter, which is a difficult problem in the engineering application of MCEKF. In this paper, the switching kernel bandwidth algorithm is employed to adaptively estimate the kernel bandwidth parameter to solve the tradeoff between convergence rate and steady-state misalignment in the MCEKF algorithm under constant kernel bandwidth. Finally, the performance of the proposed algorithm is verified by two sets of vehicle-mounted precise point positioning (PPP) and SINS tightly coupled integration experiments in urban environment. The results show that compared with EKF, the proposed method can significantly improve the positioning accuracy, that is, the PPP/SINS positioning accuracy based on GE, GR and GRE system is improved by 25.1 %, 2.5 % and 17.0 % in D196 experiment, and 23.5 %, 16.5 % and 31.8 % in D107 experiment, respectively. The velocity and attitude accuracy of the two methods are similar. The velocity errors of all schemes can reach cm/s level. The roll, pitch and heading errors of navigation-grade inertial sensor are all less than 0.12 degree. The roll and pitch errors of MEMS are less than 1.0 degree, the heading error of the G, GE, GR, and GRE systems are 1.719, 1.464, 1.676, and 1.475 degree, respectively.

Data Assimilation Impact of GNSS RO Measurements from Cube Satellites on Arctic Weather Forecasts

Abstract In this study, the effects of assimilating Global Navigation Satellite System (GNSS) radio occultation (RO) observations from existing and recently added commercial cube satellites on analyses and forecasts over the Arctic region were investigated by conducting observing system experiments (OSEs). Profiles of refractivity were assimilated with a local observation operator using the three-dimensional variational method. The analyses and forecasts from the OSEs were verified against ERA5 reanalysis, radiosonde observations, and buoy observations. In addition to the averaged impact on forecast skill, the impact of GNSS RO observations was further examined for an individual Arctic cyclone case, focusing on the added value of the cube satellite data. The effects of GNSS RO observations from existing satellites on analyses and forecasts over the Arctic region are positive, and the assimilation of GNSS RO observations from cube satellites leads to additional improvements, particularly for temperature in the upper troposphere and lower stratosphere (UTLS). Temperature biases in the UTLS are significantly reduced in the analyses, and the improved analyses result in better forecasts of upper-level potential vorticity and cyclone development when GNSS RO observations from cube satellites are assimilated. This result demonstrates the potential of GNSS RO data from cube satellites to enhance forecasts over the Arctic region.

Orbital mechanics with the Global Positioning System

The recent availability of relatively inexpensive dual-frequency receivers for signals from Global Navigation Satellite Systems (GNSS) provides access to ultra-precise, real-time data such as positions and velocities of dozens of satellites orbiting the Earth. We discuss how these data can be obtained, processed, and analyzed either with or without the actual purchase of a GNSS receiver. The positional information can be used to verify Kepler's three laws at lowest order as well as to reveal the presence of higher-order perturbations such as the oblateness of the Earth and the gravitational influences of the Sun and Moon on these satellites. The supplementary material includes both introductory laboratory exercises and Python scripts used to gather and process data suitable for intermediate courses.

Coseismic deformation and fault slip distribution of the 2023 MW7.8 and MW7.6 earthquakes in Türkiye

On February 6, 2023, a devastating earthquake with a moment magnitude of MW7.8 struck the town of Pazarcik in south-central Türkiye, followed by another powerful earthquake with a moment magnitude of MW7.6 that struck the nearby city of Elbistan 9 h later. To study the characteristics of surface deformation caused by this event and the influence of fault rupture, this study calculated the static coseismic deformation of 56 stations and dynamic displacement waveforms of 15 stations using data from the Turkish national fixed global navigation satellite system (GNSS) network. A maximum static coseismic displacement of 0.38 m for the MW7.8 Kahramanmaras earthquake was observed at station ANTE, 36 km from the epicenter, and a maximum dynamic coseismic displacement of 4.4 m for the MW7.6 Elbistan earthquake was observed at station EKZ1, 5 km from the epicenter. The rupture-slip distributions of the two earthquakes were inverted using GNSS coseismic deformation as a constraint. The results showed that the Kahramanmaras earthquake rupture segment was distinct and exposed on the ground, resulting in significant rupture slip along the Amanos and Pazarcik fault segments of the East Anatolian Fault. The maximum slip in the Pazarcik fault segment was 10.7 m, and rupture occurred at depths of 0–15 km. In the Cardak fault region, the Elbistan earthquake caused significant ruptures at depths of 0–12 km, with the largest amount of slip reaching 11.6 m. The Coulomb stress change caused by the Kahramanmaras earthquake rupture along the Cardak fault segment was approximately 2 bars, and the area of increased Coulomb stress corresponded to the subsequent rupture region of the MW7.6 earthquake. Thus, it is likely that the MW7.8 earthquake triggered or promoted the MW7.6 earthquake. Based on the cumulative stress impact of the MW7.8 and MW7.6 events, the southwestern segment of the East Anatolian Fault, specifically the Amanos fault segment, experienced a Coulomb rupture stress change exceeding 2 bars, warranting further attention to assess its future seismic hazard risk.

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Unmanned aerial vehicles (UAVs) have been used for many applications including military, construction, image and video mapping, medical, search and rescue, wireless communications, and aerial surveillance. One key research topic involving UAVs is pose estimation in autonomous navigation. A standard procedure for this process for UAVs is to combine inertial navigation system (INS) sensor information with the global navigation satellite system (GNSS) signal. However, some factors can interfere with the GNSS signal, such as natural phenomena (ionospheric scintillation) or malicious attacks (jamming or spoofing). One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images. But a high effort is required for image edge extraction. In this paper, a support vector regression (SVR) model is proposed to reduce the computational load and processing time for image edge detection. The dynamic partial reconfiguration (DPR) of part of the SVR datapath was implementated to accelerate the process, reduce the area, and analyze its granularity aspect by increasing the grain size of the reconfigurable region. Results show that the implementation in hardware is 68 times faster than that in software. This kind of architecure with DPR also facilitates the low power consumption of 4 mW, leading to a reduction of 57 % compared with that without DPR, which is also the lowest power consumption compared with other machine learning (ML) hardware implementations. Besides, the circuitry area is 41 times smaller. SVR with Gaussian kernel showed a success rate of 99.18 % and minimum square error of 0.0146 for testing with the planning trajectory. This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application, thus contributing to lower power consumption, smaller hardware area, and shorter execution time.

A simplified GNSS/LEO joint orbit determination method

Joint orbit determination (JOD) of Global Navigation Satellite Systems (GNSS) satellites and low Earth orbit (LEO) satellites has been substantiated as an efficacious approach to compensate for the ground station geometry. In the traditional JOD, the precise orbit determination (POD) of LEO satellite is mainly processed by the reduced-dynamic approach, however, this approach involves complex calculations and the credibility of the determined positions diminishes when LEO satellites undergo orbital maneuvers. Therefore, a simplified JOD method is designed that employs kinematic approach to determine the LEO satellites orbit. To verify the effectiveness of the proposed method, the orbit of GPS satellites and LEO satellites are jointly estimated utilizing the regional and global networks. 8 LEO satellites, including GRACE-C/D, SWARM-A/B/C, SENTINEL-3A/B, and JASON-3, are chosen for JOD. The comparative analysis between the proposed method and traditional method are achieved in terms of GPS orbit accuracy, LEO orbit accuracy, computation time and the JOD performance during LEO maneuvers. Under regional station scenario, the GPS orbit accuracy determined using the proposed method and the traditional method is 3.64 cm and 2.52 cm, respectively. In the case of global station scenario, the accuracies are 1.71 cm and 1.64 cm. Additionally, the traditional method yields superior enhancement and higher accuracy of the LEO orbits. However, it exhibits a noticeable increase in computation time compared to the proposed method and the performance of JOD declines significantly when LEO satellites undergo orbital maneuvers. Alternatively, although the accuracy of the LEO orbits using the proposed method is comparatively lower, it offers a substantial reduction in the overall network computation time compared to traditional method. Moreover, the proposed method based on LEO kinematic precise orbit determination (KPOD) is nearly unaffected by orbital maneuvers of LEO satellites, presenting unique advantages in practical data processing.

Real-Time Estimation of Tree Position, Tree Height, and Tree Diameter at Breast Height Point, Using Smartphones Based on Monocular SLAM

Precisely estimating the position, diameter at breast height (DBH), and height of trees is essential in forest resource inventory. Augmented reality (AR)-based devices help overcome the issue of inconsistent global point cloud data under thick forest canopies with insufficient Global Navigation Satellite System (GNSS) coverage. Although monocular simultaneous localization and mapping (SLAM) is one of the current mainstream systems, there is still no monocular SLAM solution for forest resource inventories, particularly for the precise measurement of inclined trees. We developed a forest plot survey system based on monocular SLAM that utilizes array cameras and Inertial Measurement Unit (IMU) sensors provided by smartphones, combined with augmented reality technology, to achieve a real-time estimation of the position, DBH, and height of trees within forest plots. Our results from the tested plots showed that the tree position estimation is unbiased, with an RMSE of 0.12 m and 0.11 m in the x-axis and y-axis directions, respectively; the DBH estimation bias is −0.17 cm (−0.65%), with an RMSE of 0.83 cm (3.59%), while the height estimation bias is −0.1 m (−0.95%), with an RMSE of 0.99 m (5.38%). This study will be useful in designing an algorithm to estimate the DBH and position of inclined trees using point clouds constrained by sectional planes at the breast height of the trunk, developing an algorithm to estimate the height of inclined trees utilizing the relationship between rays and plane positions, and providing observers with visual measurement results using augmented reality technology, allowing them to judge the accuracy of the estimates intuitively. Clearly, this system has significant potential applications in forest resource management and ecological research.

Integration of GNSS, Total Station, and Grid Controls: An Analysis of Combined Effect of Elevation of Topography and Map Projection Distortion to Solve the Distance Discrepancy

Heterogeneous measurements from various surveying methods need to be integrated to accomplish any survey project. The use of Global Navigation Satellite System (GNSS) and Total Station (TS) to establish a control framework along with utilizing existing national grid controls, for any survey project has been the standard practice. However, the successful integration of GNSS, TS, and existing grid controls could be challenging at times. Both survey professionals, and surveying stakeholders would benefit from the successful augmentation of heterogeneous measurements from GNSS, TS, and existing grid controls in order to provide a control framework for their survey project. In the past, the distance discrepancy/mismatch of GNSS-derived distance which is obtained from indirect measurements from GNSS survey and TS distance which is ground-based direct measurements, has created confusion. Herein, we have analyzed in detail, the combined effect of elevation of topography and map projection's distortion on the distance; demonstrate the magnitude of combined effect by numerical examples; tested this formulation with real-world GNSS and TS measurements. This way we proposed a solution to solve distance discrepancy/mismatch between various survey methods. The magnitude of the combined effect would be substantial with higher elevations and longer distances and could cross the threshold of specified/required accuracy. The effect would be more pronounced in mountainous regions suggesting combined effect should be properly taken into account. Taking combined effect into consideration brings the compatibility and comparability of GNSS, TS, and existing grid controls together. Thus, allowing both survey professionals and surveying stakeholders to utilize the mix of GNSS, TS, and existing grid controls to achieve required precision and accuracy in an economical, timely, and easy manner.

Coseismic and Early Postseismic Deformation of the 2024 Mw7.45 Noto Peninsula Earthquake

AbstractAn unexpected Mw7.45 earthquake struck the Noto Peninsula on 1 January 2024, preceded by several long‐living earthquake swarms, providing a valuable opportunity to study seismic and aseismic slips, as well as their interactions. We derived coseismic and 19‐day postseismic slip distributions by inverting co‐ and post‐seismic displacements from Global Navigation Satellite System (GNSS) data. The inverted coseismic slip distribution shows two slip patches, with a maximum slip of ∼4 m. The early postseismic afterslip is 0.1–0.25 m within coseismic slip asperity and 0.1–0.6 m northward of the rupture area. The afterslip within the rupture area is accompanied by numerous aftershocks and coincides with a ∼6 MPa stress drop, suggesting that aftershocks are likely driven by the afterslip. The pattern of poroelastic rebound implies a potential effect of fluid flow on aftershock triggering. This study sheds lights on the intricate interplay between seismic and aseismic processes following large earthquakes.

Eruption Forecasting Model for Copahue Volcano (Southern Andes) Using Seismic Data and Machine Learning: A Joint Interpretation with Geodetic Data (GNSS and InSAR)

Abstract Anticipating volcanic eruptions remains a challenge despite significant scientific advancements, leading to substantial human and economic losses. Traditional approaches, like volcano alert levels, provide current volcanic states but do not always include eruption forecasts. Machine learning (ML) emerges as a promising tool for eruption forecasting, offering data-driven insights. We propose an ML pipeline using volcano-seismic data, integrating precursor extraction, classification modeling, and decision-making for eruption alerts. Testing on six Copahue volcano eruptions demonstrates our model’s ability to identify precursors and issue advanced warnings pseudoprospectively. Our model provides alerts 5–75 hr before eruptions and achieving a high true negative rate, indicating robust discriminatory power. Integrating short- and long-term data reveals seismic sensitivity, emphasizing the need for comprehensive volcanic monitoring. Our approach showcases ML’s potential to enhance eruption forecasting and risk mitigation. In addition, we analyze long-term geodetic data (Interferometric Synthetic Aperture Radar and Global Navigation Satellite System) to assess Copahue volcano deformation trends, in which we notice an absence of noteworthy deformation in the signals associated with the six small eruptions, aligning with their small magnitude.

Travelling Ionospheric Disturbances detection: a statistical study of detrending techniques, induced period error and near real-time observables

Due to advances in remote sensing of the Earth's Ionosphere through Total Electron Content (TEC) estimates by Global Navigation Satellite System (GNSS) receivers, it is possible to detect and characterize Travelling Ionospheric Disturbances (TIDs) in both post-processing and, to some extent, in near real-time (NRT). A reliable and precise TEC filtering technique must be adopted to characterize waves accurately. Specifically, TEC detrending is widely adopted to extract the amplitude and period of the detected ionospheric waves from the background ionospheric conditions. Therefore, this study aims to understand and compare how different TEC detrending techniques and their settings impact the ability to extract such parameters. We highlight that the novel Fast Iterative Filtering (FIF) and the Savitzky-Golay filter (SGOLAY) techniques are the most reliable overall compared with moving average (MA), multi-order numerical difference (DD), polynomial detrending (POLY) and Finite Impulse response (FIR) band-pass filter (BUTF). Moreover, the impact of general algorithm settings on the extracted TID period is investigated, such as the Ionospheric Piercing Point (IPP) height and elevation cut-off angle, showing that such parameters drastically impact the retrieved period, especially for slower TIDs. Finally, due to the growing interest in real-time (RT) detection and classification of TIDs, the study proposes techniques for accurately estimating the TID amplitude in an NRT scenario. Such NRT techniques are then compared with the widely used post-processing products, such as the calibrated vertical TEC (vTEC), showing a difference that is mostly lower than the typical noise level of GNSS receivers (0.05 TECu).