Earth and Space Science最新文献

The goal of this paper is to estimate the soft X-ray signature associated with dense and fast magnetosheath jets streaming toward the dayside magnetosphere. We developed a non-self-consistent kinematic approach for simulating numerically the transport of high-speed plasma jets in the magnetosheath. Our methodology is based on global magnetohydrodynamic simulations which provide the background state of the terrestrial magnetosphere and theoretical insight on the propagation of high-speed plasma jets. We compute line-of-sight and time-integrated intensity maps of the soft X-rays generated by the charge exchange process taking place when the high-speed jet interacts with the background exosphere. The X-rays are detected by a virtual telescope launched into the simulation domain. Our results show that the soft X-ray signature of a dense and fast plasma jet is visible in the magnetosheath. We can correctly characterize the detected jets with different setups for the virtual telescope. The impact of density on the jet's X-ray signature is stronger than the impact of bulk velocity, the denser jets being more likely to be detected by an X-ray telescope than the faster ones. We discuss an image processing technique based on frame differencing which may allow an improvement of the X-ray visibility of high-speed jets. We also show that the detection of jets is enhanced considerably when the soft X-ray telescope is placed in the equatorial plane, pointing toward the magnetotail.

On 10 June and 27 September 2024, two workshops were held at GFZ Potsdam under the umbrella of the Geo. X Research Network of Geosciences to discuss the unresolved question of the overestimation and lack of scattering of modeled ring current electrons during geomagnetic storms. At the workshops, we discussed the potential contributions to the lack of scattering of electron cyclotron harmonic (ECH) waves, chorus waves, time-domain-structures (TDS), the non-linear effects of wave-particle interactions, and induced electric fields. A case study shows that the scattering by ECH waves is insufficient to account fully for the missing electron loss. More work must be done to understand the potential effects of inaccuracies in the assumed chorus wave models, TDS, and the non-linear effects of wave-particle interactions. Including induced electric fields in ring current simulations is an important step to describe the electron drifts more accurately. Explaining the missing loss process is crucial for space weather applications of surface charging effects, which rely on accurate predictions of ring current electron fluxes.

This study evaluates the capability of a robotic arm equipped with a force-torque sensor and a specially designed scoop to perform geomechanical characterization of high-fidelity lunar highlands regolith simulant. Experiments focused on pressure-sinkage, shear strength, and angle of repose to assess the performance and applicability of the system to lunar exploration and infrastructure development. Results demonstrated that pressure-sinkage measurements using the scoop reliably characterize the in situ relative density of regolith simulants, showing clear trends as a function of material density. This capability highlights the potential for real-time assessments of local regolith properties during lunar missions. Shear strength experiments identified a need for advanced robotic arm motion controls for shear testing; alternative methods and advanced modeling techniques for determining shear strength using the scoop are under active investigation. Angle of repose tests confirmed the ability of the robotic arm, scoop, and imaging hardware to measure this property accurately, showcasing the versatility of this approach for regolith characterization. The findings underscore the promise of robotic arms for performing critical geomechanical measurements on planetary surfaces given properly designed end effectors that would enable data collection essential for optimizing rover traverse paths, selecting infrastructure sites, investigating geologic history, and supporting both scientific exploration and settlement planning. These results support the inclusion of geomechanical measurement payloads in future missions, directly advancing NASA's Artemis lunar exploration program objectives.

Since the 2010 launch of Cryosat-2, a new generation of altimeters, referred to as SAR altimetry, has emerged and partially replaced the previous conventional altimeters known as Low Resolution Mode (LRM) altimetry. A surface slope correction has been previously developed for LRM altimetry. However, the differences in the way the two altimeters work, and in particular their radar footprint, make LRM altimeter slope correction inapplicable to SAR altimetry. Thus, in this paper, a slope correction model is provided for SAR altimetry, derived from the LRM-based approach. The shape of the SAR footprint induces that height correction depends on each satellite mission. Consequently, a generic method allowing to generate global maps of height correction for distinct missions is provided. The maps are computed for the Sentinel-6A mission and the importance of correcting this effect for SAR altimetry is highlighted by studying the sea surface height anomaly biases between Sentinel-6A SAR and LRM measurements. Finally, it is shown that applying the slope correction to Sentinel-6A SAR mode sea surface height anomaly measurements enhances their consistency with the latest Mean Sea Surface (MSS) model, reducing the root mean square error between the sea surface height anomaly and the MSS model by up to 1 cm.

Global Navigation Satellite System-Acoustic (GNSS-A) seafloor geodetic observation is an effective technique not only for measuring offshore crustal deformation but also for detecting underwater sound speed structures. Collecting acoustic range data along survey tracks with broad spatial coverage is typically essential to detect horizontal gradients of the sound speed structure. Although acoustic ranging data sets with broad spatial coverage have been acquired using research vessels, the acquisition of such data sets has often been difficult using an unmanned sea surface platform, such as a Wave Glider. To ensure positioning accuracy for insufficient acoustic ranging data sets, constraining the horizontal gradients of the sound speed structure using independent information is essential. In this study, we examined the applicability of ocean models to improve GNSS-A positioning accuracy for insufficient acoustic ranging data sets. We calculated the sound speed parameters expressing the horizontal gradients of the sound speed structure using the ocean models JCOPE2M and MOVE/MRI.COM, and compared them with those estimated by GNSS-A using actual acoustic ranging data sets with broad spatial coverage. The results illustrated that these ocean models have the potential to improve the positioning accuracy when large-scale horizontal inhomogeneity exists in the sound speed structure (e.g., an oceanic current). However, the GNSS-A analysis results using actual data indicate a significant influence of small-scale horizontal inhomogeneities, suggesting that higher-resolution ocean models are required to further improve positioning accuracy.

We demonstrate the increased ability to forecast hurricane impacts with a coupled numerical modeling system by simulating ocean waves, water levels, currents, sediment transport, and structural damage to predict inundation, coastal morphological change, and residential building impacts. The Coupled-Ocean-Atmosphere-Waves-Sediment-Transport (COAWST) modeling system is applied to simulate Hurricane Michael (category 5, 2018) that made landfall near Tyndall Air Force Base, FL, in the northern Gulf of America, causing severe devastation and flooding. Atmospheric forcings from the Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) are used to drive the ocean and wave models on a series of nested grids. Results identify that coastal inundation at Mexico Beach is due to surge from winds and waves, supplemented by pulses of infragravity wave motions that propagate landward into the inundation region. Seed lines observed on interior building walls also demonstrate variable changes in water level. In addition, a machine learning model was applied to hindcast structure damages, caused mostly by waves and winds, with a 72% accuracy estimate of substantial damage in proximity of landfall. The storm also created a breach across Cape San Blas, the adjacent barrier spit, due to large surge and low dune elevations. Dune locations with vegetated land cover are shown to reduce wave-energy dissipation and reduce erosion, whereas locations without land cover had increased breaching potential. The breach occurred during the maximum ocean-side water level, and the delayed high water on the bay side allowed a pressure gradient to drive flow seaward and promote breach development.

Retrievals of ocean color (OC) properties from space are important for understanding the ocean ecosystem, the carbon cycle, and monitoring events such as harmful algal blooms (HABs). The recently launched U.S. National Aeronautics and Space Administration (NASA) Earth Venture Instrument, the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO), provides a unique opportunity to examine diurnal variability in ocean ecology across coastal waters of North America and prepare for future hyperspectral geostationary OC missions. Although TEMPO does not match the spatial resolution or spectral coverage of planned coastal ocean sensors, such as NASA's Geosynchronous Littoral Imaging and Monitoring Radiometer or the U.S. National Oceanic and Atmospheric Administration Geostationary Extended Observations Ocean Color Instrument, it provides hourly observations at approximately 5 km over U.S. coastal regions and the Great Lakes. Here, we apply a newly developed atmospheric correction approach based on principal component analysis combined with machine learning (ML) to retrieve OC properties using TEMPO's hyperspectral measurements. Principal component coefficients derived from measured reflectances are used to train a neural network to estimate OC properties, including chlorophyll concentration, informed by collocated physically-based retrievals from MODIS, VIIRS, and Ocean and Land Color Instrument. This ML-based approach complements traditional radiative transfer retrievals, particularly under challenging conditions such as glint and moderate cloud coverage. This approach demonstrates the value of near-real-time OC products, with significant potential for monitoring HABs and transient oceanic phenomena.

The injection of CO₂ underground into reservoirs for carbon capture and underground storage (CCUS) is highly sensitive to heterogeneity and anisotropy. Although conventional geological modeling cannot take explicit account of heterogeneity or anisotropy or operate at a resolution that encompasses the small scales at which fluid flow is controlled, advanced fractal reservoir models (AFRMs) can be used. These AFRMs require fractal dimensions and anisotropy ratios for the target reservoir, and these values are unavailable. This paper describes the development, validation, and application of a software tool for measuring reservoir fractal dimensions. The code has been validated extensively using SynFrac data, recognizing four potential sources of systematic error, all of which can be corrected for. The resulting code has been used to measure the fractal dimension of a seismic data cube taken from the Chandon reservoir. The analysis reveals that the reservoir is multifractal with high heterogeneity (fractal dimension) at small scales and lower but still significant heterogeneity at larger scales. The fractal dimension can be calculated as a function of depth, providing a new type of log data that is not specific to a given well but rather specific to an area of seismic data.

The b-value from the Gutenberg-Richter law is a crucial parameter in the assessment of seismic hazard. Its temporal variations may also bring useful insights on the processes driving seismicity at depth, even if not yet fully understood. In this paper, we focus on the temporal evolution of the b-value in the Ubaye Region (French Western Alps) which was hit by seismic swarms (2003–2004) and complex sequences with several mainshocks (2012–2015). The swarm-like sequences show a common temporal behavior of b-value characterized by an increase and then a return to the initial level. The temporal b-value pattern for the mainshock-aftershock-like sequences is quite different. After a drop in the b-value that may follow the mainshock, the b-value increases above the background level before going back to it. Moreover, no precursory pattern can be identified before the mainshock. Fluid processes are recognized to play a crucial role in the driving mechanisms of these seismic sequences. Drawing parallel between swarms and aftershock sequences suggests that the b-value depicts fluid-processes in the Ubaye Region seismicity. We propose that b-value shows a complex behavior, with variations due to Coulomb stress-transfer from the mainshock and fluid-pressure processes. Therefore, even with a catalog made at the French national scale, the b-value variations may help to monitor the on-going processes at depth.

Interdisciplinarity is essential for addressing complex scientific problems that transcend disciplinary boundaries. Geology leverages methods from diverse domains to drive research innovation. However, quantitative evaluations of interdisciplinary connections between geology and other domains are lacking. Therefore, this study employed bibliometrics and natural language processing to assess the interdisciplinary trajectory of geology by analyzing temporal patterns in citation flows. First, a data set of geology-related publications and their cited references was collected from the Scopus database. Then, a semantic text classification approach, integrating sentence transformers and cosine similarity, was implemented to categorize cited references into eight scientific domains: mathematical and physical science, chemical science, life science, engineering and materials science, earth science, information science, management science, and health science. Longitudinal analysis of the distribution of references across these domains reveals trends in interdisciplinary collaboration over time. Finally, N-gram frequency analysis was performed on reference data associated with high-growth domains to identify specific influential techniques bridging disciplines. The results demonstrate a pronounced increase in interdisciplinarity between geology and information science, especially in applications of artificial intelligence, since 2016, with an average interdisciplinary potential of 0.1484. Key techniques driving this integration include artificial neural networks, logistic regression, support vector machines, etc. Additionally, the eight domains were expanded into 126 sub-disciplines to enable more detailed interdisciplinary analysis. Furthermore, three large language models were employed to verify the reliability of the adopted semantic analysis. The results suggest that our methodology provides robust approaches for quantifying interdisciplinary dynamics and is generalizable to other interdisciplinary fields.