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SummaryMarine observation data are plentiful for constructing seafloor topography, and the integration of multi-sources data to construct a more accurate topography model remains a significant subject that continues to be explored and studied. In this study, we use geoid height (GH), Gravity (VG) and Vertical Gravity Gradient (VGG) derived from a single rectangular prism to establish the foundational observation equations for predicting topography. The effectiveness of the foundational observation equations is verified through study cases without the use of the ship measurement depth data. Additionally, the single- and multi-beam soundings data are employed as control points to integrate into the foundational observation equations for predicting topography. The prediction results demonstrate that the prediction accuracy of combined VG anomalies with ship soundings is better than GH and VGG anomalies, which is primarily because VG anomalies are effective than GH amplify high frequency signals of topography and stronger than VGG anomalies in suppressing high frequency errors. Additionally, considering the limited accuracy of marine gravity in sea region with islands and reefs, this study incorporates satellite imagery data to identify the location and size of the islands. Then, the topography of the islands is introduced and the control equations is established to jointly predict topography. The prediction results reveal the RMS errors between prediction results and single- and multi-beam sounding data are 67.4 m, which is 37.4%, 57.8% and 62.8% higher than that of SRTM 15+, DTU and ETOPO-1 models respectively. Notably, compared with the STRM 15+ model, the algorithm improves the topography accuracy of the sea area near the islands by nearly 60.8%.

The measurement of virtual height of the sporadic E layer (h'Es) is very sensitive to the type of ionosonde used and the calibration processes. The ionosondes used by the national institute of communication an...

Determining the degree to which shifting drought conditions around the world are attributable to natural hydroclimatic variability and how much they are caused by climate change is a complicated task. Scientists often use complex computer models to simulate past climate variability and to identify unprecedented drought conditions.

It is no strange sight to see icebergs break off of the Antarctic ice cap and drift away, like the gigantic sheet of ice that is currently heading for the island of South Georgia. But climate change is making it happen more frequently, with ever-larger icebergs in the waters around Antarctica.

Each summer, more and more lake beaches are forced to close due to toxic algae blooms. While climate change is often blamed, new research suggests a more complex story: climate interacts with human activities like agriculture and urban runoff, which funnel excess nutrients into the water.

Publication date: Available online 15 February 2025

Source: Advances in Space Research

Author(s): Yang Shen, Mingjian Chen, Guangyun Li, Linyang Li, Xingyu Shi, Wei Lv

Publication date: Available online 13 February 2025

Source: Advances in Space Research

Author(s): Shuyue Fu, Shengping Gong

Publication date: Available online 13 February 2025

Source: Advances in Space Research

Author(s): Victor U. Chukwuma, Bolarinwa J. Adekoya, Eugene O. Onori, Oluwafunmilayo O. Ometan, Aghogho Ogwala

Publication date: Available online 13 February 2025

Source: Advances in Space Research

Author(s): Changchun Long, Shengping Gong, Yuying Liang

Publication date: Available online 12 February 2025

Source: Advances in Space Research

Author(s): Yinliang Zhang, Lin Zhong, Riyue Wu, Zhiwei Long, Kun Xu, Tao Zhang

Publication date: Available online 12 February 2025

Source: Advances in Space Research

Author(s): Yuan Jiang, Zhan Lei, Liying Zhu, Shuo Liu, Suliang Ma

Publication date: Available online 11 February 2025

Source: Advances in Space Research

Author(s): Jiao Wang, Boao Zhu, Chengshang Li, Chong Sun, Yawei Wan

Today we are witnessing rapid global sea level rise attributable mostly to climate change–driven melting of ice sheets and glaciers and thermal expansion of seawater. However, sea level change also occurs over millions of years as geological processes gradually reshape Earth's ocean basins and change their total storage volume.

Researchers from Tokyo Metropolitan University have carried out an unprecedentedly detailed survey of pumice rafts in the aftermath of the 2021 Fukutoku-Oka-no-Ba volcanic eruption in Japan. Using samples from 213 different locations, they considered raft density, the size and roundness of individual pumice, and biological species attached.

A team of astrophysicists, geoscientists, chemists and life scientists affiliated with a host of institutions in Japan has found evidence that billions of years ago, the Earth's oceans were green. In their study published in the journal Nature Ecology & Evolution, the group used a variety of environmental factors to create a simulation of Earth several billion years ago.

Author(s): Yong Shen, J. Q. Dong, X. D. Peng, H. D. He, and J. X. Li

The magnetohydrodynamic (MHD) instability in the HL-2A tokamak has been comprehensively studied, including ideal MHD ballooning and kink modes, as well as the operational β limit imposed jointly by them. It shows an internal ballooning mode appearing in the plasma with parabolic pressure and current…

[Phys. Rev. E 111, 025208] Published Mon Feb 24, 2025

SummaryGeological storage of captured CO₂ is essential for reducing anthropogenic greenhouse gas emissions and ensuring the sustainable use of fossil fuels. Understanding the influence of saturation, pressure, and temperature on the elastic properties of brine-saturated sandstone flooded with supercritical CO₂ is critical for interpreting seismic and sonic logging data, which aids in monitoring and quantifying subsurface changes associated with CO₂ injection. The experimental results indicate that as scCO₂ saturation increases (0∼60 per cent), the P-wave velocity decreases significantly with an average of 14 per cent drop, while the S-wave velocity increases slightly. Temperature variations (80–110°C) have a minimal effect on both velocities (1∼2 per cent), whereas elastic features show noticeable sensitivity to the variation of confining pressure (20-30 MPa) and pore pressure (10-20 MPa). Ignoring the effects of pore pressure might lead to the bias of interpreting seismic data for monitoring scCO₂ saturation change. The constructed rock physics models well capture the coupled effects of porosity and scCO₂ saturation on the P-impedance and P- and S-wave velocity ratio, which show good agreement with the experimental results. These findings are crucial for improving monitoring methods and enhancing the accuracy of predictive models for CO₂ geological storage.

Abstract

Solar photospheric abundances and CI-chondrite compositions are reviewed and updated to obtain representative solar system abundances of the elements and their isotopes. The new photospheric abundances obtained here lead to higher solar metallicity. Full 3D NLTE photospheric analyses are only available for 11 elements. A quality index for analyses is introduced. For several elements, uncertainties remain large. Protosolar mass fractions are H (X = 0.7060), He (Y = 0.2753), and for metals Li to U (Z = 0.0187). The protosolar (C+N)/H agrees within 13% with the ratio for the solar core from the Borexino experiment. Elemental abundances in CI-chondrites were screened by analytical methods, sample sizes, and evaluated using concentration frequency distributions. Aqueously mobile elements (e.g., alkalis, alkaline earths, etc.) often deviate from normal distributions indicating mobilization and/or sequestration into carbonates, phosphates, and sulfates. Revised CI-chondrite abundances of non-volatile elements are similar to earlier estimates. The moderately volatile elements F and Sb are higher than before, as are C, Br and I, whereas the CI-abundances of Hg and N are now significantly lower. The solar system nuclide distribution curves of s-process elements agree within 4% with s-process predictions of Galactic chemical evolution models. P-process nuclide distributions are assessed. No obvious correlation of CI-chondritic to solar elemental abundance ratios with condensation temperatures is observed, nor is there one for ratios of CI-chondrites/solar wind abundances.

The January 2022 Tonga volcanic eruption generated atmospheric pressure waves that propagated over the ocean’s surface and triggered a meteotsunami. This meteotsunami caused significant amplitudes exceeding 10...

Abstract

Accurately modeling and prediction the nonlinear motion of GNSS (Global Navigation Satellite System) coordinate time series holds significant theoretical and practical value for the study of geodynamics. A novel integrated network, named Ensemble Learning method based on Signal Source Driver (ELSSD), is proposed, which leverages the strengths of Long Short-Term Memory (LSTM) and Deep Self-Attention Neural Network (DSANN), while integrating GNSS loading data as an additional data source. Additionally, a multi-track synchronous sliding window data processing strategy is designed to address the challenge of multi-source data fusion input. The effectiveness of this algorithm is validated using GNSS coordinate time series from 186 global stations over a period of 10 years. Experimental results initially illustrate that, when accounting for displacement caused by environmental loading effects, there is a marked improvement in the modeling and prediction accuracy compared with GNSS input-only. Furthermore, the application of three ensemble network strategies-Bagging, Boosting, and Stacking-have further been demonstrated to enhance modeling and prediction accuracy. Compared with LSTM and DSANN networks, the proposed ELSSD algorithm achieves an average RMSE (Root Mean Square Error) of 3.6 mm for both modeling and prediction, with modeling accuracy improvements of 4.8% and 6.2%, while prediction accuracy improvements of 5.4% and 5.9%, respectively. With respect to the traditional Least Square method, there is an improvement of 22.1% and 27.9% in modeling and prediction accuracy, respectively. Regarding noise characteristics, there is a significant reduction in colored noise amplitude, with decreases of 36.7% and 36.0% observed in modeling and prediction, respectively. Simultaneously, the velocity uncertainty experiences an average reduction of 27.1% and 27.5%. The average velocity differences are measured at 0.06 mm/year and 0.24 mm/year, respectively. Hence, our findings suggest that the ELSSD algorithm emerges as an effective methodology for handling multi-source data input in GNSS coordinate time series, presenting promising practical applications in the field.