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Publication date: 15 April 2026

Source: Earth and Planetary Science Letters, Volume 680

Author(s): Daniel Sauter, Gianreto Manatschal, Nick Kusznir, Nicolas Coltice, Pauline Chenin, Marc Ulrich, Marie Garbaciak, Philippe Werner

Publication date: 15 April 2026

Source: Earth and Planetary Science Letters, Volume 680

Author(s): Anne C. Glerum, S. Brune, J. M. Magnall, P. Weis, S. A. Gleeson

Publication date: 15 April 2026

Source: Earth and Planetary Science Letters, Volume 680

Author(s): Djihane Benzeggouta, Benjamin Favier, Michael Le Bars

Publication date: 15 April 2026

Source: Earth and Planetary Science Letters, Volume 680

Author(s): Neil C. Sturchio, Hao Yan, Alian Wang, W. Andrew Jackson, Huiming Bao, Chuck Y.C. Yan, Linnea J. Heraty, Yu Wei, Quincy H.K. Qu, Kevin S. Olsen

Publication date: Available online 4 March 2026

Source: Advances in Space Research

Author(s): Andrei Chelpanov, Nikolai Kobanov

Publication date: Available online 4 March 2026

Source: Advances in Space Research

Author(s): Recep Uğur ACAR

Publication date: Available online 4 March 2026

Source: Advances in Space Research

Author(s): Mohammad Maleki, Abdulsalam Esmailzadeh, Mahmoud Edalati Ranjbar, Parisa Derakhshesh, Javad Hosseini, Mahdis Rahmati, Junye Wang, Shayan Khanmohammadidoustani, Rabee Rustum

Publication date: Available online 4 March 2026

Source: Advances in Space Research

Author(s): Zhongyi Sun, Zhanxun Che, Gengchen Zhao, Zeming Wang, Guigao Le

Publication date: Available online 4 March 2026

Source: Advances in Space Research

Author(s): TAlagu Venkatesh, Nisha Radhakrishnan

Publication date: Available online 3 March 2026

Source: Advances in Space Research

Author(s): Yuhui Ma, Jianmin Wang, Lei Zhang, Hongrui Ren

Publication date: Available online 3 March 2026

Source: Advances in Space Research

Author(s): Xufeng Wu, Liu Zhang, Guowei Fan, Quanzhi Liu, Yang Xiao, Jianpeng Han

Publication date: Available online 3 March 2026

Source: Advances in Space Research

Author(s): Roshan Beuria, Sarat Chandra Sahu, Debabrata Nandi

Publication date: Available online 3 March 2026

Source: Advances in Space Research

Author(s): Kundan Bhushan, Manoj Kumar Yadava, Praveen Kumar Yadav

In some parts of the deep ocean, it can look like it's snowing. This "marine snow" is the dust and detritus that organisms slough off as they die and decompose. Marine snow can fall several kilometers to the deepest parts of the ocean, where the particles are buried in the seafloor for millennia.

There is growing interest in the scientific community and private sector in biological approaches to marine carbon dioxide removal—strategies designed to enhance the ocean's natural ability to absorb carbon from the atmosphere. However, a study led by Megan Sullivan, a postdoctoral researcher in the University of Rhode Island's Graduate School of Oceanography (GSO), suggests that some proposals may overlook an important factor.

Changes in the Gulf Stream, a strong ocean current in the Atlantic, could serve as an early warning of the imminent collapse of the Atlantic Meridional Overturning Circulation (AMOC). The AMOC is a massive system of ocean currents that acts as a conveyor belt, moving heat from the tropics to the North Atlantic. The part of this system that flows along the east coast of the United States and then east toward Europe is the Gulf Stream. Scientists are concerned that if the AMOC were to collapse, it could trigger drastic climate shifts, especially in Europe, where temperatures could plummet.

How is carbon metabolized and processed in different ecosystems? In a study published in the journal Communications Earth & Environment, researchers led by Joely Maak, the study's first author and researcher in the Cluster of Excellence "The Ocean Floor—Earth's Uncharted Interface," examined the carbon cycle in a unique marine ecosystem.

When Saharan dust reaches the UK and Europe, as a huge country-sized cloud did over the past few days, it can transform the sky. Tiny particles drifting in the atmosphere scatter blue light while allowing reds and oranges to reach us intact, producing beautiful sunsets.

SummaryTraditional bathymetry inversion methods often fail to capture the complex nonlinear relationship between gravity data and bathymetry and lack the capability to quantify prediction uncertainty. To address these limitations, we investigated deep learning and Bayesian methods that enhance prediction accuracy and provide estimates of prediction uncertainty. Three methods—the Fully Connected Neural Network (FCNN), normalizing Flow model (Flow) and FCNN-Markov Chain Monte Carlo (FCNN-MCMC)—were developed to construct high-resolution (1′×1′) bathymetry models (FCNN, Flow, and FCNN-MCMC models) of the South China Sea (113°E-119°E, 12°N-19°N). The input data included positional, topographic, and gravity information from 4′×4′ grid points surrounding each training, validation and prediction point, while the output data corresponded to the measured bathymetry at training and validation points. At the check points, the standard deviation (STD) of the FCNN, Flow, and FCNN-MCMC models decreased by 7.13 m, 14.24 m, and 15.51 m, respectively, compared with topo_27.1, and by 18.19 m, 25.30 m, and 26.57 m, respectively, compared with ETOPO2022. The distribution of prediction uncertainties (STD) showed that over 90 per cent of the area exhibited an STD below 130 m. The prediction uncertainties exhibited a spatial distribution similar to the predicted results, with higher uncertainties mainly concentrated in shallow waters and steep areas.

SummaryHydraulic shear stimulation is a method to enhance permeability and heat extraction efficiency of geothermal systems. However, such reservoir treatments can have the risk of injection-induced seismicity. To address this issue, a novel Traffic Light System (TLS) is proposed based on the change of the seismic injection efficiency rate which is the ratio between seismic energy and hydraulic energy. In a first step, we numerically investigate the behavior of a naturally rough, slowly slipping, velocity-strengthening fracture in a granite at laboratory scale. The model is formulated on an evolution law for fractures within a rate-and-state friction framework, with fracture aperture varying as a function of both slip displacement and slip velocity. We compare the effects of the proposed Energy-based Traffic Light System (ETLS) injection protocol against those of modeled monotonic and cyclic injection, focusing particularly on aperture evolution and slip velocity. We show how implementing the ETLS criteria can reduce slip velocity by 30% compared to monotonic injection, while it was increased by 51% for cyclic injection. Even with lower slip velocity, the ETLS injection scheme sustains a similar aperture per injected volume as the monotonic scheme once larger volumes are reached. Overall, our simulations suggest that an ETLS approach could provide a safer hydraulic shear stimulation strategy for enhanced geothermal systems by minimizing slip velocity while maximizing permeability, compared to monotonic or cyclic injection.