Feed aggregator

Low energy gamma rays from the Moon and hydrogen content of the lunar surface

Publication date: 1 May 2026

Source: Advances in Space Research, Volume 77, Issue 9

Author(s): Shipra, Debabrata Banerjee, Shiv Kumar Goyal

Characterization of subsurface water ice by active neutron counting with variable source-to-detector distances

Publication date: 1 May 2026

Source: Advances in Space Research, Volume 77, Issue 9

Author(s): Teppei Takemoto, Hideaki Miyamoto, Yuta Shimizu

Corrigendum to “Advanced predictive modelling of urban expansion and land surface temperature dynamics using multi-scale machine learning approaches”. [Adv. Space Res. 77(2) (2026) 1679–1702]

Publication date: 1 May 2026

Source: Advances in Space Research, Volume 77, Issue 9

Author(s): Ahmed Ali Bindajam, Javed Mallick, Hoang Thi Hang, Chander Kumar Singh

Early twenty-first century analysis of changes in vegetation health and carbon storage in Iran: quantifying the impacts of climate variability and human activities

Publication date: 15 May 2026

Source: Advances in Space Research, Volume 77, Issue 10

Author(s): Pouyan Dehghan Rahimabadi, Bing Liu, Arash Malekian, Maliheh Behrang Manesh, Hossein Azarnivand, Weihao Sun, Bin Wang, Changkun Yang, Xiao Wang, Wen Li

Super-resolution generative adversarial network based dual channel convolutional neural network for hyperspectral image classification

Publication date: 15 May 2026

Source: Advances in Space Research, Volume 77, Issue 10

Author(s): Brajesh Kumar, Mohd. Mustafa Khan, Divyesh Varade

An integrated approach for landslide susceptibility mapping: a case study of Idukki District, South-West India

Publication date: 15 May 2026

Source: Advances in Space Research, Volume 77, Issue 10

Author(s): B. Athul, S. Sumith Satheendran, Megha Kennady, Aparna Pradeep

Dynamic semantic–collaborative multi-scale semi-supervised segmentation for remote sensing images

Publication date: 15 May 2026

Source: Advances in Space Research, Volume 77, Issue 10

Author(s): Huihui Li, Huajian Pan, Xiaoyong Liu, Zhe Li, Qiong Hu, Shaozhong Song, Yanqiu Li

Tracing urban footprints: a CA–Markov-based prediction of LULC change in a class-I city of West Bengal (2001–2041)

Publication date: 15 May 2026

Source: Advances in Space Research, Volume 77, Issue 10

Author(s): Sankar Paul, Sasmita Rout, Harekrishna Manna, Jahar Lal Giri, Rabi Narayan Behera

Refined identification and analysis of coal fire areas in Xinjiang using multi-source long-term remote sensing data

Publication date: 15 May 2026

Source: Advances in Space Research, Volume 77, Issue 10

Author(s): Hongdong Fan, Jun Wang, Zhu Li, Yangjun Teng

Ultra high-resolution mapping of urban surface temperature with multidimensional feature integration: a machine learning framework

Publication date: 15 May 2026

Source: Advances in Space Research, Volume 77, Issue 10

Author(s): Mohammad Karimi Firozjaei, Majid Kiavarz, Naeim Mijani, James Voogt

White hydrogen discovered in billion-year-old Canadian Shield rock points to potential new energy source

Phys.org: Earth science - Mon, 05/18/2026 - 19:00
Within the Canadian Shield, hydrogen gas is steadily building up naturally among some of the oldest rocks on Earth. Now, for the first time, geochemists at the University of Toronto and the University of Ottawa have measured its presence, mapped its concentration and tracked its long-term accumulation, shedding new light on this source of natural, or white, hydrogen.

Sea levels rising dramatically in some areas due to land subsidence

Phys.org: Earth science - Mon, 05/18/2026 - 16:20
Densely populated coastal regions in many parts of the world are particularly vulnerable to flooding. The sinking of land masses exacerbates the impacts of rising sea levels in these areas, according to a study by researchers from the Technical University of Munich (TUM) and Tulane University.

Small and Large Grains Move Differently in Water

EOS - Mon, 05/18/2026 - 14:24
Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: Journal of Geophysical Research: Earth Surface

Sediment transport shapes the Earth surface in different ways, by forming desert dunes and by sculpting the topography of rivers, but the physics of sediment transport initiation is still incompletely understood. For decades, models have generally assumed two basic entrainment mechanisms: a grain resting on the sediment bed is either lifted directly by fluid forces, or it is emitted from the soil indirectly, as product of a granular splash caused by the heavy impact of another grain.

However, recent breakthroughs in grain-based simulations and high-speed visualization have been offering a much clearer look at the processes that trigger grain motion. Insights from these recent advances have revealed a rather broad spectrum of indirect particle-particle and particle-fluid interactions driving entrainment, including the rearrangement of surface grains after splash and changes in near‐bed flow structure due to moving grains. These interactions exert non-local influences on transport thresholds, giving rise to a dynamic process known as collective particle entrainment—a mechanism that remains poorly understood at a fundamental level.

In a new study, Chartrand [2026] shows that collective particle entrainment is size-dependent: large grains interact primarily with their peers, while smaller grains are mobilized by both large and similar-sized particles. This distinction leads to divergent transport signatures, with a new stochastic model predicting temporally correlated motion for small grains and uncorrelated, white-noise entrainment statistics for larger particles.

Although theoretical modeling will be required to shed further light on the physics of collective entrainment, the author’s study is a step toward a quantitative model of sediment transport from a probabilistic perspective. Looking ahead, Chartrand’s ideas could now be extended to other environments, potentially transforming our understanding of entrainment in other contexts such as wind-blown transport and extraterrestrial atmospheric processes.

Citation: Chartrand, S. M. (2026). Collective particle entrainment explored with experimental data and coupled transfer functions. Journal of Geophysical Research: Earth Surface, 131, e2025JF008657. https://doi.org/10.1029/2025JF008657

—Eric Parteli, Associate Editor, JGR: Earth Surface

Text © 2026. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

Antarctic DNA offers vital clues to pinpointing rising sea levels

Phys.org: Earth science - Mon, 05/18/2026 - 14:00
Researchers say accurately predicting Antarctica's impact on global sea levels is an urgent priority that can be achieved by analyzing the DNA of tiny land animals, pinpointing the continent's icy past to paint a clearer picture of the future.

Hidden clean energy under mountains? Why erosion could shape hydrogen prospects in Alps and Pyrenees

Phys.org: Earth science - Mon, 05/18/2026 - 13:00
Hydrogen gas formed by natural processes in the subsurface of mountain ranges could represent a promising source of clean energy. A new international study led by Unil and GFZ shows that erosion plays a key and complex role in the formation and accumulation of this natural resource. The research confirms that the Pyrenees and the Alps could constitute key targets for natural hydrogen exploration.

Interactive hydrology makes a splash with students

Phys.org: Earth science - Mon, 05/18/2026 - 13:00
As climate change increases the risk of flooding worldwide, understanding how floods form has never been more important. However, the science behind flooding is notoriously difficult to grasp, involving interactions among atmospheric, terrestrial, and human systems. Creating educational tools that simplify these processes without losing their essential scientific meaning has remained a major challenge.

Intensifying droughts may be pushing tropical forests toward a dangerous threshold

Phys.org: Earth science - Mon, 05/18/2026 - 12:40
Tropical forests, often described as the lungs of the planet, may be edging closer to a dangerous threshold as droughts become more frequent and widespread across the world's humid tropics. New research suggests these ecosystems are increasingly struggling to recover from prolonged dry conditions, raising concerns that some forests could eventually shift from absorbing carbon dioxide to releasing it back into the atmosphere.

A Wavefield Separation Method Using Single-Station Six-Component Seismic Measurements

Geophysical Journal International - Mon, 05/18/2026 - 00:00
SummarySix-component (6C) seismic observations offer a more comprehensive description of the wavefield than conventional three-component methods. However, current wavefield separation techniques are often constrained by their reliance on dense arrays. This study introduces a novel wavefield separation framework based on single-station 6C polarization analysis, which enables the simultaneous identification and improved separation of major seismic wave types: P-, SV-, Rayleigh, and transversely polarized horizontal waves (SH- and Love waves). Our proposed method models the observed wavefield as a weighted linear combination of theoretical wave models and optimizes the weighting coefficients via the Broyden–Fletcher–Goldfarb–Shanno (BFGS) algorithm, achieving reliable separation. A refined polarization-based filtering strategy incorporating likelihood estimation, degree of polarization, and energy distribution ratios enhances robustness under low signal-to-noise conditions. Furthermore, the inverse Short-Time Fourier Transform (iSTFT) is adopted to mitigate energy leakage issues associated with the inverse S-transform. Validation using synthetic and real teleseismic data suggests the method’s effectiveness and provides insight into its physical limitations. This study demonstrates a polarization-based framework for seismic phase identification and wavefield separation, which may support multi-phase joint inversion in selected seismic applications.

Seismogenic index improves deep learning performance for seismicity rate forecasting in Utah FORGE and EGS Collab projects

Geophysical Journal International - Mon, 05/18/2026 - 00:00
SummaryInjection-induced seismicity poses a major challenge to the safety of Enhanced Geothermal Systems (EGS). We customized a deep learning model to forecast seismicity rate under prescribed injection schedules. The model adopts a two-stage strategy where injection pressure is first forecasted as an intermediate variable and subsequently used to support seismicity rate forecasting. In this way, seismicity rates at both the field-scale Utah Frontier Observatory for Research in Geothermal Energy (Utah FORGE) and the mine-scale EGS Collab projects could be successfully forecasted. While the model without seismogenic index (Σ) could attain low forecast errors, incorporating Σ markedly improves its ability to capture the transient variability of seismicity rate. The forecasts at Utah FORGE and EGS Collab may highlight the importance of integrating key physical parameters calculated from raw observations into data-driven frameworks for forecasting injection-induced seismicity, and may demonstrate the potential of customized deep learning models for cross-stage forecasting in next-generation EGS.

Theme by Danetsoft and Danang Probo Sayekti inspired by Maksimer