Author(s): Xiaofei Shen, Yue-Yue Chen, Karen Z. Hatsagortsyan, and Christoph H. Keitel
Relativistic electrons colliding with intense counterpropagating laser pulses are expected to lose energy through radiation reaction. However, we reveal a counterintuitive regime where reflected leptons (including incident electrons, generated electrons, and positrons) gain significant energies when…
[Phys. Rev. E 113, 035208] Published Wed Mar 18, 2026
SummaryPrevious studies of the 2021 Mw 7.4 Maduo earthquake have primarily focused on the early postseismic phase, while the dominant mechanisms driving postseismic processes and the seismic moment released by afterslip remain debated. Longer-term observational constraints are needed to address these issues. In this study, we integrate ~3.5 years of postseismic InSAR and GPS time series data to effectively separate the contributions of afterslip and viscoelastic relaxation. The results show that afterslip released a cumulative seismic moment of approximately 3.91 × 1019 N·m, accounting for ~23.3 per cent of the coseismic moment—equivalent to a new Mw 7.0 earthquake. The optimal steady-state and transient viscosities of the lower crust are estimated to be 1.35 × 1019 Pa·s and 1.5 × 1018 Pa·s, respectively. Afterslip remains the dominant mechanism driving near-field deformation throughout the observation period, while viscoelastic relaxation governed far-field deformation beginning about 4 months after the mainshock. The stress-driven afterslip is comparable with the inverted kinematic afterslip, and poroelastic rebound is negligible. These findings provide valuable insights into stress perturbations on surrounding faults induced by the coseismic rupture, afterslip, and viscoelastic relaxation, and offer new constraints on the recurrence interval of Mw 7.4 earthquake on the Jiangcuo Fault.
SummaryMachine learning models offer powerful predictive capabilities for geoscientific applications but remain limited by their ”black-box” nature and lack of rigorous uncertainty quantification. We developed a comprehensive, generalizable uncertainty quantification framework that decomposes predictive uncertainty into aleatoric and epistemic components using Quantile Regression Forests. Additionally, we applied unsupervised k-means clustering to isolate homogeneous data regimes, thereby reducing aleatoric uncertainty across spatially heterogeneous geoscientific datasets. To facilitate interpretation and quality assessment, we introduced five spatial diagnostic tools: bandwidth, variance, robustness, confidence, and explainability maps that characterize prediction reliability and identify dominant uncertainty sources. To demonstrate the framework’s applicability, we tested it on three synthetic datasets varying in size and a real-world geothermal heat flow application with 14 geophysical observables across continental Africa. Results show that clustering substantially reduces aleatoric uncertainty while maintaining stable epistemic uncertainty. Clustering also improves predictive accuracy and sharpens prediction intervals, with gains most pronounced in homogeneous regions. Applied to the African geothermal heat flow, the framework reveals region-specific geological controls (lithospheric architecture dominates stable cratons, while tectonic proximity governs active rift zones) and guides targeted data collection by distinguishing high-epistemic regions requiring additional sampling from high-aleatoric zones needing improved observables. While theoretically applicable to other geographic regions and geophysical datasets, the framework’s performance in different geological settings requires validation. This interpretable, uncertainty-aware approach enhances trustworthiness of predictions in spatially heterogeneous, data-sparse geoscientific problems.
How can we measure time more than 500 million years into the past? A study recently published in Nature Communications by researchers at the University of Lausanne presents a new geological "rock clock" that allows major climate events from the dawn of complex animal life to be dated with unprecedented precision.
New paired studies from the University of Minnesota Twin Cities show that machine learning can improve the prediction of floods. The studies, published in Water Resources Research and the Proceedings of the IEEE International Conference on Data Mining, demonstrate how "knowledge-guided" artificial intelligence can assist forecasters in saving lives and protecting infrastructure as the frequency of extreme weather increases.
Publication date: 15 March 2026
Source: Advances in Space Research, Volume 77, Issue 6
Author(s): Avinash Kumar Ranjan, Bikash Ranjan Parida, Jadunandan Dash, Amit Kumar Gorai
Publication date: 15 March 2026
Source: Advances in Space Research, Volume 77, Issue 6
Author(s): Zahraa Zawawi, Iman khudiesh, Ayah Helal
Publication date: 15 March 2026
Source: Advances in Space Research, Volume 77, Issue 6
Author(s): Peijuan Wang, Samet Aksoy, Elif Sertel
Publication date: 15 March 2026
Source: Advances in Space Research, Volume 77, Issue 6
Author(s): Yongwei Cao, Zhanghua Xu, Yuanyao Yang, Chaofei Zhang, Na Qin
Publication date: 15 March 2026
Source: Advances in Space Research, Volume 77, Issue 6
Author(s): Sirui Zhang, Bobin Cui, Shi Du, Guanwen Huang, Le Wang, Qin Zhang
Publication date: 15 March 2026
Source: Advances in Space Research, Volume 77, Issue 6
Author(s): Shuyue Wang, Jiawei Niu, Mohammed Bennamoun
Publication date: 15 March 2026
Source: Advances in Space Research, Volume 77, Issue 6
Author(s): Debasish Sing, Manas Das, Saraswati Das, Amit Kumar Mankar, Radhakanta Koner
A new study reveals an unprecedented increase in wildfires in tropical peatlands during the 20th century. "Unprecedented burning in tropical peatlands during the 20th century compared to the previous two millennia" is published in Global Change Biology.
Navigating monolithic icebergs, massive ocean waves and sub-zero snowstorms, CSIRO research vessel (RV) Investigator is a workhorse for Antarctic science. In just over 11 years and spread across seven voyages, the vessel has now spent the equivalent of one full year, or more than 10% of its time, at sea delivering crucial research in Antarctic waters.
Reliable and scalable water level prediction is crucial in hydrology for effective water resources management, especially when considering challenges owing to climate change, urbanization, improper land use, and high-water demand. It directly impacts the availability and distribution of freshwater in rivers and reservoirs. Therefore, accurate forecasting via early warning systems is a highly useful technique for flood mitigation, agricultural irrigation, ecosystem and environmental sustainability, and numerous other applications.
New research reveals that changes following the recent and dramatic decline in Antarctic sea ice could help a low-nutritional species prosper, with major ramifications for food webs and biogeochemical cycles. The findings are published in the journal Marine Ecology Progress Series.
A global analysis of more than 2,300 seawater samples from more than 20 field studies around the globe indicates that human-made chemicals make up a significant portion of organic matter in coastal oceans. The international study, led by biochemists Jarmo Kalinski and Daniel Petras at the University of California, Riverside, analyzed seawater samples collected over a decade from coastal regions from the Pacific, Atlantic, and Indian oceans.
When floods, coastal erosion or sea-level rise threaten settlements or infrastructure, European countries turn to managed retreat more often than previously assumed. Managed retreat refers to the planned, government-supported relocation of people, homes or infrastructure away from areas exposed to flooding and other climate-related hazards. A new German–Dutch study led by Kiel University in collaboration with the Dutch research institute Deltares systematically documents the extent and diversity of such measures in Europe for the first time.
SummaryLower crustal high-velocity bodies (LCHBs) are key indicators of deep magmatic addition and lithospheric modification at rifted continental margins. Integrating 3D gravity modeling with regional geophysical and geological constraints, we identify a prominent LCHB beneath the Xihu Sag of the East China Sea (ECS) shelf basin. This body is NNE–SSW elongated, ~5–7 km thick, and spatially coincides with major depocenters and fault systems. We propose a two-stage mafic emplacement model linking its formation to the tectonic transition from fore-arc compression to back-arc extension. During the early–mid Cretaceous, compressional subduction of the Paleo-Pacific Plate facilitated arc-related underplating and accumulation of mafic material in the lower crust. In the early Cenozoic, slab rollback and asthenospheric upwelling during back-arc extension renewed melt supply, further thickening the lower crust. The absence of surface volcanism indicates that magmas were largely trapped and crystallized at depth, forming dense mafic cumulates. Present-day low shallow-mantle temperatures and high densities beneath the Xihu Sag suggest that preservation of these cumulates was sustained not solely by mantle thermal conditions, but also by prolonged subsidence, sedimentary insulation, and inherited compressional structures. These results underscore the need to integrate tectonic, thermal, and structural factors to fully understand deep magmatic processes in marginal basins.
SummaryWe develop a novel comprehensive theoretical framework, the Biot-patchy-spherical-squirt (BIPSSQ) model, for wave propagation in partially saturated dual-porosity media. This model simultaneously incorporates three key fluid flow mechanisms: macroscopic flow (Biot flow or global flow), mesoscopic flow, and microscopic flow (three-dimensional spherical squirt flow). The constitutive relations and fluid pressure expressions for the BIPSSQ model are first derived and then the governing wave equations are established using a Lagrangian approach based on the system’s kinetic energy, potential energy, and dissipation functions. Through plane wave analysis, we obtain the phase velocity and attenuation of the fast P-wave. Numerical examples demonstrate that the BIPSSQ model predicts multiple dispersion transition bands and corresponding attenuation peaks, attributed to two squirt flows and two Biot global flows from two immiscible fluids. Furthermore, the presence of squirt flow significantly suppresses the mesoscopic patchy saturation effect, leading to the disappear of mesoscopic dispersion transition band and attenuation peak. The influences of permeability, saturation, porosity, squirt-flow length and inclusion radius on velocity dispersion and attenuation are also analyzed. Finally, excellent agreement between theoretical predictions and experimental measurements from an Aksu outcrop rock sample (800 kHz), a gas-water-saturated Estaillades limestone (1 kHz), and an oil-brine-saturated Vosgian sandstone (350 kHz), validates the applicability and effectiveness of the BIPSSQ model. Moreover, the BIPSSQ model can degenerate to other theories (i.e. Biot, BISSQ, BR) under certain conditions. Our proposed model provides a unified and robust tool for interpreting wave propagation phenomena in complex, partially saturated reservoir rocks.
