Publication date: 15 May 2026
Source: Advances in Space Research, Volume 77, Issue 10
Author(s): Carlo Scotto, Dario Sabbagh, Alessandro Ippolito, Loredana Perrone
Publication date: 15 May 2026
Source: Advances in Space Research, Volume 77, Issue 10
Author(s): Mefe Moses, Trisani Biswas, Haris Haralambous, Krishnendu Sekhar Paul
British demand for everyday global commodities can be linked to more than 29,000 hectares of deforestation worldwide in a single year, with tens of thousands of hectares stripped directly from overseas ecosystems. The stark figure forms the centerpiece of an environmental assessment released by the Stockholm Environment Institute (SEI) at the University of York.
The ocean is an important carbon sink that absorbs 20–30% of the total anthropogenic CO2 emissions in the industrial era (1.0–3.0 Pg annually, 1 Pg = 1015 g). Tropical cyclones are among the most devastating weather systems that profoundly disturb the upper ocean. However, their role in the global carbon cycle has been controversial: do tropical cyclones lead to net carbon absorption or release by the ocean, and does it matter?
Located in the middle of the North Pacific, between Japan and Canada, lies one of the world's largest oceanic plateaus, the so-called Hess Rise. The plateau is roughly T-shaped and extends over a length of about 1,000 kilometers. Due to its distance from the nearest mainland, the research area at Hess Rise is difficult to access and has therefore been the destination of only a few expeditions to date.
SummaryImages of the Earth’s interior can provide us with insight into the underlying properties of the Earth, such as how seismic activity might emerge and the interplay between seismic and volcanic activity. Understanding these systems requires reliable high-resolution images to understand mechanisms and estimate physical quantities. However, reliable images are often difficult to obtain due to the non-linear nature of seismic wave propagation and the ill-posedness of the related inverse problem. Reconstructions rely on good initial estimates as well as hand-crafted priors, which can ultimately bias solutions. In our work, we present a 3D reconstruction of Kilauea’s magmatic system at a previously unattained resolution. Our eikonal tomography procedure improves upon prior imaging results of Kilauea through increased resolution and per-pixel uncertainties estimated through variational inference. In particular, solving eikonal imaging using variational inference with stochastic gradient descent enables stable inversion and uncertainty quantification in the absence of strong prior knowledge of the velocity structure. Our work makes two key contributions: developing a stochastic eikonal tomography scheme with uncertainty quantification and illuminating the structure and melt quantity of the magmatic system that underlies Kilauea.
SummaryThe global energy transition has created an urgent need for expanded critical mineral supply. Projected production from existing deposits and current discovery rates remains insufficient to meet this demand. More efficient exploration strategies are therefore required, particularly in optimizing costly and low-success-rate data acquisition campaigns. To address this challenge, we introduce the concept of sequential Efficacy of Information (sequential EOI), a decision-making metric that quantifies the uncertainty reduction of target variables under proposed exploration action sequences. Unlike Value of Information (VOI), sequential EOI operates in the domain of uncertainty reduction, removing the need for an economic model that is rarely available in early-stage exploration. We demonstrate the framework using synthetic 2D and more realistic 3D porphyry copper systems, evaluating sequential combinations of exploration plans including ambient noise tomography (ANT) surveys and borehole drilling campaigns. In both cases, sequential EOI identified exploration plans that maximized the uncertainty reduction to the target variables. These results demonstrate that sequential EOI offers a principled framework for multi-physics, multi-step, and uncertainty-driven plan optimization in mineral exploration, providing exploration teams with a practical and scalable decision-analytic tool for rational campaign design without requiring economic assumptions.
Seismic waves traveling through Earth's interior often propagate at different speeds depending on their direction, a phenomenon known as seismic anisotropy. Such anisotropy is commonly detected beneath subduction zones, particularly near stagnant slabs in the mantle transition zone and uppermost lower mantle. However, the physical origin of these signals has remained uncertain.
Publication date: 15 May 2026
Source: Advances in Space Research, Volume 77, Issue 10
Author(s): Yifan Shen, Guangjian Xu, Liang Chen, Qiang Wang, Huizhong Zhu, Wei Zheng, Peifeng Kang, Shijie Zhao
Publication date: 15 May 2026
Source: Advances in Space Research, Volume 77, Issue 10
Author(s): Dan Li, Wenzuo Zhou, Yichen Hu, Xinyu Yao
Publication date: 15 May 2026
Source: Advances in Space Research, Volume 77, Issue 10
Author(s): Seyedreza Dorri, Masoud Ghodsian, Mohammad Sharifikia
Publication date: 15 May 2026
Source: Advances in Space Research, Volume 77, Issue 10
Author(s): Anja Schlicht, Jan Kodet, Mario Hannemann, Boris Strelnikov
Publication date: 15 May 2026
Source: Advances in Space Research, Volume 77, Issue 10
Author(s): Shruti Gupta, Ashish Kumar, Rahul Dev Garg, Neeraj Jain
Publication date: 15 May 2026
Source: Advances in Space Research, Volume 77, Issue 10
Author(s): Yang Sun, Pan Li, Liang Zhang, Zhiyuan Wu, Jingkai Yuan, Mingbao Wei, Meifang Wu, Kan Wang, Bao Shu, Guanwen Huang, Qin Zhang
Publication date: 15 May 2026
Source: Advances in Space Research, Volume 77, Issue 10
Author(s): Emirhan Ozdemir
AbstractAccurate and rapid magnitude prediction is critical for earthquake early warning systems, directly affecting emergency response decisions and public safety. With global seismic monitoring networks expanding to over 15,000 stations and the emergence of crowdsourcing-based IoT device monitoring systems, daily seismic data has reached petabyte scales, posing enormous challenges for real-time processing under the typical 3-10 second warning window constraint. Existing deep learning methods predominantly adopt single-modal information processing strategies, focusing either solely on temporal features of time-domain waveforms or spectral information after frequency-domain transformation, failing to fully exploit the joint evolution patterns and complementary information of seismic signals in the time-frequency domain, thereby limiting prediction accuracy and generalization performance. This paper proposes MP-Net, an end-to-end deep learning framework based on multi-scale time-frequency fusion for local magnitude (ML) prediction. The method employs a dual-branch architecture that simultaneously processes raw three-component waveforms and spectrograms: the time-domain branch captures features from microscopic waveform details to macroscopic energy evolution through parallel multi-scale convolutions; the frequency-domain branch combines hierarchical 2D convolutional networks with adaptive spectral attention mechanisms to automatically identify magnitude-related frequency components while suppressing noise; a cross-attention based fusion module achieves deep integration of complementary information from both modalities. To preserve the absolute amplitude information physically consistent with the ML definition, logarithmic amplitude features are extracted prior to waveform normalization and provided as auxiliary inputs to the fusion layer. Comprehensive experiments on the large-scale STEAD dataset demonstrate substantial improvements over baseline models: mean absolute error decreased to 0.28, coefficient of determination R2 reached 0.872, with 82.5% of predictions achieving acceptable precision (error≤0.5). The proposed approach provides an efficient and accurate solution for real-time single-station magnitude prediction, applicable to earthquake early warning systems operating in both centralized and distributed computing environments.
SummaryIn marine seismic exploration, various types of seismic sources are employed to visualize geological structures beneath the seafloor, depending on survey objectives. Airgun sources, which generate large amounts of energy by releasing compressed air underwater, are typically used for imaging deep area; however, they have limited vertical resolution due to their low peak frequencies. In contrast, sparker sources generate wavelets with high peak frequencies using bubbles produced by discharging electrical energy to vaporize water, resulting in high vertical resolution. Sparker sources are useful for the detailed imaging of shallow strata but have a shallow penetration depth due to their low source energy. This paper proposes a method to integrate airgun and sparker data to broaden the frequency bandwidth and thus achieve more accurate geological interpretations. The study used small-scale airgun data and sparker data acquired in Yeongil Bay, Pohang, South Korea. A machine-learning-based shaping filter model was developed along with synthetic training data representing the airgun and sparker source wavelet characteristics, and the trained models were applied to regularize these source wavelets. Subsequently, time-variant spectral whitening (TVSW) and weighted integration were performed to yield the flattened broadband frequency spectrum. The integrated data have enhanced penetration depth and vertical resolution compared with the original single-source datasets, thus overcoming the interpretational limitations imposed by their limited frequency bandwidth and penetration depth and enhancing the reliability of associated geological interpretations.
SummarySite amplification in the Kumamoto area, Japan, is analyzed using 985 high-quality horizontal strong-motion records from 45 aftershocks (Mj = 2.7–4.9) recorded within 24 hours following the 2016 Kumamoto Mj 7.3 earthquake, as observed by 51 K-NET and KiK-net stations. For the generalized inversion technique (GIT), a reference station is required as a standard. In the GIT process, the number of events available for analysis is limited to those recorded by the reference station, and the stations whose site effects can be estimated are restricted to those that record common events with the reference station. To overcome the limitation of the GIT, the ‘transfer-station generalized inversion method (TSGI),’ a modified GIT, is introduced to increase the number of analyzed events and stations. The site responses obtained from GIT and TSGI for the same stations exhibit a high degree of consistency, thereby demonstrating the effectiveness of the TSGI. The discrepancies between the ${{Q}_S}$ estimates of GIT and TSGI can be attributed to the gradual expansion of the region represented by ${{Q}_S}$ as more events and stations are included in the inversion. However, the results of GIT and TSGI are relative to the reference station that may itself exhibit site effects. Thus, a reference-independent technique, i.e. genetic algorithm (GA), is also introduced to obtain the absolute site amplifications. The results show that at frequencies greater than about 1 Hz, the site response of the reference station is significantly lower than the theoretical amplification factor of 2, resulting in an overestimation of the site responses at other stations. When the results of GIT are corrected with the site response of the reference station obtained from GA, these two results agree very well for most of the stations. This indicates that the results of GIT are reliable if the reference station is an ideal surface rock station, and that the GA produces accurate absolute site amplification factors for the stations investigated in this study. In addition, we analyze the high-frequency attenuation characteristics of S-waves in the Kumamoto area, and establish $\kappa $ models for different site conditions and an empirical ${{\kappa }_0}$-${{V}_{S30}}$ relationship.
Natural geological processes have been regulating Earth's climate for millions of years. Accelerated versions of these processes are now being promoted as technologies to draw down carbon from the atmosphere—and some are rapidly moving from concept to real-world deployments.
Thawing permafrost is rapidly transforming dozens of Arctic streams into acidic, metal-laden waterways, according to new research published in Science. The study shows how thawing permafrost exposes sulfide minerals that react with oxygen and water—a process similar to what occurs in mining pollution. The reactions release acidity and heavy metals such as zinc, nickel, cadmium, and aluminum into surrounding waters.
