Hasil untuk "Geophysics. Cosmic physics"

Ying Zhao

Sustainable soil–water management under climate and socio-economic pressures requires predictive capability that is both mechanistic and continuously corrected by observations. Data assimilation (DA) provides the formal machinery to merge models with heterogeneous measurements—from satellite evapotranspiration and soil moisture to cosmic-ray neutron sensing, proximal geophysics, lysimeters, and groundwater hydrographs—while propagating uncertainty. This review (based on 90 references) synthesizes frontiers in DA and modeling for soil–water systems across scales, emphasizing (i) multi-source observation operators and scaling; (ii) coupled crop–vadose–groundwater modeling frameworks and their structural hypotheses; (iii) modern DA methods (ensemble, variational, particle-based, and hybrid physics–ML) for joint estimation of states, parameters, and biases; and (iv) emerging digital twins that enable predict-then-verify management loops for irrigation, recharge enhancement, and drought risk reduction. We highlight how tracer-aided and isotope-informed components can improve evapotranspiration partitioning and recharge threshold detection, and how agent-based or socio-hydrological coupling can represent human decision feedback. Finally, we outline research gaps in uncertainty quantification, benchmarking, reproducibility, and governance needed to operationalize trustworthy soil–water digital twins for resilient food and water systems.

Kazuma Kobayashi, Samrendra Roy, S. Koric et al.

Accurate reconstruction of latent environmental fields from sparse and indirect observations is a foundational challenge across scientific domains-from atmospheric science and geophysics to public health and aerospace safety. Traditional approaches rely on physics-based simulators or dense sensor networks, both constrained by high computational cost, latency, or limited spatial coverage. We present the Temporal Radiation Operator Network (TRON), a spatiotemporal neural operator architecture designed to infer continuous global scalar fields from sequences of sparse, non-uniform proxy measurements. Unlike recent forecasting models that operate on dense, gridded inputs to predict future states, TRON addresses a more ill-posed inverse problem: reconstructing the current global field from sparse, temporally evolving sensor sequences, without access to future observations or dense labels. Demonstrated on global cosmic radiation dose reconstruction, TRON is trained on 22 years of simulation data and generalizes across 65,341 spatial locations, 8,400 days, and sequence lengths from 7 to 90 days. It achieves sub-second inference with relative L2 errors below 0.1%, representing a>58,000X speedup over Monte Carlo-based estimators. Though evaluated in the context of cosmic radiation, TRON offers a domain-agnostic framework for scientific field reconstruction from sparse data, with applications in atmospheric modeling, geophysical hazard monitoring, and real-time environmental risk forecasting.

M. Bielewicz, M. Kiecana, A. Bancer et al.

As part of the collaboration building a set of detectors for the new collider, our group was tasked with designing and building a large-scale cosmic ray detector, which was to complement the capabilities of the MPD (Dubna) detec-tor set. The detector was planned as a trigger for cosmic ray particles and to be used to calibrate and test other systems. Additional functions were to be the detection of pairs of high-energy muons originating from some parti-cle decay processes generated during collisions and con-tinuous observation of the cosmic muon stream in order to detect multi muons events. From the very beginning, the detector was designed as a scalable and universal device for many applications. The following work will present the basic features and parameters of the Modular COsmic Ray Detector (MCORD) and examples of its possible use in high energy physics, astrophysics and geology. Thanks to its universal nature, MCORD can be potential used as a fast trigger, neutron veto detector, muon detector and as a tool in muon tomography.

A. Chilingarian, T. Karapetyan, B. Sargsyan et al.

Hui Fang, Guisheng Liao, Yongjun Liu et al.

Video synthetic aperture radar (video SAR) has been successfully applied in many fields and the registration of the video SAR images has been proven to be a crucial step in their preprocessing. However, video SAR images exhibit more severe image differences because of the unique imaging mechanism and the immature imaging methods. This results in existing registration methods failing to achieve satisfactory registration outcomes for video SAR images. The convolutional neural network (CNN) can contribute to improving registration performance. Nevertheless, CNN-based registration methods must be driven by a large amount of labeled data, which is impractical for video SAR images. Therefore, to tackle these problems, we propose an unsupervised end-to-end deep registration method for video SAR images. First, an end-to-end deep registration model (DRM) is proposed to improve the registration performance for video SAR images. In the proposed DRM, the offset field is utilized to indirectly calculate the registered parameters and we construct a CNN, MUnet, to regress the offset field accurately. We also develop a differentiable H-transform and a differentiable spatial transformation to implement the mapping from end to end while allowing DRM to backpropagate the losses during the training phase. Meanwhile, we borrow intensity-based methods to further optimize the registration results. Furthermore, we propose an unsupervised deep training strategy that can use the generated pseudodata with pseudolabel to train the proposed DRM in the absence of large amounts of labeled data. Experiment results on multiple data demonstrate the effectiveness of the proposed registration method.

Hao Yi, Bo Liu, Bin Zhao et al.

Due to the limitations of small targets in remote sensing images, such as background noise, poor information, and so on, the results of commonly used detection algorithms in small target detection is not satisfactory. To improve the accuracy of detection results, we develop an improved algorithm based on YOLOv8, called LAR-YOLOv8. First, in the feature extraction network, the local module is enhanced by using the dual-branch architecture attention mechanism, while the vision transformer block is used to maximize the representation of the feature map. Second, an attention-guided bidirectional feature pyramid network is designed to generate more discriminative information by efficiently extracting feature from the shallow network through a dynamic sparse attention mechanism, and adding top–down paths to guide the subsequent network modules for feature fusion. Finally, the RIOU loss function is proposed to avoid the failure of the loss function and improve the shape consistency between the predicted and ground-truth box. Experimental results on NWPU VHR-10, RSOD, and CARPK datasets verify that LAR-YOLOv8 achieves satisfactory results in terms of mAP (small), mAP, model parameters, and FPS, and can prove that our modifications made to the original YOLOv8 model are effective.

Wentao HU, Hui LAO, Ao QIU et al.

In recent years, silicon photomultipliers (SiPMs) have emerged as preferred photoelectric conversion devices in positron emission tomography (PET) due to their outstanding performance. SiPMs possess single-photon resolution capability and time resolution below 100 ps, enabling precise photon arrival time measurements. These advances paved the way for emerging applications such as time-of-flight PET (TOF-PET), photon counting CT, and positron emission lifetime imaging, presenting new challenges to SiPM performance, the advancing of which to their physical limits has become a key focus area in next-generation SiPM research. In traditional SiPM architectures, signal processing and analog-to-digital conversion introduce noise and degrade time performance, thereby limiting the full SiPM potential. With the recent and rapid development of semiconductor manufacturing processes, SiPMs could be manufactured on standard CMOS process nodes, which marks a significant breakthrough in the SiPM field, allowing for the integration of digital logic within SiPM devices. This advancement opens the possibility of achieving more precise time, energy, and position information within a single SiPM, thereby providing potential possibilities to push SiPMs to their performance limits. In this study, we reviewed the development history, working principles, and performance parameters of SiPMs. We analyzed the limitations of traditional SiPMs, outlined key aspects of digital SiPM research, and introduced various current digital SiPM architectures. Finally, we summarized and anticipated key technologies in digital SiPMs.

Yuzo Ishikawa, Ling Bai

On January 1, 2024 ​at 16:10:09 JST, an Mj 7.6 earthquake struck the Noto Peninsula in the southern part of the Sea of Japan. This location has been experiencing an earthquake swarm for more than three years. Here, we provide an overview of this earthquake, focusing on the slip distribution of the mainshock and its relationship with the preceding swarm. We also reexamined the source areas of other large earthquakes that occurred around the Sea of Japan in the past and compared them with the Matsushiro earthquake swarm in central Japan from 1964 to 1968. The difference between the Matsushiro earthquake swarm and the Noto earthquake swarm is the surrounding stress field. The Matsushiro earthquake swarm was a strike-slip stress field, so the cracks in the crust were oriented vertically. This allowed fluids seeped from the depths to rise and flow out to the surface. On the other hand, the Noto area was a reverse fault stress field. Therefore, the cracks in the earth's crust were oriented horizontally. Fluids flowing underground in deep areas could not rise and spread over a wide area in the horizontal plane. This may have caused a large amount of fluid to accumulate underground, triggering a large earthquake. Although our proposed mechanism does not take into account other complex geological conditions into consideration, it may provide a simple way to explain why the Noto swarm is followed by a large earthquake while other swarms are not.

Cyprien Alexandre, Rodolphe Devillers, David Mouillot et al.

Detecting and tracking ships remotely is now required in a wide range of contexts, from military security to illegal immigration control, as well as the management of fisheries and marine protected areas. Among the available methods, radar remote sensing is increasingly used due to its advantages of being rarely affected by cloud cover and allowing image acquisition during both day and night. The growing availability over the past decade of free synthetic aperture radar (SAR) data, such as Sentinel-1 images, enabled the widespread use of C-band images for ship detection. There is, however, a broad range of SAR data processing methods proposed in the literature, challenging the selection of the most appropriate one for a given application. Here, we conducted a systematic review of the literature on ship detection methods using C-band SAR data from 2015 to 2022. The review shows a partition between traditional and deep learning (DL) methods. Earlier methods were mainly based on constant false alarm rate or polarimetry, which require limited computing resources but critically depend on ships’ physical environment. Those approaches are gradually replaced by DL, due to the growth of computing capacities, the wide availability of SAR images, and the publication of DL training datasets. However, access to these computing capacities may not be easy for all users, which could become a major obstacle to their development. While both methods have the same objective, they differ both technically and in their approaches to the problem. Traditional methods mainly focus on ship size in spatial units (meters), whereas DL methods are mainly based on the number of ship pixels, regardless of image resolution. These latter methods can result in a lack of information on ship size and, therefore, a lack of knowledge that could be useful to specific applications, such as fisheries and protected area management.

Glyn A. Collinson, Heli Hietala, Ferdinand Plaschke et al.

Abstract Shocklets and short large‐amplitude magnetic structures (SLAMS) are steepened magnetic fluctuations commonly found in Earth's upstream foreshock. Here we present Venus Express observations from the 26th of February 2009 establishing their existence in the steady‐state foreshock of Venus, building on a past study which found SLAMS during a substantial disturbance of the induced magnetosphere. The Venusian structures were comparable to those reported near Earth. The 2 Shocklets had magnetic compression ratios of 1.23 and 1.34 with linear polarization in the spacecraft frame. The 3 SLAMS had ratios between 3.22 and 4.03, two of which with elliptical polarization in the spacecraft frame. Statistical analysis suggests SLAMS coincide with unusually high solar wind Alfvén mach‐number at Venus (12.5, this event). Thus, while we establish Shocklets and SLAMS can form in the stable Venusian foreshock, they may be rarer than at Earth. We estimate a lower limit of their occurrence rate of ≳14%.

Einara Zahn, Elie Bou‐Zeid, Nelson Luís Dias

Abstract The Monin‐Obukhov Similarity Theory (MOST) links turbulent statistics to surface fluxes through universal functions. Here, we investigate its performance over a large lake, where none of its assumptions (flat homogeneous surface) are obviously violated. We probe the connection between the variance budget terms and departure from the nondimensional flux‐variance function for CO2, water vapor, and temperature. Our results indicate that both the variance storage and its vertical transport affect MOST, and these terms are most significant when small fluxes and near neutral conditions were prevalent. Such conditions are common over lakes and oceans, especially for CO2, underlining the limitation of using any MOST‐based methods to compute small fluxes. We further show that the relaxed eddy accumulation (REA) method is more robust and less sensitive to storage and transport, adequately reproducing the eddy‐covariance fluxes even for the smallest flux magnitudes. Therefore, we recommend REA over MOST methods for trace‐gas flux estimation.

K. Kurosawa, K. Kurosawa, S. Kotsuki et al.

<p>This study explores coupled land–atmosphere data assimilation (DA) for improving weather and hydrological forecasts by assimilating soil moisture (SM) data. This study integrates a land DA component into a global atmospheric DA system of the Nonhydrostatic ICosahedral Atmospheric Model and the local ensemble transform Kalman filter (NICAM-LETKF) and performs both strongly and weakly coupled land–atmosphere DA experiments. We explore various types of coupled DA experiments by assimilating atmospheric observations and SM data simultaneously. The results show that analyzing atmospheric variables by assimilating SM data improves the SM analysis and forecasts and mitigates a warm bias in the lower troposphere where a dry SM bias exists. On the other hand, updating SM by assimilating atmospheric observations has detrimental impacts due to spurious error correlations between the atmospheric observations and land model variables. We also find that assimilating SM by strongly coupled DA is beneficial in the Sahel and equatorial Africa from May to October. These regions are characterized by seasonal variations in the precipitation patterns and benefit from updates in the atmospheric variables through SM DA during periods of increased precipitation. Additionally, these regions coincide with those identified in the previous studies, where a global initialization of SM would enhance the prediction skill of seasonal precipitation.</p>

Josue Fonseca, Anshuman Pradhan, Tapan Mukerji

Bayesian geophysical basin modeling (BGBM) methodology is an interdisciplinary workflow that incorporates data, geological expertise, and physical processes through Bayesian inference in sedimentary basin models. Its application culminates in subsurface models that integrate the geo-history of a basin, rock physics definitions, well log and drilling data, and seismic information. Monte Carlo basin modeling realizations are performed by sampling from prior probability distributions on facies parameters and basin boundary conditions. After data assimilation, the accepted set of posterior sub-surface models yields uncertainty quantification of subsurface properties. This procedure is especially suitable for pore pressure prediction in a predrill stage. However, the high computational cost of seismic data assimilation decreases the practicality of the workflow. Therefore, we introduce and investigate seismic traveltimes criteria as computationally faster proxies for analyzing the seismic data likelihood when employing BGBM. The proposed surrogate schemes weigh the prior basin model results with the available seismic data with no need to perform expensive seismic depth-migration procedures for each Monte Carlo realization. Furthermore, we apply BGBM in a real field case from the Gulf of Mexico using a 2D section for pore pressure prediction considering different kinematics criteria. BGBM implementation with the novel seismic data assimilation proxies is compared with a computationally expensive benchmark approach. Moreover, we validate and compare the outcomes for predicted pore pressure with mud-weight data from a blind well. The fast proxy of analyzing the depth-positioning of seismic horizons proposed in this work yields similar uncertainty quantification results in pore pressure prediction compared to the benchmark. These fast proxies make the BGBM methodology efficient and practical.

Y. Stenkin

Environmental fluxes of thermal neutrons originate from two sources: cosmic rays and natural radioactivity. Owing to a long lifetime of free neutrons, they are able to propagate over rather long distances in surrounding media prior to undergoing absorption, provided that these media do not contain elements that have large cross sections for neutron capture. The real lifetime of free neutrons and distances that they travel are determined by the properties of the medium with which they are in a dynamical equilibrium. At rather large depths under the ground, natural radioactivity associated with \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(\alpha,n)$$\end{document} reactions on light nuclei of the Earth’s crust is the main source of neutrons. Radioactive gaseous radon, especially its long-lived isotope \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${}^{222}\textrm{Rn}$$\end{document}, which is able to migrate over significant distances (several tens of meters in soil and several kilometers in the Earth’s atmosphere) plays a great role in this process. This means that the changes in the medium that are caused by various geophysical processes or by Moon–Solar–Earth phenomena should also be reflected in the neutron flux escaping from the Earth’s crust. The present article gives a brief survey of studies devoted to this subject and a discussion on them.

Xin Zhang, Muhammad Atif Nawaz, Xuebin Zhao et al.

In a variety of scientific applications we wish to characterize a physical system using measurements or observations. This often requires us to solve an inverse problem, which usually has non-unique solutions so uncertainty must be quantified in order to define the family of all possible solutions. Bayesian inference provides a powerful theoretical framework which defines the set of solutions to inverse problems, and variational inference is a method to solve Bayesian inference problems using optimization while still producing fully probabilistic solutions. This chapter provides an introduction to variational inference, and reviews its applications to a range of geophysical problems, including petrophysical inversion, travel time tomography and full-waveform inversion. We demonstrate that variational inference is an efficient and scalable method which can be deployed in many practical scenarios.

Rana X. Adhikari, Luis A. Anchordoqui, Ke Fang et al.

Cosmic Probes of Fundamental Physics take two primary forms: Very high energy particles (cosmic rays, neutrinos, and gamma rays) and gravitational waves. Already today, these probes give access to fundamental physics not available by any other means, helping elucidate the underlying theory that completes the Standard Model. The last decade has witnessed a revolution of exciting discoveries such as the detection of high-energy neutrinos and gravitational waves. The scope for major developments in the next decades is dramatic, as we detail in this report.

E. Costello, R. Prechelt, A. Romero-Wolf et al.

A. Shepetov, O. Kryakunova, S. Mamina et al.

Y. Stenkin