Search

Abstract: Magmatic systems below volcanoes are often dominated by partially crystalline magma over the long term. Rejuvenation of these systems during eruptive events can impact lava composition and eruption style—sometimes resulting in more violent or explosive activity than would be expected, as was the case at Fissure 17 during Kīlauea’s 2018 eruption. Here, we explore how the crystallinity of unerupted intrusion magmas affect hybrid magma compositions and petrological signatures by constructing phase-equilibria models to evaluate mineral and melt compositions of low-MgO lavas erupted along the East Rift Zone of Kīlauea volcano on 30 to 31 January 1997 (Episode 54, Fissures A-F). We then compare calculated mixing proportions and petrologically derived magma volumes to GPS-based geodetic inversions of ground deformation and intrusion growth in an attempt to reconcile geodetic and petrologically estimated magma volumes. Open-system phase-equilibria thermodynamic models were used to constrain the composition, degree of differentiation, and thermodynamic state of a rift-stored, two pyroxene + plagioclase saturated low-MgO magma body immediately preceding its mixing with high-MgO recharge and degassed drainback (lava lake) magma from Pu‘u‘ō‘ō‘, shortly before fissure activity within Nāpau Crater began on 29 January 1997. Mixing models constructed using the Magma Chamber Simulator reproduce the mineralogy and compositions of Episode 54 lavas within uncertainties and suggest that the identity of the low-MgO magma body may be either variably differentiated remnants of un-erupted magmas intruded into Nāpau Crater in October 1968, or another spatially and compositionally similar magma body. We find that magmas derived from a single, compositionally stratified magma emplaced beneath Nāpau Crater in 1968 can mix with mafic Kīlauea magmas to reproduce average Episode 54 bulk lava, mineralogy and mineral compositions without necessitating the interaction of multiple, low-MgO rift-stored magma bodies to produce Episode 54 lava compositions. Further, by constructing phase equilibria-based mixing models of Episode 54, we can better define the pre-eruptive state of the magmatic system. The resultant mineral assemblages and compositions are consistent with the possibility that the now-fractionated, rift-stored magma body was compositionally stratified and ~ 40% to 50% crystalline at the time of mixing. Finally, we estimate the volume of the low-MgO magma body to be ~7.51 Mm3. Phase-equilibria model results corroborate field and geochemical relationships demonstrating how shallow intrusions at intraplate shield volcanoes can crystallize, evolve, and then be remobilized by new, later batches of mafic magma. Most notably, our MCS models demonstrate that the pre-eruptive conditions of an intrusive body may be recovered by examining mineral compositions within mixed lavas. Discrepancies between the geodetic constraints on volumes of stored rift versus newly intruded (recharge) magma and our best-fit results produced by MCS mixing models (which respectively are mmafic:mlow-MgO ≈ 2 vs. mmafic:mlow-MgO ≈ 0.75) are interpreted to highlight the complex nature of incomplete mixing on more localized scales as reflected in erupted lavas, compared to geodetically constrained volumes that likely reflect large spatial scale contributions to a magmatic system. These dissimilar volume relationships may also help to constrain eruptive versus unerupted volumes in magmatic systems undergoing mixing. By demonstrating the usefulness of MCS in modeling past eruptions, we highlight the potential to use it as a tool to aid in petrologic monitoring of ongoing activity.

• The results of ionospheric troughs identification from CHAMP satellite data were used for solving two actual problems. • Longitudinal variations in the main ionospheric trough position in the winter southern hemisphere were refined. • Dependence of the main ionospheric trough position on geomagnetic activity for all local time hours was determined. Based on the meticulous identification of ionization troughs, performed earlier from the CHAMP satellite data, two additional issues were resolved: (1) the longitudinal effect characteristics in the position of the main ionospheric trough (MIT) were corrected, and (2) for the first time, the dependence of the MIT position on geomagnetic activity was determined for all local time hours. A large dataset from the CHAMP satellite in the southern winter hemisphere under high solar activity was utilized. According to the refined data the amplitude of the longitudinal effect in the MIT position changes from ∼ 3° to ∼ 5° in the course of the day. The shape of the longitudinal effect varies with local time, however, the MIT in the eastern hemisphere is consistently located at higher latitudes than in the western hemisphere. The main reason for the longitudinal effect is the dependence of the equatorward boundary of auroral diffuse precipitation on the tilt angle of the Earth's dipole. The dependence on geomagnetic activity was determined as a linear regression Λ T = Λ 0 − a Kp, where Λ is the geomagnetic latitude, and the Kp index is considered for the previous 6 h. The latitude Λ 0 and coefficient a exhibited pronounced dependence on local time, with Λ 0 increasing and a decreasing when moving from night to day. Because the amplitude of the longitudinal effect decreases with increasing magnetic activity, the value of a also depends on longitude. Consequently, coefficient a was determined separately for the eastern and western hemispheres. The average values of a vary from 1.3 − 1.4° during the day to 1.8 − 1.9° at night. The difference between the eastern and western hemispheres is ∼ 0.3°. [ABSTRACT FROM AUTHOR]

Kīlauea Volcano on Hawai'i Island is host to a complex volcanic and interwoven fault system. Over the last ∼120 years, a range of seismic events, including large earthquakes such as the 1975 Mw ${M}_{w}$7.7 Kalapana earthquake, creep, and slow slip events, have occurred along the décollement underlying Kilauea's south flank. We explore both the deformation and stress changes of Kīlauea from 1896 to 2018 by collating six geodetic data sets and creating an analytical model to determine the dominant deformation sources (i.e., fault planes, rifts, magma chambers) driving this system at different times. The 1975 Kalapana earthquake significantly altered the region's state of stress and deformation; we find the average slip along the décollement was reduced from 10 cm/yr prior, to 4 cm/yr after the rupture. Prior to 1975 no slip is resolved along the décollement where the earthquake nucleated, suggesting that this portion may have been locked leading up to the rupture. After 1975, décollement slip overall is smaller and more irregular, suggesting increased control by spatial variation of mechanical properties. We find increases in shear stress along the Kīlauea décollement and a decrease in normal compressive stress within the East Rift Zone prior to the Kalapana earthquake, creating favorable conditions for failure of the décollement and subsequent magmatic intrusion. Plain Language Summary: Kīlauea Volcano on Hawai'i Island encompasses a complex volcanic and interwoven fault system. The low‐angle fault underlying the volcano has consistently been moving it southward, and has a high hazard potential due to large magnitude earthquakes. The complexity of this region leads to questions about the evolution of deformation and its seismic cycle. The 1975 Mw ${M}_{w}$7.7 Kalapana décollement earthquake is of particular interest because large magnitude ruptures can dramatically alter the state of stress within a region. We therefore explore both the deformation and stress changes of Kīlauea's décollement from 1898 to 2018 by collating a wealth of surface displacement observations. We create a model to reproduce the observed displacements throughout time and to identify key structural features causing the deformation, such as fault planes, rifts, and magma chambers. Multiple deformation sources were needed to model the observations, with more of these sources required prior to the 1975 Kalapana earthquake than afterward. We also find varied patterns and magnitudes of deformation and stress both temporally and spatially within the region. Kīlauea's history has important implications for our understanding of the relationship between magmatic and earthquake cycle processes. Key Points: Collation of ∼120 years of geodetic data at Kīlauea reveals key displacement, stress, and deformation source patterns throughout timeGreater and more complex displacement occurred within Kīlauea's south flank prior to the 1975 MW ${M}_{W}$7.7 Kalapana earthquake than after itCompressive stress decreased in East Rift Zone before/after 1975; shear stress increased before and decreased after on Kīlauea décollement [ABSTRACT FROM AUTHOR]

Motivated by an application from geodesy, we study the connected k-center problem and the connected k-diameter problem. The former problem has been introduced by Ge et al. (ACM Trans Knowl Discov Data 2(2):1–35, 2008. https://doi.org/10.1145/1376815.1376816) to model clustering of data sets with both attribute and relationship data. These problems arise from the classical k-center and k-diameter problems by adding a side constraint. For the side constraint, we are given an undirected connectivity graphG on the input points, and a clustering is now only feasible if every cluster induces a connected subgraph in G. Usually in clustering problems one assumes that the clusters are pairwise disjoint. We study this case but additionally also the case that clusters are allowed to be non-disjoint. This can help to satisfy the connectivity constraints. Our main result is an O (log 2 k) -approximation algorithm for the disjoint connected k-center and k-diameter problem. For Euclidean spaces of constant dimension and for metrics with constant doubling dimension, the approximation factor improves to O(1). Our algorithm works by computing a non-disjoint connected clustering first and transforming it into a disjoint connected clustering. We complement these upper bounds by several upper and lower bounds for variations and special cases of the model. [ABSTRACT FROM AUTHOR]

Quantifying interseismic deformation of fault networks which are predominantly deforming in a north‐south direction is challenging, because GNSS networks are usually not dense enough to resolve deformation at the level of individual faults. The alternative, interferometric synthetic aperture radar (InSAR), provides high spatial resolution but is limited by a low sensitivity to N‐S motion. We study the active normal fault network of Western Türkiye, which is undergoing rapid N‐S extension, using InSAR. Since most faults in the study region are normal faults, we overcome the low N‐S sensitivity by focusing on the vertical deformation component, which presents its own challenges. Sediment‐filled grabens show rapid anthropogenically induced subsidence, whereas urban areas tend toward erroneous uplift signals. Additionally, the morphological relief results in topographic and atmospheric disturbances of the InSAR signal. Our solution to these challenges is a systematic analysis of the high‐resolution vertical velocity field to deduce insights into regional deformation patterns, combined with detailed investigations of deformation along individual faults in the Western Anatolian Extensional Province. We show that tectonic deformation in the large graben systems is not restricted to the main faults. Smaller and seemingly less active faults are accommodating strain, favoring a continuum model of deformation over block models. We also observe a potential correlation between recent seismicity and active interseismic surface deformation. Observed deformation rates provide an estimate of current activity for many faults in the region. We discuss the potential and limitations of InSAR time series analysis for extensional regimes. Key Points: Vertical interferometric synthetic aperture radar velocity field reveals shifts from uplift to subsidence spatially correlated with faults and fault splaysResults indicate spatial variation in uplift rates along faults and could be used to infer the relative activity of faults or fault splaysGeodetic deformation rates do not generally agree with (mostly exceed) long‐term (Holocene) deformation patterns [ABSTRACT FROM AUTHOR]

The document is a corrigendum for an article on data-driven modeling of dissolved iron in the global ocean. It addresses errors in figures and equations, providing corrections to ensure accuracy. The main findings and conclusions of the study remain unaffected by these corrections, emphasizing the reliance on the dissolved iron projection derived from the random forest algorithm. The authors also provide updated data availability statements for readers to access historical versions of the simulated dissolved iron product. [Extracted from the article]

Neoproterozoic snowball Earth events lasted for multiple million years, experiencing many orbital cycles. Here we investigate whether the deglaciation of these events would be triggered more easily at certain orbital configurations than others, by using an atmosphere‐land model that considers meltpond formation on land ice. Results show that the threshold concentration of atmospheric CO2 (pCO2) required for deglaciation can vary from 6 to 10 × 104 ppmv under different orbital forcings. The threshold pCO2 decreases with the equatorial maximum monthly insolation (EMMI), which is affected most by the eccentricity and secondarily by obliquity. Therefore, we conclude that the snowball Earth deglaciation likely occurred when the eccentricity was high and obliquity was low. Compared to previous estimate that used present‐day orbital configuration which has a minimal eccentricity, the duration of snowball Earth events would likely be much shorter when the influence of orbital variations are considered. Plain Language Summary: This study explores how different orbital configurations might have influenced the termination of the Neoproterozoic snowball Earth events, during which Earth was covered by ice globally. Using a coupled atmosphere‐land model that is capable of simulating the formation of melt ponds on ice, we find that the atmospheric CO2 level needed to initiate the deglaciation varies with the Earth's orbital configurations. Specifically, the required CO2 levels are lower when the maximum monthly solar insolation received at the equator is higher, which is achieved when the Earth's orbit is more eccentric and the tilt of Earth's rotational axis is small. The results suggest that the duration of snowball Earth events could have been shorter when the influence of orbital forcing is considered. Key Points: Influence of orbital forcing on the snowball Earth deglaciation is tested using an atmosphere‐land model that considers meltpond on iceThe equatorial maximum monthly insolation is the most important factor that triggers the deglaciation of a snowball EarthThe needed CO2 to deglaciate a snowball Earth at the optimal orbital configuration is 40% lower than that under modern orbit [ABSTRACT FROM AUTHOR]

We present an approach to calculate potential fields on a planet's surface from regionally confined gradient data at spacecraft altitude, such as measurements of magnetic fields or gravitational acceleration. Our method uses altitude cognizant Slepian functions with a reference radial position that varies as a function of longitude and latitude. This is an evolution from previous altitude cognizant Slepian functions which use a fixed reference radial position. We demonstrate the advantage of these variable altitude cognizant Slepian functions over the fixed altitude cognizant Slepian functions and the classical Slepian functions using numerical tests. [ABSTRACT FROM AUTHOR]

The spatiotemporal pattern of surface displacements from large earthquakes provides crucial insights about the deformation of Earth’s crust at various scales and the interactions among tectonic plates. However, the lack of extensive and large-scale geodetic networks near such seismic events hinders our thorough understanding of the large-scale crustal deformation resulting from earthquakes. Using Türkiye’s extensive and continuous global navigation satellite system (GNSS) network during the moment magnitude 7.8 and 7.6 Kahramanmaraş earthquakes on 6 February 2023, we show that large earthquakes can induce far-field crustal deformations (>700 kilometers), exceeding current predictions from elastic dislocation models. They can lead to the mobilization of tectonic plates and the triggering of far-field earthquakes, which carries profound implications for seismic hazard assessments and necessitates a new perspective on crustal deformation and earthquake mechanics. [ABSTRACT FROM AUTHOR]

Glacial isostatic adjustment (GIA) describes the response of the solid Earth, oceans, and gravitational field to the spatio‐temporal evolution of ice sheets during a glacial cycle. Present‐day vertical displacements and sea‐level changes vary throughout eastern North America in response to the melting of the Laurentide Ice Sheet following the Last Glacial Maximum. We use the open‐source software SELEN4.0 (a SealEveL EquatioN solver) to investigate the influence of GIA on vertical land motions and sea‐level changes in eastern North America. Further, we evaluate the uncertainties associated with the lithospheric thickness and viscosity structure using an ensemble modeling approach (129,956 total simulations). We identify the best‐fitting rheological profiles by comparing modeled vertical displacements to vertical velocity rates derived from Global Positioning System (GPS). We find a general pattern of subsidence (causing accelerated relative sea‐level rise) in the eastern United States region and uplift (causing relative sea‐level fall) in the eastern Canada region consistent with previous studies for two tested ice sheet models (ICE‐6G(VM5a) and ICE‐7G(VM7)). Overall, we find lower rates of modeled vertical displacement using ICE‐6G(VM5a) compared with ICE‐7G(VM7), which produces lower residuals when compared with the GPS‐derived vertical velocity rates. Our ensemble analysis identifies adjustments to the nominal VM5a and VM7 viscosity models that improve fits to the GPS‐imaged vertical velocity rates throughout eastern North America and on the North American Atlantic Coast. The differences in our best‐fitting models for inland versus coastal regions highlight the importance of exploring lateral viscosity variations for GIA modeling throughout North America and elsewhere. Plain Language Summary: Approximately 19,000–26,000 years ago a massive ice sheet covered Canada and a portion of the northern United States. We investigate the Earth's surface movements following the melting of the ice sheet throughout eastern North America. Through computational modeling, we predict rates of vertical land motion and relative sea‐level change throughout eastern North America. We see a general pattern of downward movements (causing relative sea‐level rise) in the eastern United States region and upward movements (causing relative sea‐level fall) in the eastern Canada region. However, modeled rates vary depending on the assumptions about Earth's internal structure. We test different models of Earth structure, based upon variations of the Earth structure profiles paired with two published models of ice sheet history. Our different models vary the lithospheric thickness and radial viscosity structure (viscosity varies by depth only). By comparing the rates of Earth's surface movements generated by these different models to published data of observed vertical land motions, we determine which Earth structure models produce the best fits to the published observations. Differences in the best‐fitting Earth structure for different regions of eastern North America indicate the importance of exploring Earth structures that vary both radially and laterally. Key Points: Modeled present‐day vertical displacement and sea‐level change rates fit observed data better using ICE‐6G(VM5a) compared with ICE‐7G(VM7)Ensemble analysis suggests adjustments to VM5a and VM7 viscosity models improve fits to geodetic data throughout eastern North AmericaRegional differences in best‐fitting Earth structure support the adoption of lateral viscosity in glacial isostatic adjustment modeling [ABSTRACT FROM AUTHOR]

The seismic event of the 2016 Amatrice earthquake and its subsequent aftershocks have provided a significant opportunity to study seismic waves and their effects on the Apennine región, a very active seismic area. This research utilizes GPS data from three geodetic networks (ItalPos, NetGEO, and RING) to analyze the seismic waves generated by the earthquake, capturing seismic effects with great precision and resolution. By examining data from various GPS stations within the region, the study demonstrates the efficacy of GPS in providing detailed and accurate representations of ground motion. The study identifies co-seismic displacements and accelerations at monitoring stations by processing and analyzing GPS data, including precise point positioning strategies and wavelet adjustments. Moreover, a least squares adjustment method is employed to optimize the estimation of temporal parameters associated with seismic event detection across the spatial network of stations. The results obtained from GPS data are validated against seismic equipment, affirming their reliability in characterizing seismic events. Furthermore, the study elucidates the propagation of surface waves and path effects over the affected region, contributing to a comprehensive understanding of the earthquake impact. This research underscores the potential of GPS data as a valuable tool for rapid and precise characterization of seismic events, offering insights into ground motion dynamics and facilitating timely response and mitigation efforts. [ABSTRACT FROM AUTHOR]

Inter-satellite interferometric missions are critical in the ongoing monitoring of climate change. Next-generation Earth geodesy missions are opportunities to improve on mission cost and measurement sensitivity through revised design. To be considered feasible, mission architectures must meet an optical power requirement that factors in both shot noise and laser frequency noise. Reference-transponder mission configurations, like the Gravity Recovery and Climate Experiment-Follow On (GRACE-FO) mission, are designed for measurement down to a received carrier-to-noise density ratio of 70 dB-Hz—1.9 picowatts in shot-noise-limited detection. This work shows, through modeling and simulation, that the optical power level required to perform robust measurement varies significantly between mission configurations. Alternate configurations, such as retro-reflector-based schemes, can operate robustly down to much lower carrier-to-noise density ratios, with the example parameters considered here: down to 29 dB-Hz—150 attowatts in shot-noise-limited detection. These results motivate exploration of alternate missions configurations with revised optical power requirements, increasing the feasibility of new designs. [ABSTRACT FROM AUTHOR]

We examine whether geocoded World Bank foreign aid projects affect economic activity at the level of equally sized grid cells with a spatial resolution of 0.5 decimal degrees latitude and longitude. We use geospatial data for 58,676 grid cells during the period 1997-2013 with about one million grid-by-year observations. Using fixed effects estimating techniques, we evaluate the impact of World Bank foreign aid projects on grid cell night-time light growth. The estimates reveal that World Bank foreign aid projects contribute significantly and positively to grid cell economic activity measured by night-time light growth. We complement the fast-growing literature using geocoded data to assess aid effectiveness at the subnational level. We provide evidence that the lack of robust empirical findings at the country and subnational (e.g., ADM1 and ADM2) level can be explained due to spatial aggregation and the choice of spatial units. Additional estimates and robustness tests further corroborate our main findings. [ABSTRACT FROM AUTHOR]

According to General Relativity Theory (GRT), by comparing the frequencies between two precise clocks at two different stations, the gravity potential (geopotential) difference between the two stations can be determined due to the gravity frequency shift effect. Here, we conduct a clock-transportation experiment for measuring geopotential differences based on frequency comparisons via satellite links between two remote hydrogen atomic clocks. Based on the net frequency shift between the two clocks in two different periods, the geopotential difference between stations of the Beijing 203 Institute Laboratory (BIL) and Luojiashan Time-frequency Station (LTS) is determined. Comparisons show that the experimental result deviated from the reference of Earth gravity model EGM2008 result by (38.5 $ \pm $ ± 45.9) m in Orthometric Height (OH). The results are consistent with the frequency stabilities of the hydrogen clocks (at the level of ${10^{ - 15}}$ 10 − 15 ) used in the experiment. With the rapid development of time and frequency science and technology, the approach discussed in this study for measuring the geopotential is prospective and thus, could have broad applications. [ABSTRACT FROM AUTHOR]

Despite comprising only one per cent of global forests, mangroves provide vital ecological and economic benefits to their ecosystems. Due to its decreasing extent over the past decade, there is a rise in research innovations supporting mangrove conservation. Specifically, consumer-grade Unmanned Aerial Vehicles (UAV) were proven effective as potential remote sensing alternatives to support mangrove research and monitoring in recent studies. As most studies use custom UAV-mounted sensors for mangrove species classification, similar studies using a UAV’s default red–green–blue (RGB) cameras were scarce. This study explores the potential of high-resolution RGB aerial images through state-of-the-art object detection algorithm, YOLOv5 to detect the dominant Rhizophora mangroves in Sarawak, Malaysia. A total of 400 RGB images were equally selected from two study areas and allocated into three datasets, two corresponding to each study area and one combining all images. The annotation process was performed using a previously proposed novel method, assisted by YOLOv5 for a semi-automated annotation process with expert verification. Systematic training experiments were conducted to select an optimal epoch size across models trained with each dataset. The final models produced an average true positive rate of 73.8% and 71.7% for each study site, while the combined dataset model produced an average true positive rate of 73.7%. Overall, this study demonstrated the potential of UAV-based RGB images and deep learning object detection architectures to identify specific mangrove objects, while also highlighting key considerations for similar future research.

The motivation behind our study is to identify a robust method to enhance the accuracy of missing data, particularly chlorophyll-a data, which often goes undetected due to various factors. This study analyzes chlorophyll-a concentrations and sea level changes due to tides using three methods: Linear Interpolation, Fillgaps, and Modified Fillgaps. Two experiments were conducted: Experiment I involved random data removal (60% and 70%), and Experiment II combined sequential and random data removal (25% sequentially on the right, 35% and 45% randomly on the left). In Experiment I, the Modified Fillgaps method showed high correlation coefficients (up to 0.96) between original and reconstructed data, demonstrating its effectiveness in accurately filling significant data gaps. This method also exhibited low Root Mean Square Error and Mean Absolute Error values, confirming its predictive precision. In Experiment II, despite structured and realistic data loss patterns, the method maintained high correlation and low prediction errors, with low Normalized Root Mean Squared Error and Mean Absolute Percentage Error values, further validating its reliability. Additionally, the method excelled in two-dimensional chlorophyll-a maps, outperforming Linear Interpolation and Fillgaps methods in scenarios with 50% and 60% data loss, achieving higher correlation and lower prediction errors. These findings are crucial for environmental and climatological studies relying on satellite-derived data, confirming the Modified Fillgaps method as the most reliable and effective for handling significant data loss in chlorophyll-a map analyses. Future research should explore its application to other environmental data types and more complex data loss patterns.

The present work enhances mineral exploration in Egypt’s Eastern Desert by mapping lithological units and identifying hydrothermal alteration zones, potentially leading to the discovery of economically viable mineral deposits. This study employs a comprehensive approach of integrating multispectral bands from Landsat-9 and ASTER images with airborne radiometric data. Various image enhancement techniques such as False Color Composite (FCC), Minimum Noise Fraction (MNF), and Principal Component Analysis (PCA) are utilized to map enhanced lithological units. Additionally, image classification techniques, including Spectral Angle Mapper (SAM) and Crosta Principal Component (CROSTA PC), are applied to emphasize hydrothermal alteration minerals like alunite, calcite, hematite, illite, chlorite, epidote, kaolinite, montmorillonite, and sericite. Furthermore, radioelement ratios (eU/eTh, eU/K, eTh/K, and eU-(eTh/3.5)) and the F-parameter (K*(eU/eTh)) are utilized. Mineral percentages are determined using Scanning Electron Microscope (SEM), allowing for the observation of ore minerals from the Elkharaza-Dara area deposits, which exhibit varying compositions. Maximum values are recorded for specific elements: aluminum (10.48 wt% Al), silicon (65.38 wt% Si), silver (0.32 wt% Ag), copper (2.65 wt% Cu), gold (5.25 wt% Au), potassium (4.32 wt% K), hafnium (3.84 wt% Hf), calcium (26.94 wt% Ca), carbon (56.92 wt% C), and oxygen (53.71 wt% O). These findings offer valuable insights into the elemental composition of the mineralized deposits in the study area. The multi-algorithm integration approach has been confirmed through various methods, including comparison with existing geological maps, fieldwork, and microscopic analysis of selected samples from alteration zones across the study area.

The conservation of humpback whale populations faces ongoing challenges, including human-induced mortality, despite the ban on commercial whaling. Recent advancements in high-resolution satellite imagery offer promise for estimating whale populations, particularly in remote and inaccessible regions. However, significant research gaps persist, necessitating innovative approaches for effective monitoring and conservation efforts. This paper presents a novel methodology that integrates high- resolution satellite imagery with state-of-the-art deep learning techniques to monitor and conserve humpback whale populations, with a focus on the Indian Ocean region. Specifically, application of cutting-edge deep learning models such as YOLO for object detection and EfficientNet for classification to automate the detection, classification, and tracking of humpback whales in satellite images is explored. By leveraging deep convolutional neural networks (CNNs), the proposed ensemble system offers a robust and generalizable approach for automatically detecting, classifying, and tracking whales in space-borne satellite imagery, thereby addressing the challenge of uncertain whale populations in the world’s oceans. The results demonstrate promising accuracy and performance metrics: the Segment Anything Model(SAM) achieves an accuracy of 89.2%, YOLO achieves an accuracy of 99.2%, EfficientNet achieves an accuracy of 99% across various tasks.

Weeds are unwanted vegetation that compete with main crops for essential resources like light, water, and nutrients, leading to significant reductions in food crop yield and economic losses. Addressing this issue is crucial, particularly during the Kharif cropping season when cloud cover interferes with remote sensing capabilities. This study is an attempt to investigate the potential of satellite-based temporal analysis in weed detection from agricultural fields. The research focused on rice cultivation at the Research cum Instructional farms of Indira Gandhi Krishi Vishwavidyalaya, Raipur, Chhattisgarh. The study explored the utility of satellite imagery for assessing crop health, demonstrating how weed infestation influences vegetative indices. The study utilized satellite images from PlanetScope and Sentinel-2 to examine the temporal variation in vegetation indices across two treatments: pure rice and rice with weeds. NDVI analysis revealed a significant decline in treatments affected by weeds (upto 41% less), suggesting that time-series satellite data can serve as an early indicator of weed infestation in standing rice crops. These findings were further verified by backscatter values from the Sentinel-1 dataset, which indicated a reduction in backscatter (upto 18% less) due to the suboptimal growth conditions in weed-infested treatments compared to weed-free rice. While the technology has shown efficacy at a preliminary stage, there is significant potential for its broader application and scalability in operational contexts.

Unmanned Aerial Vehicle (UAV) technology in conjunction with aerial photography and aerial Light Detection and Ranging (LiDAR) was used for topographic surveying of the Win 3 Wind Power Project area, a complex mountainous terrain situated in the Huong Hoa district of Quang Tri province, Vietnam with the primary objective to produce a map with a scale of 1:1,000 with a contour interval of 1.0 m, accompanied by 3D point clouds, a Digital Elevation Model (DEM), and an orthomosaic. Employing the UAV LiDAR system of DJI Matrice 300 RTK, we conduct ed survey flights, achieving a high point density laser scanning of 229 points/m2 and capturing ultra-high-resolution imagery at 4.22 cm/pixel. The integration of aerial LiDAR and photographic data results in a rich and detailed information repository regarding the surveyed terrain demonstrating that UAV LiDAR technology is a robust tool for geographic data collection, particularly in challenging terrain conditions in Vietnam. We also evaluated the accuracy of the created topographic map, achieving results that surpass the technical requirements and quality standards of the project. This underscores the potential and effectiveness of UAV LiDAR technology in the field of large-scale geographic data collection and mapping in Vietnam.

Augmented reality has a long research history in computer vision and computer graphics communities. It aims to enhance the user experience for real scenes via overlapping virtual objects. Nowadays, mobile augmented reality has attracted much attention from researchers and developers due to the development of hardware techniques. Modern mobile devices such as mobile phones have a powerful computational ability for augmented reality applications. As a result, many researchers have paid attention to mobile augmented reality. From the technical viewpoint of augmented reality, mobile augmented reality largely depends on camera pose estimation. However, existing methods make it difficult to achieve the best balance between accuracy and efficiency, according to our investigation, and this may handicap the performance of mobile augmented reality systems. To overcome the problem, in this paper, we propose a novel approach to camera pose estimation based on bundle optimization. Our proposed method is evaluated on real-world datasets and is also tested in the mobile augmented reality system. Both experiments demonstrate that our proposed method has fast speed and high accuracy.

Mineral classification using hyperspectral imaging represents an essential field of research improving the understanding of geological compositions. This study presents an advancedmethodology that uses an optimized 3D-2D CNNmodel for automatic mineral identification and classification. Our approach includes such crucial steps as using the Diagnostic Absorption Band (DAB) selection technique to selectively extract bands that contain the absorption features of minerals for classification in the Cuprite zone. Focusing on the Cuprite dataset, our study successfully identified the following minerals: alunite, calcite, chalcedony, halloysite, kaolinite,montmorillonite,muscovite, and nontronite. The Cuprite dataset results with an overall accuracy rate of 95.73%underscore the effectiveness of our approach and a significant improvement over the benchmarks established by related studies. Specifically, ASMLP achieved a 94.67%accuracy rate, followed by 3D CNN at 93.86%, SAI-MLP at 91.03%, RNN at 89.09%, SPE-MLP at 85.53%, and SAMat 83.31 %. Beyond the precise identification of specific minerals, ourmethodology proves its versatility for broader applications in hyperspectral image analysis. The optimized 3D-2D CNNmodel excels in terms of mineral identification and sets a new standard for robust feature extraction and classification.

Wheat is one of the most important staple crops consumed by more than four billion people in the world. However, its production is challenged by the impact of climate change which accounts for a 5.5 % reduction in wheat yield and it is predicted to dwindle further by about 30 % in 2050, due to trends in temperature, precipitation, and carbon dioxide. An effective annual crop estimate is necessary not only to inform governments the status of national food security, but also to determine the benchmark on which agricultural commodities are priced in the market. Thus, annual crop monitoring and yield estimate is paramount to determine the amount of wheat imports required to make up for the shortfalls in the national wheat production in South Africa, which has been a net importer of wheat since 1998. This study aimed at investigating the most distinguishable crop phenology for accurate winter wheat classification during the growing season from August – December 2020 using Sentinel-2 imageries and Random Forest algorithm. The winter wheat crop was more accurately identified during the crop ‘heading’ stage in October yielding the highest user’s (75.56 %) and producer’s (92.52 %) accuracies, despite the relatively lower overall accuracy (78.14 %) compared to that of December with overall accuracy of 83.58 % obtained during the maturity stage. This study, therefore, found that the extraction of NDVI values of the winter wheat crop over the period of the growing season using the Sentinel-2 NDVI series method and grouping these values into distinct classes using the K-means unsupervised clustering techniques assist to identify the different crop phenologies based on which the winter wheat crop could be detected and mapped accurately. The phenology-based classification of the winter wheat crop during the heading stage, reduce the ambiguity of spectral confusion created with surrounding grass and maize crops.

Accurate geoid modeling is significant in geodetic, geological, and environmental sciences. Owing to challenges in establishing reference stations, particularly in rugged terrains, such as in Northern Vietnam, leveraging global geopotential models (GGMs) is imperative. Herein, we proposed a superior method that integrates GGMs with advanced artificial intelligence (AI) algorithms to enhance the accuracy and spatial resolution of regional geoid models. A total of six contemporary GGMs (XGM2019e_2159, SGG-UGM-2, SGG-UGM-1, GECO, EIGEN-6C4, and EGM2008) were systematically evaluated to identify the optimal GGM that represents the Earth’s gravitational field in Northern Vietnam. Subsequently, sophisticated AI algorithms, including tree-based ensembles, support vector machines, Gaussian linear regression, regression trees, and linear regression models, were implemented. These AI algorithms were trained on the integrated global navigation satellite system (GNSS) leveling data and corresponding height anomalies to capture complex relationships in the geopotential field. Among the six investigated GGMs, XGM2019e_2159 shows optimal performance for Northern Vietnam, displaying a standard deviation of ±0.17 m. Rigorous assessment results from cross-validation and validation against independent datasets demonstrate satisfactory accuracy across all considered models. However, the Gaussian process regression model with an exponential kernel exhibits marginal superiority, boasting a standard deviation of approximately 0.07 m. This model is therefore chosen for the construction of the geoid model by integrating ground data with optimal GGMs, which shows superior performance, particularly in challenging topographic and geophysical conditions, thereby contributing to a marked improvement in the realized spatial resolution.

Two major perenial disasters are prevalent in Indonesia, namely, peatland fires and volcanic activities associated with Andesite rock. Thus, the Indonesian Government has prioritized the prevention and mitigation of both disasters. Indonesia’s Research Center for Satellite Technology-National Research and Innovation Agency then implemented the program as a satellite payload project. In this study, we describe the design of a space-based mid-wavelength infrared (SMWIR) camera module to monitor peatland fires and volcanic activities associated with Andesite rock. Using the spectral range as the basis of design and the iteration process of general steps in designing a camera, a SMWIR camera module was successfully designed. First, the spectral range was obtained by an intersection of four methods of determining spectral bands. Subsequently, the optical section, was conducted using Zemax by applying three criteria to analyze the optical performance, such as the spot diagram, encircled energy, and modulation transfer function (MTF). Thereafter, the mechanical design was achieved through the SOLIDWORKS software. The fourth step, namely, the structure or thermal design, was achieved by both Thermal Desktop/SINDA FLUINT and Zemax. In the electronic section, both the camera and detector were developed. Finally, a calibration system was specified over the module. Results in the form of graphs, pictures, and tables indicate that all established conditions, including those of the technical side, were achieved. Therefore, high performance in terms of the image, durability, transmission, and thermal stability can easily be achieved; additionally, the module is feasible, lightweight, and compact.

Assessing the condition of historical sites at risk requires an interdisciplinary approach based on combining multiple measurement technologies. Due to the dynamic development of technology, non-invasive remote sensing methods are gaining significant importance. Among these techniques is photogrammetry based on images taken from unmanned aerial vehicles (UAVs) and those taken with a smartphone. In the study, the authors specified the possibilities and limitations of using remote photogrammetric methods to build accurate digital models of the walls of historic buildings with cracks in them. Point clouds, TIN grids, and façade orthophotos were examined. Statistical analysis was used to determine the repeatability of the data. Two parameters were identified that affect the accuracy of the data: the first – the direction of the segment between two points in the façade plane, and the second – the distance of the segment between two points in the plane of the façade. The study showed that the average accuracy of crack width measurements on the data acquired with the DJI Mavic 3 Enterprise RTK is 1 mm. Testing of crack width measurements using a Samsung Galaxy S20 FE smartphone showed an average absolute error of 0.24 mm. Based on the results, it was concluded that the imagesacquired using mobile devices can be used to determine changes in crack widths on walls.

The article presents an analysis of the effectiveness of selected machine learning methods: Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM) in the classification of land use and cover in satellite images. Several variants of each algorithm were tested, adopting different parameters typical for each of them. Each variant was classified multiple (20) times, using training samples of different sizes: from 100 pixels to 200,000 pixels. The tests were conducted independently on 3 Sentinel-2 satellite images, identifying 5 basic land cover classes: built-up areas, soil, forest, water, and low vegetation. Typical metrics were used for the accuracy assessment: Cohen’s kappa coefficient, overall accuracy (for whole images), as well as F-1 score, precision, and recall (for individual classes). The results obtained for different images were consistent and clearly indicated an increase in classification accuracy with the increase in the size of the training sample. They also showed that among the tested algorithms, the XGB algorithm is the most sensitive to the size of the training sample, while the least sensitive is SVM, which achieved relatively good results even when using training samples of the smallest sizes. At the same time, it was pointed out that while in the case of RF and XGB algorithms the differences between the tested variants were slight, the effectiveness of SVM was very much dependent on the gamma parameter – with too high values of this parameter, the model showed a tendency to overfit, which did not allow for satisfactory results.

The main aim of this paper is to introduce a solution for network analysis based on pgRouting to resolve the bicycle accessibility of forested green spaces. The proposed application uses open-source software tools such as PostgreSQL with PostGIS extension. The solution includes a complete description of how to perform network analysis using a spatial database with SQL and pgRouting. The implemented functionalities consist of solutions for finding the equidistance or isochrone area for any selected point location. The method is tested on case study data drawn from a total of 9,500 km of roads suitable for cyclists in the Poznań Metropolitan Area, located in western Poland. The results of the analysis were isochrones determining the bicycle accessibility of forested areas. The accessibility analysis was performed considering an urbanised residential area. As a result of the analysis, locations with the best and limited access to forested green areas were identified. Moreover, the described methodology is ready to be used to solve various accessibility problems.

Technical infrastructure is exposed to environmental impacts throughout its entire life cycle. For linear objects, such as roads, additional impacts resulting from their continuous use – vehicle traffic – are distinguished. Due to their communication function, these facilities are subject to ongoing condition monitoring. This procedure allows to identify any damage and indicate appropriate protective measures. Observations of the terrain as well as the road surface can be carried out using commonly used geodetic measurements. Cyclic monitoring applies especially to facilities in mining areas, where impacts in the form of continuous and discontinuous deformations significantly contribute to their damage. The research presents the results of selected geodetic measurements made on a deformed road surface affected by underground mining. The aim of the observations was to use both classic and modern measurement techniques to determine, among others, the shape of the surface or identify damage. The applied measurements included commonly used total station measurements and GNSS satellite positioning. Additionally, low-ceiling photogrammetry using an unmanned aerial vehicle and laser scanning were used to illustrate the shape of the road surface. Finally, a comparison was made of selected methods; their advantages and disadvantages allowed us to determine their usefulness in monitoring the condition of the pavement.

The diverse range of uses of unmanned aerial vehicles has garnered significant attention in research. The scientific literature that supports the data obtained from UAVs recording information from various sensors is presented in this manuscript. It summarizes current developments in remote sensing, including radar, photogrammetry, thermal imaging, light detection and ranging sensors (LiDAR), data gathering, and analysis. It is predicated on the instruments’ ability to gather and analyze accurate data. To identify some of the most urgent research problems, it also shows surveys based on research methodologies. The present research focuses on the proliferation and social effects of unmanned aerial vehicles (UAVs). It also encourages novice researchers to pursue this area of study and suggest novel approaches to the design or setup of these flying machines. UAVs have entirely transformed due to advancements in internet technology and current technologies which include camera defects, environmental monitoring, charging, impediments, crop monitoring, energy consumption, military applications, and technology gaps.

There is very little to no literature on the use of geotagging to monitor crops from aerial photos, even though many technologies have been created to do so. Current crop monitoring methods, relying on field data and lab analysis, are inefficient due to high labor, time, and potential harm, limiting their broad use. With the use of vegetation indices (VI) and geotagging, this paper highlights the benefits of crop-specific monitoring with unmanned aerial vehicles (UAV). This study systematically searched the original articles published from the 1st of January 2016 to the 7th of October 2021 in the databases of Scopus, ScienceDirect, Commonwealth Agricultural Bureaux (CAB) Direct, and Web of Science (WoS) using Boolean string: “aerial imagery” AND “vegetation index” OR “vegetation indices“ AND “crop”. Out of the papers identified, 28 eligible studies did meet our inclusion criteria and were evaluated. This review thoroughly discusses the advantages of aerial imagery, vegetation indices, and geotagging tools in the context of crop monitoring. It was found that geotagged crop monitoring using UAV empowers farmers with data-driven insights using vegetation indices, enabling them to make informed decisions before acting, transforming agriculture towards a digital future. This study offers valuable insights for researchers and industry players, helping them identify effective and context-specific crop monitoring strategies for diverse plantations, crops, and budgets. Moreover, by utilizing the advanced computational capabilities of artificial intelligence (AI), we can analyze a wide range of vegetation indices to gain a comprehensive understanding of crop health and conduct accurate predictions.

Land Use Land Cover (LULC) change is a complex phenomenon driven by various natural and anthropogenic factors, significantly impacting carbon storage potential. By applying integrated models of ANN-CA Markov, GeoDetector, and InVEST model, this study aimed to analyze LULC change, their driving factors, and implications on carbon storage in the Forest Management Unit (FMU) of Ampang Plampang in West Nusa Tenggara, Indonesia. Several data sources were utilized in the modelling approach, including DEM (Digital Elevation Model), topographical map, Landsat imageries (2011, 2016, 2021), measured carbon density (above ground, below ground, soil, dead organic), and socio-economic data (number of populations, farmer, and agricultural land). The dryland forest in the study area constitutes the most extensive LULC that has experienced significant declines due to deforestation, predominantly transforming into agricultural land, and these are predicted to continue until 2031 with different magnitudes. The significant driving factors of LULC change were elevation, population pressure on land, and distance from settlement. The LULC change also greatly influenced the decline of carbon storage historically (2011–2016) and in projected LULC (2026–2031). The conversion of forested areas to non-forest LULCs has released carbon emissions of about 1.89 Mt CO2-eq. The study findings implied that the integration of ANN-CA Markov, GeoDetector, and InVEST models has been helpful for comprehending complicated interactions among LULC change, driving factors, and carbon dynamics. The results also contribute to the scientific knowledge base for land management decision-making and policy formulation. Effective management of LULC changes through low carbon development is suggested to mitigate the loss of carbon storage capacities, foster sustainable development goals (SDGs), support Nationally Determined Contribution (NDC), and improve ecosystem resilience.

Poland as well as other countries keep extensive collections of 20th and 21st-century aerial photos, which are underexploited compared to such other archival materials as satellite imagery. Meanwhile, they offer significant research potential in various areas, including urban development, land use changes, and long-term environmental monitoring. Archival photographs are detailed, often obtained every five to ten years, and feature high resolution, from 20 cm to 1 m. Their overlap can facilitate creating precise digital models that illustrate topography and land cover, which are essential variables in many scientific contexts. However, rapidly transforming these photographs into geographically accurate measurements of the Earth’s surface poses challenges. This article explores the obstacles in automating the processing of historical photographs and presents the main scientific research directions associated with these images. Recent advancements in enhancing work˚ows, including the development of modern digital photogrammetry tools, algorithms, and machine learning techniques are also discussed. These developments are crucial for unlocking the full potential of aerial photographs, making them easier accessible and valuable for a broader range of scientific fields. These underutilized photographs are increasingly recognized as vital in various research domains due to technological advancements. Integrating new methods with these historical images offers unprecedented opportunities for scientific discovery and historical understanding, bridging the past with the future through innovative research techniques.

The research presented in this paper concerns the determination of the attraction basins of Newton’s iterative method, which was used to solve the non-linear systems of observational equations associated with the geodetic measurements. The simple observation systems corresponding to the intersections or linear and angular resections used in practice were considered. The main goal was to investigate the properties of the sets of convergent initial points of the applied iterative method. Therefore, the answers to the questions regarding the geometric structure of the basins, their limitations, connectedness, or self-similarity were sought. The research also concerned the iterative structures of the basin: maps of the number of iterations which are necessary to achieve the convergence of the Newton’s method. The determined basins were compared with the areas of convergence that result from theorems on the convergence of the Newton’s method: the conditions imposed on the eigenvalues and norms of the matrices of the studied iterative systems. One of the significant results is the indication that the obtained basins of attraction contain areas resulting from the theoretical premises. Their diameters can be comparable with the sizes of the analyzed geodetic structures. Consequently, in the analyzed cases, it is possible to construct methods that enable quick selection of the initial starting points or automation of such selection. The paper also characterizes the global convergence mechanism of the Newton’s method for disconnected basins and, as a consequence, the non-local initial points located far from the solution points.

The field of satellite navigation has seen significant advancements due to the fast development of multi-constellation Global Navigation Satellite Systems (GNSS). Around 150 satellites will be in service when all six systems – GPS, GLONASS, Galileo, BeiDou, QZSS, and NAVIC – are launched by 2030, offering both enormous potential and advantages for research and engineering applications. This study used an experiment on the accuracy, particularly for short, medium, long baselines (Wide Lane ambiguity solution) of the BeiDou, QZSS and QZSS/BeiDou combinations. It showed that with the integration of BeiDou/QZSS static measurements in the study region millimetre-centimetre accuracy for short, medium, and long baselines can be attained. Based on the results of this study, it can be concluded that the 1st (QZSS/BeiDou), 2nd (BeiDou), and 3rd (QZSS) strategies feature different horizontal accuracies for all categories. The obtained results with different satellite configurations for the Fixed-Wide-Lane integer ambiguity solution are compared with each other. Accuracy at the short baseline (BeiDou, QZSS, and BeiDou/QZSS satellites) was obtained in the range of 0.5–0.7 cm. For the medium baseline, it was computed around 1.8–82 cm. For the long baseline, the accuracy was 5.6–13.3 cm.

Abstract The time-dependent viscoelastic deformation of host rocks is important when considering the dynamics of fault behavior, specifically in brittle–ductile transitional regions or shallow subduction zones, because it relaxes stress heterogeneity and affects loading to the fault. For a rate-and-state fault embedded in a Maxwell viscoelastic medium, a previous study discovered a transition from repeated earthquakes to the permanent stuck of a rate-weakening patch (EQ–ST transition) with decreased viscoelastic relaxation time $${t}_{\text{c}}$$ t c . This transition differs from the well-known seismic–aseismic transition explained by the Hopf bifurcation at the critical stiffness of an elastic system. To better understand the EQ–ST transition, quantifying the effect of heterogeneous frictional strength is important, because this effect is characteristic to the viscoelastic medium and is absent in the elastic limit. Previous experimental studies suggest a potential contrast in frictional strength $$\Delta {f}_{*}$$ Δ f ∗ in such a way that a rate-weakening patch is stronger than a rate-strengthening region containing clay minerals. Here, we conducted two-dimensional, fully dynamic earthquake sequence simulations for a fault in a Maxwell viscoelastic medium; we investigated the EQ–ST transition in the two-dimensional parameter space of $${t}_{\text{c}}$$ t c and $$\Delta {f}_{*}$$ Δ f ∗ . With $$\Delta {f}_{*}=0.3$$ Δ f ∗ = 0.3 , the EQ–ST transition occurred at about 1 order of magnitude larger $${t}_{\text{c}}$$ t c than in the case with $$\Delta {f}_{*}=0$$ Δ f ∗ = 0 . We constructed a coarse-grained model with only two degrees of freedom based on the spatial average. Consequently, the coarse-grained model behaves remarkably similar to the continuum model, and the EQ–ST transition is associated with a homoclinic bifurcation. The EQ–ST boundary in the parameter space can be quantitatively explained by considering elastic loading due to creep in the rate-strengthening region and unloading by viscoelastic relaxation of the stress heterogeneity comparable to $$\Delta {f}_{*}$$ Δ f ∗ . A larger rate-weakening patch is anticipated to become aseismic earlier as $${t}_{\text{c}}$$ t c decreases, because the elastic loading rate is inversely correlated with the patch size. This may be qualitatively consistent with the change in the size distribution of events around the brittle–ductile transition in observations and laboratory experiments; however, further investigations on, for example, the interactions of events and changes in frictional parameters with depth are required for quantitative discussion. Graphical abstract

Abstract A prolonged earthquake swarm has persisted since June 2018 in northeastern Noto Peninsula (central Japan), with activity focused into distinct southern, western, northern, and eastern clusters. To explore the role of fluids in the occurrence of this swarm, we analyzed the focal mechanisms of the earthquakes occurring from 1 January 2018 to 30 November 2022 and performed stress tensor inversions. The western, northern, and eastern clusters were dominated by a reverse fault-type mechanism with a horizontal P-axis oriented NW–SE. One of the nodal planes of those mechanisms aligns closely with the precisely relocated hypocenter distribution. The stress fields in these three clusters, as determined by stress tensor inversion, have maximum principal stresses oriented horizontally in the NW–SE direction and minimum principal stresses oriented vertically, aligning with the regional stress field. From these focal mechanisms and this stress field, we derived small misfit angles and large slip tendencies. These findings suggest that, in these three clusters, fluids diffused into faults largely aligned with the regional stress field, resulting in earthquakes with compatible focal mechanisms. Conversely, normal and strike-slip fault-type focal mechanisms, which are unfavorable to the regional stress field, dominated in the southern cluster. The estimated stress fields, deviating from the regional stress field, have maximum principal stresses closer to vertical and minimum principal stress oriented horizontally in the ENE–WSW direction. For earthquakes deeper than 15 km, this local stress field results in relatively larger misfit angles and lower slip tendency than the other clusters. These results suggest that those earthquakes in the southern cluster occurred on misoriented fault planes due to elevated pore fluid pressures. These findings provide strong evidence of the ascent of high-pore-pressure fluids from depth in the southern cluster and their subsequent diffusion into a southeast-dipping fault zone. Graphical Abstract

Abstract Recent studies have proposed the contribution of aseismic slip (AS) to earthquake swarms. We investigated the role of AS in earthquake swarms that occurred in 2009, 2015, and 2019 at the Hakone volcano, central Japan, through highly resolved hypocenter distribution analysis, geodetic observation analysis, and identification of similar earthquakes. We observed diffusion-like migration of hypocenters during these swarms. The hydraulic diffusivity varied among the swarms, indicating differing dynamics. The 2015 swarm exhibited rapid hypocenter migration and significant crustal deformation, as revealed by the temporal sequences of tiltmeters near the swarm region. Right-lateral shear dislocation on fault planes could explain the crustal deformation observed in 2015, indicating that AS released approximately 90% of the moment. However, the 2009 swarm lacked evidence of significant AS contribution, indicating that the primary mechanism was fluid pressure diffusion. The substantial contribution of AS to the 2015 swarm might be attributed to increased fluid pressure due to the intrusion of hydrothermal fluid into the shallow part beneath the volcano during volcanic unrest. Our findings imply that the temporal and spatial patterns of seismicity can provide valuable insights into the underlying mechanics of earthquake swarms. Graphical abstract

Abstract We performed a numerical analysis of seismic velocities in mantle peridotite using the elastic constants and crystallographic preferred orientations (CPOs) of olivine, orthopyroxene, and clinopyroxene. Proportions of olivine in the samples ranged from 50 to 100% mechanically, whereas the two pyroxenes ranged from 0 to 50%. We utilized three lherzolite samples from the Horoman peridotite complex for this study. As the quantity of pyroxene increased, the P-wave velocities in the X-direction (8.35–9.14 km/s) and P-wave anisotropies (5.51–14.50%) decreased in all samples. However, the S1-wave velocities remained relatively consistent, with Vs1 ranging from 4.79 to 5.15 km/s and Vs2 from 4.55 to 4.92 km/s. The maximum S-wave anisotropies decreased as orthopyroxene contents increased. As a consequence, non-negligible effects of pyroxenes resulted in the variation of seismic velocities, suggesting the orthopyroxene/clinopyroxene contents and their CPOs could be one of critical factors for evaluating seismic anisotropy within the upper mantle. Graphical abstract

Abstract Measurements of elastic wave velocity and electrical conductivity were conducted on brine-saturated granitic rocks under confining pressure to quantitatively characterize the closure of cracks. The number of contacting asperities and radius ratio of contact over crack were estimated. Contacting asperities were assumed to deform elastically and/or plastically. The number of contacts increases steeply at low pressure (

Topography is a problem in geoid determination by the Stokes formula, a high degree Earth Gravitational Model (EGM), or for a combination thereof. Herein, we consider this problem in analytical/harmonic downward continuation of the external potential at point P to a geocentric spherical sea level approximation in geoid determination as well as to a sphere through the footpoint at the topography of the normal through P. Decomposing the topographic bias into a Bouguer shell component and a terrain component, we derive these components. It is shown that there is no terrain bias outside a spherical dome of base radius equal to the height H P of P above the sphere, and the height of the dome is about 0.4 × H P. In the case of dealing with an EGM, utilizing Molodensky truncation coefficients is one way to cope with the bias.

Abstract Distributed acoustic sensing (DAS) is a newly developed geophysical observation method and has attracted wide attention in seismology for realizing ultra-high-density observations. DAS uses fiber-optic cables and measures the strain at every point along the cable. This advantage renders DAS an effective tool for investigating near-surface geotechnical properties. Near fault zones, it is important to obtain detailed geotechnical information in advance because of the potential for significant damage in an earthquake. In this study, we recorded continuous ground motion for approximately 1 month using a 40 km-long fiber-optic communication cable running under National Route 3 in Kumamoto Prefecture, Japan. The cross-correlation function (CCF) was calculated using ambient noise, and three-station interferometry was applied to improve the signal-to-noise ratio of the CCF. Using the reconstructed CCF between channels, we calculated the dispersion curves by conducting multichannel surface wave analysis and estimated the one-dimensional velocity structure of each section from the fundamental modes of the dispersion curves. We obtained the detailed shallow S-wave velocity structure to a depth of 180 m along the Hinagu Fault for approximately 2.5 km. The obtained velocity structure showed that the low-velocity region increased abruptly with depth from the center to the latter half of the analyzed section. This velocity change occurs when the national highway running parallel to the fault gradually leaves the fault, suggesting a structural change from solid volcanic layers to thick shallow sedimentary layers derived from the Yatsushiro Plain. Graphical abstract

Abstract Ground motion prediction equations (GMPEs) in offshore regions are important not only for earthquake early warning systems and strong motion prediction but also for evaluating the durability of subsea structures and tsunami risks associated with seafloor slope failures. Since soil conditions and propagation paths differ between onshore and offshore areas, it is desirable to develop a GMPE specific to the seafloor. Previous GMPE models have some problems, such as being influenced by buried observation equipment and path effects. In this study, to predict the distribution of seafloor seismic acceleration, we developed a new GMPE regressed on the peak ground acceleration (PGA) data of S-net using minimum necessary seismic parameters as explanatory variables. Residual analysis using the conventional GMPE emphasized the path effects through the offshore area, which were corrected by the depth up to the plate boundary. The new model successfully predicted PGA with smaller errors compared to conventional onshore and offshore GMPEs. The residuals between the observed and predicted PGAs were used to examine the factors responsible for the effects of the S-net site conditions. The new GMPE can predict PGAs within 300 km of the epicenter from the moment magnitude (Mw 5.4–7.4), focal depth, earthquake type, and source distance. In this model, the distance attenuation coefficient is smaller than in conventional models, and consequently, the PGAs along the trench axis that are amplified due to path effects can be reproduced. This means that PGAs will be unexpectedly larger than those estimated by conventional GMPEs even far from the hypocenter. Our model improves the accuracy of PGA prediction and avoids underestimation in assessing seafloor slope failure and earthquake resistance near the trench. Graphical abstract

Abstract As Japan is one of the most seismically active countries, seismic data from various institutions are shared in real time and made accessible via the Web to promote research. The Japan Meteorological Agency (JMA), in collaboration with the Ministry of Education, Culture, Sports, Science, and Technology, processes these data to compile a 'unified earthquake catalog' for use in the development of strategies for disaster prevention and public safety. Based on manual arrival-time measurements provided by the JMA, we retrained PhaseNet, the deep-learning phase picker, known as neural phase picker that has gained prominence in recent years, to promote the development of high-quality seismic catalogs in Japan. We utilized the PhaseNet architecture for our model and trained it using 6.1 million three-component seismic waveforms collected in 2014–2021. The performance of the original PhaseNet model, trained with data from California, was suboptimal when applied to routine Japanese data, particularly ocean-bottom seismometer records. Retraining the model with the JMA unified catalog and corresponding waveforms significantly enhanced its performance in picking the arrival times of regular and low-frequency earthquakes. Compared with the original PhaseNet, the dependency of the model on the type of seismic station was reduced by retraining and its performance for waveforms was improved even from stations not included in the training data set. The model performance varied with earthquake magnitude, highlighting the reliance on extensive data for small events in the training set. Compared with the conventional procedure, the model identified numerous events, particularly smaller ones with undetermined magnitudes when integrated into the routine automatic processing of the JMA. Furthermore, leveraging approximately ten times more training data than the California data set, we developed and trained PhaseNetWC, doubling the number of filter channels in each convolutional layer in comparison with those of the original PhaseNet. This modified phase picker surpassed the performance of its predecessor. The dissemination of these models is anticipated to enhance the analysis of routine observational data sets in Japan. Graphical Abstract

Abstract In the field, locally applied fluid pressure can initiate fault slip, which may expand unstably over a wide area under certain conditions, generating elastic waves and inducing earthquakes. Therefore, examining the unstable expansion of the initial slip is important. However, reproducing this process in laboratory experiments, such as triaxial loading tests on small cylindrical specimens with inclined faults, is challenging. To achieve this, we prepared a sub-meter-scale cubic specimen, which was separated into two triangular prisms by a model fault. The specimen was subjected to biaxial compression of different magnitudes. A 2D array of strain gauges was embedded beneath the fault plane to measure the changes in shear strain with fault slip driven by fluid injection. Based on the experimental results, we discussed the features of the injection-induced fault slips that lead to earthquakes. The strain increased locally around the edge of the fault slipping area by approximately 10 με, which is equivalent to a shear stress of ~ 0.1 MPa. The fault slipping area first expanded gradually and then unstably beyond the fluid invasion area approximately 3 s after the slip was initiated. The unstable expansion of initial slips was suppressed by reducing the initial shear stress on the fault by 0.3 MPa. In this case, the initial shear stress was possibly too small for additional stress to accumulate at the edge of the fault-slipping area to overcome the static frictional strength of the fault. Our experimental study explicitly confirms that injection-induced aseismic slip can outpace the fluid migration front, finally leading up to a runaway rupture. It also confirms that the level of initial shear stress is important in controlling the rupture size under fluid injection. Graphical Abstract

Abstract Japan’s Quasi-Zenith Satellite System (QZSS) as an important supplement to the Global Navigation Satellite System (GNSS) can provide great positioning, navigation and timing (PNT) services in the Asia-Pacific region. Many researches related to QZSS have focused on dual-/multi-frequency precise point positioning (PPP) or real-time kinematic (RTK) technology with high user costs. In this study, the contribution of QZSS to BDS-3/2 single-frequency (SF) PPP is investigated for the first time. Experimental results show that the three-dimensional (3D) positioning accuracy of the BDS-3-only and BDS-3 + 2 ionosphere-corrected (IC) SF-PPP through QZSS enhancement can be improved by 9.3% and 1.7% to 0.883 and 0.852 m, respectively. In the ionosphere-free (IF) SF-PPP, with the introduction of QZSS, the convergence time of the BDS-3-only or BDS-3 + 2 solutions can be improved by at least 21% and 8.2% in the horizontal and vertical directions, respectively. After convergence, the positioning accuracy of the BDS-3-only and BDS-3 + 2 IF SF-PPP can be improved by about 10% to 0.15 m in horizontal and 0.25 m in vertical. Compared to the IF SF-PPP, the convergence time of the ionosphere-weighted (IW) SF-PPP in the BDS-3-only and BDS-3 + 2 solutions can be improved by at least 57.6% to 13.5 min in horizontal and no more than 23% to 35.0 min in vertical. In this case of rapid convergence, it is hard to further optimize the convergence time of the BDS-3/2 IW SF-PPP by integrating the QZSS. Similar phenomena also appeared in the positioning accuracy of the IW SF-PPP. Nowadays, the 3D positioning accuracy of both IF and IW SF-PPP using BDS-3/2 + QZSS observations can reach 0.3 m in the Asia-Pacific region. Graphical abstract

Abstract Active seismicity, which began since late 2020 under the Noto Peninsula, Japan, led to the M7.6 earthquake on the 1st January 2024. This paper analyzes the M5.9 pre-event that occurred 13 s before the M7.6 mainshock. Near-field ground motion records clearly distinguish this pre-event from the ground shaking of the mainshock. We then use six near-field ground motions over a 10-s period to obtain first the focal mechanism and then dynamic rupture models by fixing the hypocenter location. We obtain a steep dip angle of 67°, whereas the dip of the M7.6 mainshock is 33° after Japan Metrological Agency catalog. We propose a simplified inversion process that dynamic rupture models are built by varying the location of the target circular patch, the stress level and the fault dipping directions. The preferred model has an up-dip rupture directivity (rake direction) on the south–east dipping fault, i.e. propagating away from the hypocenter of the M7.6 mainshock, and the two events are not aligned on the same planar fault. Uplift of about 20 cm and more is expected on the north coast of the Noto Peninsula in the case of the stress drop of 6 MPa or less. We also analyze another M5.5 pre-event 4 min earlier, with a slightly deeper focal depth and a gentle dip angle of 36°. This earthquake has also a rupture directivity in the up-dip direction. This infers that there should have been a preparation process of the M7.6 mainshock behind these pre-events and multiple fault segmentations around the hypocenter zone might have played a role. Graphical Abstract

Abstract We propose a new reaction device for the microdiffusion analysis of CO2 in fluid samples from volcanic areas to improve the conventional analysis procedure, which requires a fixative and good handling skills. To accomplish this, a small resin reaction cell for the sample solution and acid (initiator for CO2 liberation) was devised and placed in a commercially available air-tight glass container with a Petri dish to form a microdiffusion analysis unit. The analysis was based on the principles of the conventional manual titration method that uses Ba(OH)2 as the CO2 absorbent. Using the new device, 99.5% recovery, with a relative standard deviation of 4.5% (n = 8), was achieved in a test analysis for 0.03 mmol of CO2 liberated from a Na2CO3 solution as a simulated sample. The analytical performance of the new device was almost comparable to that of conventional units under the experimental conditions of this study. Applying this new device for analyzing fluid samples from volcanic areas will contribute to enhancing the performance by reducing the possibility of CO2 contamination and by simplifying the process by eliminating the need for preparatory cleaning for fixative application. Graphical Abstract

Abstract To obtain novel information regarding normal faults that could cause tsunamis and large earthquakes on the outer slopes of the Kuril Trench, recently acquired multichannel seismic reflection (MCS) and bathymetric data are processed in this study to interpret the normal faults on the outer slope of the western Kuril Trench from near the Erimo Seamount to near the Nosappu Fracture Zone. This work also assesses the distribution and characteristics of the faults through comparisons with previous studies on the outer slope faults of the Japan Trench. The results of this study show that faulting starts approximately 100 km seaward of the trench axis, and a comparison with the normal fault distributions on the outer slope of the Japan Trench presented in previous studies reveals that the outer slope region of the Kuril Trench is narrower and more densely faulted than that of the Japan Trench. Additionally, the seismic and bathymetric data show that the Erimo and Takuyo-Daiichi Seamounts are deformed by normal faults. However, the number of faults in the seamount areas is lower than that in areas without them, which also affects the scale of the fault throws. The results of the MCS survey reveal that the thickness of the oceanic crust varies between 6 and 10 km due to the presence of these seamounts. Considering the positions of the outer swells presented in previous studies, the results suggest that the crustal and plate structures in the study area influence the distribution and formation of faults on the outer slope of the Kuril Trench. Graphical Abstract