Neutrino Earth tomography provides an observational approach to studying the Earth's deep three-dimensional structure that is distinct from seismology. However, most existing studies still rely on one-dimensional density models and therefore cannot adequately represent lateral heterogeneity within the Earth. To address this issue, this study integrates PREM, CRUST1.0, and HMSL-S06 on a tesseroid grid to construct a non-spherically symmetric three-dimensional Earth density model that includes large low-velocity provinces (LLVPs) in the deep mantle. We also develop a corresponding procedure for extracting neutrino propagation trajectories and derive closed-form expressions for the total mass and axial moment of inertia of the discrete model, which are used as global consistency checks. Within an exact three-flavor oscillation framework, we use public Super-Kamiokande data products to compare the event counts predicted by the three-dimensional model with those from a conventional one-dimensional spherically symmetric model. The results show that, under the present calculation scheme, the differences in the overall event count distributions between the three-dimensional model and the one-dimensional reference model remain limited. This study establishes a three-dimensional calculation framework that can provide a methodological basis for future investigations of how lateral density heterogeneity may affect atmospheric neutrino propagation.

Ground-penetrating radar (GPR) is a nondestructive electromagnetic (EM) method used for subsurface target detection and stratigraphic imaging. The use of a single-frequency GPR profile is inherently limited by the trade-off between penetration depth and vertical resolution. For example, low-frequency antennas provide deeper penetration with lower resolution, whereas high-frequency antennas perform better in shallow areas but attenuate rapidly. Hence, multifrequency GPR data fusion can have complementary advantages. Conventional fusion methods usually depend on handcrafted feature extraction and fusion rules, whereas supervised deep learning-based methods are difficult to apply in polar field surveys due to the unavailability of absolute paired ground-truth labels. To address these limitations, this study proposes an unsupervised dual-branch attention network (UDBA-Net) for the fusion of 400/900 MHz multifrequency GPR data and applies it to Arctic multiyear sea ice exploration. The proposed network combines frequency-specific dual-branch encoders, a convolutional block attention module (CBAM)-like channel- or spatial-attention recalibration module, and an unsupervised hybrid loss comprising local intensity, structural similarity, and Laplacian-gradient constraints. The low- and high-frequency profiles are encoded separately to reduce early cross-band feature aliasing, whereas the attention module adaptively recalibrates the concatenated features based on channel contribution and spatial reflection-event salience. The main objective of the method is the structural and morphological enhancement, rather than preservation, of absolute electromagnetic reflection coefficients. Experimental analyses using controlled synthetic data and Arctic field GPR profiles confirm that UDBA-Net enhances relative structural contrast, improves reflection-event continuity, and integrates shallow, high-resolution textures with deeper, low-frequency interfaces under label-scarce conditions.

Traditional geomagnetic field modeling requires balancing computational efficiency with the resolution of complex dynamics. A dual-architecture deep learning framework is introduced as a high-efficiency surrogate for global modeling. A sinusoidal representation network (SIREN) captures the continuous, quasi-static internal field, and a transformer with an ‘adaptive recursion’ mechanism models the dynamic external field. The recursive mechanism adjusts computational depth according to geomagnetic activity, optimizing efficiency and accuracy. Trained on CHAOS-8.4 data, the framework reproduces the global field with high fidelity. Under identical CPU hardware conditions, the SIREN-based surrogate model achieved an inference speed-up of approximately 1100 times compared to traditional implementations. Native graphics processing unit (GPU) acceleration further enables large scale parallel computations without additional engineering effort. This study validated the feasibility of deep learning as a robust real-time surrogate for complex geophysical simulations.

The diagenetic characteristics and petrological analysis of beachrocks can reflect the environmental changes during their sedimentation and reveal their formation and evolution processes. Based on field work, this study analysed the evolution of beachrocks on Zhaoshu Island by using microfossil identification, AMS14C dating, geochemical element analysis and scanning electron microscopy (SEM). This study explored the influence of sea-level changes and microbial activities on the beachrocks over the past 2000 years. This study suggests that the development of beachrocks on Zhaoshu Island has undergone three stages. The dominant factors influencing beachrock development vary across different periods. In the first stage, the sea level rose slowly, and the cementing materials were mainly an isopachous fringe of acicular cements, formed under a seawater environment. In the second stage, sea level remained stable, and the beachrocks cemented rapidly, with the multiple generations of carbonate, meniscus pendant and isopachous fringe of acicular cements. In the third stage, the sea level declined, and the cementing materials are meniscus cements and pendant cements, showing the characteristics of the joint action of seawater and freshwater. During this period, in the area with weak hydrodynamic conditions on the southern side, the cementation of beachrocks was significantly influenced by microbial activities. This study established a model of the diagenetic evolution of beachrocks in the study area since 2000 BP, and analysed the influence of sea-level changes and microbial factors, which can provide a basis for future environmental research in the South China Sea region.

The Arctic Craton has traditionally been regarded as a long-stable, ancient continental nucleus. The Chukchi Borderland (CB), as part of this craton, is generally interpreted as a fragment of thinned continental crust. Investigating the sedimentary layer and the velocity structure of the deep crust and upper mantle beneath the CB provides critical insights into the destruction mechanisms of the Arctic Craton lithosphere. This study constructed the shallow S-wave velocity structure of the CB by extracting and inverting ambient noise surface wave dispersion from passive-source data recorded by six recovered ocean-bottom seismometers during the 11th Chinese National Arctic Research Expedition. Combined with receiver function results, a joint inversion provided a high-resolution S-wave velocity model of the crust–upper mantle beneath each station. The depth of each geosphere structure, discontinuities, and low-velocity zones was determined. The results indicate that the sedimentary sequence beneath the stations may comprise two distinct layers: 1) muddy sediments composed of glacial debris mixed with marine clay, and 2) sandy sediments formed from glacial scour products. The study delineates the fine-scale velocity structure of the crustal and upper mantle in the investigated area through ambient noise surface wave dispersion and joint inversion methodology, providing novel constraints on the velocity structure of the CB. The origins of crustal thinning were analyzed from the perspective of sedimentary layer characteristics, revealing that the formation and distribution patterns of sedimentary environments may create localized zones of brittle fracture. These structural features, together with the low-velocity zones, manifest regional extensional processes that further facilitate crustal thinning.

Sea ice, as a complex and highly variable medium covering the ocean surface, exhibits intricate internal structural variations that significantly affect the ocean’s thermodynamic, dynamic, and acoustic properties. However, the extreme conditions of the polar environment pose major challenges for conducting in-situ surface experiments, resulting in limited research on the acoustic structure of Arctic sea ice. This paper presents the results obtained from cross-hole acoustic measurements conducted during the 13th Chinese National Arctic Research Expedition. A traveltime tomography method is employed to invert the acoustic velocity distribution within sea ice. The Dijkstra algorithm is used for ray tracing, and damped least-squares inversion is applied to enhance solution stability and control ray path curvature to model acoustic wave propagation paths accurately. Additionally, the attenuation characteristics of sea ice are analyzed by monitoring changes in signal amplitude. This study also investigates the influence of temperature on sound speed and amplitude. The findings of this work contribute valuable insights to future studies on sea ice-climate interactions and acoustic wave propagation in polar ocean environments.

The study of the deep crust and upper-mantle velocity structure, along with the mechanisms of crustal thinning in the Chukchi Borderland (CB), provides valuable insights into the destruction mechanisms of the Arctic craton. This article presents the results of receiver function calculations and velocity structure inversion using ocean-bottom seismometer data from six stations recovered during the 11th Chinese National Arctic Research Expedition. These results reveal the crustal velocity structure and stratigraphy of the CB. Specifically, the sedimentary layer beneath the stations is 2 km thick, and the Conrad discontinuity at a depth of 15 km divides the crust into a two-layer structure, with some areas lacking the upper crust. A significant decrease in S-wave velocity is observed within the crust, and the Moho is located at a depth of 24 km, with an average VP= VS ratio of 1.76. Beneath the Moho, a gradual transition to the asthenosphere occurs. The observed decrease in S-wave velocity across the middle crustal discontinuities, along with low-density blocks, suggests the influence of extensional stress. In addition, by integrating previous studies of the CB, this article further confirms that the formation of crustal intrusions in an extensional environment leads to reduced crustal ductility, providing new evidence for the thinning of the continental crust in the CB. These findings also highlight the general destruction of the lithospheric within the Arctic craton.

Beachrocks are common coastal sedimentary rocks in tropical and subtropical seas. They are widely spread especially in islands and coastal areas. These rocks are important for island geological evolution research. Research on beachrocks aids in protecting island ecosystems and enhances islands’ ability to prevent and mitigate damage from natural disasters. This study uses unmanned aerial vehicle (UAV) images and the U-Net model based on deep learning to identify beachrocks. To enhance identification accuracy, the efficient channel attention (ECA) mechanism was integrated, leading to improvements of 0.49% in overall accuracy, 1.41% in precision, 0.97% in recall, 1.10% in F1-score, and 2.09% in intersection over union (IoU) compared to the baseline U-Net model. The final results demonstrate that the model effectively identified beachrocks, achieving 97.47% accuracy, 93.27% precision, 94.73% recall, 93.95% F1-score, and 88.65% IoU. This study offers a valuable tool for island geological evolution research and supports the development of large-scale island conservation efforts.

The current research focus at Chukchi Boardland (CB) revolves around sediment stratification and crustal structure, but investigations into deep stress fields and mantle dynamics are limited. This article presents a study on the anisotropic characteristics of the CB. Shear-wave splitting measurements were conducted using the transverse energy minimization at six stations recovered from the 11th Chinese National Arctic Research Expedition. The observation period for these six stations ranged from 2 August 2020 to 8 September 2020. The results demonstrate significant anisotropy within the CB, with the fast shear-wave polarization direction ranging from N60 degrees E to N70 degrees E. The time delays between fast and slow shear waves were found to be similar to 0.7 s. By comparing the anisotropy observed at the CB with that at land stations in Arctic Alaska, this study suggested that the genesis of anisotropy beneath the CB was related to the formation of the Amerasian basin. The tectonic processes of rifting during basin evolution and midocean ridge spreading led to the development of anisotropy in the lithosphere beneath the CB during expansion.

The chirp sub-bottom profiler, for its high resolution, easy accessibility and cost-effectiveness, has been widely used in acoustic detection. In this paper, the acoustic impedance and grain size compositions were obtained based on the chirp sub-bottom profiler data collected in the Chukchi Plateau area during the 11th Arctic Expedition of China. The time-domain adaptive search matching algorithm was used and validated on our established theoretical model. The misfit between the inversion result and the theoretical model is less than 0.067

Earthquake data are one of the key means by which to explore our planet. At a large scale, the layered structure of the Earth is revealed by the seismic waves of natural earthquakes that go deep into its inner core. At a local scale, seismology for exploration has successfully been employed to discover massive fossil energies. As the volume of recorded seismic data becomes greater, intelligent methods for processing such a volume of data are eagerly anticipated. In particular, earthquake focal mechanisms are important for assessing the severity of tsunamis, characterizing seismogenic faults, and investigating the stress perturbations that follow a major earthquake. Here, we report a novel deep reinforcement learning method for inverting the earthquake focal mechanism. Unlike more typical deep learning applications, which require a large training dataset, a deep reinforcement learning system learns by itself. We demonstrate the validity and efficacy of the proposed deep reinforcement learning method by applying it to the Mw 7.1 mainshock of the Ridgecrest earthquakes in southern California. In the foreseeable future, deep learning technologies may greatly contribute to our understanding of the oceanographic process. The proposed method may help us understand the mechanism of marine earthquakes.

Seafloor ambient noise in the Arctic Ocean is related to sea ice. The characteristics of low-frequency seafloor ambient noise (<10 Hz) in the northern Chukchi Sea have rarely been reported. Here, we investigated the seafloor ambient noise recorded using ocean-bottom seismometers in the northern Chukchi Sea under four different sea ice-concentration periods from 3 to 23 August 2020, in the 11th Chinese National Arctic Research Expedition. The causes and mechanisms of the changes in seafloor ambient noise that correspond to the variation in sea ice concentration were discussed. The energy of infragravity waves and primary microseisms is weak compared with other oceans. Combined with the analysis of land station, we argue that the variations in sea ice extent have little effect on the energy of the primary microseisms and infragravity waves. The power of secondary microseisms is growth with the loss of sea ice and is influenced by storms. We find that a high concentration of sea ice can impede the process of storm-sea surface interaction, which in turn affects the power of microseisms inspired by storm.

Distributed acoustic sensing (DAS) is an emerging vibration signal acquisition technology that transforms existing fiber-optic communication infrastructure into an array of thousands of seismic sensors. Due to its advantages of low cost, easy deployment, continuous measurement, and long-distance measurement, DAS has rapidly developed applications in the field of marine geophysics. This paper systematically summarizes the status of DAS technology applications in marine seismic monitoring, tsunami and ocean-current monitoring, ocean thermometry, marine target monitoring, and ocean-bottom imaging; analyzes the problems faced during its development; and discusses prospects for further applications in marine geoscience and future research directions.

Studying the Arctic sea ice contributes to a comprehensive understanding of the climate system in polar regions and offers valuable insights into the interplay between polar climate change and the global climate and environment. One of the key research aspects is the investigation of the temperature, salinity, and density parameters of sea ice to obtain essential insights. During the 11th Chinese National Arctic Research Expedition, acoustic velocity was measured on an ice core at a short-term ice station, however, temperature, salinity, and density were not measured. In the present work, we utilized a genetic algorithm to invert these obtained acoustic velocity data to sea ice temperature, salinity, and density parameters on the basis of the relationship between acoustic velocity and the physical properties of Arctic summer sea ice. We validated the effectiveness of this inversion procedure by comparing its findings with those of other researchers. The results indicate that within the normalized depth range of 0.43–0.94, the ranges for temperature, salinity, and density are −0.48-−0.29 °C, 1.63–3.35, and 793.1–904.1 kgm−3, respectively.

In studies on gas hydrate, bottom-simulating reflectors (BSR) are used to determine the potential hydrate-bearing sedimentary layers. Usually, BSR detection is performed manually by experienced interpreters. Therefore, a method for implementing an automatic BSR detection process should be established. In this study, we develop a novel architecture for BSR characterization using the convolutional neural network (CNN) technique. We propose the use of Stokes' transform (ST) to obtain a time-frequency spectrum for the input of CNN. ST fully uses the frequency content of the seismic data, and a part of the 3D seismic data collected from the Blake Ridge is utilized to train the CNN. Synthetic seismic records with variable signal-to-noise ratios (SNR), as well as Blake Ridge seismic data, were used to validate the detection effect of the CNN. Results show that the CNN trained by this method exhibits excellent performance in noise-resistant testing and achieves an accuracy of more than 89% in field seismic data detection.

We applied double-difference tomography to relocate seismic events and determine the lithospheric velocity structure beneath the New Britain Island arc and the South Bismarck Sea Basin, based on the local P wave arrival time dataset collected by the International Seismological Centre. Results of the seismic relocation and velocity inversion show that the subduction of Solomon Sea Plate along the New Britain Trench is spatially different above 150 km, and the subduction angle of the slab on the west side is higher than that on the east side. The relocated earthquakes also show that there are double seismic zones at the depths of about 30–90 km beneath the New Britain Island Arc. The velocity structure shows that the dehydration of the subducting slab caused the low-velocity anomalies in mantle wedge above the slab, which are associated with the magmatic activities around the New Guinea-New Britain Island arc. Moreover, it shows that there is another low-velocity anomaly zone beneath the Bismarck mid-oceanic ridge with spatial variation. Beneath the west of the Bismarck mid-oceanic ridge, the low-velocity anomaly is weakly connected to the subducted Solomon Sea slab. Conversely, the low-velocity anomaly beneath the Manus Sea Basin is highly intertwined to the subducting slab and its mantle wedge, indicating that the subduction of the Solomon Sea Plate might be a key deep dynamic factor that drives the spreading of the Manus Sea Basin and the separation of the Bismarck Plate.

With global warming, Arctic sea ice, as one of the important factors regulating climate, has put forward new requirements for research. At present, the ground penetrating radar (GPR) is a powerful tool to obtain the structure of Arctic sea ice. Traditional offset imaging techniques no longer meet research requirements, and the two-parameter full waveform inversion (FWI) method has received widespread attention. To solve the high nonlinearity and ill-posed problem of FWI, the L-BFGS optimization algorithm and Wolfe criterion of inexact line search were used to update the model. The parameter scale factor, multiscale inversion strategy, and total variation (TV) regularization were introduced to optimize the inversion results. Finally, the inversion of anomalous bodies with different scales and different physical parameters is carried out, which verifies the reliability of the proposed method for dual-parameter imaging of Arctic sea ice and provides a powerful tool for the study of Arctic sea ice.

It has long been recognized that specific ambient seismic noise is generated within the ocean. Therefore, ocean bottom seismometers have advantages to reveal the noise characteristics directly in the deep sea. We use the seismic recordings around Tristan da Cunha collected by 20 OBSs and two island stations to investigate the ambient noise sources in the middle of the South Atlantic. We analyzed the power spectral density, ambient noise cross‐correlations, and correlations with wave heights model. While primary microseisms (∼10–20 s) in this region are deficient, the secondary microseisms are strong and dominate the ambient noise at periods of ∼1–10 s, with the peak broadening to shorter periods (1–3 s). The secondary microseisms are strongly correlated with the local wave heights, especially at shorter periods (∼1–3 s), suggesting that the microseisms are affected mostly by local wave interactions. We observed clear Empirical Green's functions at periods of ∼3–10 s, indicating the existence of distant sources of secondary microseisms. Results of asymmetry analysis of cross‐correlations suggest the prominent direction of the distant source should be located to the Southwest and show barely seasonal variations. The correlations with wave heights indicate a possible source region in the southeast Pacific Ocean. Therefore, besides the dominating local wave‐wave interactions, the secondary microseisms around Tristan region are affected simultaneously by persistent distant sources from the deep basin in the Southeast Pacific Ocean, which might be related to the intense wave‐wave interactions caused by the blocking effect of the Drake Passage on ocean currents and waves.

The New Britain‐North Solomon subduction system is located at the convergence boundary between the Indo‐Australian and Pacific plates. In the Cenozoic, the subduction system gradually evolved into a complex system containing the trench, arc, back‐arc basin and oceanic plateau with the subduction and collision of the Ontong Java Plateau, which becomes one of the best places to explore the subduction dynamics. We attempt to illustrate the morphology of the Solomon Sea slab's response to the collision of the Ontong Java Plateau along the New Britain Trench through the hypocentral distribution, focal mechanisms and density structure from the gravity anomalies. According to the hypocenters and focal mechanisms, the focal stress field of the subducted Solomon Sea Plate along the New Britain Trench is analysed and the spatial distribution pattern of the plate is determined. We found that the maximum subduction depth of the Solomon Sea Plate gradually decreases from ~600 to ~400 km and the morphology of the slab changes from the steep‐dipping subduction to slab roll‐back from the Green and Buka Islands to the Bougainville Islands. Indicated by the analysis of seismicity and gravity anomalies, the spatial differences of the subduction pattern in the Solomon Sea Plate along the New Britain Trench are closely correlated with the block of the upper mantle in the Ontong Java oceanic Plateau, which is characterized by a tendency of thickening from NW to SE along the North Solomon Trench.

The Okinawa Trough (OT) is characterized by high heat flow anomalies up to 10,000 mW/m2 (especially in the middle area), which is rarely seen in heat flow research all over the world. The genesis of the high heat flow anomaly is probably related to mantle upwelling. However, the deep genesis is yet to be studied. We used heat flow values collected from the Global Heat Flow Database to calibrate the locations of high heat flow anomalies. Gravity data were used to calculate the residual geoid and interlayer geoid anomalies, which can help in characterizing the morphology of mantle upwelling beneath the OT. Earthquake hypocenter distribution maps were presented to show the position of the Philippine Sea Plate beneath the OT. According to P-wave tomography profile and previous research, we discussed the deep genesis of the high heat flow anomaly and proposed a preliminary model to explain the process. We suggest that the deep mantle upwelling, probably from a depth of -1200 km, enters the upper mantle through the front of the subducted Pacific plate. The deep mantle upwelling converges with the molten mantle flow caused by the fluid from the dehydration of the Pacific Plate and the Philippine Sea Plate. Two mantle upwelling branches were revealed to feed the southern OT (SOT) and Changbai Volcano, and the mantle upwelling under the middle OT (MOT) was the second branch from the mantle upwelling branch under the SOT. The MOT should be more influenced by mantle upwelling from deep sources with extremely high temperatures.