Search Results (40)

The Chagai porphyry copper belt is a major component of the Tethyan metallogenic domain, which spans approximately 300 km and hosts several giant porphyry copper deposits. The tectonic setting, whether subduction-related or post-collisional, and the deep dynamic processes governing the formation of these giant deposits remain poorly understood. Mafic microgranular enclaves (MMEs), mafic dikes, and multiple porphyries have been documented in the Saindak mining area. This work examines both the ore-rich and non-ore intrusions in the Saindak porphyry Cu-Au deposit, using methods like molybdenite Re-Os dating, U-Pb zircon ages, Hf isotopes, and bulk-rock geochemical data. Geochronological results indicate that ore-fertile and barren porphyries yield ages of 22.15 ± 0.22 Ma and 22.21 ± 0.33 Ma, respectively. Both MMEs and mafic dikes have zircons with nearly identical ²⁰⁶Pb/²³⁸U weighted mean ages (21.21 ± 0.18 Ma and 21.21 ± 0.16 Ma, respectively), corresponding to the age of the host rock. Geochemical and Sr–Nd–Hf isotopic evidence indicates that the Saindak adakites were generated by the subduction of the Arabian oceanic lithosphere under the Eurasian plate, rather than through continental collision. The adakites were mainly formed by the partial melting of a metasomatized mantle wedge, induced by fluids from the dehydrating subducting slab, with minor input from subducted sediments and later crust–mantle interactions during magma ascent. We conclude that shallow subduction of the Arabian plate during the Oligocene–Miocene may have increased the flow of subducted fluids into the sub-arc mantle source of the Chagai arc. This process may have facilitated the widespread deposition of porphyry copper and copper–gold mineralization in the region. Full article

Deep-sea mining presents a critical sustainability paradox; it offers access to essential minerals for the technologies of the green transition (e.g., batteries, wind turbines, electric vehicles) yet threatens fragile marine ecosystems. As the terrestrial sources of these materials face mounting geopolitical, environmental, and ethical constraints, undersea deposits are increasingly being viewed as alternatives. However, the extraction technologies remain unproven at large scales, posing risks related to biodiversity loss, sediment disruption, and altered oceanic carbon cycles. This paper explores how deep-sea mining might be reconciled with sustainable development, arguing that its viability hinges on addressing five interdependent challenges—technological readiness, environmental protection, economic feasibility, robust governance, and social acceptability. Progress requires parallel advancements across all domains. This paper reviews the current knowledge of deep-sea resources and extraction methods, analyzes the ecological and sociopolitical risks, and proposes systemic solutions, including the implementation of stringent regulatory frameworks, technological innovation, responsible terrestrial sourcing, and circular economy strategies. A precautionary and integrated approach is emphasized to ensure that the securing of critical minerals does not compromise marine ecosystem health or long-term sustainability objectives. Full article

The deep sea is gradually being exploited, yet research on the stability of the deep seabed is scarce. In this study, the seafloor stability of the Jiaxie Seamount Group in the western Pacific Ocean was assessed using the weight-of-evidence (WoE) method based on seafloor topographic data. Slope failure features were identified by analyzing multibeam bathymetric data, revealing 21 failure zones and multiple debris accumulation areas. Topographic factors, such as water depth, slope, slope direction, planar curvature, profile curvature, and ruggedness, were selected as assessment indicators. These indicators were reclassified as evidence factors, and a WoE model was constructed to assess the failure probability in the study area. A stability zoning map indicated that over 93% of the area had high stability. In comparison, areas with low and very low stability comprised less than 4%, mainly located on steep ridges and rugged slopes. The model’s performance was validated through an ROC curve, yielding an AUC value of 0.929, indicating a high predictive capability. This study presents a statistical framework for assessing the stability of deep-sea floors and provides theoretical support for upcoming seabed mining and deep-sea engineering endeavors, despite limitations due to data constraints and dependence on visually interpreted slope failure zones. Full article

Typhoon is one of the most destructive natural disasters, and it affects human society significantly. To reduce the negative impacts, many deep learning models for predicting future typhoon tracks have appeared. However, most of these models use all of the data they obtain as input, which may cause the diversity of typhoon tracks to have a negative impact on the prediction outcomes. In this paper, a joint method is proposed. The method mainly includes two parts: First, use a spatiotemporal similarity feature mining model to find out paths that are similar to the ongoing typhoon. Second, a deep learning model for processing sequence data is trained by these similar paths and then used for predicting the future track points’ latitude and longitude. The joint method bridges the gap in deep learning models’ ability to process spatial information and the shortcomings of spatiotemporal similarity feature mining models in predicting future data. In the experiment, we use a spatiotemporal similarity feature mining model to generate different input datasets by changing the number of similar paths in it, which can compare the model’s accuracy in different inputs. Also, real typhoon data recorded in the North West Pacific Ocean are used in the experiment. Through a comparison between the real path and prediction results in longitude and latitude, we find that 100–250 similar typhoon tracks as input have the best prediction effect in different tasks and are more accurate in long-term prediction. Full article

The weathering of seafloor hydrothermal sulfides is facilitated by microbial activities, yet the specific mechanisms of different sulfide types are not well understood. Previous studies have primarily been carried out under laboratory conditions, making it difficult to accurately replicate the complex in situ conditions of deep-sea hydrothermal fields. Herein, we deployed two well-characterized pyrite (Py)-dominated and chalcopyrite (Ccp)-dominated sulfide slices, which were placed 300 m from an active venting site in the Wocan-1 hydrothermal field (Carlsberg Ridge, Northwest Indian Ocean) for an 18-month in situ incubation experiment. Microscopic observations and organic matter analyses were conducted on the recovered sulfide slices to investigate the microbial weathering features of different sulfide types. Our results demonstrated that the weathering of the Py-dominated sulfide sample was primarily mediated by extracellular polymeric substances (EPSs) through indirect interactions, whereas the Ccp-dominated sulfide sample exhibited both direct microbial dissolution, resulting in the formation of distinct dissolution pits, and indirect EPS-mediated interactions. Four distinct phases of microbe–sulfide interactions were identified: approach, adsorption, stable attachment, and extensive colonization. Furthermore, the weathering products and biomineralization structures differed significantly between the two sulfide types, reflecting their different microbial colonization processes. Our study confirms that microorganisms are crucial in seafloor sulfide weathering. These findings advance our understanding of microbial-driven processes in sulfide mineral transformations and their role in marine ecosystems. Our findings are also valuable for future research on biogeochemical cycles and for developing bioremediation strategies for deep-sea mining. Full article

Marine cobalt-rich crusts, extensively used in industries such as aerospace, automotive, and electronics, are crucial mineral resources located on the ocean floor. To effectively exploit these valuable resources, underwater imaging is essential for real-time detection and distribution mapping in mining areas. However, the presence of suspended particles in the seabed mining environment severely degrades image quality due to light scattering and absorption, hindering the effective identification of the target objects. Traditional image processing techniques—including spatial and frequency domain methods—are ineffective in addressing the interference caused by suspended particles and offer only limited enhancement effects. This paper proposes a novel underwater image restoration method that combines polarization imaging and homomorphic filtering. By exploiting the differences in polarization characteristics between suspended particles and target objects, polarization imaging is used to separate backscattered light from the target signal, enhancing the clarity of the cobalt crust images. Homomorphic filtering is then applied to improve the intensity distribution and contrast of the orthogonal polarization images. To optimize the parameters, a genetic algorithm is used with image quality evaluation indices as the fitness function. The proposed method was compared with traditional image processing techniques and classical polarization imaging methods. Experimental results demonstrate that the proposed approach more effectively suppresses backscattered light, enhancing the clarity of target object features. With significant improvements in image quality confirmed by several no-reference quality metrics, the method shows promise as a solution for high-quality underwater imaging in turbid environments, particularly for deep-sea mining of cobalt-rich crusts. Full article

Deep-sea Fe-Mn polymetallic nodules formed nowadays at the deep-sea ocean floor were evaluated as promising critical raw materials (CRMs). Here, we report results of polymetallic nodules from the H22_NE block of the Interoceanmetal (IOM) exploration area in the eastern part of the Clarion–Clipperton Zone (CCZ), NE Pacific Ocean. The polymetallic nodules were studied with X-ray Diffraction, Raman spectroscopy, SEM-EDS, and LA-ICP-MS (bulk nodules and in situ nodule layers). Additionally, we combine geochemical data of polymetallic nodules with the previously reported data of pore waters and sediments from six stations. Our study aims to define the mineral composition and determine the content of CRMs in the polymetallic nodules and to assess the main factors controlling metal deposition and nodule enrichment in some CRMs. Mn content and the Mn/Fe ratio of the nodules classify them mostly as mixed hydrogenetic–diagenetic type. They are also enriched in Ni, Cu, Co, Zn, Mo, W, Li, Tl, and REE. The in situ REE patterns exhibit MREE and HREE enrichment and a variable Ce anomaly that argues for a changing oxic/suboxic environment and periodically changing of diagenetic and hydrogenetic nodule growth. The results of the joint study of the bottom sediments, pore waters, and polymetallic nodules show a complexity of processes that influence the formation of these deposits. The changing oxic and anoxic conditions are well documented in the chemistry of the nodule layers. Probably the most important controlling factors are sedimentation rate, bioturbation, adsorption, desorption, and oxidation. In addition, growth rates, water depth variations, electro-chemical speciation, phosphatization, and the structures of the Fe-Mn adsorbents are also considered. The polymetallic nodule deposits in the IOM contract area are estimated for future mining for Ni, Cu, Co, and Mn resources. They, however, contain additional metals of economic importance, such as REE and other trace elements (referred to as CRMs) that are potential by-products for metal mining. They can significantly increase the economic importance of exploited polymetallic nodules. Full article

Pursuing sustainable development is increasingly urgent due to resource depletion and environmental degradation, compounded by the need for a green energy transition requiring significant mineral resources. Traditional mining practices result in several environmental impacts, prompting the exploration of alternatives, like mining the ocean floor. This method offers a potentially less invasive way to obtain critical minerals. Notwithstanding, our understanding of the ocean ecosystem, which is crucial to Earth’s life support system, is still too limited. This study aimed to assess an educational intervention on sea mining for polymetallic nodules while improving scientific literacy and system thinking and supporting Sustainable Development Goals (SDG) 4, 13, and 14. A pre-/post-intervention design was implemented with 17 adolescents (aged 12–16 years) from an underprivileged non-formal context. The mixed-methods approach involved role-playing and modelling activities focused on the question: “Do you agree with mining polymetallic nodules in deep-sea waters”? The Wilcoxon test revealed that the intervention changed participants’ opinions about the theme, showing a statistically significant difference in student responses before and after the intervention (Z = −2.165; p = 0.030). A content analysis showed enhanced argumentation, understanding of Earth’s subsystems, and decision-making abilities. These findings suggest that the educational resource positively impacted students’ scientific literacy on the topic. This approach can be extended to other contexts and inform future investigations. Full article

Deep-sea mining pipes are different from traditional ocean risers articulated at both ends: they are free-suspended, weakly constrained at the bottom, and have an intermediate silo at the end, compared to which relatively little research has been carried out on vortex-induced vibration in mining pipes. In this study, a sophisticated quasi-3D numerical model with two degrees of freedom for the flow field domain and structural dynamics of a deep-sea mining pipe is developed through a novel slicing method. The investigation explores how the vortex-induced vibrations of the mining pipe behave in various scenarios, including uniform and oscillating flows, as well as changes in the mass of the relay bin. The findings indicate that the displacement of the deep-sea mining pipe increases continuously as it moves from top to bottom along its axial direction. The upper motion track appears chaotic, while the middle and lower tracks exhibit a stable “8” shape capture, with the tail capturing a “C” shape track. Furthermore, with an increase in flow velocity, both transverse vibration frequency and vibration modes of the mining pipe progressively rise. Under oscillating flow conditions, there exists a “delay effect” between vibration amplitude and velocity. Additionally, an increase in oscillation frequency leads to gradual sparsity in the vibration envelope of the mining pipe in transverse flow direction without affecting its overall vibration frequency. Under the same flow velocity and different bottom effects, the main control frequency of the deep-sea mining pipe is basically unchanged, but the vibration mode of the mining pipe is changed. Full article

We show that analyzing archived and future multibeam backscatter and bathymetry data, in tandem with regional environmental parameters, can help to identify polymetallic nodule fields in the world’s oceans. Extensive archived multibeam transit data through remote areas of the world’s oceans are available for data mining. New multibeam data will be made available through the Seabed 2030 Project. Uniformity of along- and across-track backscatter, backscatter intensity, angular response, water depth, nearby ground-truth data, local slope, sedimentation rate, and seafloor age provide thresholds for discriminating areas that are permissive to nodule presence. A case study of this methodology is presented, using archived multibeam data from a remote section of the South Pacific along the Foundation Seamounts between the Selkirk paleomicroplate and East Pacific Rise, that were collected during the 1997 Foundation–Hotline expedition on R/V Atalante. The 12 kHz Simrad EM12D multibeam data and the other forementioned data strongly suggest that a previously unknown nodule occurrence exists along the expedition transit. We also compare the utility of three different backscatter products to demonstrate that scans of printed backscatter maps can be a useful substitute for digital backscatter mosaics calculated using primary multibeam data files. We show that this expeditious analysis of legacy multibeam data could characterize benthic habitat types efficiently in remote deep-ocean areas, prior to more time-consuming and expensive video and sample acquisition surveys. Additionally, utilizing software other than specialty sonar processing programs during this research allows an exploration of how multibeam data products could be interrogated by a broader range of scientists and data users. Future mapping, video, and sampling cruises in this area would test our prediction and investigate how far it might extend to the north and south. Full article

Metals in deep-sea polymetallic nodules are indispensable for battery production and play a crucial role in facilitating the socio-economic green transition. A three-dimensional laser scanning model of nodules in the northwest Pacific Ocean has yielded an amount of data on volume, shape, and particle size. To deeply mine the correlation between the characteristics of the nodules, a joint probability density function (JPDF) based on copula theory is used. A univariate probability density function (PDF) linked to the particle size, burred depth, shape factor, and mass of the ores is established. The trend of nodule density with particle size is analyzed. Then, bivariate joint distribution using the copula method is constructed for mass and particle size. Furthermore, trivariate joint distribution using the copula method for nodule mass, particle size, and shape factor is derived. The results of this paper provide data to support the resource assessment of polymetallic nodules and optimize the design of mining systems. Full article

The geologic framework of western Washington, USA, is the result of collisional tectonics, where oceanic plate materials were subducted beneath the continental margin. As part of this process, fragments of mantle peridotites were transported into the upper crust along deep faults. The hydration of these ultramafic materials produced bodies of serpentinite. Subsequent regional metamorphism caused metasomatism of the serpentinite to produce a variety of minerals, which include nephrite jade, grossular, chlorite, diopside, vesuvianite, and pumpellyite. Many of the nephrite-bearing rocks are located along the Darrington–Devils Mountain Fault Zone in Skagit and Snohomish Counties. Intense prospecting has led to the establishment of many mining claims, but recreational collecting remains a popular activity. Full article

With the rapid development of the modern world, it is imperative to achieve effective and efficient monitoring for territories of interest, especially for the broad ocean area. For surveillance of ship targets at sea, a common and powerful approach is to take advantage of satellite synthetic aperture radar (SAR) systems. Currently, using satellite SAR images for ship classification is a challenging issue due to complex sea situations and the imaging variances of ships. Fortunately, the emergence of advanced satellite SAR sensors has shed much light on the SAR ship automatic target recognition (ATR) task, e.g., utilizing dual-polarization (dual-pol) information to boost the performance of SAR ship classification. Therefore, in this paper we have developed a novel cross-polarimetric interaction network (CPINet) to explore the abundant polarization information of dual-pol SAR images with the help of deep learning strategies, leading to an effective solution for high-performance ship classification. First, we establish a novel multiscale deep feature extraction framework to fully mine the characteristics of dual-pol SAR images in a coarse-to-fine manner. Second, to further leverage the complementary information of dual-pol SAR images, we propose a mixed-order squeeze–excitation (MO-SE) attention mechanism, in which the first- and second-order statistics of the deep features from one single-polarized SAR image are extracted to guide the learning of another polarized one. Then, the intermediate multiscale fused and MO-SE augmented dual-polarized deep feature maps are respectively aggregated by the factorized bilinear coding (FBC) pooling method. Meanwhile, the last multiscale fused deep feature maps for each single-polarized SAR image are also individually aggregated by the FBC. Finally, four kinds of highly discriminative deep representations are obtained for loss computation and category prediction. For better network training, the gradient normalization (GradNorm) method for multitask networks is extended to adaptively balance the contribution of each loss component. Extensive experiments on the three- and five-category dual-pol SAR ship classification dataset collected from the open and free OpenSARShip database demonstrate the superiority and robustness of CPINet compared with state-of-the-art methods for the dual-polarized SAR ship classification task. Full article

Deep-sea mining, as a critical direction for the future development of mineral resources, places significant importance on the mechanical characteristics of its transportation pipelines for the safety and efficiency of the entire mining system. This paper establishes a simulation model of the deep-sea mining system based on oceanic environmental loads and the mechanical theory of deep-sea mining transportation pipelines. Through a static analysis, the effective tension along the pipeline length, the maximum values of bending moment, and the minimum values of bending radius are determined as critical points for the dynamic analysis of pipeline mechanical characteristic monitoring. A dynamic simulation analysis of the pipeline’s mechanical characteristics was conducted, and simulation sensor data were obtained as inputs for the prediction model construction. A prediction model of pipeline mechanical characteristics based on the BP neural network was constructed, with the model’s prediction correlation coefficients all exceeding 0.95, enabling an accurate prediction of pipeline state parameters. Full article

This study introduces an innovative analytical methodology for examining the interconnections among the atmosphere, ocean, and society. The primary area of interest pertains to the North Atlantic Oscillation (NAO), a notable phenomenon characterised by daily to decadal fluctuations in atmospheric conditions over the Northern Hemisphere. The NAO has a prominent impact on winter weather patterns in North America, Europe, and to some extent, Asia. This impact has significant ramifications for civilization, as well as for marine, freshwater, and terrestrial ecosystems, and food chains. Accurate predictions of the surface NAO hold significant importance for society in terms of energy consumption planning and adaptation to severe winter conditions, such as winter wind and snowstorms, which can result in property damage and disruptions to transportation networks. Moreover, it is crucial to improve climate forecasts in order to bolster the resilience of food systems. This would enable producers to quickly respond to expected changes and make the required modifications, such as adjusting their food output or expanding their product range, in order to reduce potential hazards. The forecast centres prioritise and actively research the predictability and variability of the NAO. Nevertheless, it is increasingly evident that conventional analytical methods and prediction models that rely solely on scientific methodologies are inadequate in comprehensively addressing the transdisciplinary dimension of NAO variability. This includes a comprehensive view of research, forecasting, and social ramifications. This study introduces a new framework that combines sophisticated Big Data analytic techniques and forecasting tools using a generalised additive model to investigate the fluctuations of the NAO and the interplay between the ocean and atmosphere. Additionally, it explores innovative approaches to analyze the socio-economic response associated with these phenomena using text mining tools, specifically modern deep learning techniques. The analysis is conducted on an extensive corpora of free text information sourced from media outlets, public companies, government reports, and newspapers. Overall, the result shows that the NAO index has been reproduced well by the Deep-NAO model with a correlation coefficient of 0.74. Full article