Marine Geophysical Research最新文献

Porosity and acoustic impedance are important in the study of subsurface properties of rocks and soil. Porosity is influenced by the type of minerals, and fluids, and their distribution within the subsurface material. Acoustic impedance is a key parameter in seismic inversion because it governs the reflection and transmission of seismic waves at interfaces between different rock layers. Mapping porosity and acoustic impedance using seismic inversion poses several challenges such as low resolution, longer convergence times compared to other optimization techniques, and handling large datasets. To address these challenges, our current study has employed a semi-hybrid optimization approach by incorporating a pattern search (PS) method into the globally recognized simulated annealing (SA) technique. In our devised methodology, seismic data is meticulously inverted, trace by trace, initially utilizing the simulated annealing process and subsequently integrating the pattern search which further reduces computational Complexity. The output from SA serves as the foundation for the PS optimization, preventing it from getting trapped in local minima or maxima. To evaluate the algorithm, we initiated a systematic analysis using synthetic data. The hybrid optimization method performed well, yielding highly accurate inversion results with a remarkable high resolution and correlation between original and inverted impedance. We then applied this approach to actual seismic reflection data from the Blackfoot field in Alberta, Canada. Notably, the inversion identified a sand channel between 1055 and 1070 ms two-way travel time, characterized by low impedance and high porosity, suggesting the potential presence of hydrocarbon reservoirs. The level of performance demonstrated in this context may not be anticipated when utilizing SA or PS optimization alone. Hence, the newly devised semi-hybrid optimization approach emerges as a highly recommended solution, offering the potential to address the constraints of individual optimization methods and deliver thorough subsurface insights.

The compression index (C_c) is a crucial parameter for evaluating the consolidation settlement of marine infrastructure, but measuring it experimentally is challenging. This study presents C_c prediction models for marine seabed soils using linear, nonlinear, and artificial neural network modeling techniques. Large experimental oedometer test results for marine clays, collected from the available literature, are used to develop valuable models based on easily measurable soil properties, applicable to a wide range of marine soils. The initial void ratio and plasticity index have a greater impact on C_c estimation compared to the liquid limit and natural water content. The predictive capacity of the models is validated with independent oedometer test data, confirming the reliability of the results. The proposed models aid geotechnical designers in determining the required C_c for initial settlement assessments for marine infrastructure, resulting in cost and time savings. The predicted C_c values can be further adjusted by conducting traditional consolidation tests on selected seabed samples collected from the coastal site.

The Biak tsunami event on February 17, 1996, was triggered by a Mw 8.2 earthquake at 5:59 UTC (14:59 local time). Based on the field survey, the maximum tsunami height was not located on the coast that directly faces the earthquake epicenter. The maximum tsunami of up to 7.7 m was recorded at Farusi village on the opposite coast. In addition to the high tsunami hit, the fast arrival time in this village was an anomaly that raised questions regarding the multiple tsunami sources. Previous studies suspected a landslide when a rupture occurred, but no one had yet identified the dimensions and mechanism of the landslide. The purpose of this research is to increase understanding of tsunami generators and answer that question. The COMCOT software is used to perform tsunami simulations, integrating fault and landslide sources simultaneously. This study obtains the Biak tsunami generator from a fault source model with a length of 272 km, a width of 110 km, an average dislocation of 8 m, and a maximum slip of 10.6 m. Also, there are three landslides occurred in the south coast. One of the major landslide source model has dimensions length and width of 5.629 km and 14.595 km, respectively, and a thickness of landslide material of 50 m, with an average slope of the slip plane of 10° located in the Ramardori. These two source models answer the particular questions of the Biak tsunami incident.

The hadal zone (water depths > 6000 m) are unlike the overlying shallower marine regions (bathyal and abyssal) as it does not follow a continuum from the continental shelves to abyssal plains, but rather exhibits a globally disjunct series of discrete deep-sea habitats confined within geomorphological features. From an ecological perspective, hadal communities are often endemic to individual or adjacent features and are partitioned and isolated by geomorphological structures. To examine the size, shape, depth and degree of isolation of features where hadal fauna inhabit, this study explores the broad seafloor geomorphology, and distinctly partitioned hadal areas, across the Southwest Pacific and East Indian oceans using global bathymetric datasets. This research revealed the area occupied by hadal depths to be 716,915 km² of which 58% are accounted for by trenches, 37% in basins and troughs, and 5% fracture zones. The largest feature in terms of area > 6000 m depth is the Wharton Basin with 218,030 km² spanning 376 discrete areas. The largest continuous hadal habitats were the Kermadec and Tonga trenches at 145,103 and 111,951 km² respectively, whereas features such as the Java Trench comprise two hadal components partitioned by a bathymetric high. Conversely, no physical barrier exists between the New Britain and Bougainville trenches thus any literature pertaining to hadal species or habitats from these trenches can be merged. This study highlights that the hadal zone mainly comprises two main geomorphological features (trenches and basins) that differ in size, depth and complexity. Hadal basins cover vast, generally shallower areas, comparable to abyssal plains, whereas trenches, despite a lesser footprint, represent greater depth ranges and complexity. As such, sampling designs and interpretation of ecological data must differ and hadal basins likely play an increasingly important role in understanding ecological shifts from abyssal to hadal ecosystems.

Seafloor spreading along the Carlsberg and Central Indian ridges has steered the tectonic evolution of the western Indian Ocean. These spreading ridges display variations in spreading rate, segmentation, and morphological characteristics, providing clues to the long-term evolution of the oceanic lithosphere in this region. To assess the influence of two notable off-axis thermal sources, the Réunion plume and the Indian Ocean Diffuse Boundary Zone, on factors such as rigidity and seafloor subsidence along these ridges, we computed the effective elastic thickness (Te), residual geoid-age slopes, and residual depth anomalies (RDA) of the region using gravity and geoid data. The results reveal a weaker lithosphere at the northern Central Indian Ridge (Te: ~ 8.5–8.9 km) compared to the neighboring segments of the southern Central Indian Ridge (Te: ~ 10.5–12.7 km) and the Carlsberg Ridge (Te: ~ 10.5–14.7 km). Residual geoid and RDA variations suggest asymmetric seafloor spreading and subsidence along the entire ridge system. The asymmetric subsidence across the Central Indian Ridge is largely due to upper mantle contamination from the Réunion plume, while across the Carlsberg Ridge, it may be linked to its complex tectonic history. The rigidity and seafloor spreading patterns along the northern Central Indian Ridge are notably affected by thermal perturbations from the regional heat flow anomaly of the ongoing diffuse deformation zone. Moreover, the Te and segmentation patterns roughly correlate along the ridge system, suggesting a causal relationship between the two or the presence of underlying factors such as regional thermal structure influencing both.

Accurate identification of marine species is essential for ecological monitoring, habitat assessment, biodiversity conservation, and sustainable resource management. To address the challenges associated with diverse and complex marine environments, the paper proposes a integrated model that combines the strengths of a Vision Transformer (ViT) and Transfer Learning (TL). The paper introduces a novel methodology for the classification of marine species images by integrating the capabilities of a Amended Dual Attention oN Self-locale and External (ADANSE) Vision Transformer and a DenseNet-169 Transfer Learning model. The ADANSE-ViT, serving as the foundational architecture, excels in capturing long-range dependencies and intricate patterns in large-scale images, forming a robust basis for subsequent classification tasks. On Fine-tuning further, it customizes the model for marine species images. Additionally, we utilize transfer learning with the DenseNet-169 architecture, pre-trained on a comprehensive dataset, to extract relevant features and enhance classification effectiveness specifically for marine species. This synergistic combination enables a comprehensive analysis of both local and semantic features in species images, leading to accurate classification results. Experimental evaluations conducted on self-collected and benchmark datasets showcase the efficacy of our approach, surpassing existing fish classifiers and TL variants in terms of classification accuracy. Our integrated model achieves an impressive accuracy of 96.21% for the self-collected dataset and 95.09% for the benchmarked dataset.

The northwestern region of the Sicily Channel hosts a great number of morphological highs, the widest of which is the Adventure Plateau that is part of the Sicilian Maghrebian Fold and Thrust Belt system, formed since the Neogene. The Adventure Plateau was shaped in the Early Pliocene by an extensional phase that produced high-angle normal faults mostly WNW-ESE to N-S oriented. Through these faults, magmatic fluids ascended and produced widespread volcanic manifestations often associated to fluid flow processes. The interpretation of multibeam echosounder, seismic reflection (sparker, airgun) and well-log data allow us to identify several features related to the presence of fluids in the study area. The morpho-structural analysis showed a NW–SE oriented fault system and a string of pockmarks that follow the same trend. A detailed well-log analysis confirmed the presence of oil traces, at a depth of ~ 250 m, and gas (i.e., CO₂) at a depth of ~ 450 m. The seismo-stratigraphic analysis highlighted seismic signals located below the pockmarks, (e.g. seismic chimneys, bright spots) which suggest the presence of fluids that would rise to a few meters’ depth. Based on the observations, two sources and two corresponding rising mechanisms have been identified. Morphometric analysis of pockmarks has been performed to delineate their possible interaction with the bottom currents. A fluids pathway model has been reconstructed, revealing the source of fluids emissions at depth in the Adventure Plateau, and providing new insights into the identification of fluid leakage pathways.

The undrained shear strength of marine sediment is of vital importance because of its critical role in seafloor slope stability, seafloor infrastructure, and influencing sediment dynamics that can lead to underwater landslides. Therefore, understanding the undrained shear strength of marine sediments and its influencing factors is a fundamental requirement for both offshore engineering and geoscience studies. Core data obtained from 198 sites across 46 legs of the Ocean Drilling Program/International Ocean Discovery Program (ODP/IODP) were used to analyze the undrained shear strength of marine sediments and their influencing factors. The undrained shear strength of marine sediments is significantly influenced by the depositional environment. Sediments deposited in active continental margins exhibit a higher undrained shear strength than those deposited in deep-sea and carbonate platform environments due to seismic strengthening and over-consolidation. It was found that fine-grained siliciclastic lithofacies with less than 50% carbonate content exhibited high variability and a rapid increase in the undrained shear strength with depth. In contrast, fine-grained carbonate lithofacies with more than 50% carbonate, as well as reef-facies carbonates, showed low variability and only a gradual increase in undrained shear strength with depth. Additionally, we showed a positive association between the undrained shear strength and physical characteristics including bulk density and P-wave velocity, as well as an inverse correlation with porosity. An exponential relationship was found between these physical properties and the undrained shear strength, with coefficients of determination (R²) values of 0.71, 0.74, and 0.69, respectively.

Empirical methods often fail to accurately depict in-situ gas hydrate saturation distributions, despite their relationships with petrophysical and elastic properties remaining partially unclear. We proposed a data-driven approach to estimate gas hydrate saturation employing several machine learning techniques, including radial basis function neural network (RBFNN), random forest (RF), extreme gradient boosting (XGBoost), Adaptive Boosting (AdaBoost), support vector machines (SVM), and k-nearest neighbors (kNN). This study involved pre-processing data from laterolog deep resistivity and p-wave velocity logs, defining their increments as differences from the lowest values in gas hydrate zones. We identified data-driven patterns between pairs of laterolog deep resistivity and p-wave velocity increments, as well as core information corroborated with the traditionally predicted gas hydrate saturations, by adopting machine learning (ML) approaches. The approach tested on four wells in the Krishna-Godavari (KG) offshore basin (India) is extremely feasible. During the training and test phases, the minimum correlation coefficient between the true and predicted responses exceeds 0.94 and 0.88, respectively. The model accuracy hierarchy was RBFNN > AdaBoost > RF > XGBoost > KNN > SVM during training, and AdaBoost > XGBoost > RF > RBFNN > KNN > SVM during testing. This approach allows interpreters to select the most accurate ML model based on training phase performance. The proposed ML-based method is efficient, synergising p-wave and resistivity data increment, significantly improving gas hydrate saturation predictions, and avoiding the complexities of traditional calculations. The study indicates that gas hydrate saturation in the Krishna-Godavari region ranges from 0.17 to 86.84%.

Lake Fuxian is a tectonic lake located on the Yunnan–Guizhou Plateau in southwest China. It is the deepest freshwater tectonic lake in the Yunnan Plateau. The present study focused on examining the structural changes, faulting patterns, and their influence on fault subsidence in the Lake Fuxian basin. Seismic interpretation showed uplift in the SSW area and subsidence in the NNE region. Subsidence is more pronounced on the northern survey lines, where the sedimentary strata had a maximum sedimentation of 1200 m. The seismic interpretation findings showed a horst block in the southern basin and a graben block in the northern half of the basin. L-14 demonstrated the steeper with maximum throw and parallel character of normal faults and provided the evidence of crustal extensional regime. Thirteen main faults were identified by fault modeling in the lake basin. The analysis of fault characteristics revealed that faults in the northern basin are characterized by greater depth, steeper angles, maximum displacement, and are actively moving owing to low resistance and negative asperity values, and poor edge detection values. Faults in the southern basin have an opposite character to those in the northern basin. Major faults in the northern lake basin have a stronger influence of fault subsidence compared to faults in the center and southern lake basins. Overall, the lake Fuxian basin showed horst-graben structure with parallel normal faulting with a crustal extensional regime.