Seafloor Types Research Articles

Principles of landscape-bionomic zoning of the Barents Sea

Landscape-bionomic zoning reveals the effect of hydrological and geomorphological links of the physical-geographical process on the bionomic differentiation of the Barents Sea floor. The polar front separates the Arctic water masses and communities from the subarctic ones; under the influence of warm Atlantic Current branches, extrazonal boreal biomes are formed. The geomorphological structure represents the lithogenic basis that determines the formation of bionomic types of the Barents Sea floor. It was established that each geomorphological region corresponds to one macrozoobenthos group.

Why, and where, is commercial fishing gear lost? A global review and case study of Pacific Canada

Derelict fishing gear is a global problem, damaging marine ecosystems via habitat degradation and trapping marine life, thereby impacting fisheries. We conducted a global review of reasons for commercial gear loss, and used the findings to design a survey focused on coastal British Columbia (BC), Canada. We conducted dockside and on-line surveys of commercial fishers to record their experiences with lost gear across net, line, and trap gear types. The most common reasons for gear loss from the global review were interactions with other fishing vessels and their gear, marine weather, and snagging on submerged features. Survey results of 29 fishers in BC indicated that snagging gear on rough substrate was the most important reason for loss across all gear categories, followed by seafloor type. Other reasons for gear loss varied by net, line, and trap gear type. Understanding reasons for gear loss is important to reduce losses.

A convolutional vision transformer for semantic segmentation of side-scan sonar data

Distinguishing among different marine benthic habitat characteristics is of key importance in a wide set of seabed operations ranging from installations of oil rigs to laying networks of cables and monitoring the impact of humans on marine ecosystems. The Side-Scan Sonar (SSS) is a widely used imaging sensor in this regard. It produces high-resolution seafloor maps by logging the intensities of sound waves reflected back from the seafloor. In this work, we leverage these acoustic intensity maps to produce pixel-wise categorization of different seafloor types. We propose a novel architecture adapted from the Vision Transformer (ViT) in an encoder–decoder framework. Further, in doing so, the applicability of ViTs is evaluated on smaller datasets. To overcome the lack of CNN-like inductive biases, thereby making ViTs more conducive to applications in low data regimes, we propose a novel feature extraction module to replace the Multi-layer Perceptron (MLP) block within transformer layers and a novel module to extract multiscale patch embeddings. A lightweight decoder is also proposed to complement this design in order to further enhance multiscale feature extraction. With the modified architecture, we achieve state-of-the-art results and also meet real-time computational requirements. We make our code available at https://github.com/hayatrajani/s3seg-vit.

Shallow Water Object Detection, Classification, and Localization via Phase-Measured, Bathymetry-Mode Backscatter

Detection, classification, and localization (DCL) techniques are being developed around the use of a phase-measuring sidescan sonar (PMSS) in very shallow waters. The instrument simultaneously collects co-located sidescan imagery and bathymetry in extreme shallow water environments (<1 m water depth). In addition to the bathymetry, an uncalibrated backscatter data set, referred to in this study as phase-measured, bathymetry-mode backscatter (BMB), is also collected. This BMB has been minimally addressed in the literature. This work aims to use the BMB to detect and differentiate between various objects on the seafloor, including unexploded ordnance (UXO), and placed marine debris, or ‘clutter’, such as lobster pots, boat propellers, and car tires. The differentiation from multiple seafloor types including mud, sand, and gravel and different types of objects occurred through various statistical analysis methods including binomial and multinomial logistic regression. These methods have been applied to create statistical regression models for several variables including phase-measured, bathymetry-mode backscatter amplitude, sounding distance from nadir, per-ping vessel roll, orientation offset between per-ping vessel heading and object orientation, and all combinations of these variables. These statistical tests produced maximum likelihood odds ratios of individual soundings being associated with the various seafloor and object types. Results from these analyses shows that DCL could be possible with phase-measured, bathymetry-mode backscatter from this PMSS system, though these results may not be representative for all bed types and phase-measuring systems.

Structure and metabolic potential of the prokaryotic communities from the hydrothermal system of Paleochori Bay, Milos, Greece.

Shallow hydrothermal systems share many characteristics with their deep-sea counterparts, but their accessibility facilitates their study. One of the most studied shallow hydrothermal vent fields lies at Paleochori Bay off the coast of Milos in the Aegean Sea (Greece). It has been studied through extensive mapping and its physical and chemical processes have been characterized over the past decades. However, a thorough description of the microbial communities inhabiting the bay is still missing. We present the first in-depth characterization of the prokaryotic communities of Paleochori Bay by sampling eight different seafloor types that are distributed along the entire gradient of hydrothermal influence. We used deep sequencing of the 16S rRNA marker gene and complemented the analysis with qPCR quantification of the 16S rRNA gene and several functional genes to gain insights into the metabolic potential of the communities. We found that the microbiome of the bay is strongly influenced by the hydrothermal venting, with a succession of various groups dominating the sediments from the coldest to the warmest zones. Prokaryotic diversity and abundance decrease with increasing temperature, and thermophilic archaea overtake the community. Relevant geochemical cycles of the Bay are discussed. This study expands our limited understanding of subsurface microbial communities in acidic shallow-sea hydrothermal systems and the contribution of their microbial activity to biogeochemical cycling.

Predicting the Sound Speed of Seafloor Sediments in the East China Sea Based on an XGBoost Algorithm

Based on the acoustic and physical data of typical seafloor sediment samples collected in the East China Sea, this study on the super parameter selection and contribution of the characteristic factors of the machine learning model for predicting the sound speed of seafloor sediments was conducted using the eXtreme gradient boosting (XGBoost) algorithm. An XGBoost model for predicting the sound speed of seafloor sediments was established based on five physical parameters: density (ρ), water content (w), void ratio (e), sand content (S), and average grain size (Mz). The results demonstrated that the model had the highest accuracy when n_estimator was 75 and max_depth was 5. The model training goodness of fit (R2) was as high as 0.92, and the mean absolute error and mean absolute percent error of the model prediction were 7.99 m/s and 0.51%, respectively. The results demonstrated that, in the study area, the XGBoost prediction method for the sound speed of seafloor sediments was superior to the traditional single- and two-parameter regressional equation prediction methods, with higher prediction accuracy, thus providing a new approach to predict the sound speed of seafloor sediments.

Integration of Photogrammetric and Spectral Techniques for Advanced Drone-Based Bathymetry Retrieval Using a Deep Learning Approach

Shallow bathymetry mapping using proximal sensing techniques is an active field of research that offers a new perspective in studying the seafloor. Drone-based imagery with centimeter resolution allows for bathymetry retrieval in unprecedented detail in areas with adequate water transparency. The majority of studies apply either spectral or photogrammetric techniques for deriving bathymetry from remotely sensed imagery. However, spectral methods require a certain amount of ground-truth depth data for model calibration, while photogrammetric methods cannot perform on texture-less seafloor types. The presented approach takes advantage of the interrelation of the two methods, in order to predict bathymetry in a more efficient way. Thus, we combine structure-from-motion (SfM) outputs along with band-ratios of radiometrically corrected drone images within a specially designed deep convolutional neural network (CNN) that outputs a reliable and robust bathymetry estimation. To achieve effective training of our deep learning system, we utilize interpolated uncrewed surface vehicle (USV) sonar measurements. We perform several predictions at three locations in the southern Mediterranean Sea, with varying seafloor types. Our results show low root-mean-square errors over all study areas (average RMSE ≅ 0.3 m), when the method was trained and tested on the same area each time. In addition, we obtain promising cross-validation performance across different study areas (average RMSE ≅ 0.9 m), which demonstrates the potential of our proposed approach in terms of generalization capabilities on unseen data. Furthermore, areas with mixed seafloor types are suitable for building a model that can be applied in similar locations where only drone data is available.

Seasonal occurrence of the potentially toxic benthic armoured dinoflagellates in Nha Trang Bay, South Central Coast of Viet Nam

Seasonal variations of the potentially toxic benthic dinoflagellates (bHABs) were studied during one year in the Bai Lan, Nha Trang Bay, South Central Viet Nam. Physical conditions (temperature, salinity, photosynthetically active radiation) and nutrient (NO3-N, PO4-P, DIN) concentrations were measured. The quantitative density of bHABs on the window screen sampling devices was determined in a range of seafloor types (dead coral, sand, and seaweeds) and retrieved within 24 h after placement. Results showed that twenty-one species of bHABs were recorded with Coolia malayensis, Ostreopsis lenticularis, O cf. ovata, O. siamensis, Prorocentrum emarginatum, and P. lima occurring throughout the year. The cell density of bHABs showed unclear seasonality, with Bystmatrum spp., Gambierdiscus spp., Ostreopsis lenticularis, and Coolia spp. absent in November 2020, concurrent with a peak in rainfall. The non-metric multidimensional scaling (NMDS) analysis showed that there was a high similarity between Gambierdiscus abundance and phosphate content. The densities of O. lenticularis, O. siamensis, and P. emarginatum were, in contrast, more closely associated with photosynthetically active radiation (PAR). Ostreopsis cf. ovata was less influenced by environmental conditions.

Fusion of Drone-Based RGB and Multi-Spectral Imagery for Shallow Water Bathymetry Inversion

Shallow bathymetry inversion algorithms have long been applied in various types of remote sensing imagery with relative success. However, this approach requires that imagery with increased radiometric resolution in the visible spectrum be available. The recent developments in drones and camera sensors allow for testing current inversion techniques on new types of datasets with centimeter resolution. This study explores the bathymetric mapping capabilities of fused RGB and multispectral imagery as an alternative to costly hyperspectral sensors for drones. Combining drone-based RGB and multispectral imagery into a single cube dataset provides the necessary radiometric detail for shallow bathymetry inversion applications. This technique is based on commercial and open-source software and does not require the input of reference depth measurements in contrast to other approaches. The robustness of this method was tested on three different coastal sites with contrasting seafloor types with a maximum depth of six meters. The use of suitable end-member spectra, which are representative of the seafloor types of the study area, are important parameters in model tuning. The results of this study are promising, showing good correlation (R2 > 0.75 and Lin’s coefficient > 0.80) and less than half a meter average error when they are compared with sonar depth measurements. Consequently, the integration of imagery from various drone-based sensors (visible range) assists in producing detailed bathymetry maps for small-scale shallow areas based on optical modelling.

Evaluation of radiometric calibration of drone-based imagery for improving shallow bathymetry retrieval

ABSTRACT Extracting high resolution, shallow bathymetry from drone-based imagery using empirical methods is a novel approach that gains increasing interest. However, due to the rapidly expanding character of this field, there are not sufficient guidelines for optimal pre-processing of drone-based imagery regarding bathymetry retrieval. This study explores the suitability of imagery resulted from commonly used drone cameras in terms of producing good correlation with ground-truth depth measurements obtained from an unmanned surface vehicle (USV). Thus, imagery with similar spectral responses, from two commercial sensors (DJI Phantom 4 Pro and MicaSense-RedEdge multispectral camera), is examined along with and without radiometric corrections applied, using proprietary software. The results show that radiometric image corrections using a reference reflectance panel, greatly enhance the correlation coefficient between bathymetry data and single-band or band-ratio reflectance mosaics at two study areas with mixed seafloor types. However, it was observed that the Red band and its associated logarithmic band ratios (Blue/Red, Green/Red) from both sensors show good correlation with depth as well, regardless of radiometric corrections. This result applies mainly to areas with favourable optical properties of water. This study suggests that radiometric calibration is crucial in empirical bathymetry retrieval from drone-based imagery. Furthermore, radiometric corrections applied on common Red, Green, Blue (RGB) cameras provide useful reflectance mosaics at lower costs compared to multispectral cameras.

Measurement of Seafloor Acoustic Backscatter Angular Dependence at 150 kHz Using a Multibeam Echosounder

Acoustic seafloor measurements with multibeam echosounders (MBESs) are currently often used for submarine habitat mapping, but the MBESs are usually not acoustically calibrated for backscattering strength (BBS) and cannot be used to infer absolute seafloor angular dependence. We present a study outlining the calibration and showing absolute backscattering strength values measured at a frequency of 150 kHz at around 10–20 m water depth. After recording bathymetry, the co-registered backscattering strength was corrected for true incidence and footprint reverberation area on a rough and tilted seafloor. Finally, absolute backscattering strength angular response curves (ARCs) for several seafloor types were constructed after applying sonar backscattering strength calibration and specific water column absorption for 150 kHz correction. Thus, we inferred specific 150 kHz angular backscattering responses that can discriminate among very fine sand, sandy gravel, and gravelly sand, as well as between bare boulders and boulders partially overgrown by red algae, which was validated by video ground-truthing. In addition, we provide backscatter mosaics using our algorithm (BBS-Coder) to correct the angle varying gain (AVG). The results of the work are compared and discussed with the published results of BBS measurements in the 100–400 kHz frequency range. The presented results are valuable in extending the very sparse angular response curves gathered so far and could contribute to a better understanding of the dependence of backscattering on the type of bottom habitat and improve their acoustic classification.

Design and field testing of a non-linear single-beam echosounder for multi-frequency seabed characterization

Seabed mapping and characterization are best performed using several frequencies and several angles of incidence. This is often an issue because of the need to employ different sonars, with distinct frequencies but co-located as much as possible to image the same patch of seafloor. This article presents the design, calibration and field testing of a multiple-frequency single-beam echosounder (SBES), mounted on a mechanical pan-and-tilt head. It uses very high transmitting levels to produce non-linear effects and generate harmonics of a 100 kHz fundamental frequency. PZT transducers are used to transmit high acoustic powers and PDVF transducers enable the reception of scattering levels over a very broad frequency band (for the different harmonics). Tank experiments are used to verify effective harmonic generation. The shock distance (at which harmonics are at their maximum level) is measured as 2 m from the transmitter and recommended as the minimum far-field range. Non-linear transmission losses (distinct from linear losses) are calibrated using a full metal sphere 38.1 mm in diameter and of known frequency response, up to ranges commensurate with the depths expected in the field (⩽30 m). The −3 dB beamwidth varies from 5.8° at 100 kHz to 2.8° at 300 kHz. Harmonics are used to resolve phase ambiguities in detecting seabed depths. Backscattering strengths BS are matched to the Generic Seafloor Acoustic Backscatter (GSAB) model to derive the best-fitting parameters. Field validation took place in the Bay of Brest (France) in May 2016, over three different types of seafloor (namely: sandy mud; gravel; gravelly coarse sand with maerl). Additional in situ calibration was used. The echosounder was pointed at angles from 0° (nadir) to 60° by 5° steps. One of the areas surveyed (“Carré Renard”), commonly used for instrument calibration and comparison with other measurements, showed differences <1 dB at 200 kHz. Videos and photographs of the seafloor were used to ground truth interpretations of the BS curves. The results show that these BS curves measured with the echosounder are relevant for seabed classification and characterization. The different shapes and levels of BS when compared to ground truth are coherent with the Jackson model. The main limit of this prototype of echosounder is the signal to noise ratio, in particular for high frequency harmonics (⩾400 kHz). The in situ calibration is unavoidable because of the non-linear parameter variations with water characteristics (temperature, salinity…). Calibrated BS curves from 100 kHz to 300 kHz can be directly compared to other measurements, for example to calibrate other instruments.

The onset of seafloor spreading at the northeastern continent-ocean boundary of the South China Sea

The transition from continental to oceanic crust and the initial formation of typical oceanic crust at passive rifted margin are still poorly understood. In this study, deep crustal structures in the transitional domain of the northeastern South China Sea (SCS) margin are investigated and studied according to newly-acquired high-resolution reflection seismic data and gravity modeling. Three strong reflectors (R1, R2 and R3) are imaged in the narrow transitional domain. About 1 s in the two-way travel time (TWTT) beneath the top basement, R1 shows a similar morphology to the top basement, and quite likely represents a partial serpentinization interface. The presence of R1 suggests a tectonically dominated extensional stage prior to the final lithospheric rupture of the SCS. R2 is from magmatic intrusions or plutons (sills or dikes) from decompression melting. R3 is observed at ~2.5–2.2 s TWTT beneath the top basement but shoals seawards. R3 likely resulted from magmatic relicts in the paleo-melt transfer channels and/or from frozen molten materials in a shear zone of the lithospheric and asthenospheric mantle. The occurrence of R3 indicates the onset of a robust seafloor spreading system. The gradual seaward increase in magmatic activity highlights that, towards the final lithospheric breakup, tectonic extension was overtaken by magmatism. Increased magmatism and partial serpentinization formed a narrow hybrid transitional crust. Nevertheless, the breakup was not an instantaneous event. The last-stage transient magmatism occurred in a very narrow zone and does not rule out that the margin was predominantly amagmatic during the rifting and breakup. Based on reflection seismic and gravity modeling, we propose that the onset of typical seafloor spreading occurred at about 30 Ma around the study area, and the continent-ocean boundary (COB) should be located further seawards in the northeastern SCS margin.

Shallow-water hydrothermalism at Milos (Greece): Nature, distribution, heat fluxes and impact on ecosystems

Submarine hydrothermal activity is responsible for heat and chemical exchanges through the seafloor. Shallow-water hydrothermal systems (SWHS), while identified around the globe, are often studied in a way that is less comprehensive than their deep-ocean counterparts (e.g., along ridges), where systematic optical and acoustic mapping is more prevalent and coupled to in situ observations and sampling. Using aerial drones, an AUV, and temperature measurements at 10–40 cm subseafloor, we investigated in 2019 one of the most extensive SWHS known to date, in Paleochori and nearby Spathi and Agia Kyriaki Bays (south of Milos, Greece). Hydrothermal venting, found from the shore to water depths of almost 500 m, shows emissions of gases and high-temperature fluids, often associated with bacterial mats and/or hydrothermal mineral precipitates. This study provides extensive drone mapping coupled with local AUV surveys for seafloor characterization and ground-truthing from the shore to ~20 m water depth. Seafloor photomosaics also provide a detailed context to samples, measurements and observations carried in situ. We interpret the photomosaics to define distinct seafloor types, linked to this hydrothermal activity. White hydrothermal patches (WHPs) often show a clear polygonal organization, together with outflow areas that are both more dispersed and distributed. Polygonal patterns likely result from fluid convection in a sandy porous medium heated from below. These WHPs display elevated subseafloor temperatures, typically >50°C, with maximum values of ~75°C. Photomosaics also display textures of biological origin, including seagrass and bioturbation patterns. Widespread bioturbation by burrowing shrimps is often associated with WHPs, bounding them, but also occurs on sandy seafloor away from hydrothermal patterns. Subseafloor temperatures at these bioturbated areas are of ~30–40°C, and are thus transitional between hot WHPs and sedimented seafloor unaffected by hydrothermal activity (~24°C). In addition to linking subseafloor temperature data and interpreted seafloor photomosaics, our results provide a comprehensive general overview of this SWHS, of the organization of its hydrothermal outflow through the seafloor, and of the underlying subseafloor fluid circulation. This paper also gives the first perspectives on the heat fluxes of the system, and constitutes a background for other studies on the nature and distribution of microbial communities, controlled by this hydrothermal activity.

Classification of various algae canopy, algae turf, and barren seafloor types using a scientific echosounder and machine learning analysis

Diverse algae form algae canopies and turfs with various community structures, which play an important ecological role in coastal waters. Acoustic methods have been suggested and applied as effective quantitative methods for some algae canopy measurements across a large-scale area. However, these approaches face difficulties in accurately classifying turfs from barren seafloor due to weak backscattering strength. Thus, to estimate the community structure of various algae assemblages, we developed a classification method using a combination of acoustic-derived physical distance and backscattering strength parameters using a scientific echosounder. The prediction accuracy for algae or barren seafloor using four machine learning methods based on seven parameters was higher than that for the manual classification results based only on the acoustic physical distance. The classification accuracies of six types of algae canopy, turf, and barren seafloor were also higher than those obtained based only on commonly used seafloor parameters. Hence, machine learning methods based on the seven derived parameters from acoustic data are suggested to be effective for the classification. Applications in the classification and distribution estimations of various types of algae canopies, turfs, and potential algae habitat areas are promising.

Mapping seafloor habitats in the Bay of Fundy to assess megafaunal assemblages associated with Modiolus modiolus beds

Dense aggregations of horse mussels exist in the Bay of Fundy and are thought to be associated with high biodiversity compared to surrounding habitats. Previous research show correlations between these aggregations and long narrow flow-parallel bedforms. In this study, high resolution seafloor photographs were compared to multibeam echosounder data to investigate benthic community and biodiversity patterns across the Bay of Fundy. Biophysical seafloor types (benthoscapes) were mapped with 71% accuracy using Object Based Image Analysis (OBIA) with fuzzy c-means classification. Community patterns were found to shift gradually across benthoscapes and mega-epifaunal biodiversity was highest in areas of mixed sediments and silty gravel and anemones. Horse mussels occurred mainly within the sand benthoscape where the flow parallel bedforms exist. Community patterns were also assessed at fine scales (5 m) using a by-eye interpretation of the flow parallel bedforms, where gradational shifts across the bedforms and surrounding areas were evident and abundance of horse mussels were found to be significantly greater on the bedforms. These findings will ultimately facilitate decisions around fisheries management, marine spatial planning, and monitoring of the horse mussel habitats and the surrounding benthoscapes within the Bay of Fundy.

Seafloor classification based on deep-sea multibeam data—Application to the Southwest Indian Ridge at 50.47°E

Seafloor classification based on multibeam data is an attractive approach due to its wide-coverage capability and relatively low cost. It has been successfully applied in shallow water, but may not work well in deep sea, particularly that with complex seafloor topography. In this paper, a method for deep-sea seafloor classification based on multibeam data is proposed. Firstly, bathymetry data are processed to obtain local slopes. Secondly, backscattering strength (BS) data from depth datagrams are corrected by calculated incident angles and insonified areas on the seafloor. Then BS data from several consecutive pings along the track are grouped into a set of small units in the across-track direction and the angular response curve of BS in each unit is extracted. Moreover, inversions of angular response curves are carried out to obtain the empirical parameter BSO based on a functional model. BSO values related to seafloor types and the standard deviations of inversion results are acquired at the same time. Finally, with the help of the prior information gained by analyzing the relationship between BSO values and ground truth, BSO values of all the units can be statistically analyzed and classified to generate a seafloor classification map. This method has been applied in a complex terrain area around the Duanqiao hydrothermal field on the Southwest Indian Ridge (SWIR) at 50.47°E, and six seafloor types have been classified according to the statistical analysis on BSO values and the visual ground truth.

A Combined Machine Learning and Residual Analysis Approach for Improved Retrieval of Shallow Bathymetry from Hyperspectral Imagery and Sparse Ground Truth Data

Mapping shallow bathymetry by means of optical remote sensing has been a challenging task of growing interest in recent years. Particularly, many studies exploit earlier empirical models together with the latest multispectral satellite imagery (e.g., Sentinel 2, Landsat 8). However, in these studies, the accuracy of resulting bathymetry is (a) limited for deeper waters (&gt;15 m) and/or (b) is being influenced by seafloor type albedo. This study explores further the capabilities of hyperspectral satellite imagery (Hyperion), which provides several spectral bands in the visible spectrum, along with existing reference bathymetry. Bathymetry predictors are created by applying the semi-empirical approach of band ratios on hyperspectral imagery. Then, these predictors are fed to machine learning regression algorithms for predicting bathymetry. Algorithm performance is being further compared to bathymetry predictions from multiple linear regression analysis. Following the initial predictions, the residual bathymetry values are interpolated by applying the Ordinary Kriging method. Then, the predicted bathymetry from all three algorithms along with their associated residual grids is used as predictors at a second processing stage. Validation results show that by using a second stage of processing, the root-mean-square error values of predicted bathymetry is being improved by ≈1 m even for deeper water (up to 25 m). It is suggested that this approach is suitable for (a) contributing wide-scale, high-resolution shallow bathymetry toward the goals of the Seabed 2030 program and (b) as a coarse resolution alternative to effort-consuming single-beam sonar or costly airborne bathymetric laser surveying.

The isotopic (He, Ne, Sr, Nd, Hf, Pb) signature in the Indian Mantle over 8.8 Ma

The eastern Southwest Indian Ridge, between 61° and 67°E, has a very low melt supply and comprises several corridors of nearly amagmatic spreading that expose mantle-derived serpentinized peridotite. More volcanically active ridge portions separate these corridors. He, Ne, Pb, Nd, Hf and Sr isotopes were analyzed in basalt glasses dredged on two types of seafloor: volcanic and ultramafic. Basalts dredged on on-axis ultramafic seafloor tend to be slightly more depleted for heavy radiogenic isotopes and show slightly higher 3He/4He isotope ratios than basalts dredged on volcanic seafloor, with no systematic difference in neon isotope ratios. We propose that both types of basalts are derived from the same mantle source, but that the basalts dredged on ultramafic seafloor are more affected by melt/mantle reactions, which slightly modify their isotopic signatures. Our dataset also includes a few basalts, dredged on off-axis ultramafic seafloor, that range in age between 2.6 and 8.8 Ma. These few and widely-spaced off-axis samples, erupted at the ridge axis, are a rare opportunity to capture the potential geochemical variability of the mantle source in an ultramafic seafloor corridor over 8.8 Ma. This temporal variability appears to be minor compared to the overall range of isotopic variability of the on-axis lavas from the 61°-67°E region.

Effects of seafloor topography on underwater acoustic channel

Sound wave is the main means of underwater communication at present. Focusing on effects of seafloor topography on underwater acoustic channel, some acoustic propagation models were compared and the BELLHOP method was chosen based on the experiment data of typical seafloor topography. Then the date of OFES model and SOM method were used to get the marine parameters of temperature, salinity, depth and sounding velocity along SS section near Diaoyu Island. At last, the acoustic propagations along the SS section in flat bottom and real seafloor topography were simulated using BELLHOP method. The comparison of acoustic propagation loss was presented. The results show that the BELLHOP method can be used to get authentic results of acoustic propagation under different seafloor topographies, and seafloor topography has a significient effect on acoustic propagation characteritics, which are also influenced by the depth of sound source and sound receiver.