Bathymetric Model Research Articles

Predicting intermediate wavelength depth using a deep neural network combined with gravity data in the Sea of Japan

ABSTRACT Influenced by various geophysical factors, gravity and bathymetry exhibit significant correlations only at certain intermediate wavelengths. In this study, a deep neural network (DNN) combined with multi-source gravity data was used to recovery the intermediate wavelength (15–160 km) depth in the Sea of Japan (37–42°N, 130–138°E), to analyze the contributions of different gravity data to the bathymetric model. Subsequently, the gravity anomaly (GA), vertical gravity gradient (VGG), and vertical deflection were used as training data with a suitable DNN framework and designed training strategy. Evaluation results show that: the precision of the final DNN training models at test points surpasses that of the topo_25.1 model by 13 to 14 m; the TC1 model, with GA, VGG, and vertical deflection, is optimal, and the accuracy is 7.2 m higher than that of the (gravity geology method) GGM model on check cruises. The three types of gravity data can complement and constrain each other. In addition, predicting depths at intermediate wavelengths with significant coherence, as opposed to directly predicting absolute depths, results in higher accuracy by 20–30 m at test points, and an improvement of approximately 10 m in check accuracy.

Accuracy and Precision of Shallow-Water Photogrammetry from the Sea Surface

Mapping shallow-water bathymetry and morphology represents a technical challenge. In fact, acoustic surveys are limited by water depths reachable by boat, and airborne surveys have high costs. Photogrammetric approaches (either via drone or from the sea surface) have opened up the possibility to perform shallow-water surveys easily and at accessible costs. This work presents a simple, low-cost, and highly portable platform that allows gathering sequential photos and echosounder depth values of shallow-water sites (up to 5 m depth). The photos are then analysed in conjunction with photogrammetric techniques to obtain digital bathymetric models and orthomosaics of the seafloor. The workflow was tested on four repeated surveys of the same area in the Western Mediterranean and allowed obtaining digital bathymetric models with centimetric average accuracy and precision and root mean square errors within a few decimetres. The platform presented in this work can be employed to obtain first-order bathymetric products, enabling the contextual establishment of the depth accuracy of the final products.

Recovering Bathymetry Using BP Neural Network Combined with Modified Gravity–Geologic Method: A Case Study in the South China Sea

The gravity–geologic method (GGM) is widely used for bathymetric predictions. However, the conventional GGM cannot be applied in regions without actual bathymetric data. The modified gravity–geologic method (MGGM) enhances the accuracy of bathymetric models by supplementing short-wavelength gravity anomalies with an a priori bathymetric model, but it overlooks the significance of actual bathymetric data in the prediction process. In this study, we used the BP neural network (BPNN), incorporating shipborne depth soundings and coastline data as zero-depth estimates combined with the MGGM to produce a bathymetric model (BPGGM_BAT) for the South China Sea (105°E–122°E, 0°N–26°N). The results indicate that the BPGGM_BAT model decreases the root-mean-square (RMS) of bathymetry differences from 154.33 m to approximately 140.43 m relative to multibeam depth data. Additionally, the RMS differences between the BPGGM_BAT model and multibeam depth data show further improvements of 19.63%, 20.10%, and 19.54% when compared with the recently released SRTM15_V2.6, GEBCO_2022, and topo_V27.1 models, respectively. The precision of the BPGGM_BAT model is comparable to that of the SDUST2023BCO model, as verified using multibeam depth data in open sea regions. The BPGGM_BAT model outperforms existing models with RMS differences of 8.54% to 32.66%, as verified using Electronic Navigational Chart (ENC) bathymetric data in the regions around the Zhongsha and Nansha Islands. A power density analysis suggests that the BPGGM_BAT model is superior to the MGGM_BAT model for predicting seafloor topography within wavelengths shorter than 15 km, and its performance is closely consistent with that of the topo_V27.1 and SDUST2023BCO models. Overall, this integrated method demonstrates significant potential for improving the accuracy of bathymetric predictions.

Assessing the feasibility of mapping channel bathymetry of long river corridors from hyperspectral data across a range of flow conditions

ABSTRACT Hyperspectral imagery is a promising tool for bathymetric retrieval because that might be more robust than traditional imagery. However, in fluvial sciences, this approach has only been applied to short reaches for which sparse bathymetric information is available and studies extrapolating such models to longer reaches are lacking. In this paper, we use linear regression and random forest (RF) regression to derive bathymetric estimates from hyperspectral data acquired under two different flow conditions: an unmanned aerial vehicle (UAV) acquisition at 27 m3.s−1 (base flow) and an airplane acquisition at 127 m3.s−1 (mean flow) for reaches of 0.35 km and 20 km, respectively. Using a 2D bathymetric model based on a green LiDAR acquisition, we generate calibration and validation data for the 20 km reach and the two flow conditions. First, we show that the airplane dataset is able to retrieve base flow bathymetry with a similar accuracy to the UAV acquisition (5–10 cm) despite having been acquired at a higher discharge. Then, we show that we can extrapolate bathymetric models calibrated on a smaller reach to the 20 km study reach with a loss in accuracy compared to an RF model calibrated on the study reach (20–30 cm vs. 5–10 cm). By analysing the errors from our models, we show that the accuracy of the extrapolated models dropped due to an underprediction of deep pool areas and changes in the optical properties of the water column and substratum across the study reach, with some spectral bands performing more poorly. The better performance of the random forest calibrated on the study reach led us to suggest using multi-band machine learning models when attempting bathymetric predictions over long river corridors, and highlights the need to cover the range of environmental conditions in the target reach when acquiring field data.

Bathymetric analysis using multifrequency multibeam echosounder

Making a nautical chart for safe navigation is a bathymetric survey’s primary goal. Multifrequency MBES have been developed over the last few decades, and their introduction has dramatically improved the efficiency, accuracy, and spatial resolution of coastal and ocean mapping. The goal of multifrequency MBES is to increase the subsurface’s detection resolution. To obtain an accurate picture of the seabed, the user can lessen the impact of this subsidence by running surveys in three different modes at once. With the help of multifrequency MBES, this study will analyze bathymetry in shallow coastal waters. The digital bathymetric model’s (DBM) frequencies are remarkably close. The depth value of the study site ranges from –20 m to–70 m with reference to lowest water surface (LWS) based on the produced DBM. Generally, the difference between 100 kHz, 200 kHz, and 400 kHz is as small as 0–30 cm, and a small part is 30–60 cm. The volume between frequencies for an area of 1 ha is between 90 m3 to 440 m3. If the thickness of the dredged sediment is 1 m, then the difference in volume between frequencies is less than 5%. The bathymetry difference between 100 kHz and 400 kHz frequencies to –10 cm is dominated by the region of 0 cm. Dredging volume inter frequency ranges from 0.042 m3/m2 to 0.068 m3/m2.

Refined Bathymetric Prediction Based on Feature Extraction of Gravity Field Signals: BATHY‐FE

AbstractThe resolution of altimetry‐derived gravity signals renders traditional bathymetry inversion methods inadequate for detecting short‐wavelength bathymetric features. This study presents a new global seafloor topography model by merging regional models constructed via a deep learning approach recently developed by the authors. Feature extraction techniques were used to refine each regional bathymetric model, resulting in sharpened features that are fuzzily revealed (or not seen) in global bathymetric models such as GEBCO_2023 and SRTM15+V2, especially in areas close to the poles. This is proof that, with regard to current gravity field products, conventional bathymetry inversion methods are weaker at generalizing to uncharted locations of the global ocean floor. At test points, the global seafloor model has mean error and error standard deviation of 1.75 ± 81.15 m. The refined seafloor topography can be used to supplement existing seafloor maps, especially in the polar regions. It is useful for studying seafloor geography, and also for studies in which seafloor ruggedness is required.

Arctic puzzle: Pioneering a northern shrimp (Pandalus borealis) habitat model in Disko Bay, West Greenland

Recent advancements in spatial modelling leverage remote sensing data and statistical species-environment relationships to forecast the distribution of a specific species. Our study focuses on Disko Bay in West Greenland, recognized as a significant marine biodiversity hotspot in the region. We conducted comprehensive analyses using multiple datasets spanning from 2010 to 2019, incorporating shrimp and fish surveys, commercial shrimp fishery catches, high-resolution (25 × 25 m) multibeam bathymetry and backscatter data along with a medium-resolution (200 × 200 m) bathymetric model, measured and modelled oceanographic data, and satellite chlorophyll data. Through multivariate regression analysis, we tested the significance of various physical factors (seafloor depth, sediment class, bottom water temperature, bottom water salinity, bottom current velocity, space, and time), biological factors (chlorophyll a, Greenland halibut (Reinhardtius hippoglossoides)), and anthropogenic impact (shrimp fishery; standardized catch per unit effort) on the density of northern shrimp in the area. Our results indicate a significant association between northern shrimp density, seafloor depth, and sediment class, explaining 36 % of the variation in shrimp density. Subsequently, we developed a high-resolution (optimized) spatial linear mixed–effect model to map the distribution of northern shrimp across Disko Bay, representing the first model of its kind developed for an Arctic area. The optimal habitat for northern shrimp is characterized by medium-deep waters (approximately 150–350 m), turbulent conditions, and mixed sediments, predominantly located in the northern and southern regions of Disko Bay. Notably, the northern region hosts a relatively diverse benthic community, with northern shrimp and sponges as the primary contributors of epibenthic biomass. This novel high-resolution model significantly enhances our understanding of the physical drivers and detailed spatial patterns influencing the distribution of northern shrimp in the Arctic.

Exploring Ancillary Parameters for Quantifying Interpolation Uncertainty in Digital Bathymetric Models

The oceans remain one of Earth’s most enigmatic frontiers, with approximately 75% of the world’s oceans still unmapped. To create a seamless digital bathymetric model (DBM) from sparse bathymetric datasets, interpolation is employed, but this introduces uncertainties of unknown nature. This study aims to estimate and characterize these uncertainties, which is important in many fields, particularly nautical charting, and navigational safety. Complete seafloor coverage sonar depth data for five testbeds that varied in slope and roughness are sampled at a range of densities (1% to 50%) and interpolated across an entire area using Spline, Inverse Distance Weighting (IDW), and Linear interpolation. The resulting uncertainties are evaluated from both scientific and operational perspectives. Employing linear regression and machine learning techniques, the relationships between these uncertainties and ancillary parameters (distance to the nearest measurement, seabed roughness, and slope) are examined for quantifying and characterizing the interpolation uncertainty. Results indicate insignificant operational differences among the three interpolators in depth estimation, as well as the statistical significance of the examined uncertainty predictors. Additionally, findings suggest the potential presence of unaccounted-for factors shaping uncertainty, yet this work lays a foundational understanding for improving the estimate of uncertainty in DBMs.

The geoecological analysis of Lake Tuchne (Volyn Polissia) and assessment of sapropel reserves in it


 
 
 The administrative-territorial reform in Ukraine, which began in 2015, opened up prospects for newly created territorial communities to determine the resource potential of local territories under control and develop strategies for balanced nature use and their progressive development. The lakes, which have a significant potential for sapropel resources, take an important place in the Polissia region of Ukraine. Given the above, there is a need for a comprehensive study of specific lakes and justification of recommendations for the rational use and protection of their water and sapropel resources. The object of our research is Lake Tuchne, Volyn Polissia. The catchment area of Lake Tuchne is 10.05 km2, and the reservoir itself is 0.39 km2. A bathymetric model of the lake was built with a depth interval of 0.5 m. The vegetation index (NDVI) for the reservoir over the past five years has been varying from -0.53 (12/02/2021) to +0.48 (07/15/2021). The research presents a stratigraphic section of the reservoir sediments. The area of accumulated sapropel is 20.5 hectares, the average thickness is 4.46 m, and the maximum is 10.0 m. The ash content of sapropel varies within 17.5-37.5% (on dry substance). The content of Fe2O3 in the core ranges from 2.53% to 7.26%. The distribution of CaO compounds in the samples ranges from 2.09 to 8.12%. The CO2 content varies from 1.97 to 9.72%. The concentration of Р2О5 in sapropel samples is 0.19-0.53%. The distribution of S in sapropel deposits ranges from 0.23 to 1.09%. The pH (salt extract) distribution in the core samples varies from moderately alkaline (7.63) to close to neutral – 5.97. A digital landscape map of the lake was constructed with the selection of the littoral and sublittoral-profundal aqua sub-areas and five types of aquafacies. A landscape-metric analysis of the natural and aquatic complex of the lake was carried out. The potential of water resources of Tuchne lake is 827.0 thousand m3. Sapropel reserves of category A are 914,000 m3, and based on a conditional 60.0% humidity, they are 155,000 tons. The species composition of sapropel is represented by zoogenic, organo-ferrous, and organo-limestone species. The total stock of sapropel in the lake basin is 52.5% of its volume. The reservoir is promising for extracting sapropel due to its quality characteristics and hydro-technical prerequisites for operation. Mined sapropel can be used in agriculture, medicine, recreation, etc. Implementing this task will improve the geoecological state of the lake and the quality of its water resources.
 
 

Modern digital hydrography and the new international hydrographic organisation bathymetric survey standards

The current state of the International Hydrographic organization bathymetric survey Standards S44 (6.1.0) is examined in this study. The introduction of the S44 survey Standards fourth edition in 1998 heralded the beginning of a new era of modern digital hydrography based on the concept of the survey orders resulting from implementation of the Global Navigation Satellite Systems of positioning and hydrographic information systems. The fundamental changes between the 4th and 6th editions of the Standards for Hydrographic Surveys (S-44) are described. The differences between the 5th and 6th editions of the Standards for Hydrographic Surveys (S-44) are evaluated. Particular attention is paid to the conceptual evolution of such definitions as “bathymetric model” and “ features detection”. The main new S44 (6.1.0) survey Standards paradigm became the concept of “bathymetric coverage” and the rejection of the “ full sea floor coverage” concept which was introduced in the previous 5th edition. Introducing the “bathymetric coverage” and “ feature search” notions in the 6th S44 (2022) survey Standards provides the possibility to avoid the implementation of such previously important hydrographic definition as “line spacing”. It is especially emphasized that the introduction and combined application of such definitions as “bathymetric coverage”, “ feature detection” and “ feature search” necessitate the usage in the 6th S44 (2022) survey Standards of the new special tool named “specification matrix”. This tool is important for hydrographic survey drafting taking into consideration the various stakeholder requirements as well as providing a short hydrographic survey description. The introduction of gridding concept in hydrography provided in the S44 (6.1.0) survey Standards for the first time is also mentioned. Critical comments on the interpretation of gridding methods provided in the annex D which are not considered as the part of the Standards are presented. Some recommendations concerning general quality control requirements and survey posteriori quality control provided in the annex C and B are also commented on. It should be especially emphasized that three last editions of S44 Standards use the “standard deviation” term without specifying its confidence level for position uncertainty (Total Horizontal Uncertainty — THU) calculation. The position uncertainty (2D) quantities are stated as “standard deviation” multiplied by coefficient 2.45. The “standard deviation” term needs additional explanation and clarification; it should not be confused with the widely used term «position standard deviation =distance root mean squared». It is also pointed out the insufficient study of the 3D hydrographic technology provisions presented in the 6th S44 survey Standards edition associated with the alternative method of sea level correction determination based on the precise ellipsoid (geodetic) heights measurements of the tide gauges benchmarks and chart datum using GNSS observations. The necessity of inclusion of the requirements for survey posteriori quality control as the result of survey data post-processing, using statistical comparison of check lines crossing the regular lines, is substantiated. Finally, the urgency of developing new Russian hydrographic standards and specifications based on IHO S44 (6.1.0) on the basis of modern technology is emphasized.

High-precision 1′ × 1′ bathymetric model of Philippine Sea inversed from marine gravity anomalies

Abstract. The Philippine Sea, located at the edge of the northwestern Pacific Ocean, possesses complex seabed topography. Developing a high-precision bathymetric model for this region is of paramount importance, as it provides fundamental geoinformation essential for Earth observation and marine scientific research, including plate motion, ocean circulation, and hydrological characteristics. The gravity–geologic method (GGM), based on marine gravity anomalies, serves as an effective bathymetric prediction technique. To further strengthen the prediction accuracy of conventional GGM, we introduce the improved GGM (IGGM). The IGGM considers the effects of regional seafloor topography by employing weighted averaging to more accurately estimate the short-wavelength gravity component, along with refining the subsequent modeling of long-wavelength gravity component. In this paper, we focus on seafloor topography modeling in the Philippine Sea based on the IGGM, combining shipborne bathymetric data with the Scripps Institution of Oceanography (SIO) V32.1 gravity anomaly. To reduce computational complexity, the optimal parameter values required for IGGM are first calculated before the overall regional calculation, and then, based on the terrain characteristics and distribution of sounding data, we selected four representative local sea areas as the research objects to construct the corresponding bathymetric models using GGM and IGGM. The analysis indicates that the precision of the IGGM models in four regions is improved to varying degrees, and the optimal calculation radius is 2′. Based on the above finding, a high-precision 1′×1′ bathymetric model of the Philippine Sea (5–35° N, 120–150° E), known as the BAT_PS model, is constructed using IGGM. Results demonstrate that the BAT_PS model exhibits a higher overall precision compared to the General Bathymetric Chart of the Oceans (GEBCO), topo_25.1, and DTU18 models at single-beam shipborne bathymetric points.

METHODS FOR SATELLITE DERIVED BATHYMETRY FROM SENTINEL-2 IMAGES: A COMPARISON

Abstract. In recent decades, research has been developed to estimate near-shore bathymetry depth values using satellite imagery. Visible and infrared bands are used to derive elevation profile estimates, so to obtain bathymetric in rapid way without mobilisation of persons or equipment and saving the costs. For consequence, Satellite Derived Bathymetry (SDB) is seen as a valid approach for shallow waters survey: strongly supported by the activity of scholars and researchers, multiple methods are available in the literature. This article aims to investigate and compare different SDB methods for sea depth extraction from Sentinel-2 satellite multispectral images, with particular attention to the accuracy of the results. The experiments are conducted on imagery including Blue, Green, Red and Near Infrared bands, with 10 m resolution, concerning the Bay of Pozzuoli (Italy). After removing the glint, the effects caused by the reflection of sunlight through single scattering from sea surface, three methods are applied: Band Ratio method (BRM), 3rd-degree polynomial regression line method (3DPM), and principal component analysis method (PCAM). 3DPM can be seen as a variant of the BRM where the linear law that interprets the correlation between the band ratio values and the depth values is replaced by the third order function. Models are trained using depth data extracted from an Electronic Navigational Chart (ENC) at 1:7,500 scale, which is also used to verify result accuracy. The experiments demonstrate that the 3DPM is better able to obtain a more precise bathymetric model, confirming the greater adaptability of the 3rd order function to interpretate the variability of the interaction of light with water along the water column.

A regional digital bathymetric model fusion method based on topographic slope: A case study of the south China sea and surrounding waters

High-resolution seafloor topography is important in scientific research and marine engineering in regard to marine resource development and environmental protection monitoring. In this study, multi-dimensional comparisons were made between GEBCO_2022, SRTM15_V2.5.5, SRTM30_PLUS, SYNBATH_V1.0, ETOPO_2022, and topo_25.1 in the South China Sea and surrounding waters (SCS). This study has found that ETOPO_2022 had the best overall accuracy and reliability. Based on the results of the model accuracy analysis and by considering the topographic slope, ETOPO_2022, GEBCO_2022, and SRTM15_V2.5.5 were weighted and fused to form a fusion model. The error of the fusion model was 94.80% concentrated in (−100–100 m). When compared with GEBCO_2022, SRTM15_V2.5.5, SRTM30_PLUS, SYNBATH_V1.0, ETOPO_2022, and topo_25.1, the RMSE was reduced by 2%, 9%, 62%, 15%, 1%, and 73%, respectively. The slope-based weighted fusion method has been shown that it can overcome the limitations of a single data source and provide a reference for timely reconstruction and updating of large-scale seafloor topography.

Coastal bathymetry in central Dronning Maud Land controls ice shelf stability

Knowledge of the bathymetry of Antarctica’s margins is crucial for models and interpretations of ice-ocean interactions and their influence on ongoing and future sea level change, but remains patchy where ice shelves and multi-year sea ice block measurements. Here, we present a bathymetric model for the central Dronning Maud Land margin, based on a constrained inversion of airborne gravity data. It shows the cavities beneath the region’s two ice shelves to be much deeper than in existing bathymetric compilations, but to be shielded from Warm Deep Water ingress and basal melting by the presence of shallow bathymetric sills along the continental shelf. Over areas of multi-year sea ice, the model returns bathymetric estimates of similar accuracy to gravity interpolation-based methods over open water. Airborne gravity thus presents an opportunity to bathymetrically map hundreds of thousands of square kilometres of the most inaccessible margins of Antarctica at resolutions adequate for regional and global oceanographic and glaciological modelling and interpretation.

Satellite bathymetry estimation in the optically complex northern Baltic Sea

Satellite bathymetry estimation (SBE) is a cost-effective method for producing depth information for shallow marine and aquatic areas. However, most SBE methods have been developed and are refined in clear water conditions and their feasibility in optically complex waters is poorly understood. This hinders the use of SBE in optically complex waters.To test the feasibility of SBE in optically complex waters, we used multi-temporal Sentinel-2 (S-2) imagery and in-situ measured depth information from dive computers to fit bathymetric models for 14 study areas in the northern Baltic Sea. We compared different methods of using multi-temporal S2-scenes, atmospheric correction, and statistical modeling, as well as different band combinations to define the optimal model for each study area. We tested the dependence of the depth estimation on 13 environmental variables with potential influence on the accuracy of the SBE.The optimal bathymetric models differed among the study sites, with the differences manifested in different requirements for atmospheric correction, band combination, statistical model, and the use of multiple S2-scenes. The coefficients of determination (R2) of the optimal models varied between 0.42 and 0.79, with mean (X) R2 equaling to 0.65. The coefficients of determination were positively related to the proportion of sea within the 5 km search kernel and to the exposure of the study site. At the predicted depth of 2 m, the differences between observed and predicted depths varied between 0.11 and 0.60 m (X = 0.35 m). At the predicted depth of 1 m, the differences varied between 0.04 and 0.34 m (X = 0.21 m).SBE provides accurate depth estimates in optically complex waters, even when in-situ calibration data are limited. However, the maximum detectable depth obtained with SBE in optically complex waters is significantly lower than in clear water conditions. The SBE process can be fine-tuned to the specific characteristics of an area, improving its performance in optically complex conditions. In particular, the identification of the optimal band combination is critical for successful SBE. The depth estimates produced in this study can be used in a variety of applications that require accurate information on the seafloor bathymetry.

Pixel-level bathymetry mapping of optically shallow water areas by combining aerial RGB video and photogrammetry

Combining geometric and optical methods based on a low-cost UAV platform can achieve high-resolution bathymetry mapping without ground-truth data in optically shallow water areas. However, with the increasing spatial resolution, water surface fluctuation interferes with the imaging. In this study, we propose a bathymetry mapping approach that combines video and geometric-optical principles. The multi-sampling of the video data allows for a temporal averaging window of each pixel. A motion-based frame registration method was developed to compose an image from a video acquired by UAV push-broom sampling to mitigate the instantaneous changes caused by water surface fluctuations. The composite images were used for bathymetry mapping using data from the photometric point clouds from the UAV images for calibration. Then, the improved effect of video multi-sampling on the optical bathymetric model was evaluated by comparing the results of bathymetric inversion based on single images and video composite images. An evaluation case in the coastal area of Hainan Island demonstrates that results based on composite images processed with three optical bathymetric models of different complexity increased the coefficient of determination from 0.8111, 0.8652, 0.9255 to 0.8652, 0.8750, 0.9363, and reduced the root mean square error from 0.234 m, 0.192 m, 0.143 m to 0.197 m, 0.178 m, 0.133 m, respectively. Qualitatively, using composite images from aerial RGB videos for bathymetry mapping effectively removes radiative anomalies due to wave focusing or reflection and provides a more accurate description of underwater objects' shape than single images.

New digital bathymetric model of Lake Onego (Verhne-Svirskoe reservoir): development, verification, application

A new digital bathymetric model of Lake Onego (Verhne-Svirskoe Reservoir) has been developed based on navigation charts. Verification of the model was carried out by comparing the modeled bathymetry with the cartographic materials, echo sounder measurements, and previous bathymetric models. Spatial resolution of the developed model is 50 m in plane and 2 m in depth. Despite these identified deviations in depth, the model has a good agreement between raster values and measured depth, which can be considered sufficient to calculate the main morphometric characteristics of the lake and solve other applied tasks. Morphometric characteristics of the water body (within the boundaries of the lake and reservoir separately) and its limnic regions were updated based on the developed digital model. In particular, values of the water area, water volume, average and maximum depth, littoral area (up to isobaths 2, 5, and 10 m) of limnic regions were specified for the first time based on the developed bathymetric model. The Central and South Onego are the largest regions by water area, while the Large Onego, the Central part of the lake and the Povenets Bay are the deepest. The largest areas of the lake in terms of the littoral area value are the Central part and the South Onego, while the maximum percentage of the littoral area was observed in the Kizhi skerriesand for most of thenorthern bays of the lake. The digital bathymetric model was applied to reconstruct the lake development in the post-glacial period, as well as in mapping the Quaternary deposits of the lake basin. The digital model can also be used as a basis for 3D modeling of geomorphologic, thermo-hydrodynamic and sedimentation processes.

High-Resolution Digital Bathymetric Model (DBM) With Uncertainty Reconstructed by Deep Learning With Dropout

High-Resolution Digital Bathymetric Model (DBM) With Uncertainty Reconstructed by Deep Learning With Dropout

Research on Multibeam Bathymetric Strategy Based on Optimization Algorithm and Geometric Analysis

This paper presents a comprehensive examination of the application of multibeam bathymetric strategies, specifically focusing on the transition from two-dimensional cross-sections to three-dimensional space. Firstly, a two-dimensional bathymetric model is established through geometric and mathematical analysis, considering the influence of the seabed slope. This allows for accurate calculation of the coverage width and overlap rate. Secondly, the model is expanded to incorporate three-dimensional sea conditions, considering multiple factors such as the relative angle between the survey line direction and the seabed slope. Lastly, an optimization algorithm is employed to minimize the total length of the survey line. The optimal solution is determined to be 206 survey lines with a spacing of 36.05m between them, resulting in a total length of the shortest survey line of 7425.7m.

Methodology for Creating a Digital Bathymetric Model Using Neural Networks for Combined Hydroacoustic and Photogrammetric Data in Shallow Water Areas.

This study uses a neural network to propose a methodology for creating digital bathymetric models for shallow water areas that are partially covered by a mix of hydroacoustic and photogrammetric data. A key challenge of this approach is the preparation of the training dataset from such data. Focusing on cases in which the training dataset covers only part of the measured depths, the approach employs generalized linear regression for data optimization followed by multilayer perceptron neural networks for bathymetric model creation. The research assessed the impact of data reduction, outlier elimination, and regression surface-based filtering on neural network learning. The average values of the root mean square (RMS) error were successively obtained for the studied nearshore, middle, and deep water areas, which were 0.12 m, 0.03 m, and 0.06 m, respectively; moreover, the values of the mean absolute error (MAE) were 0.11 m, 0.02 m, and 0.04 m, respectively. Following detailed quantitative and qualitative error analyses, the results indicate variable accuracy across different study areas. Nonetheless, the methodology demonstrated effectiveness in depth calculations for water bodies, although it faces challenges with respect to accuracy, especially in preserving nearshore values in shallow areas.