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The conventional technique for positioning seafloor geophones in ocean bottom seismic exploration encounters several challenges, including the significant impact of outliers on positioning results, underutilization of high-precision observations, and low efficiency in real-time data processing. These issues inevitably affect the quality of seismic exploration outcomes. To address these challenges and enhance the accuracy of geophone positioning, this paper proposes a rigorous real-time acoustic positioning method for geophones based on sequential adjustment and Baarda's outlier detection approach. The proposed method comprises three key steps: grouping the original acoustic observations, constructing the intra-group acoustic positioning model, and synthesizing the positioning results across the different groups. The validity and practicality of this approach are confirmed through a simulation experiment as well as the field experiment conducted in the Bohai Sea, China. The results demonstrate that the proposed method effectively eliminates outliers in the original observations and maximizes the utilization of high-quality observations. Compared to traditional acoustic positioning methods, it significantly reduces positioning errors from meters to decimeters, and in some cases can achieve centimeter-level precision. When the sound velocity profile in the operating sea area is measured, the method can attain the posterior standard deviation at the millimeter level and positioning errors within 10 cm. When the sound velocity profile is unknown, the method can achieve the posterior standard deviation at centimeter-level and positioning errors of approximately 20 cm. [ABSTRACT FROM AUTHOR]

With the development of ultra-wide coverage technology, multibeam echo-sounder (MBES) system has put forward higher requirements for localization accuracy and computational efficiency of ray tracing method. The classical equivalent sound speed profile (ESSP) method replaces the measured sound velocity profile (SVP) with a simple constant gradient SVP, reducing the computational workload of beam positioning. However, in deep-sea environment, the depth measurement error of this method rapidly increases from the central beam to the edge beam. By analyzing the positioning error of the ESSP method at edge beam, it is discovered that the positioning error increases monotonically with the incident angle, and the relationship between them could be expressed by polynomial function. Therefore, an error correction algorithm based on polynomial fitting is obtained. The simulation experiment conducted on an inclined seafloor shows that the proposed algorithm exhibits comparable efficiency to the original ESSP method, while significantly improving bathymetry accuracy by nearly eight times in the edge beam. [ABSTRACT FROM AUTHOR]

Understanding the topographic patterns of the seafloor is a very important part of understanding our planet. Although the science involved in bathymetric surveying has advanced much over the decades, less than 20% of the seafloor has been precisely modeled to date, and there is an urgent need to improve the accuracy and reduce the uncertainty of underwater survey data. In this study, we introduce a pretrained visual geometry group network (VGGNet) method based on deep learning. To apply this method, we input gravity anomaly data derived from ship measurements and satellite altimetry into the model and correct the latter, which has a larger spatial coverage, based on the former, which is considered the true value and is more accurate. After obtaining the corrected high-precision gravity model, it is inverted to the corresponding bathymetric model by applying the gravity-depth correlation. We choose four data pairs collected from different environments, i.e., the Southern Ocean, Pacific Ocean, Atlantic Ocean and Caribbean Sea, to evaluate the topographic correction results of the model. The experiments show that the coefficient of determination (R2) reaches 0.834 among the results of the four experimental groups, signifying a high correlation. The standard deviation and normalized root mean square error are also evaluated, and the accuracy of their performance improved by up to 24.2% compared with similar research done in recent years. The evaluation of the R2 values at different water depths shows that our model can achieve performance results above 0.90 at certain water depths and can also significantly improve results from mid-water depths when compared to previous research. Finally, the bathymetry corrected by our model is able to show an accuracy improvement level of more than 21% within 1% of the total water depths, which is sufficient to prove that the VGGNet-based method has the ability to perform a gravity-bathymetry correction and achieve outstanding results. [ABSTRACT FROM AUTHOR]

Three long-term fixed acoustic Doppler current profilers were first used for investigating the vertical structure of tidal currents in Xuliujing Section of Changjiang River Estuary. Moreover, three different periods (spring, summer and fall) were also considered for investigating seasonal variations. The semi-diurnal tides were the most energetic, with along-channel speed of up to 80 cm/s for M2 constituent, which dominates at all stations with percent energy up to 65%–75% during seasons. The shape of tidal ellipses of the most energetic semi-diurnal constituent M2 showed obvious polarization of the flow paralleling to the riverbank, with the minor semi-axis being generally less than 20% of the major one. The maximum velocity of mean current is appeared in top layers at all the three stations, and the velocity decreased with the depth. The seasonal variations of direction are also observed, which is probably caused by complex local topography since the erosion and deposition in riverbed. Observed vertical variation of four parameters of M2 ellipses, agreed well with the optimally fit frictional solutions in top and middle layers. However, there was an obvious difference between frictional model and observed data in the lower water column. Discrepancies are probably on account of stratification, which strengthens in summer and fall due to the freshening influence of the Changjiang River Estuary outflow. [ABSTRACT FROM AUTHOR]

Today is a reality that the novel coronavirus SARS-Cov-2 has become a global pandemic. For this reason, the study of real microscopic images of this coronavirus is of great importance, as it allows us to carry out a more precise research on it. However, as we pointed out in a former paper as reported by Roberto Rodríguez (SARS-CoV-2: Enhancement and Segmentation of High-Resolution Microscopy Images. Part I", Sent to Signal, Image and Video Processing Video Processing, Springer, New York, 2020), many times these microscopic images present some blurring problems, which are always susceptible to be improved. The aim of this work is to carry out a theoretical analysis of the proposed algorithms to enhancement and segmentation of these microscopic images, which is important for the design and development of future algorithms before new epidemics. [ABSTRACT FROM AUTHOR]