Search

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]

Conventional sediment classification methods based on Multibeam Echo System (MBES) data have low accuracy since the correlation between features and sediment has not been fully considered. Moreover, their poor resistance to the residual error of MBES backscatter strength (BS) processing also degrades their performances. Toward these problems, we propose a seabed sediment classification method using spatial statistical features extracted from angular response curve (ARC), topography, and geomorphology. First, to reduce interference of noise and residual error of beam pattern correction, we propose a robust method combining the Generic Seafloor Acoustic Backscatter (GSAB) model and Huber loss function to estimate the parameters of ARC which is strongly correlated with seabed sediments. Second, a feature set is constructed by AR features composed of GSAB parameters, BS mosaic and its derivatives, and seabed topography and its derivatives to characterize seabed sediments. After that, feature selection and probability map acquisition are employed based on the random forest algorithm (RF). Finally, a denoising and final sediment map generation method is proposed and applied to probability maps to obtain the sediment map with reasonable sediment distribution and clear boundaries between classes. We implement experiments and achieve the classification accuracy of 93.3%, which verifies the validity of our method. [ABSTRACT FROM AUTHOR]

Sailing‐circle GPS‐acoustic method, prevalently applied for measuring ocean floor crustal deformation, allows the precise positioning of an underwater transponder in a global frame. In the method, distance estimation requires prior information, such as transponder depth or incident angle. However, sometimes the prior information is inaccurate or absent. Under these circumstances, this study proposes two kinds of positioning methods that utilize an iteration composed of an inverse or direct ray tracing method and the least squares approach to constantly modify the initial data to obtain an accurate incident angle and coordinates of an underwater transponder. Even though an accurate incident angle can be got and there is no time delay or spatial‐temporal heterogeneities of SSP (sound speed profile), SSP density and geometric incident angle still influence ranging errors and PDOP (position dilution of precision) during distance intersection. In order to reduce the two kinds of impacts, this paper deduces a positioning error model based on the principle of sailing‐circle method. On the one hand, the proposed algorithms and model are verified by a simulated experiment for various SSP density, geometric incident angle, and transponder depth. On the other hand, in a practical experiment, average positioning accuracy of 0.025 m and vertical accuracy of −0.02 m are achieved, and the improved accuracy is about 85% higher than that of the ordinary sailing‐circle method. Plain Language Summary: GPS‐acoustic method is a kind of method that utilizes GPS, which is usually placed in our vehicle for navigation, and sonars, which can measure the distance between two points in water, for determining the position of a fixed point underwater. By continuously monitoring the location of this point, the position change is obtained and the change can help us predict earthquakes and study earthquakes. Among GPS‐acoustic methods, sailing‐circle method that the vessel equipped with GPS and sonars drives a circle centered on the underwater point can gain the best positioning accuracy. However, the method requires accurate prior information, such as transponder depth or incident angle. When the prior information is inaccurate or absent, accuracy will decrease and therefore, we propose two kinds of new sailing‐circle methods to deal with this situation. And it is noted that whether the prior information is accurate or not, there will be errors in the final positioning results caused by instruments, algorithms, and environments. Consequently, based on the principle of sailing‐circle method, we deduce the positioning error model to guide us design various parameters in a practical experiment, such as sailing radius, and improve the positioning accuracy. The ultimate goal of this paper is to improve the positioning accuracy of sailing‐circle method. To check the effectiveness of the two proposed methods and model, on the one hand, they are verified by a simulated experiment for various situations. On the other hand, in a practical experiment, positioning accuracy of the proposed method is about 85% higher than that of the ordinary method. Key Points: Two new sailing‐circle GPS‐acoustic methodsPositioning error model with respect to geometric incident angle (or sailing radius) and the density of sound speed profile is deducedGeometric incident angle should be controlled within 50° and the smaller the density of sound speed profile, the better [ABSTRACT FROM AUTHOR]