scholarly journals Joint Tracking of Source and Environment Using Improved Particle Filtering in Shallow Water

2021 ◽  
Vol 9 (11) ◽  
pp. 1203
Author(s):  
Miao Dai ◽  
Yaan Li ◽  
Jinying Ye ◽  
Kunde Yang
Keyword(s):  
Shallow Water ◽  
Source Localization ◽  
Particle Filtering ◽  
Source Parameters ◽  
Simulated Data ◽  
Environmental Parameters ◽  
Performance Comparison ◽  
Depth Range ◽  
Parameter Uncertainties ◽  
Joint Tracking

Shallow water is a complex sound propagation medium, which is affected by the varying spatial–temporal ocean environment. Taking this complexity into account, the classical processing techniques of source localization and environmental inversion may be improved. In this work, a joint tracking approach for the moving source and environmental parameters of the range-dependent and time-evolving environment in shallow water is presented. The tracking scheme treats both the source parameters (e.g., source depth, range, and speed) and the environmental parameters (e.g., water column sound speed profile (SSP) and sediment parameters) at the source location as unknown variables that evolve as the source moves. To counter sample impoverishment and robustly characterize the evolution of the parameters, an improved particle filter (PF), which is an extension of the standard PF, is proposed. Two examples with simulated data in a slowly changing environment and experimental data collected during the ASIAEX experiment are utilized to demonstrate the effectiveness of the joint approach. The results show that we were able to track the source and environmental parameters simultaneously, and the uncertainties were evaluated in the form of time-evolving posterior probability densities (PPDs). The performance comparison confirms that the improved PF is superior to the standard PF, as it can reduce the parameter uncertainties. The tracking capabilities of the improved PF were verified with high accuracy in real-time source localization and well-estimated rapidly varying parameters. Moreover, the influence of different particle numbers on the improved PF tracking performance is also illustrated.

scholarly journals Joint Inversion for Sound Speed Field and Moving Source Localization in Shallow Water

2019 ◽  
Vol 7 (9) ◽  
pp. 295 ◽  
Author(s):  
Dai ◽  
Li ◽  
Yang
Keyword(s):  
Kalman Filter ◽  
Shallow Water ◽  
Source Localization ◽  
Sound Speed ◽  
Joint Inversion ◽  
Source Parameters ◽  
Water Environment ◽  
Depth Range ◽  
Moving Source ◽  
The Impact

This paper develops a joint approach for time-evolving sound speed field (SSF) inversion and moving source localization in shallow water environment. The SSF is parameterized in terms of the first three empirical orthogonal function (EOF) coefficients. The approach treats both first three EOF coefficients and source parameters (e.g., source depth, range and speed) as state vectors of evolving with time, and a measurement vector that incorporates acoustic information via a vertical line array (VLA), and then the inversion problem is formulated in a state-space model. The processors of the extended Kalman filter (EKF) and ensemble Kalman filter (EnKF) are used to estimate the evolution of those six parameters. Simulation results verify the proposed approach, which enable it to invert the SSF and locate the moving source simultaneously. The root-mean-square-error (RMSE) is employed to evaluate the effectiveness of this proposed approach. The interfile comparison shows that the EnKF outperform the EKF. For the EnKF, the robustness of the approach under the sparse vertical array configuration is verified. Moreover, the impact of the source-VLA deployment on the estimation is also concerned.

TOMOGRAPHIC INVERSION FOR GEOACOUSTIC PARAMETERS IN SHALLOW WATER

2000 ◽  
Vol 08 (02) ◽  
pp. 285-293 ◽  
Author(s):  
A. TOLSTOY
Keyword(s):  
Shallow Water ◽  
Simulated Data ◽  
Environmental Parameters ◽  
Inversion Method ◽  
Future Directions ◽  
Tomographic Inversion ◽  
The Matrix ◽  
Matrix Condition Number ◽  
Matrix Condition ◽  
Tomography Inversion

This paper examines the linearized tomographic inversion of simulated data for a shallow water, multi-array, multi-source scenario. The environments represented include simulations of (1) highly idealized constant regions as well as (2) the Haro Strait Test of June 1996 which displays range, depth, and azimuthal variability, i.e., 3-D dependence on environmental parameters where these parameters can include water depths and multiple sediment sound-speed profiles, densities, depths, and attenuations. This tomographic inversion method is independent of the number of parameters to be determined. However, the method does assume that some inversion method (such as RIGS, simulated annealing, genetic algorithms, etc.) has already estimated range-independent average source-to-receiver environmental parameters. These average parameters are then input into the tomographic inversion which relies on a matrix of path-cell distances. The matrix condition number, Λ, is a determining feature for the inversion accuracy where Λ is a function of source and receiver distributions and their subsequent path distances through the region cells. Additionally, the accuracy of the input estimates for the average geoacoustic properties is also an important factor in the final 3-D tomographic inversion accuracy. Results using this (linearized) tomography inversion method show a potential for excellent error estimates (much less than 1%) for the environmental parameters assuming exact, idealized input values. Errors are still quite reasonable (well under 10%) if more realistic, i.e., erroneous, input values are assumed. This paper will conclude with a discussion of upcoming future directions.

Moray Firth marine fouling communities

1986 ◽  
Vol 91 ◽  
pp. 213-220 ◽  
Author(s):  
Gordon B. Picken
Keyword(s):  
Shallow Water ◽  
Depth Range ◽  
Soft Corals ◽  
Fouling Communities ◽  
Algal Cover ◽  
Marine Fouling ◽  
Moray Firth ◽  
Hanging Chain ◽  
Fouling Organisms

SynopsisFouling communities typical of shallow water inshore sites were found at three locations in the Moray Firth. At each, an initial background cover of solitary tubeworms and barnacles was overgrown by secondary fouling organisms. On the piles of Nigg jetty, overgrowth consisted of mussels in the depth range 0–6 m and hydroids, sponges, soft corals and anemones from 6–26 m. Buoys in the approaches to Cromarty Firth were completely covered by a mixture of algae and mussels. Sunlit areas of the float cleaned annually bore a diverse algal cover, whereas uncleaned shaded areas and the freely hanging chain had three-year-old mussels up to 7 cm long. Mussel fouling extended down the chain to within 1 m of the seabed at 26 m depth. Concrete anchor blocks on the seabed were covered with solitary tubeworms and hydroids. Steel piled platforms in the Beatrice Field were completely fouled after four years. Mussels and seaweeds were abundant from 0–5 m. In the depth range 8–35 m the background calcareous layer was overgrown by soft corals up to 10 cm long and hydroids. From 35 m to the seabed at 46 m, soft overgrowth was provided mainly by hydroids and ascidians, with only a few small corals.

Strain to Ground Motion Conversion of DAS Data for Earthquake Magnitude and Stress Drop Determination

2021 ◽  
Author(s):  
Itzhak Lior ◽  
Anthony Sladen ◽  
Diego Mercerat ◽  
Jean-Paul Ampuero ◽  
Diane Rivet ◽  
...  
Keyword(s):  
Early Warning ◽  
Stress Drop ◽  
Source Parameters ◽  
Simulated Data ◽  
Earthquake Early Warning ◽  
Body Waves ◽  
Ocean Bottom ◽  
Correlated Noise ◽  
S Wave ◽  
Earthquake Source Parameters

<p>The use of Distributed Acoustic Sensing (DAS) presents unique advantages for earthquake monitoring compared with standard seismic networks: spatially dense measurements adapted for harsh environments and designed for remote operation. However, the ability to determine earthquake source parameters using DAS is yet to be fully established. In particular, resolving the magnitude and stress drop, is a fundamental objective for seismic monitoring and earthquake early warning. To apply existing methods for source parameter estimation to DAS signals, they must first be converted from strain to ground motions. This conversion can be achieved using the waves’ apparent phase velocity, which varies for different seismic phases ranging from fast body-waves to slow surface- and scattered-waves. To facilitate this conversion and improve its reliability, an algorithm for slowness determination is presented, based on the local slant-stack transform. This approach yields a unique slowness value at each time instance of a DAS time-series. The ability to convert strain-rate signals to ground accelerations is validated using simulated data and applied to several earthquakes recorded by dark fibers of three ocean-bottom telecommunication cables in the Mediterranean Sea. The conversion emphasizes fast body-waves compared to slow scattered-waves and ambient noise, and is robust even in the presence of correlated noise and varying wave propagation directions. Good agreement is found between source parameters determined using converted DAS waveforms and on-land seismometers for both P- and S-wave records. The demonstrated ability to resolve source parameters using P-waves on horizontal ocean-bottom fibers is key for the implementation of DAS based earthquake early warning, which will significantly improve hazard mitigation capabilities for offshore and tsunami earthquakes.</p>

scholarly journals Measuring the Impact of Observations on the Predictability of the Kuroshio Extension in a Shallow-Water Model

2012 ◽  
Vol 42 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Werner Kramer ◽  
Henk A. Dijkstra ◽  
Stefano Pierini ◽  
Peter Jan van Leeuwen
Keyword(s):  
Shallow Water ◽  
Particle Filtering ◽  
Predictive Power ◽  
Kuroshio Extension ◽  
Water Model ◽  
Ensemble Prediction ◽  
Shallow Water Model ◽  
One Year ◽  
The Impact ◽  
The Kuroshio

Abstract In this paper, sequential importance sampling is used to assess the impact of observations on an ensemble prediction for the decadal path transitions of the Kuroshio Extension. This particle-filtering approach gives access to the probability density of the state vector, which allows the predictive power—an entropy-based measure—of the ensemble prediction to be determined. The proposed setup makes use of an ensemble that, at each time, samples the climatological probability distribution. Then, in a postprocessing step, the impact of different sets of observations is measured by the increase in predictive power of the ensemble over the climatological signal during one year. The method is applied in an identical-twin experiment for the Kuroshio Extension using a reduced-gravity shallow-water model. This study investigates the impact of assimilating velocity observations from different locations during the elongated and the contracted meandering states of the Kuroshio Extension. Optimal observation locations correspond to regions with strong potential vorticity gradients. For the elongated state the optimal location is in the first meander of the Kuroshio Extension. During the contracted state it is located south of Japan, where the Kuroshio separates from the coast.

scholarly journals Elephant Herding Optimization for Energy-Based Localization

2018 ◽  
Author(s):  
Sérgio Correia ◽  
Marko Beko ◽  
Luís Cruz ◽  
Slavisa Tomic
Keyword(s):  
Source Localization ◽  
Wireless Sensors ◽  
Performance Comparison ◽  
Sensor Nodes ◽  
Convex Relaxations ◽  
Noisy Environments ◽  
New Approach ◽  
Metaheuristic Methods ◽  
Simulation Results ◽  
First Time

This work addresses the energy-based source localization problem in wireless sensors networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex relaxations and approximations (like all existing approaches do), we here tackle it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics is applied to this type of problems. More specifically an elephant herding optimization (EHO) algorithm is applied. Through extensive simulations, the key parameters of the EHO algorithm are optimized such that they match the energy decay model between two sensor nodes. A detailed analysis of the computational complexity is presented, as well as performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms the existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.

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