Abstract :
Great strides have been made in the ability to model and predict oceanography (temperature, salinity, currents, etc.) accurately and in a timely manner. There exists a need to characterize the variability of the ocean based on its acoustic propagation characteristics. That is, how and where does the evolution or variability of the environment significantly impact the acoustic propagation characteristics of an oceanographic waveguide? Due to the complexity of the acoustic propagation in a waveguide, variability in the oceanography is not always indicative of the variability in the acoustic propagation. For example, a significant change in temperature in an area may not significantly impact the acoustic propagation in the area. There is also a limit on the ability to sense the oceanography. Sensor availability and coverage, as well as time put constraints on efforts to measure a large ocean area. The work presented here shows that analysis of acoustic variability computed using predicted oceanography over an area provides a better insight into the oceanographic variability for the purposes of sensor placement. Acoustic coverage integrated over many source depths is a representation of how energy travels in a waveguide and can therefore provide a good estimate of the propagation properties of the environment over a large area. A method of estimating the acoustic variability over a period of time using this integrated acoustic coverage (IAC) computation, which is derived from estimated transmission loss is presented here. Two and three dimensional oceanographic model predictions of temperature and salinity (converted to sound velocity) over a time period are used as inputs to the acoustic model. The parabolic equation Range-dependent Acoustic Model (RAM, [Collins, M. D., "Applications and time-domain solution of higher-order parabolic equations in underwater acoustics," J. Acoust. Soc. Am, 86 (3), 1097-1102, 1989]) is used to compute the complex pressure and transmissio- n loss (TL) for calculation of a range independent IAC. This quantity is computed for each time period and variability over time is computed for the ocean volume (longitude, latitude and depth). The RAM is then run for range dependent (RD) environments across user-selected, highly variable tracks for estimation of (range dependent) IAC variance. The acoustic computation is currently done over the whole area in a range independent mode to save computation time. The capability to compute along selected tracks adds a necessary RD evaluation of the areas with the most variability. Comparisons of the range independent (RI) IAC over the volume and RD over the highly variable track show that the use of the range independent IAC parameter is valid for acoustic variance estimation over an area. This work shows that the integrated range independent acoustic variability provides a better estimate of variability than does the oceanographic variability, for the purposes of sensor placement. As work continues, more capability can be added to include functionality such as range dependence.
Keywords :
acoustic wave propagation; acoustic waveguides; ocean temperature; oceanographic techniques; underwater sound; acoustic propagation characteristics; acoustic transmission loss; integrated acoustic coverage computation; ocean acoustic environment variability; ocean currents; ocean salinity; ocean temperature; oceanographic prediction; oceanographic waveguide; range-dependent acoustic model; sensor placement; Acoustic propagation; Acoustic sensors; Acoustic waveguides; Availability; Equations; Ocean temperature; Predictive models; Propagation losses; Temperature sensors; Underwater acoustics;
