By Topic

Generating acoustic provinces for the U.S. Navy's Low frequency bottom loss database using geographic information systems

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

1 Author(s)
Timothy H. Ruppel ; Acoustic Modeling and Database Division, Acoustic Analysis Branch Naval Océanographie Office Stennis Space Center, MS 39522 USA

The U.S. Navy's Low Frequency Bottom Loss (LFBL) database is released and maintained by the Naval Oceanographic Office (NAVOCEANO) and used as an environmental input to many of the Navy-standard tactical decision aids. It is designed to support the Navy's operational requirements in determining the nature of the interaction of 20-1000 Hz acoustic energy with the sea bottom. The general methodology of generating shallow-water LFBL databases was described in a previous paper. [Harvey, D.W., Lowrie, A., and Filipczyk, R. D., Oceans '02 MTS/IEE Volume 1, pages 358-362.] LFBL is based on acoustic and seismic measurements performed at-sea by NAVOCEANO survey teams and subsequent geo-acoustic inversions of the measured data using the Navy Standard Parabolic Equation (NSPE) model and the resources of the Navy Department of Defense Supercomputing Resource Center (Navy DSRC). LFBL is composed of a global database that is available worldwide and four regional databases for which additional acoustic and geological measurements were made. The global and regional databases use different parameter sets to describe bottom interactions. In the global database, a set of 10 parameters is used to describe an effective one-layer bottom. In the regional databases the bottom is parameterized using a set of effective acoustic layers described by sound speed, density, and attenuation at the top and bottom of each layer. The acoustic properties of the effective layers may not correspond to the actual density, sound speed, or attenuation of the physical bottom at a given location, but collectively, they are found to accurately predict acoustic propagation of bottom-interacting sound paths, and the values are constrained in the geo-acoustic inversion to geologically reasonable values. Hence, the regional databases are also known as "N-Layer" databases. Starting with LFBL Version 11.1, LFBL has also included error metrics in the regional databases to indicate the degree of confidence for th- values therein in order to provide improved guidance for operational decision-making. For both the global and regional databases, the geographic region is divided into provinces over which it is assumed that the sub-bottom acoustic parameters are constant, even if the sub-bottom layer depths in the N-Layer regions vary. The sub-bottom layer depths are determined from geological interpretation of acoustic two-way travel times using data gathered concurrent with, but with independent equipment from, the acoustic transmission loss (TL) measurements. A new technique has been devised for determining province boundaries in the regional databases using geographic information systems (GIS). In brief, rough provinces are sketched out by geologists at NAVOCEANO. The locations of the TL measurements are added onto this map, and colored lines (dubbed "geo-acoustic connections") are drawn between the TL measurements to indicate how accurately the inverted parameters from one measurement predict the results of the other. The province boundaries are then adjusted to include (as much as possible) only measurements whose inverted parameters accurately predict TL at the other measurement sites within the province. Parameters are chosen for a given province by selecting the parameter set within the province that produces the best confidence metrics.

Published in:


Date of Conference:

26-29 Oct. 2009