Geophysical Data Base Variable Resolution (GDBV): An Object-Oriented Database for Dynamic Geo-Acoustic Data Storage

摘要

The Geophysical Data Base-Variable resolution (GDBV) is a modem, object-oriented database product that is designed to accommodate the dynamically derived parameters of the Geo-Acoustic Inversion Techniques (GAIT). Sponsored by the Oceanographer of the Navy (CNO N096) via PEO(C4I and Space) PMW-155, GAIT/ GDBV is a Through-the-Sensors (TTS) program that includes a flexible data model for the assimilation of data at local, regional, and global levels of operation. In addition to its dynamic capabilities, GDBV also includes support for historical database roles similar to the Naval Oceanographic Office's (NAVOCEANO) Low Frequency Bottom Loss (LFBL) database. In order to demonstrate its highly extendable design, this paper explores GDBV's database format and data model. In both the historical and dynamic capacity, GDBV must be capable of evolving with new system specifications. GDBV's multiple levels of organization and object-oriented implementation provide an efficient solution to these requirements. In addition its dynamic operational requirements, the GDBV database will accommodate the parameters from each of the following OAML databases produced by NAVOCEANO: High Frequency Bottom Loss (HFBL), LFBL, Mine Ware (MIW) Surface Sediments, and LFBL's N-Layer portion (GDB). Support for these static databases will facilitate the absorption of each individual database into a single broad database that provides a complete description of the ocean bottom, OAML GDBV. The complete parameter set of GDBV is presented along with a physical representation of the parameters. The concept of operation for GDBV in TEDServices is presented for local, regional, and global levels of operation. The overall data flow is very similar to a preceding Through-The- Sensors (TTS) database system, PUMA-TEDS. This working example of GDBV in GAIT/ TEDServices reinforces the portability of the database design and the benefits of generation.