Blind Ocean Acoustic Tomography

Transcription

1 Blind Ocean Acoustic Tomography presentation at the ENEA Workshop on Ocean Acoustic Tomography: results and perspectives 28 November 22 Santa Teresa, Lerici, Itália by S.M. Jesus and C.Soares SiPLAB, FCT - Universidade do Algarve Campus de Gambelas, PT-8 Faro, Portugal

2 Blind Ocean Acoustic Tomography S.M. Jesus and C. Soares SiPLAB, FCT - Universidade do Algarve, Campus de Gambelas, PT - Faro, Portugal Supported under ATOMS, FCT contract PDCTM/P/MAR/15296/1999 and TOMPACO, CNR, Italy.

3 Outline the game of the name and motivation the INTIFANTE sea trial environmental inversion active tomography passive tomography summary

4 source receiver X Ocean Acoustic Tomography - synoptic acoustic pressure y r( ζ ) Correlator Optimization p( ζ ) Acoustic Model ζ new environment source signal s(t) (r,z) ^ ζ

5 Motivation decrease the dependency on source knowledge (emitted signal and source position) increase the (time-space) adaptivity ability to use non-co-operative (noise) sources

6 INTIFANTE Experimental Site -9 3' -9 15' -9 ' -8 45' -8 3' 39 ' 39 ' 38 45' 38 45' LISBON Setubal 38 3' 38 3' Sesimbra Espichel Cape INTIFANTE 99 Area 38 15' 38 15' 38 ' 38 ' -9 3' -9 15' -9 ' -8 45' -8 3'

7 Geometry and Bathymetry

8 Vertical Line Array

9 Radio Buoy: deployment and setup

10 Recovering the Telemetry Unit

11 Sound source and emitted signals

12 Events 2 and 5: bathymetry and runs

13 Temperature evolution (XBT)

14 Soundspeed (m/s) 1 N ˆN = min n=1 λ 2 n { >.8} N M m=1 λ 2 m 8 Depth (m) 6 Depth (m) 4 n=1 α nun ceof = ĉ + N 2 Empirical Orthogonal Functions st EOF 2nd EOF Soundspeed (m/s)

15 Depth (m) Physical model - NW/NE track m/s VA Source (63 m) Sediment 2. m 175 m/s α=.8 db/λ ρ=1.9 g/cm 3 18 m/s α=.8 db/λ ρ=1.9 g/cm 3 Subbottom Range (km)

16 Inversion results for Event 2 Bartlett Power Range (km) Depth (m) Sensordepth (m) Speed in sed. (m/s) Sediment thickness (m) Speed in sub bottom (m/s) Array tilt (rad) α α

17 Depth (m) Physical model - NW/NE track m/s VA 6 Source (63 m) 119 Sediment 2. m 165 m/s α=.8 db/λ ρ=1.9 g/cm 3 α=.8 db/λ ρ=1.9 g/cm 3 18 m/s Subbottom Range (km)

18 Inversion results for Event Bartlett Power Range (km) Depth (m) Sensordepth (m) Speed in sed. (m/s) Sediment thickness (m) Speed in sub bottom (m/s) Array tilt (rad) α α

19 Tide evolution and VA moving

20 Depth (m) Temperature ( o C) Temperature ( o C) XBT Thermistors 8 XBT Thermistors 6 Depth (m) Estimated temperature profiles - Event 2 & 5

21 NRP D. Carlos I Overall length (m) 68 Beam (m) 13 Gross displa. (ton) 28 Two diesel-electric (HP) 8 Max speed (kn) 11 Crew 34 Scientific 15

22 Militar Long. (km) x x ULVA 2:14 Militar Lat. (km) 15 3:14 2: Event 6 x 152 x x x x 2: Event 2 NW leg Event 5 NE leg Event 6: bathymetry and source run Depth (m)

23 NRP D. Carlos I speed and heading 14 (a) Ship speed (kn) (b) Bearing (deg) Julian date

24 Depth (m) Physical model and search parameters Source (3 m) Sediment m/s 2. m 165 m/s 18 m/s VA α=.8 db/λ ρ=1.9 g/cm 3 α=.8 db/λ ρ=1.9 g/cm 3 Symbol Unit Search int./steps α1 m/s α2 m/s sr km sd m rd m θ rad Subbottom Range (km)

25 Frequency (Hz) 19 Power (db) Frequency (Hz) 25 Frequency (Hz) db NRP D. Carlos I radiated noise

26 Inversion results for Event 6 Bartlett Power Range (km) Depth (m) Sensordepth (m) Array tilt (rad) α α Depth (m)

27 Linear broadband data model: Theoretical background Y (θ) = [Y T (ω1) Y T (ωl)] T = H(θ)S + U Minimum variance signal estimator: Ŝ(ˆθ) = [(H H (ˆθ)H(ˆθ)] 1 H H (ˆθ) N 1 Y n(θ) n= Broadband conventional processor: Pinc(θ) = K k=1 Ŝ(ω k) 2 H H (ωk, θ)ĉy Y (ωk, ωk)h(ωk, θ) H(θ)Ŝ 2

28 Frequency (Hz) Likelihood k=1 V (ω, k) K v(ω) = K Likelihood measure: V (ω, k) = 1 T T [Y (ω, t, k) µ Y (ω, k)] 2 dt, Variance at the k th hydrophone: Frequency selection

29 α 2 Sensordepth (m) Bartlett Power Temperature o C 1 in focus off focus XBT 8 Depth (m) Array tilt (rad) α Range (km) 2 Depth (m) Event 6: Inversion results

30 Frequency (Hz) Event 6: Ship power spectrum estimation

31 Summary This is a preliminary test for ship noise tomography in a (assumed) unknown environment. strong correlation between ship speed/emitted power and accurate source tracking when in focus geometrical and environmental parameter assume credible values more extensive data for oceanographic features observation enhanced search of ship noise spectral components