Uncertainty of low frequency sound attenuation estimate in marine - PowerPoint PPT Presentation

Uncertainty of low frequency sound attenuation estimate in marine sediment Yong-Min Jiang and N. Ross Chapman University of Victoria Victoria BC Canada University of Victoria, Victoria, BC, Canada Work supported by ONR Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

Objective: • Measurement of marine sediment attenuation at frequencies lower than 5 kHz • Evaluation of the uncertainty of the attenuation estimate Outline: Outline: • Experimental geometry • Sediment attenuation estimation method S di t tt ti ti ti th d • Factors that affect the uncertainty of attenuation estimates • fluctuation of the signals • fluctuation of the signals • uncertainties of sediment sound speed and layer thickness • The results • Summary and acknowledgements Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

Methods of estimating sound attenuation in marine sediment : • For attenuation at high frequencies (f > 10 kHz): • For attenuation at high frequencies (f > 10 kHz): In situ measurements by using two embedded probes • For attenuation at low frequencies (f < 1 kHz): Inferences from different kinds of inversions of sound Inferences from different kinds of inversions of sound propagation data • An alternate way of estimating the sediment attenuation: The signal used in this study is LFM pulse with frequency bandwidth of 1.5 kHz to 4.5 kHz Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

Experimental geometry: Sound travel path in the sediment Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

Example of the received signal: om Sub-botto Surface Bottom Direct arrival Direct BRSR SRBR Surface reflection Bottom reflection Bottom reflection Sub-bottom reflection Bottom reflection surface reflection Surface reflection bottom reflection Surface reflection bottom reflection Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

Method of estimating sediment attenuation: Signal from bottom reflection: θ ξ φ η Src Re c - Directional Src p V D ( f , , ) D ( f , , ) D − α + = ⋅ ( neper ) ( r r ) 0 b b b p ( f ) e w 1 2 + response of b r r 1 2 source Signal from sub bottom reflection: θ ξ φ η Src Re c p T T V D ( f , , ) D ( f , , ) = D Re - Directional 0 ws sw sb sb sb p ( f ) c + + + sb r r r r 3 4 5 6 response of f ( neper ) − α ⋅ + − α ( neper ) + ⋅ ⋅ ( f ) ( r r ) ( ) r r 5 6 receiver e w 3 4 e sb The ratio of reflections from bottom to sub bottom (dB): Δ = − = + α ⋅ + P ( f ) 20 log p ( f ) 20 log p ( f ) B ( f ) ( r r ) 10 b 10 sb sb 5 6 Linear frequency dependence: or Nonlinear frequency dependence: β Δ = + ⋅ α ⋅ + Δ = + ⋅ α ⋅ + f ( f ) P ( f ) ( r r ) f B P ( f ) ( r r ) ( f f ) B 0 5 6 sb 5 6 sb 0 α α is in dB/m • kHz is in dB/m @1 kHz, f o is 1 kHz ( f ) f 0 sb sb α λ = α ⋅ in dB/ λ ( ) ( f ) c / 1000 sb sb sb Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

The uncertainty of attenuation estimate: Linear frequency dependence: or Nonlinear frequency dependence: β Δ Δ = + + ⋅ α α ⋅ + + Δ Δ = + + ⋅ α α ⋅ + + ( f ) f P P ( ( f f ) ) ( ( r r r r ) ) f f B B P P ( ( f f ) ) ( ( r r r r ) ) 0 ( ( f f f f ) ) B B 5 5 6 6 sb b 5 5 6 6 sb b 0 0 in dB/m • kHz is in dB/m @1 kHz, f o is 1 kHz ⋅ + λ Δ = ⋅ α ⋅ + 1000 ( r r ) ( ) P ( f ) f B 5 6 sb c sb in dB/ λ in dB/ λ The uncertainty of the attenuation estimate: The uncertainty of the attenuation estimate: � the measurement � signal amplitude fluctuation � signal amplitude fluctuation � the uncertainty of sound speed and layer thickness estimates � Bayesian travel time inversion � Bayesian travel time inversion Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

Estimate attenuation from bottom and sub-bottom reflections: b b fl i Matched filtered waveforms ∆ P(f) at different frequencies Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

Example of uncertainty due to signal fluctuation Determine the frequency dependence of the attenuation in terms of: • the width of 95% of the credibility interval • the consistency of the estimates from different source-receiver pairs Monte Carlo approach, linear fitting Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

Linear fitting for data from four source-receiver pairs Linear fitting for data from four source-receiver pairs Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

The uncertainty of sediment sound speed and L Layer thickness estimates: thi k ti t Optimization: p Water column SSP, source & receiver geometry: Range, Water depth, Array tilt Source depth, Receiver depth Bayesian inversion: Bayesian inversion: Uncertainty of sediment sound speed and layer thickness and layer thickness Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

Uncertainty of sediment sound speed and layer Uncertainty of sediment sound speed and layer thickness estimates from Bayesian travel time inversion: inversion: Sound speed Layer thickness grazing angle 1550 1600 1650 10 20 30 10 20 30 40 Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

Example of uncertainty of attenuation estimate: Sound path length Sound path length/sound speed 0.1 distribution 0.08 0.06 Probability d 0.04 0.02 0 6 8 10 12 70 70 80 80 90 90 100 100 110 110 0 04 0.04 0 05 0.05 0 06 0.06 0 07 0.07 ( r 5 + r 6 ) × α (f) (f) r + 5 + sb ( r ) + ⋅ α ( r r ) / c ( f ) ( r r ) 5 6 6 sb 5 6 sb 1.4 1.4 1.2 1.2 1 1 0.8 0.8 0 6 0.6 0 6 0.6 0.4 0.4 0.2 0.2 0 0 0.1 0 1 0.12 0 12 0 14 0.14 0 16 0.16 0.18 0 18 0.2 0 2 0 0 0.075 0 08 0 08 0.08 0.085 0 09 0 09 0.09 0.095 0 1 0.1 0.105 0 10 0.11 0 11 α α sb ( λ in dB/ λ ( f ) in dB/m • kHz ) sb Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009

Summary: • Marine sediment sound attenuation at low frequency is estimated from single bounce sub-bottom reflections • Frequency dependence of the attenuation is determined by the Frequency dependence of the attenuation is determined by the measured data • The uncertainty of the attenuation estimate is mapped from the fluctuation of measured signal and the uncertainty of the fluctuation of measured signal and the uncertainty of the sediment property estimates from Bayesian travel time inversion • VLA and source at different depths experimental geometry Acknowledgements: • Office of Naval Research: for sponsoring the research Offi f N l R h f i th h • Drs. William Hodgkiss and Peter Gerstoft from MPL (acoustic data) • Dr. David Knobles from ARL (navigation and source depth data) • Dr. John Goff from IG, Univ. of Texas at Austin (seismic reflection ) Jiang and Chapman ASA157, Portland, Oregon, 18 - 22 May 2009