Stochastic scattering model predictions for reverberation from the near‐surface oceanic bubble layer

1992 ◽  
Vol 92 (4) ◽  
pp. 2348-2349
Author(s):  
Kenneth E. Gilbert
Keyword(s):  
Near Surface ◽  
Scattering Model ◽  
Model Predictions ◽  
Bubble Layer

Temporal coherence of acoustic signals propagating through the near surface bubble layer

2002 ◽  
Vol 112 (5) ◽  
pp. 2424-2424
Author(s):  
R. Lee Culver ◽  
David Bradley ◽  
Jon Reeves
Keyword(s):  
Acoustic Signals ◽  
Temporal Coherence ◽  
Near Surface ◽  
Bubble Layer ◽  
Surface Bubble

Distribution of zooplankton inferred from hydroacoustic backscatter data in coastal waters off Ningaloo Reef, Western Australia

2002 ◽  
Vol 53 (6) ◽  
pp. 1005 ◽  
Author(s):  
Steven G. Wilson ◽  
Timothy Pauly ◽  
Mark G. Meekan
Keyword(s):  
Western Australia ◽  
Coastal Waters ◽  
Near Surface ◽  
Rhincodon Typus ◽  
Ningaloo Reef ◽  
The North ◽  
Whale Sharks ◽  
Bubble Layer ◽  
Surface Bubble

Hydroacoustic surveys were used to examine zooplankton distributions in coastal waters off Ningaloo Reef, Western Australia. Surveys were timed to coincide with the seasonal aggregation of whale sharks, Rhincodon typus, and other large zooplanktivores in these waters. The surveys examined scattering features of lagoon/shelf fronts, a series of cross-shelf transects and waters surrounding whale sharks swimming at the surface. These suggested that lagoon waters flow intrusively into shelf waters at reef passages in a layered exchange. Cross-shelf transects identified three vertical scattering layers: a surface bubble layer; a near-surface minimum layer; and a bottom maximum layer. Regions of intense mixing of lagoon and shelf waters were detected seaward and to the north of reef passages. Integrated acoustic mean volume backscatter of the bottom maximum layer increased with depth and distance offshore. Large subsurface aggregations of unidentified fauna were detected beneath whale sharks in the same area that manta rays and surface schools of euphausiids were also observed.

FDTD modeling of low‐frequency sea surface reverberation in the presence of a near‐surface bubble layer

2000 ◽  
Vol 107 (5) ◽  
pp. 2922-2922
Author(s):  
Richard S. Keiffer ◽  
Robert A. Zingarelli ◽  
Jorge C. Novarini
Keyword(s):  
Low Frequency ◽  
Sea Surface ◽  
Near Surface ◽  
Bubble Layer ◽  
Surface Bubble

scholarly journals A combined surface-and volume-scattering model for ice-sheet radar altimetry

1993 ◽  
Vol 39 (133) ◽  
pp. 675-686 ◽  
Author(s):  
Curt H. Davis ◽  
Richard K. Moore
Keyword(s):  
Ice Sheets ◽  
Ice Sheet ◽  
Altimeter Data ◽  
Wave Form ◽  
Combined Model ◽  
Volume Scattering ◽  
Near Surface ◽  
Scattering Model ◽  
Wave Forms ◽  
Antarctic Ice Sheets

AbstractOver the last 15 years, satellite-altimeter data have been used to produce surface-elevation maps of the Greenland and Antarctic ice sheets with a 2 m accuracy. Analysis of Seasat and Geosat cross-over points showed that satellite altimeters can measure changes in the mass balance of the ice sheets. The retracking algorithm used to extract surface elevations from Seasat and Geosat return wave forms is based upon a modified form of the Brown surface-scattering model. Recent work has shown that altimeter wave forms over higher-altitude regions of the ice sheets are affected by sub-surface volume-scattering. Here, we develop a theoretical model for altimeter return wave forms over the ice sheets that is based on a combination of surface-and volume-scattering. By approximating the altimeter’s antenna pattern and transmitted pulse shape with Gaussian functions, we derive a closed-form analytical solution for the return-power volume-scattered from beneath the ice-sheet surface. We then combine the volume-scattering model with the Brown model and apply it to average wave forms from the Greenland and Antarctic ice sheets. The results show that the combined model accurately describes variations in altimeter wave-form shapes that are produced by differing contributions of surface-and volume-scattering to the received power. The combined model is then used to simulate return wave forms from a dual-frequency altimeter. The simulation shows that a two-frequency system can provide quantitative estimates of the absorption and scattering coefficients for near-surface snow.

scholarly journals A stochastic model for scattering from the near‐surface oceanic bubble layer.

1991 ◽  
Vol 90 (4) ◽  
pp. 2301-2302 ◽  
Author(s):  
Kenneth E. Gilbert ◽  
Lintao Wang ◽  
Ralph R. Goodman
Keyword(s):  
Stochastic Model ◽  
Near Surface ◽  
Bubble Layer

scholarly journals Frequency-domain electromagnetic induction inversion with randomized tensor decomposition

2021 ◽  
Author(s):  
João Narciso ◽  
Mingliang Liu ◽  
Ellen Van De Vijver ◽  
Leonardo Azevedo ◽  
Dario Grana
Keyword(s):  
Spatial Distribution ◽  
Data Assimilation ◽  
Frequency Domain ◽  
Electromagnetic Induction ◽  
Three Dimensional ◽  
Inversion Method ◽  
Geophysical Inversion ◽  
Near Surface ◽  
Model Predictions ◽  
Value Decomposition

<p>Near-surface systems can be complex and highly heterogeneous. The complex nature of these systems makes their numerical modelling a challenging problem in geosciences. Geophysical survey methods combined with direct measurements have been widely used to characterize the spatial distribution of the near-surface physical properties. Within this scope, geophysical inversion has been a preferable tool to predict quantitively the spatial distribution of the relevant near-surface properties. Ensemble-based data assimilation techniques are common geophysical inversion methods used in problems related to subsurface modelling and characterization. These methods allow the accurate prediction of the spatial distribution of the subsurface properties and have the ability to assess the uncertainty about the model predictions. However, these are computationally demanding inversion techniques, which makes their applicability to large data sets prohibitive.</p><p>This study presents the application of a computationally efficient ensemble-based data assimilation technique for inversion of a large-scale frequency-domain electromagnetic induction survey data set. The inversion method is based on randomized high-order singular value decomposition. We combine randomized linear algebra with high-order singular value decomposition, which allows to perform data assimilation in a low-dimensional model and data space. This inversion approach satisfies two objectives: it reduces the computational burden of the inversion and has the same characteristics as conventional ensemble-based data assimilation methods. The inversion method presented herein predicts the spatial distribution of subsurface electrical conductivity and magnetic susceptibility from frequency-domain electromagnetic induction data (as related to the in-phase and quadrature FDEM signal components).</p><p>The method is illustrated in a three-dimensional real case application where a set of geophysical and borehole data is available. The log-set composed by electrical conductivity and magnetic susceptibility is used as conditioning data to generate a prior ensemble of numerical three-dimensional models with geostatistical simulation. The predicted posterior distribution generates synthetic frequency-domain electromagnetic induction data that reproduces the observed data. The model predictions at a blind well location, not used in the generation of the prior ensemble, agree will with the observed log data, validating the quality of the applied method.</p>

scholarly journals A combined surface-and volume-scattering model for ice-sheet radar altimetry

1993 ◽  
Vol 39 (133) ◽  
pp. 675-686 ◽  
Author(s):  
Curt H. Davis ◽  
Richard K. Moore
Keyword(s):  
Ice Sheets ◽  
Ice Sheet ◽  
Altimeter Data ◽  
Wave Form ◽  
Combined Model ◽  
Volume Scattering ◽  
Near Surface ◽  
Scattering Model ◽  
Wave Forms ◽  
Antarctic Ice Sheets

AbstractOver the last 15 years, satellite-altimeter data have been used to produce surface-elevation maps of the Greenland and Antarctic ice sheets with a 2 m accuracy. Analysis of Seasat and Geosat cross-over points showed that satellite altimeters can measure changes in the mass balance of the ice sheets. The retracking algorithm used to extract surface elevations from Seasat and Geosat return wave forms is based upon a modified form of the Brown surface-scattering model. Recent work has shown that altimeter wave forms over higher-altitude regions of the ice sheets are affected by sub-surface volume-scattering. Here, we develop a theoretical model for altimeter return wave forms over the ice sheets that is based on a combination of surface-and volume-scattering. By approximating the altimeter’s antenna pattern and transmitted pulse shape with Gaussian functions, we derive a closed-form analytical solution for the return-power volume-scattered from beneath the ice-sheet surface. We then combine the volume-scattering model with the Brown model and apply it to average wave forms from the Greenland and Antarctic ice sheets. The results show that the combined model accurately describes variations in altimeter wave-form shapes that are produced by differing contributions of surface-and volume-scattering to the received power. The combined model is then used to simulate return wave forms from a dual-frequency altimeter. The simulation shows that a two-frequency system can provide quantitative estimates of the absorption and scattering coefficients for near-surface snow.

Sound propagation through the near‐surface ocean bubble layer

1996 ◽  
Vol 100 (4) ◽  
pp. 2840-2840 ◽  
Author(s):  
Holly Burch ◽  
Michael Buckingham
Keyword(s):  
Sound Propagation ◽  
Near Surface ◽  
Surface Ocean ◽  
Bubble Layer

Inverse scattering of surface waves: A new look at surface Consistency

Geophysics ◽  
1994 ◽  
Vol 59 (6) ◽  
pp. 963-972 ◽  
Author(s):  
Bastian Blonk ◽  
Gérard C. Herman
Keyword(s):  
Inverse Scattering ◽  
Seismic Data ◽  
Scattering Theory ◽  
Synthetic Data ◽  
Surface Scattering ◽  
Frequency Filtering ◽  
Near Surface ◽  
Scattering Model ◽  
Static Corrections

A method is presented for eliminating near‐surface scattered noise from seismic data. Starting from an appropriately chosen background model, a surface‐consistent scattering model is determined using linearized elastodynamic inverse scattering theory. This scattering model does not necessarily equal the actual scatterer distribution, but it enables one to calculate, approximately, the near‐surface scattered part of the data. The method honors at least some of the complexity of the near‐surface scattering process and can be applied in cases where traditional methods, like wavenumber‐frequency filtering techniques and methods for static corrections, are ineffective. From a number of tests on synthetic data, we conclude that the method is rather robust; its main sensitivity is because of errors in the determination of the background Rayleigh‐wave velocity.

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