Model-based localization of deep-diving cetaceans using towed line array acoustic data

2021 ◽  
Vol 150 (2) ◽  
pp. 1120-1132
Author(s):  
Yvonne M. Barkley ◽  
Eva-Marie Nosal ◽  
Erin M. Oleson
Keyword(s):  
Acoustic Data ◽  
Model Based ◽  
Deep Diving ◽  
Line Array

Model-based dispersive processing of borehole dipole wave data using an equivalent-tool theory

Geophysics ◽  
2016 ◽  
Vol 81 (1) ◽  
pp. D35-D43 ◽  
Author(s):  
Sheng-Qing Lee ◽  
Xiao-Ming Tang ◽  
Yuan-da Su ◽  
Chun-Xi Zhuang
Keyword(s):  
Data Processing ◽  
Dispersion Curve ◽  
Low Frequency ◽  
Wave Dispersion ◽  
Flexural Wave ◽  
Dispersion Characteristics ◽  
Acoustic Data ◽  
Model Based ◽  
Dispersion Data ◽  
Application Procedure

We have developed a model-based processing technique for borehole dipole S-wave logging data to estimate formation shear slowness from the data. During dipole acoustic logging, the presence of the logging tool can significantly affect the dispersion characteristics of flexural waves. Therefore, modeling the effects of the tool is essential for model-based processing. We have determined that an equivalent-tool theory using only two parameters, tool radius, and modulus, can adequately model the flexural-wave-dispersion characteristics. We used this theory, together with a calibration procedure, to determine the tool parameters to formulate an inversion method for the logging data processing. Our use of the equivalent tool theory played an important role in fitting the theoretical dispersion curve to the actual flexural-wave-dispersion data, enabling fast processing of the field acoustic data. An advantage of this model-based method is its prediction power, which, in the absence of low-frequency dispersion data, allows for predicting formation shear slowness from the low-frequency limit of the model-fitted dispersion curve. We have also developed an application procedure of the method for field-data processing and demonstrated its effectiveness in the dispersion correction using field acoustic data from fast and slow formations.

MODEL-BASED SIGNAL ENHANCEMENT FOR THE HUDSON CANYON EXPERIMENT

1994 ◽  
Vol 02 (03) ◽  
pp. 327-344
Author(s):  
J. V. CANDY ◽  
E. J. SULLIVAN
Keyword(s):  
Sensor Arrays ◽  
Recursive Estimation ◽  
Propagation Model ◽  
Space Representation ◽  
Propagation Models ◽  
Acoustic Data ◽  
State Space Representation ◽  
Measurement Models ◽  
Model Based

Model-based signal processing is a well-defined methodology enabling the inclusion of environmental (propagation) models, measurement (sensor arrays) models, and noise (shipping, measurement) models into a sophisticated processing algorithm. Here we investigate the design of a space-varying, nonstationary, model-based processor (MBP) for the Hudson Canyon experiment. In this shallow water application, a state space representation of the normal mode propagation model is used. The processor is designed such that it allows in situ recursive estimation of the both the pressure field and modal functions. It is shown that the MBP can be effectively utilized to "validate" the performance of the model on noisy ocean acoustic data. In fact, a set of processors is designed, one for each source range, and the results are reasonable, implying that the propagation model with measured parameters adequately represents the data.

Geoacoustic Inversions of Horizontal and Vertical Line Array Acoustic Data From a Surface Ship Source of Opportunity

2010 ◽  
Vol 35 (1) ◽  
pp. 79-102 ◽  
Author(s):  
S.A. Stotts ◽  
R.A. Koch ◽  
S.M. Joshi ◽  
V.T. Nguyen ◽  
V.W. Ferreri ◽  
...  
Keyword(s):  
Vertical Line ◽  
Acoustic Data ◽  
Surface Ship ◽  
Line Array

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

2001 ◽  
Vol 7 (S2) ◽  
pp. 578-579
Author(s):  
David W. Knowles ◽  
Sophie A. Lelièvre ◽  
Carlos Ortiz de Solόrzano ◽  
Stephen J. Lockett ◽  
Mina J. Bissell ◽  
...  
Keyword(s):  
Image Analysis ◽  
Mammary Epithelial Cells ◽  
Nuclear Organization ◽  
Critical Role ◽  
Quantitative Model ◽  
Mammary Epithelial ◽  
Cell Phenotype ◽  
Proliferating Cells ◽  
Mitotic Apparatus ◽  
Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

Model-Based Software Testing and Analysis with C#

2007 ◽  
Author(s):  
Jonathan Jacky ◽  
Margus Veanes ◽  
Colin Campbell ◽  
Wolfram Schulte
Keyword(s):  
Software Testing ◽  
Model Based
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