scholarly journals Forward modeling for marine sediment characterization using chirp sonars

Geophysics ◽  
2011 ◽  
Vol 76 (4) ◽  
pp. T91-T99 ◽  
Author(s):  
Sandrine Rakotonarivo ◽  
Michel Legris ◽  
Rozenn Desmare ◽  
Jean-Pierre Sessarégo ◽  
Jean-François Bourillet
Keyword(s):  
Layer Thickness ◽  
Forward Modeling ◽  
Forward Model ◽  
Central Frequency ◽  
Transitional Layer ◽  
Quantitative Characterization ◽  
Buried Layers ◽  
Chirp Sonar ◽  
Good Agreement

This paper investigates the forward modeling of chirp-sonar data for the quantitative characterization of marine subbottom sediment between 1 and 10 kHz. The forward modeling, based on a transfer function approach, included impacts of layering or impedance mismatch, attenuation, roughness, and transitional layers, i.e., continuous impedance variations. The presented approach provided the best compromise between the number of available geoacoustic parameters from chirp-sonar data and the subbottom modeling accuracy. The forward model was tested on deep-sea chirp-sonar data acquired at a central frequency of 3.5 kHz. Comparisons between synthetic and experimental seismograms showed good agreement for the first 15 m of buried layers. Performance of the inversion using this forward model was also examined through sensitivity analysis. The results suggested that estimations of layer thickness, impedance, and transitional layer thickness were robust, whereas roughness and attenuation estimations were subject to wavelength and layer thickness conditions.

Fine structure in Raman spectroscopy and X-ray reflectometry and its uses for characterization of superlattices

1992 ◽  
Vol 70 (10-11) ◽  
pp. 843-851 ◽  
Author(s):  
P. X. Zhang ◽  
D. J. Lockwood ◽  
J.-M. Baribeau
Keyword(s):  
Fine Structure ◽  
Test Sample ◽  
Quantitative Characterization ◽  
Interface Quality ◽  
X Ray ◽  
Special Cases ◽  
Period Fluctuation ◽  
Phonon Velocity ◽  
Good Agreement

Fine structure in the folded longitudinal acoustic (FLA) phonon peaks from high-resolution Raman spectra of Si/Si1−xGex superlattices has been observed. The observed FLA fine structure is attributed to fluctuations in the superlattice periodicity. A 0.2 cm−1 splitting in the FLA peaks is observed in the Raman spectrum of a 15-period superlattice. According to the Rytov theory for the FLA phonons, the peak splitting corresponds to a change in period of about four monolayers (~0.5 nm) during growth. X-ray reflectometry on these samples also shows a similar doublet formation on the main reflection peaks, consistent with a 2–3 monolayer period fluctuation. A test sample with a built-in period variation was also well characterized by both techniques. The good agreement between the two techniques, as well as between the theories and the experiments, demonstrates that both techniques are capable of high precision and allow detailed and quantitative characterization of the superlattice perfection. In addition to determining the phonon velocity, dispersion relation, layer thickness, period, and composition of the superlattices, these results demonstrate the possibility of measuring the fluctuation in the period and the interface quality, as well as the numbers of given periods in some special cases.

Nonlinear inversion of subsidence signatures induced by tunnels detected with surface and remote sensing measurements

2019 ◽  
Vol 38 (7) ◽  
pp. 550-553
Author(s):  
Jorge Parra ◽  
Jonathan Parra ◽  
Marius Necsoiu
Keyword(s):  
Remote Sensing ◽  
State Of The Art ◽  
Cost Effective ◽  
Forward Modeling ◽  
Nonlinear Inversion ◽  
Inversion Algorithm ◽  
Gap Parameter ◽  
Remote Sensing Methods ◽  
Good Agreement

The state of the art in predicting tunnel-induced subsidence settlements is based on empirical and analytical methods. Empirical methods are useful when the equations are implemented with host medium properties where tunnels have been excavated. Analytical solutions can predict tunneling-induced ground movements, with the predictions accounting for tunnel radius and depth as well as ground-loss parameters in soft soils. The drawback is that these methods require human intervention, as each model must be adjusted manually by the interpreter until the model signature fits the observed data. It would take tremendous effort to evaluate displacement anomalies detected by remote sensing methods using such forward-modeling methods. Therefore, we present a method based on an inversion algorithm that automatically inverts subsidence signatures for tunnel radius, depth, Poisson's ratio, and the gap parameter. It is an advancement over conventional methods because it does not require a first guess, and it can invert several subsidence signatures in a matter of minutes. The algorithm, coupled with remote sensing-based displacement maps, is a cost-effective solution in operational characterization of displacement anomalies. We demonstrate that observed and predicted subsidence signatures are in good agreement with existing tunnel data in uniform clay and that the inversion parameters correspond to those predicted with forward modeling alone.

scholarly journals A forward model-based validation of cardiovascular system identification

2001 ◽  
Vol 281 (6) ◽  
pp. H2714-H2730 ◽  
Author(s):  
Ramakrishna Mukkamala ◽  
Richard J. Cohen
Keyword(s):  
System Identification ◽  
Cardiovascular System ◽  
Autonomic Function ◽  
Forward Modeling ◽  
Forward Model ◽  
Theoretical Evaluation ◽  
System Identification Method ◽  
Identification Method ◽  
Arterial Blood

We present a theoretical evaluation of a cardiovascular system identification method that we previously developed for the analysis of beat-to-beat fluctuations in noninvasively measured heart rate, arterial blood pressure, and instantaneous lung volume. The method provides a dynamical characterization of the important autonomic and mechanical mechanisms responsible for coupling the fluctuations (inverse modeling). To carry out the evaluation, we developed a computational model of the cardiovascular system capable of generating realistic beat-to-beat variability (forward modeling). We applied the method to data generated from the forward model and compared the resulting estimated dynamics with the actual dynamics of the forward model, which were either precisely known or easily determined. We found that the estimated dynamics corresponded to the actual dynamics and that this correspondence was robust to forward model uncertainty. We also demonstrated the sensitivity of the method in detecting small changes in parameters characterizing autonomic function in the forward model. These results provide confidence in the performance of the cardiovascular system identification method when applied to experimental data.

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