Near-specular acoustic scattering from a buried submarine mud volcano.

Submarine mud volcanoes are objects that form on the seafloor due to the emission of gas and fluidized sediment from the Earth's interior. They vary widely in size, can be exposed or buried, and are of interest to the underwater acoustics community as potential sources of active sonar clutter. Coincident seismic reflection data and low frequency bistatic scattering data were gathered from one such buried mud volcano located in the Straits of Sicily.

Correlation of RF signals during angular compounding.

A theoretical analysis of the correlation between radio-frequency (RF) echo signal data acquired from the same location but at different angles is presented. The accuracy of the theoretical results is verified with computer simulations. Refinements to previous analyses of the correlation of RF signals originating from the same spatial location at different angular positions are made.

Optimization of angular and frequency compounding in ultrasonic attenuation estimations.

Previous reports have shown that the variance in ultrasound attenuation measurements is reduced when spatial and frequency compounding were applied in data acquisition and analysis. This paper investigates factors affecting the efficiency of compound attenuation imaging methods. A theoretical expression is derived that predicts the correlation between attenuation versus frequency slope (beta) estimates as a function of the increment between measurement frequencies (deltaf ) and the angular separation between beam lines (Delta (theta)).

Improved parametric imaging of scatterer size estimates using angular compounding.

The feasibility of estimating and imaging scatterer size using backscattered ultrasound signals and spectral analysis techniques was demonstrated previously. In many cases, size estimation, although computationally intensive, has proven to be useful for monitoring, diagnosing, and studying disease. However, a difficulty that is encountered in imaging scatterer size is the large estimator variance caused by statistical fluctuations in echo signals from random media.