Near bottom sediment characterization offshore SW San Clemente Island

Normal incidence, 23.5 kHz seafloor acoustic backscatter data and bottom video were measured with the Deep Tow instrument package of the Scripps Institution of Oceanography in 100 meter water depth south of San Clemente Island, CA. The collected data were processed using an echo envelopesediment characterization method, to derive geoacoustic parameters such as particle mean grain size and the strength of the power law characterizing the roughness energy density spectrum of thesediment-water interface. Two regions, sand and silt, were selected based on available ground truth, perceived along-track sediment homogeneity, data quality and tow fish stability. Distinction between sand and fine grain sediments can be accomplished by creation of feature vectors comprised of mean grain size (MΦ) and interface roughness spectral strength (w2). Estimates for mean grain size and roughness spectral strength (MΦ, w2) were (1.5, 0.0095) for sand, and (6.7, 0.0033) for silt, where MΦ is expressed in PHI units, and w2 has units cm4. These results are consistent with local ground truth measurements and illustrate the potential of this sediment characterization method in survey mode

Remote sensing of sediment characteristics by optimized echo-envelope matching

A sediment geoacoustic parameter estimation technique is described which compares bottom returns, measured by a calibrated monostatic sonar oriented within 15° of vertical and having a 10°–21° beamwidth, with an echo envelope model based on high-frequency (10–100 kHz) incoherent backscattertheory and sediment properties such as: mean grain size, strength, and exponent of the power law characterizing the interface roughness energy density spectrum, and volume scattering coefficient. An average echo envelope matching procedure iterates on the reflection coefficient to match the peak echo amplitude and separate coarse from fine-grain sediments, followed by a global optimization using a combination of simulated annealing and downhill simplex searches over mean grain size, interface roughness spectral strength, and sediment volume scattering coefficient. Error analyses using Monte Carlo simulations validate this optimization procedure. Moderate frequencies (33 kHz) and orientations normal with the interface are best suited for this application. Distinction between sands and fine-grain sediments is demonstrated based on acoustic estimation of mean grain size alone. The creation of feature vectors from estimates of mean grain size and interface roughness spectral strength shows promise for intraclass separation of silt and clay. The correlation between estimated parameters is consistent with what is observed in situ

Human Perception-Inspired Grain Segmentation Refinement Using Conditional Random Fields

Accurate segmentation of interconnected line networks, such as grain boundaries in polycrystalline material microstructures, poses a significant challenge due to the fragmented masks produced by conventional computer vision algorithms, including convolutional neural networks. These algorithms struggle with thin masks, often necessitating intricate post-processing for effective contour closure and continuity. Addressing this issue, this paper introduces a fast, high-fidelity post-processing technique, leveraging domain knowledge about grain boundary connectivity and employing conditional random fields and perceptual grouping rules. This approach significantly enhances segmentation mask accuracy, achieving a 79% segment identification accuracy in validation with a U-Net model on electron microscopy images of a polycrystalline oxide. Additionally, a novel grain alignment metric is introduced, showing a 51% improvement in grain alignment, providing a more detailed assessment of segmentation performance for complex microstructures. This method not only enables rapid and accurate segmentation but also facilitates an unprecedented level of data analysis, significantly improving the statistical representation of grain boundary networks, making it suitable for a range of disciplines where precise segmentation of interconnected line networks is essential