arXiv subjects

Mikhail B. Salin

Publications and source records attributed to Mikhail B. Salin.

4 recordsLinked to original sources

arXiv2023

Usage of single-camera video recording to measure sea surface roughness with machine learning methods

Photometry is a convenient operational method for monitoring such dynamically evolving phenomena as wind waves. Nowadays machine learning allows one to avoid explicit derivation of the solution to the problem, describing all the instructions for transforming the input data into the final result. Instead, an algorithm is used to independently find solutions through the integrated use of statistical data, from which patterns are derived, on the basis of which forecasts are made. An example of a problem for which a regular solution has traditionally been applied is the prediction of wave height from the input brightness of a water surface. The task is complicated by the multitude of possible physical models and the need to apply calibration coefficients. In this paper, we solve the problem of how, basing on the obtained brightness values and the corresponding heights, to train the neural network to further predict the heights from the incoming brightness values with the greatest accuracy.

physics.ao-ph↗

arXiv2022

Surface waves prediction based on long-range acoustic backscattering in a mid-frequency range

New data was obtained for a frequency band that had not been so well-studied for sea surface probing applications before. During the described 2-weeks sea experiment 1-3 kHz tonal pulses were emitted from a platform, located on the northern Black Sea shelf, and Doppler spectrum of reverberation was studied. We believe that this band is worth further studying due the sound propagation range is large enough to meet practical needs in coastal zone while the angle-distance resolution is quite moderate. However it is quite difficult to interpret the obtained data since backscattering spectrum shape is influenced by a series of effects and has a complicated link to wind waves and currents parameters. Backscattering of acoustical signals was received for distances around 2 nautical miles. Significant wave height, dominant wave frequency were estimated as the result of such signals processing with the use of machine learning tools. A decision-tree-based mathematical regression model was trained to solve the inverse problem. Wind waves prediction is in a good agreement with direct measurements, made on the platform, and machine learning results allow physical interpretation.

physics.ao-ph↗

arXiv2018

Examples of usage of nearfield acoustic holography methods for far field estimations: Part 1. CW signals

The paper is devoted to the usage of nearfield acoustic holography methods for estimating far field of the object. An experiment was carried out in anechoic chamber. First, acoustic filed was recorded in a plane that was close to source. This signals records were used to reconstruct the far field by computation routines. Second, the signal in the far field is measured and the results are compared. Several methods are tested and research on possible reduction of the microphone array size is carried out. The most significant reduction of the measurement facility complexity is usage a linear array in stead of the planar array that is made possible due to introduced computation routines

eess.AS↗

arXiv2014

Methods Of Measurement The Three-Dimensional Wind Waves Spectra, Based On The Processing Of Video Images Of The Sea Surface

Optical instruments for measuring surface-wave characteristics provide a better spatial and temporal resolution than other methods, but they face difficulties while converting the results of indirect measurements into absolute levels of the waves. We have solved this problem to some extent. In this paper, we propose an optical method for measuring the 3D power spectral density of the surface waves and spatio-temporal samples of the wave profiles. The method involves, first, synchronous recording of the brightness field over a patch of a rough surface and measurement of surface oscillations at one or more points and, second, filtering of the spatial image spectrum. Filter parameters are chosen to maximize the correlation of the surface oscillations recovered and measured at one or two points. In addition to the measurement procedure, the paper provides experimental results of measuring multidimensional spectra of roughness, which generally agree with theoretical expectations and the results of other authors.

physics.ao-ph↗