Turbulent Patch Identification in Microstructure Profiles: A Method Based on Wavelet Denoising and Thorpe Displacement Analysis

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Abstract
A new method based on wavelet denoising and the analysis of Thorpe displacements dT profiles is presented for turbulent patch identification. Thorpe profiles are computed by comparing the observed density profile ρ(z) and the monotonic density profile ρm(z), which is constructed by reordering ρ(z) to make it gravitationally stable. This method is decomposed in two main algorithms. The first, based on a wavelet denoising procedure, reduces most of the noise present in the measured profiles. This algorithm has been tested from theoretical profiles and has demonstrated a high efficiency in noise reduction, only some limitations were detected in very low-density gradient conditions. The second algorithm is based on a semiquantitative analysis of the Thorpe displacements. By comparing each displacement dT with its potential error EdT, it is possible to classify samples in three possible states: Z (dT = 0), U (dT < EdT), and S (dT > EdT). This classification makes it possible to compute two statistical... Abstract A new method based on wavelet denoising and the analysis of Thorpe displacements dT profiles is presented for turbulent patch identification. Thorpe profiles are computed by comparing the observed density profile ρ(z) and the monotonic density profile ρm(z), which is constructed by reordering ρ(z) to make it gravitationally stable. This method is decomposed in two main algorithms. The first, based on a wavelet denoising procedure, reduces most of the noise present in the measured profiles. This algorithm has been tested from theoretical profiles and has demonstrated a high efficiency in noise reduction, only some limitations were detected in very low-density gradient conditions. The second algorithm is based on a semiquantitative analysis of the Thorpe displacements. By comparing each displacement dT with its potential error EdT, it is possible to classify samples in three possible states: Z (dT = 0), U (dT < EdT), and S (dT > EdT). This classification makes it possible to compute two statistical...

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