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acoused/Model/acoustic_inversion_method_h...

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# ============================================================================== #
# mainwindow.py - AcouSed #
# Copyright (C) 2024 INRAE #
# #
# This program is free software: you can redistribute it and/or modify #
# it under the terms of the GNU General Public License as published by #
# the Free Software Foundation, either version 3 of the License, or #
# (at your option) any later version. #
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with this program. If not, see <https://www.gnu.org/licenses/>. #
# by Brahim MOUDJED #
# ============================================================================== #
# -*- coding: utf-8 -*-
import logging
import numpy as np
import settings as stg
from Model.GrainSizeTools import demodul_granulo, mix_gaussian_model
from tools import trace
logger = logging.getLogger("acoused")
class AcousticInversionMethodHighConcentration():
""" Thi class compute acoustic inversion method adapted for high suspended sediments concentration
For instance, the case of dam flush downstream Rhone-Isere confluence (07/01/2018) evaluated at ~ 10g/L """
def __init__(self):
pass
# ==========================================
# Functions
# ==========================================
# ---------- Computing sound speed ------------- #
def water_velocity(self, T):
"""Computing sond speed from Bilaniuk and Wong 1993"""
C = 1.40238744 * 1e3 + 5.03836171 * T - 5.81172916 * 1e-2 * T ** 2 + 3.34638117 * 1e-4 * T ** 3 - \
1.48259672 * 1e-6 * T ** 4 + 3.16585020 * 1e-9 * T ** 5
return C
# -------- Computing water attenuation coefficient ----------- #
def water_attenuation(self, freq, T):
"""Computing attenuation from François and Garrison 1982"""
if T > 20:
alpha = (3.964 * 1e-4 - 1.146 * 1e-5 * T + 1.45 * 1e-7 * T ** 2 - 6.5 * 1e-10 * T ** 3) * 1e-3 * \
(np.log(10) / 20) * (freq * 1e-3) ** 2
else:
alpha = (4.937 * 1e-4 - 2.59 * 1e-5 * T + 9.11 * 1e-7 * T ** 2 - 1.5 * 1e-8 * T ** 3) * 1e-3 * \
(np.log(10) / 20) * (freq * 1e-3) ** 2
return alpha
# --- Gaussian mixture ---
def compute_particle_size_distribution_in_number_of_particles(self, num_sample, r_grain, frac_vol_cumul):
min_demodul = 1e-6
max_demodul = 500e-6
sample_demodul = demodul_granulo(r_grain, frac_vol_cumul[num_sample], min_demodul, max_demodul)
resampled_log_array = np.log(np.logspace(-10, -2, 3000))
proba_vol_demodul = mix_gaussian_model(resampled_log_array,
sample_demodul.demodul_data_list[2].mu_list,
sample_demodul.demodul_data_list[2].sigma_list,
sample_demodul.demodul_data_list[2].w_list)
proba_vol_demodul = proba_vol_demodul / np.sum(proba_vol_demodul)
ss = np.sum(proba_vol_demodul / np.exp(resampled_log_array) ** 3)
proba_num = proba_vol_demodul / np.exp(resampled_log_array) ** 3 / ss
return proba_num
# ------------- Computing ks ------------- #
def form_factor_function_MoateThorne2012(self, a, freq, C):
"""This function computes the form factor based on the equation of
Moate and Thorne (2012)"""
# computing the wave number
k = 2 * np.pi * freq / C
x = k * a
f = (x ** 2 * (1 - 0.25 * np.exp(-((x - 1.5) / 0.35) ** 2)) * (1 + 0.6 * np.exp(-((x - 2.9) / 1.15) ** 2))) / (
42 + 28 * x ** 2)
return f
def ks(self, proba_num, freq, C):
# --- Compute k_s by dividing two integrals ---
resampled_log_array = np.log(np.logspace(-10, -2, 3000))
a2f2pdf = 0
a3pdf = 0
for i in range(len(resampled_log_array)):
a = np.exp(resampled_log_array)[i]
a2f2pdf += a**2 * self.form_factor_function_MoateThorne2012(a, freq, C)**2 * proba_num[i]
a3pdf += a**3 * proba_num[i]
ks = np.sqrt(a2f2pdf / a3pdf)
return ks
# ------------- Computing sv ------------- #
def sv(self, ks, M_sand):
sv = (3 / (16 * np.pi)) * (ks ** 2) * M_sand
return sv
# ------------- Computing X ------------- #
def X_exponent(self, freq1, freq2, sv_freq1, sv_freq2):
X = np.log(sv_freq1 / sv_freq2) / np.log(freq1 / freq2)
return X
# -------------- Computing Kt -------------- #
def kt_corrected(self, r, water_velocity, RxGain, TxGain, kt_ref):
"""Computing the instrument constant Kt that depends on gain and temperature"""
# Cell size
delta_r = r[1] - r[0]
# Pulse length
tau = 2 * delta_r / 1500
# Sound speed
cel = water_velocity
# Reference pulse length
tau_ref = 13.33333e-6
# Reference sound speed
c_ref = 1475
# Gain
gain = 10 ** ((RxGain + TxGain) / 20)
# Computing Kt
kt = kt_ref * gain * np.sqrt(tau * cel / (tau_ref * c_ref)) # 1D numpy array
return kt
# ------------- Computing J_cross_section ------------- #
def j_cross_section(self, BS, r2D, kt):
J_cross_section = (3 / (16 * np.pi)) * ((BS**2 * r2D**2) / kt**2)
return J_cross_section
# ------------- Computing alpha_s ------------- #
def alpha_s(self, sv, j_cross_section, depth, alpha_w):
alpha_s = (np.log(sv / j_cross_section) / (4 * depth)) - alpha_w
return alpha_s
# ------------- Computing interpolation of fine SSC -------------
def M_profile_SCC_fine_interpolated(self, sample_depth, M_profile, range_cells, r_bottom):
'''Computing interpolation of fine SSC data obtained from water sampling
collected at various depth in the vertical sample'''
res = np.zeros((len(range_cells),)) * np.nan
l0 = sample_depth
l1 = [l0.index(x) for x in sorted(l0)]
l2 = [l0[k] for k in l1]
c1 = [list(M_profile)[j] for j in l1]
for i in range(len(c1) - 1):
# print("i = ", i)
r_ini = l2[i]
c_ini = c1[i]
r_end = l2[i + 1]
c_end = c1[i + 1]
# Computing the linear equation
a = (c_end - c_ini) / (r_end - r_ini)
b = c_ini - a * r_ini
# Finding the indices of r_ini and r_end in the interpolated array
loc = (range_cells >= r_ini) * (range_cells < r_end)
# Filling the array with interpolation values
res[loc] = range_cells[loc] * a + b
# Filling first and last values
i = 0
while np.isnan(res[i]):
res[i] = c1[0]
i += 1
# Filling the last values
i = -1
while np.isnan(res[i]):
res[i] = c1[-1]
i += -1
if r_bottom.size != 0:
res[np.where(range_cells > r_bottom)] = np.nan
loc_point_lin_interp0 = range_cells[np.where((range_cells > l2[0]) & (range_cells < l2[-1]))]
res0 = res[np.where((range_cells > l2[0]) & (range_cells < l2[-1]))]
loc_point_lin_interp = loc_point_lin_interp0[np.where(loc_point_lin_interp0 > l2[0])]
res = res0[np.where(loc_point_lin_interp0 > l2[0])]
return (loc_point_lin_interp, res)
# ------------- Computing zeta ------------- #
def zeta(self, alpha_s, r, M_profile_fine):
delta_r = r[1] - r[0]
zeta = alpha_s / (np.sum(np.array(M_profile_fine)*delta_r))
return zeta
# ------------- Computing VBI ------------- #
def VBI_cross_section(self, freq1, freq2,
zeta_freq1, zeta_freq2,
j_cross_section_freq1, j_cross_section_freq2,
r2D,
water_attenuation_freq1, water_attenuation_freq2,
X):
logVBI = ((zeta_freq2 *
np.log(j_cross_section_freq1 * np.exp(4 * r2D * water_attenuation_freq1) /
(freq1 ** X)) -
zeta_freq1 *
np.log(j_cross_section_freq2 * np.exp(4 * r2D * water_attenuation_freq2) /
(freq2 ** X))) /
(zeta_freq2 - zeta_freq1))
return np.exp(logVBI)
# ------------- Computing SSC fine ------------- #
def SSC_fine(self, zeta, r2D, VBI, freq, X, j_cross_section, alpha_w):
SSC_fine = (1/zeta) * ( 1/(4 * r2D) * np.log((VBI * freq**X) / j_cross_section) - alpha_w)
return SSC_fine
# ------------- Computing SSC sand ------------- #
def SSC_sand(self, VBI, freq, X, ks):
SSC_sand = (16 * np.pi * VBI * freq ** X) / (3 * ks**2)
return SSC_sand
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