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The sediment calibration tab is added to the software AcouSed. It includes selection of frequency, fine and sand sediments, linear interpolation of the profile of fine sediments concentration, computation of parameter ks, sv, X, alphaS and zeta.

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brahim committed 2024-07-24 21:59:35 +02:00
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commit fe52e097e7
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Model/acoustic_inversion_method_high_concentration.py
+25 -22
View File
@@ -51,7 +51,7 @@ class AcousticInversionMethodHighConcentration():
min_demodul = 1e-6
max_demodul = 500e-6
sample_demodul = demodul_granulo(r_grain, frac_vol_cumul[num_sample, :], min_demodul, max_demodul)
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,
@@ -64,9 +64,9 @@ class AcousticInversionMethodHighConcentration():
# sample_demodul.plot_interpolation()
# sample_demodul.plot_modes(N_modes)
print(f"mu_list : {sample_demodul.demodul_data_list[3 - 1].mu_list}")
print(f"sigma_list : {sample_demodul.demodul_data_list[3 - 1].sigma_list}")
print(f"w_list : {sample_demodul.demodul_data_list[3 - 1].w_list}")
# print(f"mu_list : {sample_demodul.demodul_data_list[3 - 1].mu_list}")
# print(f"sigma_list : {sample_demodul.demodul_data_list[3 - 1].sigma_list}")
# print(f"w_list : {sample_demodul.demodul_data_list[3 - 1].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)
@@ -75,7 +75,7 @@ class AcousticInversionMethodHighConcentration():
return proba_num
# ------------- Computing ks ------------- #
def form_factor_function_MoateThorne2012(self, a, freq, C=1500):
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
@@ -86,18 +86,19 @@ class AcousticInversionMethodHighConcentration():
# print(f"form factor = {f}")
return f
def ks(self, num_sample, freq, pdf):
# def ks(self, num_sample_sand, radius_grain_sand, frac_vol_sand_cumul, freq, C):
def ks(self, proba_num, freq, C):
# --- Calcul de la fonction de form ---
# form_factor = self.form_factor_function_MoateThorne2012(a, freq)
# print(f"form_factor shape = {form_factor}")
# print(f"form_factor = {form_factor}")
#--- Particle size distribution ---
proba_num = (
self.compute_particle_size_distribution_in_number_of_particles(
num_sample=num_sample, r_grain=stg.radius_grain, frac_vol_cumul=stg.frac_vol_sand_cumul))
# proba_num = (
# self.compute_particle_size_distribution_in_number_of_particles(
# num_sample=num_sample_sand, r_grain=radius_grain_sand, frac_vol_cumul=frac_vol_sand_cumul[num_sample_sand]))
print(f"proba_num : {proba_num}")
# print(f"proba_num : {proba_num}")
# --- Compute k_s by dividing two integrals ---
resampled_log_array = np.log(np.logspace(-10, -2, 3000))
@@ -105,11 +106,12 @@ class AcousticInversionMethodHighConcentration():
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)**2 * proba_num[i]
a2f2pdf += a**2 * self.form_factor_function_MoateThorne2012(a, freq, C)**2 * proba_num[i]
a3pdf += a**3 * proba_num[i]
print(f"a2f2pdf = {a2f2pdf}")
print(f"a3pdf = {a3pdf}")
# print("form factor ", self.form_factor_function_MoateThorne2012(a, freq, C))
# print(f"a2f2pdf = {a2f2pdf}")
# print(f"a3pdf = {a3pdf}")
ks = np.sqrt(a2f2pdf / a3pdf)
@@ -176,8 +178,8 @@ class AcousticInversionMethodHighConcentration():
# print("BS min : ", np.nanmin(BS))
# print("BS max : ", np.nanmax(BS))
# print("r2D : ", r2D)
print("kt shape : ", kt.shape)
print("kt : ", kt)
# print("kt shape : ", kt.shape)
# print("kt : ", kt)
# print("--------------------------------")
# for k in range(1):
# J_cross_section[k, :, :] = (3 / (16 * np.pi)) * ((BS[k, :, :]**2 * r2D[k, :, :]**2) / kt[k, :, :]**2)
@@ -247,12 +249,13 @@ class AcousticInversionMethodHighConcentration():
while np.isnan(res[i]):
res[i] = M_profile[-1]
i += -1
if stg.r_bottom.size != 0:
res[np.where(range_cells > r_bottom)] = np.nan
# print(f"2. res.shape = {res.shape}")
# print(f"res.shape = {res.shape}")
# print(f"res = {res}")
# print(f"r_bottom.shape = {r_bottom.shape}")
# print(f" = {res}")
if r_bottom.shape != (0,):
res[np.where(range_cells > r_bottom)] = np.nan
loc_point_lin_interp0 = range_cells[np.where((range_cells > sample_depth[0]) & (range_cells < sample_depth[-1]))]
# print(f"range_cells : {range_cells}")
@@ -276,9 +279,9 @@ class AcousticInversionMethodHighConcentration():
def zeta(self, alpha_s, r, M_profile_fine):
delta_r = r[1] - r[0]
zeta = alpha_s / (np.sum(M_profile_fine*delta_r))
zeta = alpha_s / (np.sum(np.array(M_profile_fine)*delta_r))
print(f"np.sum(M_profile_fine*delta_r) : {np.sum(M_profile_fine*delta_r)}")
# print(f"np.sum(M_profile_fine*delta_r) : {np.sum(M_profile_fine*delta_r)}")
# zeta0 = np.array([0.021, 0.035, 0.057, 0.229])
# zeta = zeta0[ind]
# zeta0 = np.array([0.04341525, 0.04832906, 0.0847188, np.nan])
Model/granulo_loader.py
+1 -1
View File
@@ -16,7 +16,7 @@ class GranuloLoader:
self._y = self._data.iloc[:, 1].tolist() # distance from left bank (m)
self._z = self._data.iloc[:, 2].tolist() # depth (m)
self._r_grain = 1e-6 * self._data.columns.values[5:] / 2
self._r_grain = 1e-6 * np.array(self._data.columns.values)[5:].astype(float) / 2
# self._r_grain = 1e-6 * np.array(self._data.columns.values)[5:].astype(float) / 2 # grain radius (m)
self._Ctot = self._data.iloc[:, 3].tolist() # Total concentration (g/L)