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已同步 2025-09-19 01:03:50 +08:00
葛峻恺
311
10_ice&water_content.py
普通文件
311
10_ice&water_content.py
普通文件
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import numpy as np
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from scipy.interpolate import interp1d
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import matplotlib.pyplot as plt
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import matplotlib.colors as mcolors
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from numpy.lib.stride_tricks import sliding_window_view
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def smooth_rows_reflect(arr, window):
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"""
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Row-wise moving average with reflective padding.
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Parameters
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----------
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arr : array-like, shape (N_rows, N_cols)
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Input 2D array.
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window : int
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Window length for the moving average (>=1).
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Returns
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-------
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smoothed : ndarray
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Array with the same shape as `arr`, smoothed along axis=1.
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"""
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arr = np.asarray(arr)
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w = int(window)
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if w <= 1:
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return arr.copy()
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# Ensure the window does not exceed the number of columns
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w = min(w, arr.shape[1])
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# Make output length equal to input: left + right = w - 1
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left = w // 2
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right = w - 1 - left # for even w: (left, right) = (w//2, w//2 - 1)
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padded = np.pad(arr, ((0, 0), (left, right)), mode='reflect')
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# Slide along axis=1 and take the mean within each window
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win = sliding_window_view(padded, window_shape=w, axis=1)
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return win.mean(axis=-1)
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def custom_rainbow():
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"""
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Create a custom colormap with expanded red and blue regions,
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de-emphasizing the yellow/green middle.
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"""
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colors = [
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(0.00, 'red'),
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(1.00, 'blue'),
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]
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return mcolors.LinearSegmentedColormap.from_list("custom_rainbow", colors)
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def plot_water_constant(data, path, line_length=400, time_length=10):
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"""
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Plot estimated water content (%) image.
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Parameters
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----------
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data : ndarray, shape (N_time, N_dist)
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2D array of water content in fraction (0–1).
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path : str
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Output image path.
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line_length : float
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Profile length in meters (x-axis extent).
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time_length : float
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Time window in ns (y-axis extent).
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"""
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num_points, num_lines = data.shape
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plt.rcParams.update({'font.family': 'Times New Roman', 'font.size': 20})
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plt.figure(figsize=(12, 4))
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im = plt.imshow(
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data * 100, aspect='auto', cmap='YlGnBu',
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extent=[0, line_length, time_length, 0],
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vmin=20, vmax=40
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)
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plt.xlabel('Distance (m)', fontsize=20)
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plt.xticks([0, 20, 40, 60, 80, 100])
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plt.ylabel('Time (ns)', fontsize=20)
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plt.yticks([0, 2, 4, 6, 8, 10])
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cbar = plt.colorbar(im)
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cbar.set_label('Water content (%)', fontsize=20)
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plt.tick_params(axis='both', direction='in', width=1)
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for spine in plt.gca().spines.values():
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spine.set_linewidth(1)
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plt.grid(False)
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plt.tight_layout()
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plt.savefig(path, dpi=300)
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plt.show()
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def plot_ice_constant(data, path, line_length=400, time_length=10):
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"""
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Plot estimated ice content (%) image.
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Parameters
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----------
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data : ndarray, shape (N_time, N_dist)
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2D array of ice content in fraction (0–1).
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path : str
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Output image path.
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line_length : float
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Profile length in meters (x-axis extent).
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time_length : float
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Time window in ns (y-axis extent).
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"""
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num_points, num_lines = data.shape
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plt.rcParams.update({'font.family': 'Times New Roman', 'font.size': 20})
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plt.figure(figsize=(12, 4))
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im = plt.imshow(
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data * 100, aspect='auto', cmap='PuBu',
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extent=[0, line_length, time_length, 0],
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vmin=10, vmax=40
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)
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plt.xlabel('Distance (m)', fontsize=20)
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plt.xticks([0, 20, 40, 60, 80, 100])
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plt.ylabel('Time (ns)', fontsize=20)
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plt.yticks([0, 2, 4, 6, 8, 10])
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cbar = plt.colorbar(im)
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cbar.set_label('Ice content (%)', fontsize=20)
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plt.tick_params(axis='both', direction='in', width=1)
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for spine in plt.gca().spines.values():
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spine.set_linewidth(1)
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plt.grid(False)
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plt.tight_layout()
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plt.savefig(path, dpi=300)
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plt.show()
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def plot_permittivity_constant(data, path, line_length=400, time_length=10):
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"""
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Plot inverted relative permittivity image.
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Parameters
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----------
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data : ndarray, shape (N_time, N_dist)
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2D array of relative permittivity.
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path : str
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Output image path.
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line_length : float
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Profile length in meters (x-axis extent).
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time_length : float
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Time window in ns (y-axis extent).
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"""
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num_points, num_lines = data.shape
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plt.rcParams.update({'font.family': 'Times New Roman', 'font.size': 20})
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plt.figure(figsize=(12, 4))
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im = plt.imshow(
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data, aspect='auto', cmap='rainbow_r',
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extent=[0, line_length, time_length, 0],
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vmin=5, vmax=25
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)
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plt.xlabel('Distance (m)', fontsize=20)
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plt.xticks([0, 20, 40, 60, 80, 100])
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plt.ylabel('Time (ns)', fontsize=20)
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plt.yticks([0, 2, 4, 6, 8, 10])
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cbar = plt.colorbar(im)
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cbar.set_label('Permittivity', fontsize=20)
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plt.tick_params(axis='both', direction='in', width=1)
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for spine in plt.gca().spines.values():
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spine.set_linewidth(1)
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plt.grid(False)
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plt.tight_layout()
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plt.savefig(path, dpi=300)
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plt.show()
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def estimate_water_ice(eps_map, n_porosity=0.45,
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eps_s=4.7, eps_w=86.0, eps_i=3.15):
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"""
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Estimate volumetric ice and water contents from relative permittivity.
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Parameters
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----------
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eps_map : ndarray
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Relative permittivity (inverted) of shape (N_time, N_dist).
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n_porosity : float
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Porosity (m³/m³).
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eps_s : float
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Permittivity of the solid phase.
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eps_w : float
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Permittivity of liquid water.
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eps_i : float
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Permittivity of ice.
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Returns
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-------
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theta_i_crim : ndarray
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Ice content (m³/m³) estimated via CRIM.
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theta_w_emp : ndarray
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Water content (m³/m³) estimated via empirical relation.
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"""
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# --- Ice content via CRIM ---
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sqrt_eps = np.sqrt(eps_map)
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sqrt_es = np.sqrt(eps_s)
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sqrt_ew = np.sqrt(eps_w)
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sqrt_ei = np.sqrt(eps_i)
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# θ_i = [√ε - (1-n)√ε_s - n√ε_w] / (√ε_i - √ε_w)
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theta_i_crim = (sqrt_eps - (1 - n_porosity) * sqrt_es - n_porosity * sqrt_ew) / (sqrt_ei - sqrt_ew)
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theta_i_crim = np.clip(theta_i_crim, 0.0, n_porosity)
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# Water content (active layer) via empirical relation
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theta_w_emp = 0.458 * (eps_map ** 0.26) - 0.664
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theta_w_emp = np.clip(theta_w_emp, 0.0, n_porosity)
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# If needed, CRIM water content would be: theta_w_crim = n_porosity - theta_i_crim
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return theta_i_crim, theta_w_emp
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def convert2depth(dielectric_img, dt=1e-9, dz_interval=0.05, max_samples=399):
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"""
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Convert a time-domain permittivity section to a depth-domain permittivity section.
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Parameters
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----------
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dielectric_img : ndarray, shape (N_time, N_x)
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Relative permittivity in time domain.
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dt : float
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Time sampling interval in seconds.
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dz_interval : float
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Target depth sampling interval in meters.
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max_samples : int
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Use only the first `max_samples` samples per trace when integrating.
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Processing
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----------
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For each column (trace):
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1) Compute local EM velocity v = c / sqrt(eps).
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2) Accumulate depth: depth_z = cumsum(v * dt) / 2 (two-way time to depth).
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3) Interpolate permittivity onto a uniform depth grid with spacing `dz_interval`.
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Returns
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-------
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depth_converted_img : ndarray, shape (N_depth, N_x)
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Permittivity in depth domain (rows = depth, cols = distance).
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max_depth_m : float
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Maximum depth covered across traces (in meters).
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"""
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c = 3e8 # m/s
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n_time, n_x = dielectric_img.shape
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depth_img_list = []
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z_stack_length = []
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for i in range(n_x):
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eps_line = dielectric_img[:, i][:max_samples]
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v_line = c / np.sqrt(eps_line)
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depth_z = np.cumsum(v_line * dt) / 2
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# Anchor at depth 0 with the first permittivity value
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depth_z = np.insert(depth_z, 0, 0)
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eps_line = np.insert(eps_line, 0, eps_line[0])
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max_depth = np.max(depth_z)
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dest_depth = np.arange(0, max_depth, dz_interval)
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interp_func = interp1d(depth_z, eps_line, kind='linear', bounds_error=False, fill_value=np.nan)
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eps_interp = interp_func(dest_depth)
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depth_img_list.append(eps_interp)
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z_stack_length.append(len(dest_depth))
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max_length = np.max(z_stack_length)
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depth_img_padded = [
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np.pad(v, (0, max_length - len(v)), constant_values=np.nan) for v in depth_img_list
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]
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depth_converted_img = np.array(depth_img_padded).T
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max_depth_m = np.max(z_stack_length) * dz_interval
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return depth_converted_img, max_depth_m
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# ------------------------- Script usage -------------------------
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data_file = './time_result_csv/DONGRONG.csv'
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path = './IMG/DONGRONG_depth.png'
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# Load data, drop the first row (e.g., file index), scale by 30
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data = np.delete(np.loadtxt(data_file, delimiter=",", skiprows=0), [0], axis=0) * 30
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# Row-wise smoothing with reflective padding
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data = smooth_rows_reflect(data.T, window=10)
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data = smooth_rows_reflect(data.T, window=30)
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depth_converted_img, max_depth = convert2depth(data, dt=0.2e-9, dz_interval=0.01, max_samples=1000)
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plot_permittivity_constant(depth_converted_img, path, line_length=100, time_length=10)
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ice, water = estimate_water_ice(depth_converted_img, n_porosity=0.4, eps_s=7, eps_w=81, eps_i=3.15)
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plot_ice_constant(ice, './IMG/ice.png', line_length=100, time_length=10)
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plot_water_constant(water, './IMG/water.png', line_length=100, time_length=10)
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