n/a

load_main.py

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+import segyio
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.signal import tukey, hilbert
+import mig_fk  # Custom FK migration module
+
+
+def write_to_dat_append(data, x, y, file):
+    """
+    Append 3D point cloud data to a .dat file in the format (x, y, z, value)
+    
+    Parameters:
+        data (2D array): Processed data to export
+        x (float): X offset for the current profile
+        y (float): Y offset for the current profile
+        file (file object): Output file opened in write/append mode
+    """
+    nz, nx = data.shape
+    x_coords = np.linspace(x + 8, 213, nx)
+    z_coords = np.linspace(0, 85, nz)
+
+    for i in range(nz):
+        for j in range(nx):
+            file.write(f"{x_coords[j]} {y + 3} {z_coords[i]} {data[i, j]}\n")
+
+
+def sliding_window_mean(data, window_size):
+    """
+    Compute local background mean using a sliding window
+    
+    Parameters:
+        data (2D array): Input data of shape (n_traces, n_samples)
+        window_size (int): Size of the sliding window (must be odd)
+    
+    Returns:
+        2D array: Local mean values with the same shape as input
+    """
+    if window_size % 2 == 0:
+        raise ValueError("Window size must be an odd number.")
+
+    half_window = window_size // 2
+    padded_data = np.pad(data, ((0, 0), (half_window, half_window)), mode='reflect')
+    local_means = np.empty_like(data)
+
+    for i in range(data.shape[1]):
+        local_means[:, i] = np.mean(padded_data[:, i:i + window_size], axis=1)
+
+    return local_means
+
+
+def taper_field(in_array): 
+    """
+    Apply a Tukey window-based taper to suppress edge noise
+    
+    Parameters:
+        in_array (1D array): Input radar trace
+    
+    Returns:
+        1D array: Tapered trace
+    """
+    window = tukey(500, 0.3)
+    window[65:] = 1  # Force central part to remain unchanged
+    window = window ** 2
+    return in_array * window
+
+
+def load(filename, static, trace_remove, x, y, sub_aver, amplify):
+    """
+    Load and process GPR data from a SEG-Y file
+    
+    Parameters:
+        filename (str): Path to SEG-Y file
+        static (int): Number of top samples to remove (static correction)
+        trace_remove (int): Number of traces to remove from the end
+        x (float): X-coordinate offset
+        y (float): Y-coordinate offset
+        sub_aver (int): Window size for background subtraction
+        amplify (float): Amplitude scale factor for visualization
+    """
+    with segyio.open(filename, 'r', ignore_geometry=True) as segyfile:
+        raw_traces = segyfile.trace.raw[:]
+        data_meta = raw_traces.T[static:, :-trace_remove]
+        data=data_meta[0:500]
+
+        # Subtract local mean background (clutter removal)
+        reference_mean = sliding_window_mean(data, sub_aver)
+        data = data - reference_mean
+
+        # Apply tapering to suppress edge effects
+        data = np.apply_along_axis(taper_field, 0, data)
+
+        # Perform FK migration (custom)
+        data, twtt, mig_pos = mig_fk.fkmig(data, dt=0.02425e-9, dx=0.005, v=1.10e8)
+
+        # Compute envelope using Hilbert transform
+        data_envelop = np.abs(hilbert(data, axis=0))
+
+    # Plot migrated image
+    plt.title(filename[7:-4])
+    plt.imshow(data * amplify, cmap='gray_r', aspect='auto',
+               vmin=-np.max(np.abs(data)), vmax=np.max(np.abs(data)))
+    plt.colorbar()
+    plt.show()
+
+    # Plot envelope
+    plt.title(filename[7:-4] + " Envelope")
+    plt.imshow(data_envelop * amplify, cmap='gray_r', aspect='auto',
+               vmin=0, vmax=0.0040)
+    plt.colorbar()
+    plt.show()
+
+    # Save envelope to .dat file
+    write_to_dat_append(data_envelop, x, y, file)
+
+
+# Main process for batch loading and writing
+with open('3D.dat', 'w') as file:
+    load('./data/x0y0.sgy', static=35, trace_remove=1, x=0, y=0, sub_aver=51, amplify=3)
+    load('./data/x0y5.sgy', static=35, trace_remove=1, x=0, y=5, sub_aver=51, amplify=5)
+    load('./data/x0y10.sgy', static=35, trace_remove=1, x=0, y=10, sub_aver=51, amplify=10)
+    load('./data/x0y15.sgy', static=35, trace_remove=1, x=0, y=15, sub_aver=51, amplify=10)
+    load('./data/x0y20.sgy', static=35, trace_remove=1, x=0, y=20, sub_aver=51, amplify=10)
+    load('./data/x10y25.sgy', static=35, trace_remove=1, x=10, y=25, sub_aver=51, amplify=10)
+    load('./data/x20y30.sgy', static=35, trace_remove=1, x=20, y=30, sub_aver=51, amplify=10)
+    load('./data/x30y35.sgy', static=35, trace_remove=15, x=30, y=35, sub_aver=51, amplify=10)
+    load('./data/x30y40.sgy', static=35, trace_remove=1, x=30, y=40, sub_aver=51, amplify=10)
+    load('./data/x40y45.sgy', static=35, trace_remove=1, x=40, y=45, sub_aver=51, amplify=10)
+    load('./data/x45y50.sgy', static=35, trace_remove=14, x=45, y=50, sub_aver=51, amplify=10)
+