update: datasets links

.gitignore

@@ -1,4 +1,6 @@
+__pycache__/
 backup/
+# Backup/
 Article-Figures/
 python/
 Figures/

Datasets-Construction/OpenSWI-shallow/Backup/0.5-5s-varyingThickness/readme.md

@@ -1,5 +0,0 @@
-
-v1: t range: 0.5-5s, 100 samples
-    t = generate_mixed_samples(num_samples=100,start=0.5,end=5,uniform_num=30,log_num=30,random_num=40)
-v2: t range: 0.01-5s, 120 samples
-    t = generate_mixed_samples(num_samples=120,start=0.01,end=5,uniform_num=40,log_num=40,random_num=40)

Datasets-Construction/OpenSWI-shallow/Backup/0.5-5s/01_1_OpenFWI-FlatVel-A.ipynb

@@ -1,282 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imshow 1 of the subsets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "data_base_path = \"/home/bingxing2/ailab/group/ai4earth/liufeng/OpenFWI/FlatVel_A/model\"\n",
-    "\n",
-    "models_list = os.listdir(data_base_path)\n",
-    "\n",
-    "models_list = sorted(models_list, key=lambda x: int(x.split(\".\")[0].replace(\"model\", \"\")))\n",
-    "\n",
-    "models_path_list = []\n",
-    "for model_name in models_list:\n",
-    "    model_path = os.path.join(data_base_path, model_name)\n",
-    "    models_path_list.append(model_path)\n",
-    "\n",
-    "\n",
-    "vel_model_subsets0 = np.load(models_path_list[0])\n",
-    "vel_model_subsets0 = vel_model_subsets0.squeeze()\n",
-    "nrows = 10\n",
-    "ncols = 10\n",
-    "fig,axs = plt.subplots(nrows,ncols,figsize=(10,10))\n",
-    "for i in range(nrows):\n",
-    "    for j in range(ncols):\n",
-    "        axs[i,j].imshow(vel_model_subsets0[i*ncols+j],cmap=\"jet\")\n",
-    "        axs[i,j].set_xticks([])\n",
-    "        axs[i,j].set_yticks([])\n",
-    "plt.subplots_adjust(wspace=0.05,hspace=0.05)\n",
-    "# plt.savefig(\"Datasets/JupyterNotebook/Figures/FlatVel_A_2D.png\",bbox_inches='tight',dpi=300)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Datasets Preparing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from DispFormer.dispersion import *\n",
-    "from p_tqdm import p_map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vel_model_subset = np.load(models_path_list[0]).squeeze()\n",
-    "vel_models0 = transform_vp_to_vel_model(vel_model_subset[100,:,10]/1000)\n",
-    "vel_models1 = transform_vs_to_vel_model(vel_models0[:,2],depth=vel_models0[:,0])\n",
-    "disp0 = calculate_dispersion(vel_models0)\n",
-    "disp1 = calculate_dispersion(vel_models1)\n",
-    "depth = vel_models0[:,0]\n",
-    "\n",
-    "plt.figure(figsize=(10,5))\n",
-    "plt.subplot(121)\n",
-    "plt.step(vel_models0[:,2],depth)\n",
-    "plt.step(vel_models1[:,2],depth)\n",
-    "plt.gca().invert_yaxis()\n",
-    "\n",
-    "\n",
-    "plt.subplot(122)\n",
-    "plt.scatter(disp0[:,0],disp0[:,1])\n",
-    "plt.scatter(disp1[:,0],disp1[:,1])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "save_base_path = \"/home/bingxing2/ailab/scxlab0055/project/04_Inversion/SurfWaveInv/DispFormer-local/Datasets/OpenSWI/Datasets/OpenSWI-shallow/0.2-10s-Aug/\"\n",
-    "\n",
-    "# Initialize empty lists to store data\n",
-    "disp_data_all  = []\n",
-    "vel_models_all = []\n",
-    "\n",
-    "try:\n",
-    "    # Process each model file\n",
-    "    for path in models_path_list:\n",
-    "        # Load and preprocess velocity model\n",
-    "        vel_model_subset = np.load(path).squeeze()\n",
-    "        \n",
-    "        # Convert velocity from m/s to km/s and transform to velocity model\n",
-    "        vel_models = p_map(transform_vp_to_vel_model, vel_model_subset[:,:,10]/1000)\n",
-    "        vel_models = np.array(vel_models)\n",
-    "        vs_models  = list(vel_models[:,:,2])\n",
-    "        vel_models = p_map(transform_vs_to_vel_model,vs_models)\n",
-    "\n",
-    "        # Calculate dispersion curves\n",
-    "        disp_data = p_map(calculate_dispersion, vel_models)\n",
-    "        disp_data = np.array(disp_data)\n",
-    "        \n",
-    "        # Store results\n",
-    "        disp_data_all.append(disp_data)\n",
-    "        vel_models_all.append(vel_models)\n",
-    "\n",
-    "    # Convert lists to numpy arrays and reshape\n",
-    "    disp_data_all = np.array(disp_data_all).reshape(-1, *np.array(disp_data_all).shape[2:])\n",
-    "    vel_models_all = np.array(vel_models_all).reshape(-1, *np.array(vel_models_all).shape[2:])\n",
-    "    \n",
-    "    # Filter out zero dispersion curves and corresponding velocity models\n",
-    "    valid_indices = ~np.all(disp_data_all == 0, axis=(1,2))\n",
-    "    disp_data_all = disp_data_all[valid_indices]\n",
-    "    vel_models_all = vel_models_all[valid_indices]\n",
-    "\n",
-    "    # Create output directory if it doesn't exist\n",
-    "    os.makedirs(save_base_path, exist_ok=True)\n",
-    "    \n",
-    "    # Save processed data as compressed npz files\n",
-    "    np.savez_compressed(os.path.join(save_base_path, \"FlatVelA_model.npz\"),\n",
-    "                       data=vel_models_all.astype(np.float32))\n",
-    "    np.savez_compressed(os.path.join(save_base_path, \"FlatVelA_disp.npz\"),\n",
-    "                       data=disp_data_all.astype(np.float32))\n",
-    "                       \n",
-    "except Exception as e:\n",
-    "    print(f\"An error occurred during processing: {str(e)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Datasets Imshow\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "def plot_vel_disp(vel_model, disp_data):\n",
-    "    \"\"\"Plot velocity model and dispersion curves\n",
-    "    \n",
-    "    Args:\n",
-    "        vel_model: numpy array with columns [thickness, vp, vs, density]\n",
-    "        disp_data: numpy array with columns [period, phase_vel, group_vel]\n",
-    "    \"\"\"\n",
-    "    fig, axs = plt.subplots(1,2,figsize=(10,6))\n",
-    "    \n",
-    "    depth = vel_model[:,0]\n",
-    "    \n",
-    "    # Left subplot - Velocity model\n",
-    "    axs[0].step(vel_model[:,1], depth, label='P-wave velocity (km/s)', linewidth=2)\n",
-    "    axs[0].step(vel_model[:,2], depth, label='S-wave velocity (km/s)', linewidth=2) \n",
-    "    axs[0].step(vel_model[:,3], depth, label='Density (g/cm³)', linewidth=2)\n",
-    "    axs[0].invert_yaxis()\n",
-    "    axs[0].set_xlabel('Velocity (km/s) / Density (g/cm³)')\n",
-    "    axs[0].set_ylabel('Depth (m)')\n",
-    "    axs[0].set_title('Velocity Model')\n",
-    "    axs[0].grid(True, linestyle='--', alpha=0.7)\n",
-    "    axs[0].legend()\n",
-    "\n",
-    "    # Right subplot - Dispersion curves\n",
-    "    axs[1].scatter(disp_data[:,0], disp_data[:,1], label='Phase velocity', s=50)\n",
-    "    axs[1].scatter(disp_data[:,0], disp_data[:,2], label='Group velocity', s=50)\n",
-    "    axs[1].set_xlabel('Period (s)')\n",
-    "    axs[1].set_ylabel('Velocity (km/s)')\n",
-    "    axs[1].set_title('Dispersion Curves')\n",
-    "    axs[1].grid(True, linestyle='--', alpha=0.7)\n",
-    "    axs[1].legend()\n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "    \n",
-    "# Test the function\n",
-    "idx = 1100\n",
-    "plot_vel_disp(vel_models_all[idx], disp_data_all[idx])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create subplots for Vp, Vs and density\n",
-    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12,5))\n",
-    "\n",
-    "# Get depth array from first model\n",
-    "depth = vel_models_all[0,:,0]\n",
-    "\n",
-    "# Plot Vp models with alpha blending\n",
-    "ax1.step(vel_models_all[:,:,1].T, depth, color='r', alpha=0.01)\n",
-    "ax1.set_title('P-wave Velocity Models')\n",
-    "ax1.set_xlabel('Velocity (km/s)')\n",
-    "ax1.set_ylabel('Depth (m)')\n",
-    "ax1.invert_yaxis()\n",
-    "ax1.grid(True)\n",
-    "\n",
-    "# Plot Vs models\n",
-    "ax2.step(vel_models_all[:,:,2].T, depth, color='b', alpha=0.01)\n",
-    "ax2.set_title('P-wave Velocity Models')\n",
-    "ax2.set_xlabel('Velocity (km/s)')\n",
-    "ax2.set_ylabel('Depth (m)')\n",
-    "ax2.invert_yaxis()\n",
-    "ax2.grid(True)\n",
-    "\n",
-    "# Plot density models\n",
-    "ax3.step(vel_models_all[:,:,3].T, depth, color='gray', alpha=0.01)\n",
-    "ax3.set_title('P-wave Velocity Models')\n",
-    "ax3.set_xlabel('Velocity (km/s)')\n",
-    "ax3.set_ylabel('Depth (m)')\n",
-    "ax3.invert_yaxis()\n",
-    "ax3.grid(True)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "vel_models_all = np.load(\"/home/bingxing2/ailab/scxlab0055/project/04_Inversion/SurfWaveInv/DispFormer-local/Datasets/OpenSWI/Datasets/OpenSWI-shallow/0.2-10s-Aug/FlatVelA_model.npz\")[\"data\"]\n",
-    "disp_data_all = np.load(\"/home/bingxing2/ailab/scxlab0055/project/04_Inversion/SurfWaveInv/DispFormer-local/Datasets/OpenSWI/Datasets/OpenSWI-shallow/0.2-10s-Aug/FlatVelA_disp.npz\")[\"data\"]\n",
-    "\n",
-    "vel_models_all.shape,disp_data_all.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "ADinversion",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

Datasets-Construction/OpenSWI-shallow/Backup/0.5-5s/01_4_OpenFWI-CurveFault-A.ipynb

@@ -1,310 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Imshow 1 of the subsets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "\n",
-    "data_base_path = \"/home/bingxing2/ailab/group/ai4earth/liufeng/OpenFWI/CurveFault_A/model\"\n",
-    "\n",
-    "models_list = os.listdir(data_base_path)\n",
-    "\n",
-    "# models_list = sorted(models_list, key=lambda x: int(x.split(\".\")[0].replace(\"model\", \"\")))\n",
-    "\n",
-    "models_path_list = []\n",
-    "for model_name in models_list:\n",
-    "    model_path = os.path.join(data_base_path, model_name)\n",
-    "    models_path_list.append(model_path)\n",
-    "\n",
-    "\n",
-    "vel_model_subsets0 = np.load(models_path_list[0])\n",
-    "vel_model_subsets0 = vel_model_subsets0.squeeze()\n",
-    "nrows = 10\n",
-    "ncols = 10\n",
-    "fig,axs = plt.subplots(nrows,ncols,figsize=(10,10))\n",
-    "for i in range(nrows):\n",
-    "    for j in range(ncols):\n",
-    "        axs[i,j].imshow(vel_model_subsets0[i*ncols+j],cmap=\"jet\")\n",
-    "        axs[i,j].set_xticks([])\n",
-    "        axs[i,j].set_yticks([])\n",
-    "plt.subplots_adjust(wspace=0.05,hspace=0.05)\n",
-    "# plt.savefig(\"Datasets/JupyterNotebook/Figures/CurveFault_A_2D.png\",bbox_inches='tight',dpi=300)\n",
-    "plt.show()\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Datasets Preparing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from DispFormer.dispersion import *\n",
-    "from p_tqdm import p_map"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vel_model_subset = np.load(models_path_list[0]).squeeze()\n",
-    "vel_models0 = transform_vp_to_vel_model(vel_model_subset[80,:,20]/1000)\n",
-    "vel_models1 = transform_vs_to_vel_model(vel_models0[:,2],depth=vel_models0[:,0])\n",
-    "disp0 = calculate_dispersion(vel_models0)\n",
-    "disp1 = calculate_dispersion(vel_models1)\n",
-    "depth = vel_models0[:,0]\n",
-    "\n",
-    "plt.figure(figsize=(10,5))\n",
-    "plt.subplot(121)\n",
-    "plt.step(vel_models0[:,2],depth)\n",
-    "plt.step(vel_models1[:,2],depth)\n",
-    "plt.gca().invert_yaxis()\n",
-    "\n",
-    "\n",
-    "plt.subplot(122)\n",
-    "plt.scatter(disp0[:,0],disp0[:,1])\n",
-    "plt.scatter(disp1[:,0],disp1[:,1])\n",
-    "plt.show()\n",
-    "\n",
-    "vel_models0.dtype"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "vel_model_subset = np.load(models_path_list[0]).squeeze()\n",
-    "\n",
-    "model_idx = 10\n",
-    "\n",
-    "plt.figure()\n",
-    "plt.subplot(121)\n",
-    "plt.imshow(vel_model_subset[model_idx])\n",
-    "for i in range(0,70,2):\n",
-    "    plt.axvline(x=i)\n",
-    "\n",
-    "plt.subplot(122)\n",
-    "depth = np.arange(70)*0.04\n",
-    "for i in range(0,70,2):\n",
-    "    plt.step(vel_model_subset[model_idx,:,i],depth)\n",
-    "plt.gca().invert_yaxis()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "save_base_path = \"/home/bingxing2/ailab/scxlab0055/project/04_Inversion/SurfWaveInv/DispFormer-local/Datasets/OpenSWI/Datasets/OpenSWI-shallow/0.2-10s-Aug/\"\n",
-    "\n",
-    "# Initialize empty lists to store data\n",
-    "disp_data_all = []\n",
-    "vel_models_all = []\n",
-    "\n",
-    "try:\n",
-    "    # Process each model file\n",
-    "    for path in models_path_list:\n",
-    "        # Load and preprocess velocity model\n",
-    "        vel_model_subset = np.load(path).squeeze()\n",
-    "        \n",
-    "        # Convert velocity from m/s to km/s and transform to velocity model\n",
-    "        vel_model_subset = np.swapaxes(vel_model_subset, 1, 2)\n",
-    "        vel_model_subset = vel_model_subset.reshape(-1, vel_model_subset.shape[-1])[::7]\n",
-    "        vel_models = p_map(transform_vp_to_vel_model, vel_model_subset/1000)\n",
-    "        vel_models = np.array(vel_models)\n",
-    "        vs_models  = list(vel_models[:,:,2])\n",
-    "        vel_models = p_map(transform_vs_to_vel_model,vs_models)\n",
-    "        \n",
-    "        # Calculate dispersion curves\n",
-    "        disp_data = p_map(calculate_dispersion, vel_models)\n",
-    "        disp_data = np.array(disp_data)\n",
-    "        \n",
-    "        # Store results\n",
-    "        disp_data_all.append(disp_data)\n",
-    "        vel_models_all.append(vel_models)\n",
-    "\n",
-    "    # Convert lists to numpy arrays and reshape\n",
-    "    disp_data_all = np.array(disp_data_all).reshape(-1, *np.array(disp_data_all).shape[2:])\n",
-    "    vel_models_all = np.array(vel_models_all).reshape(-1, *np.array(vel_models_all).shape[2:])\n",
-    "    \n",
-    "    # Filter out zero dispersion curves and corresponding velocity models\n",
-    "    valid_indices = ~np.all(disp_data_all == 0, axis=(1,2))\n",
-    "    disp_data_all = disp_data_all[valid_indices]\n",
-    "    vel_models_all = vel_models_all[valid_indices]\n",
-    "\n",
-    "    # Create output directory if it doesn't exist\n",
-    "    os.makedirs(save_base_path, exist_ok=True)\n",
-    "    \n",
-    "    # Save processed data as compressed npz files\n",
-    "    np.savez_compressed(os.path.join(save_base_path, \"CurveFaultA_model.npz\"),\n",
-    "                       data=vel_models_all.astype(np.float32))\n",
-    "    np.savez_compressed(os.path.join(save_base_path, \"CurveFaultA_disp.npz\"),\n",
-    "                       data=disp_data_all.astype(np.float32))\n",
-    "                       \n",
-    "except Exception as e:\n",
-    "    print(f\"An error occurred during processing: {str(e)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Datasets Imshow\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "\n",
-    "vel_models_all = np.load(\"/home/bingxing2/ailab/scxlab0055/project/04_Inversion/SurfWaveInv/DispFormer-local/Datasets/OpenSWI/Datasets/OpenSWI-shallow/0.2-10s-Aug/CurveFaultA_model.npz\")[\"data\"]\n",
-    "disp_data_all = np.load(\"/home/bingxing2/ailab/scxlab0055/project/04_Inversion/SurfWaveInv/DispFormer-local/Datasets/OpenSWI/Datasets/OpenSWI-shallow/0.2-10s-Aug/CurveFaultA_disp.npz\")[\"data\"]\n",
-    "\n",
-    "vel_models_all.shape,disp_data_all.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "def plot_vel_disp(vel_model, disp_data):\n",
-    "    \"\"\"Plot velocity model and dispersion curves\n",
-    "    \n",
-    "    Args:\n",
-    "        vel_model: numpy array with columns [thickness, vp, vs, density]\n",
-    "        disp_data: numpy array with columns [period, phase_vel, group_vel]\n",
-    "    \"\"\"\n",
-    "    fig, axs = plt.subplots(1,2,figsize=(10,6))\n",
-    "    \n",
-    "    depth = vel_model[:,0]\n",
-    "    \n",
-    "    # Left subplot - Velocity model\n",
-    "    axs[0].step(vel_model[:,1], depth, label='P-wave velocity (km/s)', linewidth=2)\n",
-    "    axs[0].step(vel_model[:,2], depth, label='S-wave velocity (km/s)', linewidth=2) \n",
-    "    axs[0].step(vel_model[:,3], depth, label='Density (g/cm³)', linewidth=2)\n",
-    "    axs[0].invert_yaxis()\n",
-    "    axs[0].set_xlabel('Velocity (km/s) / Density (g/cm³)')\n",
-    "    axs[0].set_ylabel('Depth (m)')\n",
-    "    axs[0].set_title('Velocity Model')\n",
-    "    axs[0].grid(True, linestyle='--', alpha=0.7)\n",
-    "    axs[0].legend()\n",
-    "\n",
-    "    # Right subplot - Dispersion curves\n",
-    "    axs[1].scatter(disp_data[:,0], disp_data[:,1], label='Phase velocity', s=50)\n",
-    "    axs[1].scatter(disp_data[:,0], disp_data[:,2], label='Group velocity', s=50)\n",
-    "    axs[1].set_xlabel('Period (s)')\n",
-    "    axs[1].set_ylabel('Velocity (km/s)')\n",
-    "    axs[1].set_title('Dispersion Curves')\n",
-    "    axs[1].grid(True, linestyle='--', alpha=0.7)\n",
-    "    axs[1].legend()\n",
-    "\n",
-    "    plt.tight_layout()\n",
-    "    plt.show()\n",
-    "    \n",
-    "# Test the function\n",
-    "idx = 120001\n",
-    "plot_vel_disp(vel_models_all[idx], disp_data_all[idx])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create subplots for Vp, Vs and density\n",
-    "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(12,5))\n",
-    "\n",
-    "# Get depth array from first model\n",
-    "depth = vel_models_all[0,:,0]\n",
-    "\n",
-    "# Plot Vp models with alpha blending\n",
-    "ax1.step(vel_models_all[::100,:,1].T, depth, color='r', alpha=0.01)\n",
-    "ax1.set_title('P-wave Velocity Models')\n",
-    "ax1.set_xlabel('Velocity (km/s)')\n",
-    "ax1.set_ylabel('Depth (m)')\n",
-    "ax1.invert_yaxis()\n",
-    "ax1.grid(True)\n",
-    "\n",
-    "# Plot Vs models\n",
-    "ax2.step(vel_models_all[::100,:,2].T, depth, color='b', alpha=0.01)\n",
-    "ax2.set_title('S-wave Velocity Models')\n",
-    "ax2.set_xlabel('Velocity (km/s)')\n",
-    "ax2.set_ylabel('Depth (m)')\n",
-    "ax2.invert_yaxis()\n",
-    "ax2.grid(True)\n",
-    "\n",
-    "# Plot density models\n",
-    "ax3.step(vel_models_all[::100,:,3].T, depth, color='gray', alpha=0.01)\n",
-    "ax3.set_title('Density Models')\n",
-    "ax3.set_xlabel('Density (g/cm³)')\n",
-    "ax3.set_ylabel('Depth (m)')\n",
-    "ax3.invert_yaxis()\n",
-    "ax3.grid(True)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
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