diff --git "a/Datasets-Construction/OpenSWI-deep/1s-100s-Aug/12_LITHO1.ipynb" "b/Datasets-Construction/OpenSWI-deep/1s-100s-Aug/12_LITHO1.ipynb" deleted file mode 100644--- "a/Datasets-Construction/OpenSWI-deep/1s-100s-Aug/12_LITHO1.ipynb" +++ /dev/null @@ -1,922 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# LITHO1.0\n", - "\n", - "Description of the model\n", - "- The LITHO1.0 model is a 1° tessellated model of the crust and uppermost mantle of the earth, extending into the upper mantle to include the lithospheric lid and underlying asthenosphere. The model is parameterized laterally by tessellated nodes and vertically as a series of geophysically identified layers, such as water, ice, sediments, crystalline crust, lithospheric lid, and asthenosphere. LITHO1.0 was created by constructing an appropriate starting model, including CRUST1.0 as starting model for the crust. We then perturbed the model to fit high-resolution surface wave dispersion maps (Love, Rayleigh, group, phase) over a wide frequency band (5-40 mHz).\n", - "- The model was generated by examination and discussion of the model with respect to key lithospheric parameters, such as average crustal velocity, crustal thickness, upper mantle velocity, and lithospheric thickness. We then compared the constructed model with those from a number of select studies at regional and global scales and find general consistency. Details can be found in Pasyanos et al. (2014).\n", - "- It appears that LITHO1.0 represents a reasonable starting model of the earth's shallow structure (crust and uppermost mantle) for the purposes in which these models are used, such as travel time tomography or in efforts to create a 3D reference earth model. The model matches surface wave dispersion over a frequency band wider than the band used in the inversion. There are several avenues for improving the model in the future by including attenuation and anisotropy, as well as making use of surface waves at higher frequency.\n", - "- Each of the nodes has a unique profile where the layers are\n", - " - water\n", - " - ice\n", - " - upper sediments\n", - " - middle sediments\n", - " - lower sediments\n", - " - upper crust\n", - " - middle crust\n", - " - lower crust\n", - " - lithospheric mantle (lid)\n", - " - asthenospheric mantle\n", - " - ak135\n", - "- Crustal parameterization was adopted from CRUST1.0 though the depth to Moho and a uniform perturbation in the crystalline crust was allowed in the inversions. Parameters of layer thickness, VP, VS, rho, and Q (placeholder values) are given explicitly for all layers. The parameters below the asthenosphere blend into the ak135 model (Kennett et al., 1995).\n", - "\n", - "Reference\n", - "- Pasyanos, M.E., T.G. Masters, G. Laske, and Z. Ma (2014). LITHO1.0: An updated crust and lithospheric model of the Earth, J. Geophys. Res., 119 (3), 2153-2173, DOI: 10.1002/2013JB010626..\n", - "\n", - "Pages: https://igppweb.ucsd.edu/~gabi/litho1.0.html" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 1: Extract 1D Velocity Model\n", - "\n", - "Note!!: \n", - "1. Consider the larger variation of the moho depth of LITHO1.0 model, this model is not suggested use the augmentation method. \n", - "\n", - "2. The number of LITHO1.0 model is larger enough, so the augmentation method is not suggested.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(40962,\n", - " ['node1.model',\n", - " 'node2.model',\n", - " 'node3.model',\n", - " 'node4.model',\n", - " 'node5.model',\n", - " 'node6.model',\n", - " 'node7.model',\n", - " 'node8.model',\n", - " 'node9.model',\n", - " 'node10.model'])" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np \n", - "import os \n", - "import matplotlib.pyplot as plt\n", - "\n", - "data_folder = \"../../../Datasets/Original/OpenSWI-deep/LITHO1.0/LITHO1.0/litho_model\"\n", - "data_files = os.listdir(data_folder)\n", - "data_files.sort(key= lambda x:int(x.replace(\"node\",\"\").replace(\".model\",\"\")))\n", - "len(data_files),data_files[:10]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(40962, 3)" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# node, latitude, glatitude, longitude\n", - "lon_lat_file = \"../../../Datasets/Original/OpenSWI-deep/LITHO1.0/LITHO1.0/Icosahedron_Level7_LatLon_mod.txt\"\n", - "loc_data = np.loadtxt(lon_lat_file)\n", - "loc_data.shape" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 2: Quality Control & Interpolation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from scipy.interpolate import interp1d\n", - "import sys\n", - "sys.path.append('../../../')\n", - "from SWIDP.process_1d_deep import *\n", - "from SWIDP.dispersion import *\n", - "\n", - "def extract_vel(files_idx):\n", - " depth,rho,vp,vs,layer_name = [],[],[],[],[]\n", - " # depth(m) density(kg/m3) Vp(m/s) Vs(m/s) Qkappa Qmu Vp2(m/s) Vs2(m/s) eta layername\n", - " lines = open(os.path.join(data_folder,data_files[files_idx]),'r').readlines()\n", - " for i,line in enumerate(lines):\n", - " line = line.strip().split()\n", - " if i>0:\n", - " depth.append(float(line[0])/1e3)\n", - " rho.append(float(line[1])/1e3)\n", - " vp.append(float(line[2])/1e3)\n", - " vs.append(float(line[3])/1e3)\n", - " layer_name.append(line[-1])\n", - " depth = np.array(depth)[::-1]\n", - " rho = np.array(rho)[::-1]\n", - " vp = np.array(vp)[::-1]\n", - " vs = np.array(vs)[::-1]\n", - " layer_name = np.array(layer_name)[::-1]\n", - " return depth,vp,vs,rho,layer_name\n", - "\n", - "# remove interface and water layer\n", - "def remove_interface_and_water(depth,vp,vs,rho):\n", - " # remove the interface\n", - " depth, depth_unique_indices = np.unique(depth, return_index=True)\n", - " vs = vs[depth_unique_indices]\n", - " vp = vp[depth_unique_indices]\n", - " rho = rho[depth_unique_indices]\n", - "\n", - " # remove the water layer\n", - " water_mask = vs>0\n", - " depth = depth[water_mask]\n", - " vs = vs[water_mask]\n", - " vp = vp[water_mask]\n", - " rho = rho[water_mask]\n", - " \n", - " return depth,vp,vs,rho\n", - "\n", - "def interp_data(depth,vp,vs,rho,interp_method='linear'):\n", - " # interp\n", - " max_depth = 300+0.1\n", - " interp_depth = np.concatenate([\n", - " np.arange(0, max_depth, 1)\n", - " ])\n", - " interp_depth_temp = interp_depth+depth.min()\n", - " \n", - " # interpolate velocity model\n", - " f = interp1d(depth,vs,kind=interp_method)\n", - " interp_vs = f(interp_depth_temp)\n", - " f = interp1d(depth,vp,kind=interp_method)\n", - " interp_vp = f(interp_depth_temp)\n", - " f = interp1d(depth,rho,kind=interp_method)\n", - " interp_rho = f(interp_depth_temp)\n", - " \n", - " # return\n", - " new_depth = interp_depth\n", - " new_vp = interp_vp\n", - " new_vs = interp_vs\n", - " new_rho = interp_rho\n", - " \n", - " return np.hstack((new_depth.reshape(-1,1),new_vp.reshape(-1,1),new_vs.reshape(-1,1),new_rho.reshape(-1,1)))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(40962, 301)" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# ---------------------------------\n", - "# Interpolate the velocity model\n", - "# ---------------------------------\n", - "vs_interp = [] \n", - "for files_idx in range(len(data_files)):\n", - " depth_temp,vp_temp,vs_temp,rho_temp,layer_name = extract_vel(files_idx)\n", - " depth_temp,vp_temp,vs_temp,rho_temp = remove_interface_and_water(depth_temp,vp_temp,vs_temp,rho_temp)\n", - " interp_vel_model = interp_data(depth_temp,vp_temp,vs_temp,rho_temp,interp_method=\"linear\")\n", - " vs_interp.append(interp_vel_model[:,2])\n", - "depth_interp = interp_vel_model[:,0]\n", - "vs_interp = np.array(vs_interp)\n", - "vs_interp.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nrows = 5\n", - "ncols = 5\n", - "fig,axs = plt.subplots(nrows,ncols,figsize=(10,10))\n", - "for i in range(nrows):\n", - " for j in range(ncols):\n", - " sta_idx = np.random.randint(0,len(vs_interp))\n", - " axs[i,j].step(vs_interp[sta_idx,:],depth_interp,c='r')\n", - " axs[i,j].invert_yaxis()\n", - " axs[i,j].set_title(f'sta_idx: {sta_idx}')\n", - " if j == 0:\n", - " axs[i,j].set_ylabel('Depth (km)')\n", - " else:\n", - " axs[i,j].tick_params(labelleft=False)\n", - " if i == nrows-1:\n", - " axs[i,j].set_xlabel('Vs (km/s)')\n", - " else:\n", - " axs[i,j].tick_params(labelbottom=False)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "964a05bd59ea4990b0d1ce86dc5d9fb6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/40962 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nrows = 5\n", - "ncols = 5\n", - "fig,axs = plt.subplots(nrows,ncols,figsize=(10,10))\n", - "for i in range(nrows):\n", - " for j in range(ncols):\n", - " sta_idx = np.random.randint(0,len(vs_interp))\n", - " axs[i,j].step(vs_interp[sta_idx,:],depth_interp,c='k')\n", - " axs[i,j].step(vs_interp_rm_sandwich[sta_idx,:],depth_interp,c='r')\n", - " axs[i,j].invert_yaxis()\n", - " axs[i,j].set_title(f'sta_idx: {sta_idx}')\n", - " if j == 0:\n", - " axs[i,j].set_ylabel('Depth (km)')\n", - " else:\n", - " axs[i,j].tick_params(labelleft=False)\n", - " if i == nrows-1:\n", - " axs[i,j].set_xlabel('Vs (km/s)')\n", - " else:\n", - " axs[i,j].tick_params(labelbottom=False)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 3: Find the Moho (not suggested)\n", - "\n", - "* you can get the right depth of moho from the LITHO1.0 original datasets\n", - "* the wrong augmentation will not influence the quality of datasets (the mapping bettwen the dispersion curves and 1D profiles follow the physical relationship)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a3a3c22edeee4bc1bab9c049645f033f", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/40962 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nrows = 5\n", - "ncols = 5\n", - "fig,axs = plt.subplots(nrows,ncols,figsize=(10,10))\n", - "for i in range(nrows):\n", - " for j in range(ncols):\n", - " sta_idx = np.random.randint(0,len(depth_interp))\n", - " moho_depth_idx = moho_idxs[sta_idx]\n", - " axs[i,j].step(vs_interp_rm_sandwich[sta_idx,:],depth_interp,c='k')\n", - " axs[i,j].axhline(y = depth_interp[moho_depth_idx],color='r',linestyle='--')\n", - " axs[i,j].scatter(vs_interp_rm_sandwich[sta_idx,moho_depth_idx],depth_interp[moho_depth_idx],c='r',marker='x',s=100)\n", - " axs[i,j].invert_yaxis()\n", - " axs[i,j].set_title(f'sta_idx: {sta_idx}')\n", - " if j == 0:\n", - " axs[i,j].set_ylabel('Depth (km)')\n", - " else:\n", - " axs[i,j].tick_params(labelleft=False)\n", - " if i == nrows-1:\n", - " axs[i,j].set_xlabel('Vs (km/s)')\n", - " else:\n", - " axs[i,j].tick_params(labelbottom=False)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 4: Perturbation the Moho depth and velocity" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "(100, 301)" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# Generate perturbed models single\n", - "i = 400\n", - "vs_orig = vs_interp_rm_sandwich[i]\n", - "# Find Moho\n", - "moho_idx = moho_idxs[i]\n", - "plt.figure()\n", - "plt.step(vs_orig,depth_interp,c='k')\n", - "plt.scatter(vs_orig[moho_idx],depth_interp[moho_idx],c='r',marker='x')\n", - "plt.axhline(y=depth_interp[moho_idx],color='r',linestyle='--')\n", - "\n", - "vs_perts = []\n", - "perturb_num = 100\n", - "random_seeds = np.random.randint(0,1000000,perturb_num)\n", - "for i in range(perturb_num):\n", - " # Generate perturbed profile\n", - " vs_pert, controle_node_t, controle_node_vs = augment_crust_moho_mantle(vs_orig, \n", - " depth_interp, \n", - " moho_idx, \n", - " vs_perturb_range=[-0.2,0.2],\n", - " crust_nodes_range=[3,8],\n", - " mantle_nodes_range=[8,12],\n", - " moho_shift_range=5,\n", - " gaussian_smooth_sigma=1.5,\n", - " return_nodes=True,\n", - " random_seed=random_seeds[i])\n", - " # plot the perturbed profile\n", - " plt.step(vs_pert,depth_interp,c='pink' if i !=0 else 'r',alpha=0.1 if i !=0 else 1, zorder=1 if i !=0 else 2)\n", - " # plt.scatter(controle_node_vs,controle_node_t,c='b',alpha=0.2)\n", - " vs_perts.append(vs_pert)\n", - "vs_perts = np.array(vs_perts)\n", - "plt.gca().invert_yaxis()\n", - "plt.show()\n", - "np.unique(vs_perts,axis=0).shape" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((40962, 301), (40962,))" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vs_interp_rm_sandwich.shape,moho_idxs.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "bcdd7a4a981f4364a5718fe81d5f5cee", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/40962 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nrows = 5\n", - "ncols = 5\n", - "fig,axs = plt.subplots(nrows,ncols,figsize=(10,10))\n", - "for i in range(nrows):\n", - " for j in range(ncols):\n", - " sta_idx = np.random.randint(0,len(vs_interp_rm_sandwich))\n", - " axs[i,j].step(vs_interp_rm_sandwich[sta_idx,:],depth_interp,c='k',label='original', linewidth=2)\n", - "\n", - " for k in range(perturb_num):\n", - " axs[i,j].step(aug_vs_list[k,sta_idx,:],depth_interp,c='r',label='perturbed' if k == 0 else None, linewidth=1)\n", - "\n", - " axs[i,j].invert_yaxis()\n", - " axs[i,j].set_title(f'sta_idx: {sta_idx}')\n", - " if j == 0:\n", - " axs[i,j].set_ylabel('Depth (km)')\n", - " else:\n", - " axs[i,j].tick_params(labelleft=False)\n", - " if i == nrows-1:\n", - " axs[i,j].set_xlabel('Vs (km/s)')\n", - " else:\n", - " axs[i,j].tick_params(labelbottom=False)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Step 5: Dispersion Curve Inversion" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "dc0848ca35254a25a7ae504a720c9603", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/245772 [00:00" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nrows = 10\n", - "ncols = 10\n", - "fig,axs = plt.subplots(nrows,ncols,figsize=(10,10))\n", - "\n", - "for i in range(nrows):\n", - " for j in range(ncols//2):\n", - "\n", - " sta_idx = np.random.randint(0,vs_interp_rm_sandwich.shape[0])\n", - " \n", - " # velocity profile\n", - " for k in range(perturb_num+1):\n", - " axs[i,j*2].step(vel_models[k*vs_interp_rm_sandwich.shape[0]+sta_idx][:,2],\n", - " vel_models[k*vs_interp_rm_sandwich.shape[0]+sta_idx][:,0],\n", - " linewidth=1 if k == 0 else 0.5,color='k' if k == 0 else 'r')\n", - " axs[i,j*2].set_xticks([])\n", - " axs[i,j*2].set_yticks([])\n", - " axs[i,j*2].invert_yaxis()\n", - "\n", - " # dispersion curve\n", - " for k in range(perturb_num+1):\n", - " axs[i,j*2+1].plot(disp_data[k*vs_interp_rm_sandwich.shape[0]+sta_idx][:,0],\n", - " disp_data[k*vs_interp_rm_sandwich.shape[0]+sta_idx][:,1],\n", - " linewidth=1 if k==0 else 0.5,c='r' if k==0 else \"pink\", zorder=2 if k==0 else 1)\n", - " axs[i,j*2+1].plot(disp_data[k*vs_interp_rm_sandwich.shape[0]+sta_idx][:,0],\n", - " disp_data[k*vs_interp_rm_sandwich.shape[0]+sta_idx][:,2],\n", - " linewidth=1 if k==0 else 0.5,c='b' if k==0 else \"lightblue\", zorder=2 if k==0 else 1)\n", - " axs[i,j*2+1].set_xticks([])\n", - " axs[i,j*2+1].set_yticks([])\n", - "plt.subplots_adjust(wspace=0.05,hspace=0.05)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig,axs = plt.subplots(1,2,figsize=(8,4))\n", - "\n", - "sta_idx = np.random.randint(0,vs_interp_rm_sandwich.shape[0])\n", - " \n", - "# velocity profile\n", - "for k in range(perturb_num+1):\n", - " axs[0].step(vel_models[k*vs_interp_rm_sandwich.shape[0]+sta_idx][:,2],\n", - " vel_models[k*vs_interp_rm_sandwich.shape[0]+sta_idx][:,0],\n", - " linewidth=0.2,color='k' if k == 0 else 'silver')\n", - "axs[0].set_xticks([])\n", - "axs[0].set_yticks([])\n", - "axs[0].invert_yaxis()\n", - "\n", - "# dispersion curve\n", - "for k in range(perturb_num+1):\n", - " axs[1].scatter(disp_data[k*vs_interp_rm_sandwich.shape[0]+sta_idx][:,0],\n", - " disp_data[k*vs_interp_rm_sandwich.shape[0]+sta_idx][:,1],\n", - " s=1,c='r' if k==0 else \"pink\", zorder=2 if k==0 else 1)\n", - " axs[1].scatter(disp_data[k*vs_interp_rm_sandwich.shape[0]+sta_idx][:,0],\n", - " disp_data[k*vs_interp_rm_sandwich.shape[0]+sta_idx][:,2],\n", - " s=1,c='b' if k==0 else \"lightblue\", zorder=2 if k==0 else 1)\n", - "axs[1].set_xticks([])\n", - "axs[1].set_yticks([])\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "((245771, 301, 4), (245771, 300, 3))" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "phase_mask = np.sum(disp_data[:,:,1] == 0,axis=1)==0\n", - "group_mask = np.sum(disp_data[:,:,2] == 0,axis=1)==0\n", - "mask = phase_mask*group_mask\n", - "\n", - "\n", - "# loc = np.hstack((LON.reshape(-1,1),LAT.reshape(-1,1)))[mask,:]\n", - "vel_models = vel_models[mask,:,:]\n", - "disp_data = disp_data[mask,:,:]\n", - "\n", - "vel_models.shape,disp_data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "save_base_path = \"../../../Datasets/OpenSWI-deep/1s-100s-Aug\"\n", - "# Save processed data as compressed npz files\n", - "# np.savez_compressed(os.path.join(save_base_path, \"LITHO1_loc.npz\"),\n", - "# data=loc.astype(np.float32))\n", - "np.savez_compressed(os.path.join(save_base_path, \"LITHO1_model.npz\"),\n", - " data=vel_models.astype(np.float32))\n", - "np.savez_compressed(os.path.join(save_base_path, \"LITHO1_disp.npz\"),\n", - " data=disp_data.astype(np.float32))" - ] - }, - { - "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 -}