Skip to content

Commit

Permalink
first commit
Browse files Browse the repository at this point in the history
  • Loading branch information
Gao committed Oct 31, 2024
1 parent a9bbc96 commit 6b4a94d
Show file tree
Hide file tree
Showing 19 changed files with 3,149 additions and 0 deletions.
Binary file added __pycache__/lossf.cpython-39.pyc
View file
Binary file not shown.
Binary file added __pycache__/mlps.cpython-39.pyc
View file
Binary file not shown.
Binary file added __pycache__/models.cpython-39.pyc
View file
Binary file not shown.
Binary file added __pycache__/plot.cpython-39.pyc
View file
Binary file not shown.
Binary file added __pycache__/utils.cpython-39.pyc
View file
Binary file not shown.
322 changes: 322 additions & 0 deletions case1.ipynb
View file

Large diffs are not rendered by default.

333 changes: 333 additions & 0 deletions case2.ipynb
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,333 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import plot\n",
"import models\n",
"from feature_ecoding.clip_surface import Fault_feature\n",
"import torch\n",
"import pandas as pd\n",
"import os\n",
"import importlib"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"GPU amount: 1\n",
"device: cuda\n"
]
}
],
"source": [
"torch.manual_seed(0)\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"if torch.cuda.is_available():\n",
" num_GPU = torch.cuda.device_count()\n",
" print(f\"GPU amount: {num_GPU}\")\n",
"else:\n",
" num_GPU = 0\n",
"\n",
"print(f\"device: {device}\")\n",
"root_path = os.path.abspath('.')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1. load observational data"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"surface_points = pd.read_csv(os.path.join(root_path, 'data', 'case2', 'original_data', 'filtered_surface_points.csv'))\n",
"orientation_points = pd.read_csv(os.path.join(root_path, 'data', 'case2', 'original_data', 'filtered_orientations.csv'))\n",
"extent = [451000,456000, 6782000, 6784000, -6927, -4953]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"plot.observation(surface_points, orientation_points, extent)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2. modeling fault surfaces"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finish modeling fault3 | Loss_i: 0.016700157895684242, Loss_o: 0.05635257437825203, Loss_ab:0.0\n",
"Finish modeling fault4 | Loss_i: 0.012287864461541176, Loss_o: 0.058752622455358505, Loss_ab:0.0\n",
"------Finish-------\n"
]
}
],
"source": [
"fault_mesh = models.fault_ConcatMLP(surface_points, # original data read from csv file\n",
" orientation_points, # original data read from csv file\n",
" extent, # domain boundary\n",
" resolution=[60, 30, 30], # resolution of the fault mesh, lower resolution to reduce the computation time in feature encoding,\n",
" in_dim=3, # input dimension of neural network\n",
" hidden_dim=256, # hidden layer's dimension of neural network\n",
" out_dim=1, # output dimension of neural network, the only output is a scalar value\n",
" n_hidden_layers=2, # number of hidden layers\n",
" activation='Softplus', # activation function, default is 'Softplus'\n",
" beta_list=[25, 70], # `beta` values for Softplus activation function, the length related to the number of faults\n",
" concat = True, # whether to concat the input features to hidden layers\n",
" epochs=3000, # number of forward and backward propagation process \n",
" lr=0.001, # learning rate\n",
" above_below=True) # whether to use `above_below` loss function, default is False"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"plot.fault_mesh(fault_mesh, surface_points, orientation_points, extent)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"#importlib.reload(plot)\n",
"plot.observation_fault_mesh(surface_points, orientation_points, extent, fault_mesh)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3. fault feature encoding"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"domain_mesh_features, label_interf_all, orie_points_all = Fault_feature.encoding(extents=extent, # domain boundary\n",
" resolution=[100, 100, 100], # resolution of the domain mesh\n",
" mesh_list=fault_mesh, # fault mesh list\n",
" surface_points=surface_points, # original data read from csv file\n",
" orientation_points=orientation_points, # original data read from csv file\n",
" movement=['down','down'], # 'up' or 'down', relative movement in fault right side\n",
" fault_direct=['right','right'], # fault surface direction, insures correct feature encoding\n",
" decimate=0.0) # reserve all the points in fault mesh"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"\" plot.feature_encoding(mesh_list=fault_mesh, # fault mesh list after editing\\n number=0, # 0 is the first mesh, 1 is the second mesh\\n extent=extent, # domain boundary\\n type='obs_point', # 'mesh_point' or 'obs_point', visualization type\\n side='up', # 'up' or 'down', 'up' is the side which assigned value 1 in the feature encoding\\n domain_mesh_features=domain_mesh_features, # meshgrid points of interpreated domain\\n label_interf_all=label_interf_all, # strtigraphic points with labels\\n orie_points_all=orie_points_all) # orientation points \""
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\"\"\" plot.feature_encoding(mesh_list=fault_mesh, # fault mesh list after editing\n",
" number=0, # 0 is the first mesh, 1 is the second mesh\n",
" extent=extent, # domain boundary\n",
" type='obs_point', # 'mesh_point' or 'obs_point', visualization type\n",
" side='up', # 'up' or 'down', 'up' is the side which assigned value 1 in the feature encoding\n",
" domain_mesh_features=domain_mesh_features, # meshgrid points of interpreated domain\n",
" label_interf_all=label_interf_all, # strtigraphic points with labels\n",
" orie_points_all=orie_points_all) # orientation points \"\"\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4. modeling the discontinuious stratigraphic"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training losses | Loss_i: 0.482204794883728, Loss_o: 0.5115153789520264\n",
"each epoch training time : 0.002317742586135864 seconds\n",
"Inference time: 6.364690065383911 seconds\n",
"------Finish-------\n"
]
}
],
"source": [
"stratigraphic_mesh, scalar_field_strat = models.stratigraphic_ConcatMLP(interface_data=label_interf_all, # stratigraphic points with labels\n",
" orientation_data=orie_points_all, # orientation points\n",
" meshgrid_data=domain_mesh_features, # meshgrid points of interpreated domain\n",
" extent=extent, # domain boundary\n",
" resolution=[100, 100, 100], # resolution of the domain mesh\n",
" in_dim=5, # input dimension of neural network\n",
" hidden_dim=512, # hidden layer's dimension of neural network\n",
" out_dim=1, # output dimension of neural network, the only output is a scalar value\n",
" n_hidden_layers=2, # number of hidden layers\n",
" activation='Softplus', # activation function, default is 'Softplus'\n",
" beta=210, # `beta` value for Softplus activation function\n",
" concat=True, # whether to concat the input features to hidden layers\n",
" epochs=1000, # number of forward and backward propagation process\n",
" lr=0.001, # learning rate\n",
" alpha=1) # the efficiency of orientation loss, default is 0.1"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"plot.scalar_field(scalar_field_strat, fault_mesh)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"5. modeling the unconformity layer"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Finish modeling BCU | Loss_i: 0.004268969874829054, Loss_o: 0.010167177766561508\n",
"------Finish-------\n"
]
}
],
"source": [
"unconformity_mesh, scalar_field_unconf = models.unconformity_ConcatMLP(surface_points, \n",
" orientation_points, \n",
" extent,\n",
" resolution=[100, 100, 100], \n",
" in_dim=3,\n",
" hidden_dim=256, \n",
" out_dim=1,\n",
" n_hidden_layers=2,\n",
" activation='Softplus', # Softplus, ReLU , LeakyReLU, Tanh, Sigmoid, and ELU can be selected\n",
" beta_list=[100], \n",
" concat = False,\n",
" epochs=2000,\n",
" lr=0.001,)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"#importlib.reload(plot)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"plot.final_structure(fautl_mesh_list = fault_mesh,\n",
" stratigraphic_mesh = stratigraphic_mesh,\n",
" unconformity_mesh_list = unconformity_mesh,\n",
" surface_points = surface_points,\n",
" orientation_points = orientation_points,\n",
" extent=extent,\n",
" unconformity=True,)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"plot.layer(scalar_field_strat, fault_mesh, extent)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "ml_modeling",
"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": "3.9.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
13 changes: 13 additions & 0 deletions data/case1/original_data/tut_ch1-5_orientations.csv
View file
Original file line number Diff line number Diff line change
@@ -0,0 +1,13 @@
X,Y,Z,dx,dy,dz,formation,group,type
550,500,-600,-0.894934362,9.58E-18,0.446197813,fault2,fault2,fault
350,500,-700,0.894934362,1.00E-12,0.446197813,fault1,fault1,fault
400,500,-625,1.00E-12,1.00E-12,1,rock4,B,stratigraphic
150,500,-550,1.00E-12,1.00E-12,1,rock4,A,stratigraphic
850,500,-525,1.00E-12,1.00E-12,1,rock4,C,stratigraphic
400,500,-675,1.00E-12,1.00E-12,1,rock3,B,stratigraphic
150,500,-600,1.00E-12,1.00E-12,1,rock3,A,stratigraphic
850,500,-575,1.00E-12,1.00E-12,1,rock3,C,stratigraphic
850,500,-625,1.00E-12,1.00E-12,1,rock2,C,stratigraphic
150,500,-650,1.00E-12,1.00E-12,1,rock2,A,stratigraphic
150,500,-700,1.00E-12,1.00E-12,1,rock1,A,stratigraphic
850,500,-675,1.00E-12,1.00E-12,1,rock1,C,stratigraphic
Loading

0 comments on commit 6b4a94d

Please sign in to comment.