GeoFWI is a large velocity model dataset for deep-learning-based FWI benchmarking. This project is inspired by the OpenFWI project (https://sites.google.com/site/youzuolin044/openfwi) but includes more realistic geological models (e.g., folding layers, faults, and salt bodies).
Currently, we call it lightweight because this is the first step toward an eventually "heavyweight," very realistic, highly generalizable 3D velocity model dataset for unprecedented high-efficacy deep-learning-empowered FWI studies.
https://utexas.box.com/s/scbh25utyy5jz3mq7b0hp3wyluka7gaq
https://utexas.box.com/s/t6rmre575qxb2x0yvdanst6pamzvsnxp
NOTE 1: Please put the folders train/valid/test in the geofwi_train folder, which includes 50 samples for training, 10 samples for validation, and 111 samples for the completely independent test.
NOTE 2: The test set is not used in training the best SeisInvNet model, trained from 40000 samples, validated on 5000 samples, and tested on 4476 samples. It is downloadable from https://utexas.box.com/s/6ak8omw3rlvatdnh51ngig1n94e02mss
NOTE 3: For larger-scale training, e.g., a scale of thousands or tens of thousands of velocity samples, we recommend generating the shot gathers by the users themselves. That's why we only focus on a "lightweight" velocity model dataset (not shot gathers) in the GeoFWI project.
Li et al., 2025, GeoFWI: A large velocity model dataset for benchmarking full waveform inversion using deep learning - upcoming, TBD.
BibTeX:
@Article{geofwi,
author={Chao Li and Yiran Shen and Sergey Fomel and Umair Bin Waheed and Alexandros Savvaidis and Yangkang Chen},
title = {GeoFWI: A large velocity model dataset for benchmarking full waveform inversion using deep learning},
journal={TBD},
year=2025,
volume=TBD,
number=TBD,
issue=TBD,
pages={TBD},
doi={TBD},
}
GEOFWI developing team, 2024-present
MIT License
Using the latest version
git clone https://github.com/aaspip/geofwi
cd geofwi
pip install -v -e .
or using Pypi
pip install geofwi
or (recommended, because we update very fast)
pip install git+https://github.com/aaspip/geofwi
Mac OS, Linux, Windows (need Microsoft C++ Build Tools)
Here is a sneak peek of some of the structures and initial results of the GeoFWI project.
Figure below shows a few good/bad predictions using the data-driven DL-based method (SeisInvNet) detailed in Li et al., 2020, Deep learning Inversion of Seismic Data, IEEE TGRS.
An example below shows a few shot gathers simulated from the velocity models on top.
Read the GeoFWI data and check its dimension
import numpy as np
sizes=np.load("data/geofwi-size-layer-fault-salt-1-10.npy")
GeoFWI_path='data/geofwi.npy' #suppose you put geofwi.npy in "./data/" directory
geofwi=np.load(GeoFWI_path)
print('geofwi.shape',geofwi.shape)
print('NO of samples in GeoFWI is ',geofwi.shape[0])
print('Each sample has a dimension of %d * %d '%(geofwi.shape[1],geofwi.shape[2]))
The output will be like
geofwi.shape (49476, 100, 100)
NO of samples in GeoFWI is 49476
Each sample has a dimension of 100 * 100
Get some statistics from GeoFWI
import numpy as np
sizes=np.load("data/geofwi-size-layer-fault-salt-1-10.npy")
print('length of sizes:',len(sizes))
print('Two-layer folding model: %d samples'%sizes[0])
print('Two-layer fault model: %d samples'%sizes[1])
print('Two-layer salt model: %d samples'%sizes[2])
print('Three-layer folding model: %d samples'%sizes[3])
print('Three-layer fault model: %d samples'%sizes[4])
print('Three-layer salt model: %d samples'%sizes[5])
print('Four-layer folding model: %d samples'%sizes[6])
print('Four-layer fault model: %d samples'%sizes[7])
print('Four-layer salt model: %d samples'%sizes[8])
print('Five-layer folding model: %d samples'%sizes[9])
print('Five-layer fault model: %d samples'%sizes[10])
print('Five-layer salt model: %d samples'%sizes[11])
print('Six-layer folding model: %d samples'%sizes[12])
print('Six-layer fault model: %d samples'%sizes[13])
print('Six-layer salt model: %d samples'%sizes[14])
print('Seven-layer folding model: %d samples'%sizes[15])
print('Seven-layer fault model: %d samples'%sizes[16])
print('Seven-layer salt model: %d samples'%sizes[17])
print('Eight-layer folding model: %d samples'%sizes[18])
print('Eight-layer fault model: %d samples'%sizes[19])
print('Eight-layer salt model: %d samples'%sizes[20])
print('Nine-layer folding model: %d samples'%sizes[21])
print('Nine-layer fault model: %d samples'%sizes[22])
print('Nine-layer salt model: %d samples'%sizes[23])
print('Ten-layer folding model: %d samples'%sizes[24])
print('Ten-layer fault model: %d samples'%sizes[25])
print('Ten-layer salt model: %d samples'%sizes[26])
print('Eleven-layer folding model: %d samples'%sizes[27])
print('Eleven-layer fault model: %d samples'%sizes[28])
print('Eleven-layer salt model: %d samples'%sizes[29])
The output will be like
Two-layer folding model: 2826 samples
Two-layer fault model: 2754 samples
Two-layer salt model: 1888 samples
Three-layer folding model: 2649 samples
Three-layer fault model: 2473 samples
Three-layer salt model: 1758 samples
Four-layer folding model: 2491 samples
Four-layer fault model: 2161 samples
Four-layer salt model: 1674 samples
Five-layer folding model: 2245 samples
Five-layer fault model: 1948 samples
Five-layer salt model: 1508 samples
Six-layer folding model: 1974 samples
Six-layer fault model: 1653 samples
Six-layer salt model: 1340 samples
Seven-layer folding model: 1797 samples
Seven-layer fault model: 1478 samples
Seven-layer salt model: 1226 samples
Eight-layer folding model: 1635 samples
Eight-layer fault model: 1325 samples
Eight-layer salt model: 1076 samples
Nine-layer folding model: 1453 samples
Nine-layer fault model: 1117 samples
Nine-layer salt model: 976 samples
Ten-layer folding model: 1309 samples
Ten-layer fault model: 1000 samples
Ten-layer salt model: 881 samples
Eleven-layer folding model: 1200 samples
Eleven-layer fault model: 829 samples
Eleven-layer salt model: 832 samples
Extract a few five-layer fault models from GeoFWI
import numpy as np
sizes=np.load("data/geofwi-size-layer-fault-salt-1-10.npy")
GeoFWI_path='data/geofwi.npy' #suppose you put geofwi.npy in "./data/" directory
geofwi=np.load(GeoFWI_path)
#first index for five-layer fault models,
ind=sum(sizes[0:10])+1 #refer to last example for checking the index for each type of model
no=8 #make it 4,8,16
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(16, 8))
for ii in range(no):
plt.subplot(2,int(no/2),ii+1)
plt.imshow(geofwi[ind+ii,:,:],clim=[1500,4000]);
plt.colorbar(orientation='horizontal',cax=fig.add_axes([0.37,0.07,0.3,0.01]),shrink=1,label='Velocity (m/s)');
plt.show()
The output will be like
Extract a few five-layer salt models from GeoFWI
import numpy as np
sizes=np.load("data/geofwi-size-layer-fault-salt-1-10.npy")
GeoFWI_path='data/geofwi.npy' #suppose you put geofwi.npy in "./data/" directory
geofwi=np.load(GeoFWI_path)
#first index for five-layer salt models,
ind=sum(sizes[0:11])+1 #refer to last example for checking the index for each type of model
no=8 #make it 4,8,16
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(16, 8))
for ii in range(no):
plt.subplot(2,int(no/2),ii+1)
plt.imshow(geofwi[ind+ii,:,:],clim=[1500,4000]);
plt.colorbar(orientation='horizontal',cax=fig.add_axes([0.37,0.07,0.3,0.01]),shrink=1,label='Velocity (m/s)');
plt.show()
The output will be like
Extract one velocity with a certain number of layers and a specific type of fold/fault/salt.
from geofwi import get_vel
lay=5;mod='salt';
vel=get_vel(layer=lay,mode=mod,datapath='./data',indx=0) #indx=0 means the first sample of this type
import matplotlib.pyplot as plt
plt.imshow(vel,cmap=plt.jet());plt.colorbar(label='Velocity (m/s)');
plt.xlabel('X sample'); plt.ylabel('Z sample');
plt.title("%d-layer %s model"%(lay,mod))
plt.show()
The output will be like
Here is a benchmarking test using different loss functions for the data-driven FWI (we use SeisInvNet, Li et al., 2020).
Here is a benchmarking test using different diffusion model scenarios. Generated by benchmark/DiffusionModel/test_compare_three_DMs.py
The development team welcomes voluntary contributions from any open-source enthusiast.
If you want to make contribution to this project, feel free to contact the development team.
Regarding any questions, bugs, developments, collaborations, please contact
Yangkang Chen
chenyk2016@gmail.com
The gallery figures of the geofwi project can be found at https://github.com/aaspip/gallery/tree/main/geofwi All figures in the gallery directory can be reproduced by their corresponding DEMO scripts in the "demo" directory. These gallery figures are also presented below.
Generated by demos/test_quick_visualize.py
First scenario: randomly chosen 25 samples from the whole GeoFWI dataset
Second scenario: the first 25 samples from the whole GeoFWI dataset
Third scenario: the last 25 samples from the whole GeoFWI dataset
Fourth scenario: the last 25 samples in the "11-layer fault category" (e.g., sizes=np.load('data/geofwi-size-layer-fault-salt-1-10.npy'); index is chosen from 49476-sizes[-1]-1 to sizes[-1]-25) from the whole GeoFWI dataset
Below is an example for a simple FWI example using GeoFWI velocity samples. It requires the pywave package (https://github.com/chenyk1990/pywave) for modeling and inversion.
Generated by demos/test_fwi_pywave.py
Below are two examples for applying the diffusion model trained from the GeoFWI dataset to a completely independent complex model (Marmousi or Overthrust). It can be observed that 1) L2-only FWI suffers from cycle skipping and artifacts; 2) L2 + TV FWI improves continuity but over-smooths the structure; 3) L2 + SGDS (Split Gibbs Diffusion Sampling) FWI recovers high-resolution features like salt bodies and faults, closely matching the ground truth.
