Seismic Waveform Inversion Toolbox-1.0

Last update: Dec 29, 2022

Overview

Seismic Waveform Inversion Toolbox (SWIT-1.0)

By Haipeng Li @ USTC

Contact: [email protected]

First look at SWIT

Contents of SWIT

Workflow of SWIT

SWIT Installation

Step 1: Install gfortran

# Install gcc and gfortran
sudo apt-get install build-essential
sudo apt install gfortran

Step 2 : Install OpenMPI

# Download the latest OpenMPI package, or go to  http://www.open-mpi.org/software/ompi to download the desired version
wget https://download.open-mpi.org/release/open-mpi/v4.1/openmpi-4.1.1.tar.gz 
tar xvfz openmpi-4.1.1.tar.gz
cd openmpi-4.1.1

# Configure the installation files and install OpenMPI (this would take quite a while)
./configure --prefix=/usr/local/openmpi CC=gcc FC=gfortran
make    # make -j8  # use 8 cores to speed up the make process
sudo make install

# Add env path at your ~/.bashrc
vim ~/.bashrc
export PATH=/usr/local/openmpi/bin:$PATH
source ~/.bashrc

# Check OpenMPI is successfully installed
which mpirun

Step 3 : Install Anaconda Environment (Otherwise, just install Python dependencies as you like)

# Anaconda is recommended. For installing Anaconda, please refer to https://docs.anaconda.com/anaconda/install/linux/
# 1. download package from: https://www.anaconda.com/products/individual/download-success
# 2. bash your_downloaded_Anaconda_package

# Create the conda environment for SWIT if you use Anaconda
conda create --name SWIT python=3.7.5
conda activate SWIT

# Install dependencies using USTC mirrors (whether use Anaconda or not)
pip install numpy obspy scipy matplotlib multiprocess PySimpleGUI psutil Pillow -i https://pypi.mirrors.ustc.edu.cn/simple/

Step 4 : Install & Run SWIT

# Complie the fd2dmpi forward solver with the default fortran compiler (mpif90).
# If you want to use other fortran compiler, you can edit the Makefile.config file (line 18) under ~/SWIT-1.0/fd2dmpi/.
cd /your/own/path/to/SWIT-1.0/fd2dmpi/
rm *.mod
make clean
make

# Add fd2dmpi and Python toolbox to the env path at your ~/.bashrc 
vim ~/.bashrc 
export PATH=/your/own/path/to/SWIT-1.0/bin:$PATH
export PYTHONPATH=/your/own/path/to/SWIT-1.0/toolbox
source ~/.bashrc

# Option 1. Run SWIT via GUI
cd /your/own/path/to/SWIT-1.0/toolbox/
python runswit_Linux.py    # or python runswit_MacOS.py 

# Option 2. Run SWIT via the Python script
cd /your/own/path/to/SWIT-1.0/example/some_case/
./run_workflow     # You need to modify all the paths in the Python script before running

# Notice:
# If you use the Intel Compiler, you need to make the following change in forward and adjoint functions in toolbox/solver.py: 
# Before:     
#	   solver_cmd = 'mpirun -np %d  fd2dmpi par=%s' % (mpiproc, parfile)
# After:
#    solver_cmd = 'mpiexec -np %d  fd2dmpi par=%s' % (mpiproc, parfile)

Systems

SWIT-1.0 has been tested on Ubuntu 16.04, 18.04, 20.04, Centos 7.
It seems that SWIT-1.0 cannot run on MacOS properly due to the problem with the Python multiprocess module. 
This issue will be fixed in the near future.

FWI examples (keep updating)

No.	Acquisition	Model	Misfit	Features	Optimization	Size
1	Land	Marmousi	Waveform	-	NLCG	481x121, 25 m
2	Land	Overthrust	Waveform	-	NLCG	401x101, 25 m
3	Marine	Marmousi	Waveform	-	NLCG	481x141, 25 m
4	Marine	Overthrust	Waveform	-	NLCG	401x121, 25 m
5	Land	Marmousi	Traveltime & Waveform	1D initial model	NLCG	401x121, 25 m
6	Land	Overthrust	Waveform	Multi-scale Inversion	NLCG	401x101, 25 m

Citations :

If you find SWIT is useful, please cite the following work:

1. Li, H., Li, J., Liu, B., Huang, X. (2021). Application of full-waveform tomography on deep seismic profiling dataset for tectonic fault characterization. International Meeting for Applied Geoscience & Energy.

2. Schuster, G. T. (2017). Seismic inversion. Society of Exploration Geophysicists. https://library.seg.org/doi/book/10.1190/1.9781560803423

Few more words:

Simplicity is the Greatest Virtue Ever.
The Seismic WIT always lies within.

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Comments

Problem make installing the package

Hi Haipeng,

I am installing this package on my macbook pro M1. I have openmpi installed:

 mpif90 --version
GNU Fortran (Homebrew GCC 11.2.0_3) 11.2.0
Copyright (C) 2021 Free Software Foundation, Inc.
This is free software; see the source for copying conditions.  There is NO
warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.

However, when I run make in the fd2dmpi folder I got the following error indicating grammar error. Could this be a problem with my mpi library？

❯ make
/opt/homebrew/bin/mpif90 -o .obj/global.o global.f90 -O3 -w  -c -funroll-loops -J.mod
/opt/homebrew/bin/mpif90 -o .obj/parser.o parser.f90 -O3 -w  -c -funroll-loops -J.mod
/opt/homebrew/bin/mpif90 -o .obj/datatype.o datatype.f90 -O3 -w  -c -funroll-loops -J.mod
/opt/homebrew/bin/mpif90 -o .obj/mmi_mpi.o mmi_mpi.f90 -O3 -w  -c -funroll-loops -J.mod
/opt/homebrew/bin/mpif90 -o .obj/string.o string.f90 -O3 -w  -c -funroll-loops -J.mod
/opt/homebrew/bin/mpif90 -o .obj/su.o su.f90 -O3 -w  -c -funroll-loops -J.mod
/opt/homebrew/bin/mpif90 -o .obj/io.o io.f90 -O3 -w  -c -funroll-loops -J.mod
io.f90:296:17:

  296 |   call MPI_BCAST(den(:,ix),nz_pml,MPI_INTEGER,0,MPI_COMM_WORLD,ierr)
      |                 1
......
  798 | call MPI_BCAST(fs,nx_pml,MPI_INTEGER,0,MPI_COMM_WORLD,ierr)
      |               2
Error: Type mismatch between actual argument at (1) and actual argument at (2) (REAL(4)/INTEGER(4)).
io.f90:261:17:

Best, Xin

opened by RunningXinLiu 11

Questions about free surface

Thank you for your work.

PML =50, whether free surface is set to Yes or No and the result seems to be the same. I don't know why.

In addition, on line 154 of base.py, "self.nz_pml = self.nz + self.pml * (2 - self.fs)". Can you explain what it means and where self.nz_pml is used

I would appreciate it if you could take some time to answer my questions.

opened by ZYX68926 1
The data filtering becomes very slow due to large OMP_NUM_THREADS for Scipy

I add a default specification for OMP_NUM_THREADS in base.py (Line 70):

os.environ["OMP_NUM_THREADS"] = "1" # export OMP_NUM_THREADS=1

The data filtering speed is faster now.

opened by Haipeng-ustc 0

Releases(v1.0.0)

v1.0.0(Oct 11, 2022)

Seismic Waveform Inversion Toolbox-1.0 (SWIT-1.0)
Source code(tar.gz)
Source code(zip)

Owner

Haipeng Li

I'm a geophysics graduate student.

GitHub Repository

Seismic Waveform Inversion Toolbox-1.0

Related tags

Overview

Seismic Waveform Inversion Toolbox (SWIT-1.0)

First look at SWIT

Contents of SWIT

Workflow of SWIT

SWIT Installation

Step 1: Install gfortran

Step 2 : Install OpenMPI

Step 3 : Install Anaconda Environment (Otherwise, just install Python dependencies as you like)

Step 4 : Install & Run SWIT

Systems

FWI examples (keep updating)

Citations :

Few more words:

You might also like...

Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data recorded in NumPy array

A Python Tools to imaging the shallow seismic structure

OpenQuake's Engine for Seismic Hazard and Risk Analysis

UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

efficient neural audio synthesis in the waveform domain

Unofficial PyTorch Implementation of UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

The official implementation of the Interspeech 2021 paper WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution.

Unofficial PyTorch Implementation of UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

Official implementation of the paper Chunked Autoregressive GAN for Conditional Waveform Synthesis

A python package to manage the stored receiver-side Strain Green's Tensor (SGT) database of 3D background models and able to generate Green's function and synthetic waveform

WAL enables programmable waveform analysis.

A collection of resources on GAN Inversion.

[CVPR 2021] Unsupervised 3D Shape Completion through GAN Inversion

[IJCAI-2021] A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation"

A Simplied Framework of GAN Inversion

Style-based Neural Drum Synthesis with GAN inversion

Implementation for HFGI: High-Fidelity GAN Inversion for Image Attribute Editing

Chunkmogrify: Real image inversion via Segments

Official implementation for "Style Transformer for Image Inversion and Editing" (CVPR 2022)

Comments

Problem make installing the package

Questions about free surface

The data filtering becomes very slow due to large OMP_NUM_THREADS for Scipy

Releases(v1.0.0)

v1.0.0(Oct 11, 2022)

Owner

Haipeng Li

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