Seismic Waveform Inversion Toolbox-1.0

Related tags

Data Visualizationseismologygui-applicationfull-waveform-inversion
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:

  1. Simplicity is the Greatest Virtue Ever.

  2. The Seismic WIT always lies within.

You might also like...
Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data recorded in NumPy array
Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data recorded in NumPy array

shindo.py Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data stored in NumPy array Introduction Japa

RR_Inyo 3 Sep 23, 2022
A Python Tools to imaging the shallow seismic structure

ShallowSeismicImaging Tools to imaging the shallow seismic structure, above 10 km, based on the ZH ratio measured from the ambient seismic noise, and

Xiao Xiao 9 Aug 9, 2022
OpenQuake's Engine for Seismic Hazard and Risk Analysis
OpenQuake's Engine for Seismic Hazard and Risk Analysis

OpenQuake Engine The OpenQuake Engine is an open source application that allows users to compute seismic hazard and seismic risk of earthquakes on a g

Global Earthquake Model 281 Dec 21, 2022
UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

UnivNet UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation. Training python train.py --c

Rishikesh (ऋषिकेश) 55 Dec 26, 2022
efficient neural audio synthesis in the waveform domain
efficient neural audio synthesis in the waveform domain

neural waveshaping synthesis real-time neural audio synthesis in the waveform domain paper • website • colab • audio by Ben Hayes, Charalampos Saitis,

Ben Hayes 169 Dec 23, 2022
Unofficial PyTorch Implementation of UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation
Unofficial PyTorch Implementation of UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

UnivNet UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation This is an unofficial PyTorch

MINDs Lab 170 Jan 4, 2023
The official implementation of the Interspeech 2021 paper WSRGlow: A Glow-based Waveform Generative Model for Audio Super-Resolution.

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

Kexun Zhang 96 Jan 3, 2023
Unofficial PyTorch Implementation of UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation
Unofficial PyTorch Implementation of UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

UnivNet UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation This is an unofficial PyTorch

MINDs Lab 54 Aug 30, 2021
Official implementation of the paper Chunked Autoregressive GAN for Conditional Waveform Synthesis

Chunked Autoregressive GAN (CARGAN) Official implementation of the paper Chunked Autoregressive GAN for Conditional Waveform Synthesis [paper] [compan

Descript 150 Dec 6, 2022
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

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

Liang Ding 7 Dec 14, 2022
WAL enables programmable waveform analysis.

This repro introcudes the Waveform Analysis Language (WAL). The initial paper on WAL will appear at ASPDAC'22 and can be downloaded here: https://www.

Institute for Complex Systems (ICS), Johannes Kepler University Linz 40 Dec 13, 2022
A collection of resources on GAN Inversion.

This repo is a collection of resources on GAN inversion, as a supplement for our survey

null 857 Dec 29, 2022
[CVPR 2021] Unsupervised 3D Shape Completion through GAN Inversion
[CVPR 2021] Unsupervised 3D Shape Completion through GAN Inversion

ShapeInversion Paper Junzhe Zhang, Xinyi Chen, Zhongang Cai, Liang Pan, Haiyu Zhao, Shuai Yi, Chai Kiat Yeo, Bo Dai, Chen Change Loy "Unsupervised 3D

null 100 Dec 22, 2022
[IJCAI-2021] A benchmark of data-free knowledge distillation from paper
[IJCAI-2021] A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation"

DataFree A benchmark of data-free knowledge distillation from paper "Contrastive Model Inversion for Data-Free Knowledge Distillation" Authors: Gongfa

ZJU-VIPA 47 Jan 9, 2023
A Simplied Framework of GAN Inversion

Framework of GAN Inversion Introcuction You can implement your own inversion idea using our repo. We offer a full range of tuning settings (in hparams

Kangneng Zhou 13 Sep 27, 2022
Style-based Neural Drum Synthesis with GAN inversion
Style-based Neural Drum Synthesis with GAN inversion

Style-based Drum Synthesis with GAN Inversion Demo TensorFlow implementation of a style-based version of the adversarial drum synth (ADS) from the pap

Sound and Music Analysis (SoMA) Group 29 Nov 19, 2022
Implementation for HFGI: High-Fidelity GAN Inversion for Image Attribute Editing
Implementation for HFGI: High-Fidelity GAN Inversion for Image Attribute Editing

HFGI: High-Fidelity GAN Inversion for Image Attribute Editing High-Fidelity GAN Inversion for Image Attribute Editing Update: We released the inferenc

Tengfei Wang 371 Dec 30, 2022
Chunkmogrify: Real image inversion via Segments
Chunkmogrify: Real image inversion via Segments

Chunkmogrify: Real image inversion via Segments Teaser video with live editing sessions can be found here This code demonstrates the ideas discussed i

David Futschik 112 Jan 4, 2023
Official implementation for
Official implementation for "Style Transformer for Image Inversion and Editing" (CVPR 2022)

Style Transformer for Image Inversion and Editing (CVPR2022) https://arxiv.org/abs/2203.07932 Existing GAN inversion methods fail to provide latent co

Xueqi Hu 153 Dec 2, 2022
Comments
  • Problem make installing the package

    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

    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

    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)
Owner
Haipeng Li
I'm a geophysics graduate student.
Haipeng Li
GitHub Repository
A package for plotting maps in R with ggplot2

Attention! Google has recently changed its API requirements, and ggmap users are now required to register with Google. From a user’s perspective, ther

David Kahle 719 Jan 04, 2023
Area-weighted venn-diagrams for Python/matplotlib

Venn diagram plotting routines for Python/Matplotlib Routines for plotting area-weighted two- and three-circle venn diagrams. Installation The simples

Konstantin Tretyakov 400 Dec 31, 2022
GUI for visualization and interactive editing of SMPL-family body models ie. SMPL, SMPL-X, MANO, FLAME.

Body Model Visualizer Introduction This is a simple Open3D-based GUI for SMPL-family body models. This GUI lets you play with the shape, expression, a

Muhammed Kocabas 207 Jan 01, 2023
Create Badges with stats of Scratch User, Project and Studio. Use those badges in Github readmes, etc.

Scratch-Stats-Badge Create customized Badges with stats of Scratch User, Studio or Project. Use those badges in Github readmes, etc. Examples Document

Siddhesh Chavan 5 Aug 28, 2022
A python script to visualise explain plans as a graph using graphviz

README Needs to be improved Prerequisites Need to have graphiz installed on the machine. Refer to https://graphviz.readthedocs.io/en/stable/manual.htm

Edward Mallia 1 Sep 28, 2021
Here I plotted data for the average test scores across schools and class sizes across school districts.

HW_02 Here I plotted data for the average test scores across schools and class sizes across school districts. Average Test Score by Race This graph re

7 Oct 27, 2021
Python Data Validation for Humans™.

validators Python data validation for Humans. Python has all kinds of data validation tools, but every one of them seems to require defining a schema

Konsta Vesterinen 670 Jan 09, 2023
A small script written in Python3 that generates a visual representation of the Mandelbrot set.

Mandelbrot Set Generator A small script written in Python3 that generates a visual representation of the Mandelbrot set. Abstract The colors in the ou

1 Dec 28, 2021
Movie recommendation using RASA, TigerGraph

Demo run: The below video will highlight the runtime of this setup and some sample real-time conversations using the power of RASA + TigerGraph, Steps

Sudha Vijayakumar 3 Sep 10, 2022
web application for flight log analysis & review

Flight Review This is a web application for flight log analysis. It allows users to upload ULog flight logs, and analyze them through the browser. It

PX4 Drone Autopilot 145 Dec 20, 2022
A blender import/export system for Defold

defold-blender-export A Blender export system for the Defold game engine. Setup Notes There are no exhaustive documents for this tool yet. Its just no

David Lannan 27 Dec 30, 2022
demir.ai Dataset Operations

demir.ai Dataset Operations With this application, you can have the empty values (nan/null) deleted or filled before giving your dataset to machine le

Ahmet Furkan DEMIR 8 Nov 01, 2022
A script written in Python that generate output custom color (HEX or RGB input to x1b hexadecimal)

ColorShell ─ 1.5 Planned for v2: setup.sh for setup alias This script converts HEX and RGB code to x1b x1b is code for colorize outputs, works on ou

Riley 4 Oct 31, 2021
This is a place where I'm playing around with pandas to analyze data in a csv/excel file.

pandas-csv-excel-analysis This is a place where I'm playing around with pandas to analyze data in a csv/excel file. 0-start A very simple cheat sheet

Chuqin 3 Oct 05, 2022
649 Pokémon palettes as CSVs, with a Python lib to turn names/IDs into palettes, or MatPlotLib compatible ListedColormaps.

PokePalette 649 Pokémon, broken down into CSVs of their RGB colour palettes. Complete with a Python library to convert names or Pokédex IDs into eithe

11 Dec 05, 2022
Apache Superset is a Data Visualization and Data Exploration Platform

Apache Superset is a Data Visualization and Data Exploration Platform

The Apache Software Foundation 49.9k Jan 02, 2023
DataVisualization - The evolution of my arduino and python journey. New level of competence achieved

DataVisualization - The evolution of my arduino and python journey. New level of competence achieved

1 Jan 03, 2022
A curated list of awesome Dash (plotly) resources

Awesome Dash A curated list of awesome Dash (plotly) resources Dash is a productive Python framework for building web applications. Written on top of

Luke Singham 1.7k Dec 26, 2022
Lime: Explaining the predictions of any machine learning classifier

lime This project is about explaining what machine learning classifiers (or models) are doing. At the moment, we support explaining individual predict

Marco Tulio Correia Ribeiro 10.3k Dec 29, 2022
Use Perspective to create the chart for the trader’s dashboard

Task Overview | Installation Instructions | Link to Module 3 Introduction Experience Technology at JP Morgan Chase Try out what real work is like in t

Abdulazeez Jimoh 1 Jan 22, 2022