BranchesTags

Repository files navigation

swprocess - A Python Package for Surface Wave Processing

Joseph P. Vantassel, jpvantassel.com

DOIPyPI - LicenseCircleCIDocumentation StatusPyPI - Python VersionCodacy Badgecodecov

Table of Contents

About swprocess

swprocess is a Python package for surface wave processing. swprocess was developed by Joseph P. Vantassel under the supervision of Professor Brady R. Cox at The University of Texas at Austin. swprocess continues to be developed and maintained by Joseph P. Vantassel and his research group at Virginia Tech.

If you use swprocess in your research or consulting, we ask you please cite the following:

Vantassel, J. P. (2021). jpvantassel/swprocess: latest (Concept). Zenodo. https://doi.org/10.5281/zenodo.4584128

Vantassel, J. P. & Cox, B. R. (2022). "SWprocess: a workflow for developing robust estimates of surface wave dispersion uncertainty". Journal of Seismology. https://doi.org/10.1007/s10950-021-10035-y

Note: For software, version specific citations should be preferred to general concept citations, such as that listed above. To generate a version specific citation for swprocess, please use the citation tool on the swprocessarchive.

Why use swprocess

swprocess contains features not currently available in any other open-source software, including:

  • Multiple pre-processing workflows for active-source [i.e., Multichannel Analysis of Surface Waves (MASW)] measurements including:
    • time-domain muting,
    • frequency-domain stacking, and
    • time-domain stacking.
  • Multiple wavefield transformations for active-source (i.e., MASW) measurements including:
    • frequency-wavenumber (Nolet and Panza, 1976),
    • phase-shift (Park, 1998),
    • slant-stack (McMechan and Yedlin, 1981), and
    • frequency domain beamformer (Zywicki 1999).
  • Post-processing of active-source and passive-wavefield [i.e., microtremor array measurements (MAM)] data from swprocess and Geopsy, respectively.
  • Interactive trimming to remove low quality dispersion data.
  • Rigorous calculation of dispersion statistics to quantify epistemic and aleatory uncertainty in surface wave measurements.

Examples

Active-source processing

Interactive trimming

Calculation of dispersion statistics

Getting Started

Installing or Upgrading swprocess

  1. If you do not have Python 3.8 or later installed, you will need to do so. A detailed set of instructions can be found here.

  2. If you have not installed swprocess previously use pip install swprocess. If you are not familiar with pip, a useful tutorial can be found here. If you have an earlier version and would like to upgrade to the latest version of swprocess use pip install swprocess --upgrade.

  3. Confirm that swprocess has installed/updated successfully by examining the last few lines of the text displayed in the console.

Using swprocess

  1. Download the contents of the examples directory to any location of your choice.

  2. Start by processing the provided active-source data using the Jupyter notebook (masw.ipynb). If you have not installed Jupyter, detailed instructions can be found here.

  3. Post-process the provided passive-wavefield data using the Jupyter notebook (mam_fk.ipynb).

  4. Perform interactive trimming and calculate dispersion statistics for the example data using the Jupyter notebook (stats.ipynb). Compare your results to those shown in the figure above.

  5. Enjoy!

About

Python package for surface wave processing.

pypi.org/project/swprocess/

Topics

geopsymammaswprocessingsurfacewave

Resources

Readme
License
Activity

Stars

122 stars

Watchers

5 watching

Forks

37 forks
Report repository

Releases

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
swprocess - A Python Package for Surface Wave Processing
Joseph P. Vantassel, jpvantassel.com
DOIPyPI - LicenseCircleCIDocumentation StatusPyPI - Python VersionCodacy Badgecodecov
Table of Contents
About swprocess
swprocess is a Python package for surface wave processing. swprocess was
developed by Joseph P. Vantassel under the
supervision of Professor Brady R. Cox at The University of Texas at Austin.
swprocess continues to be developed and maintained by
Joseph P. Vantassel and his research group at Virginia Tech.
If you use swprocess in your research or consulting, we ask you please cite
the following:
Vantassel, J. P. (2021). jpvantassel/swprocess: latest (Concept). Zenodo.
https://doi.org/10.5281/zenodo.4584128
Vantassel, J. P. & Cox, B. R. (2022). "SWprocess: a workflow for developing robust
estimates of surface wave dispersion uncertainty". Journal of Seismology.
https://doi.org/10.1007/s10950-021-10035-y
Note: For software, version specific citations should be preferred to
general concept citations, such as that listed above. To generate a version
specific citation for swprocess, please use the citation tool on the swprocessarchive.
Why use swprocess
swprocess contains features not currently available in any other open-source
software, including:
  • Multiple pre-processing workflows for active-source [i.e., Multichannel
Analysis of Surface Waves (MASW)] measurements including:

    • time-domain muting,
    • frequency-domain stacking, and
    • time-domain stacking.
  • Multiple wavefield transformations for active-source (i.e., MASW) measurements
including:

    • frequency-wavenumber (Nolet and Panza, 1976),
    • phase-shift (Park, 1998),
    • slant-stack (McMechan and Yedlin, 1981), and
    • frequency domain beamformer (Zywicki 1999).
  • Post-processing of active-source and passive-wavefield [i.e., microtremor
array measurements (MAM)] data from swprocess and Geopsy, respectively.
  • Interactive trimming to remove low quality dispersion data.
  • Rigorous calculation of dispersion statistics to quantify epistemic and
aleatory uncertainty in surface wave measurements.
Examples
Active-source processing
Interactive trimming
Calculation of dispersion statistics
Getting Started
Installing or Upgrading swprocess
  1. If you do not have Python 3.8 or later installed, you will need to do
so. A detailed set of instructions can be found
here.
  2. If you have not installed swprocess previously use pip install swprocess.
If you are not familiar with pip, a useful tutorial can be found
here. If you have
an earlier version and would like to upgrade to the latest version of
swprocess use pip install swprocess --upgrade.
  3. Confirm that swprocess has installed/updated successfully by examining the
last few lines of the text displayed in the console.
Using swprocess
  1. Download the contents of the
examples
directory to any location of your choice.
  2. Start by processing the provided active-source data using the
Jupyter notebook (masw.ipynb). If you have not installed Jupyter,
detailed instructions can be found
here.
  3. Post-process the provided passive-wavefield data using the
Jupyter notebook (mam_fk.ipynb).
  4. Perform interactive trimming and calculate dispersion statistics for the
example data using the Jupyter notebook (stats.ipynb). Compare your results
to those shown in the figure above.
  5. Enjoy!
About
Python package for surface wave processing.
pypi.org/project/swprocess/
Topics
geopsymammaswprocessingsurfacewave
Resources
Readme
License
Activity
Stars
122 stars
Watchers
5 watching
Forks
37 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
swprocess - A Python Package for Surface Wave Processing
Joseph P. Vantassel, jpvantassel.com
DOIPyPI - LicenseCircleCIDocumentation StatusPyPI - Python VersionCodacy Badgecodecov
Table of Contents
About swprocess
swprocess is a Python package for surface wave processing. swprocess was
developed by Joseph P. Vantassel under the
supervision of Professor Brady R. Cox at The University of Texas at Austin.
swprocess continues to be developed and maintained by
Joseph P. Vantassel and his research group at Virginia Tech.
If you use swprocess in your research or consulting, we ask you please cite
the following:
Vantassel, J. P. (2021). jpvantassel/swprocess: latest (Concept). Zenodo.
https://doi.org/10.5281/zenodo.4584128
Vantassel, J. P. & Cox, B. R. (2022). "SWprocess: a workflow for developing robust
estimates of surface wave dispersion uncertainty". Journal of Seismology.
https://doi.org/10.1007/s10950-021-10035-y
Note: For software, version specific citations should be preferred to
general concept citations, such as that listed above. To generate a version
specific citation for swprocess, please use the citation tool on the swprocessarchive.
Why use swprocess
swprocess contains features not currently available in any other open-source
software, including:
  • Multiple pre-processing workflows for active-source [i.e., Multichannel
Analysis of Surface Waves (MASW)] measurements including:

    • time-domain muting,
    • frequency-domain stacking, and
    • time-domain stacking.
  • Multiple wavefield transformations for active-source (i.e., MASW) measurements
including:

    • frequency-wavenumber (Nolet and Panza, 1976),
    • phase-shift (Park, 1998),
    • slant-stack (McMechan and Yedlin, 1981), and
    • frequency domain beamformer (Zywicki 1999).
  • Post-processing of active-source and passive-wavefield [i.e., microtremor
array measurements (MAM)] data from swprocess and Geopsy, respectively.
  • Interactive trimming to remove low quality dispersion data.
  • Rigorous calculation of dispersion statistics to quantify epistemic and
aleatory uncertainty in surface wave measurements.
Examples
Active-source processing
Interactive trimming
Calculation of dispersion statistics
Getting Started
Installing or Upgrading swprocess
  1. If you do not have Python 3.8 or later installed, you will need to do
so. A detailed set of instructions can be found
here.
  2. If you have not installed swprocess previously use pip install swprocess.
If you are not familiar with pip, a useful tutorial can be found
here. If you have
an earlier version and would like to upgrade to the latest version of
swprocess use pip install swprocess --upgrade.
  3. Confirm that swprocess has installed/updated successfully by examining the
last few lines of the text displayed in the console.
Using swprocess
  1. Download the contents of the
examples
directory to any location of your choice.
  2. Start by processing the provided active-source data using the
Jupyter notebook (masw.ipynb). If you have not installed Jupyter,
detailed instructions can be found
here.
  3. Post-process the provided passive-wavefield data using the
Jupyter notebook (mam_fk.ipynb).
  4. Perform interactive trimming and calculate dispersion statistics for the
example data using the Jupyter notebook (stats.ipynb). Compare your results
to those shown in the figure above.
  5. Enjoy!
About
Python package for surface wave processing.
pypi.org/project/swprocess/
Topics
geopsymammaswprocessingsurfacewave
Resources
Readme
License
Activity
Stars
122 stars
Watchers
5 watching
Forks
37 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
swprocess - A Python Package for Surface Wave Processing
Joseph P. Vantassel, jpvantassel.com
DOIPyPI - LicenseCircleCIDocumentation StatusPyPI - Python VersionCodacy Badgecodecov
Table of Contents
About swprocess
swprocess is a Python package for surface wave processing. swprocess was
developed by Joseph P. Vantassel under the
supervision of Professor Brady R. Cox at The University of Texas at Austin.
swprocess continues to be developed and maintained by
Joseph P. Vantassel and his research group at Virginia Tech.
If you use swprocess in your research or consulting, we ask you please cite
the following:
Vantassel, J. P. (2021). jpvantassel/swprocess: latest (Concept). Zenodo.
https://doi.org/10.5281/zenodo.4584128
Vantassel, J. P. & Cox, B. R. (2022). "SWprocess: a workflow for developing robust
estimates of surface wave dispersion uncertainty". Journal of Seismology.
https://doi.org/10.1007/s10950-021-10035-y
Note: For software, version specific citations should be preferred to
general concept citations, such as that listed above. To generate a version
specific citation for swprocess, please use the citation tool on the swprocessarchive.
Why use swprocess
swprocess contains features not currently available in any other open-source
software, including:
  • Multiple pre-processing workflows for active-source [i.e., Multichannel
Analysis of Surface Waves (MASW)] measurements including:

    • time-domain muting,
    • frequency-domain stacking, and
    • time-domain stacking.
  • Multiple wavefield transformations for active-source (i.e., MASW) measurements
including:

    • frequency-wavenumber (Nolet and Panza, 1976),
    • phase-shift (Park, 1998),
    • slant-stack (McMechan and Yedlin, 1981), and
    • frequency domain beamformer (Zywicki 1999).
  • Post-processing of active-source and passive-wavefield [i.e., microtremor
array measurements (MAM)] data from swprocess and Geopsy, respectively.
  • Interactive trimming to remove low quality dispersion data.
  • Rigorous calculation of dispersion statistics to quantify epistemic and
aleatory uncertainty in surface wave measurements.
Examples
Active-source processing
Interactive trimming
Calculation of dispersion statistics
Getting Started
Installing or Upgrading swprocess
  1. If you do not have Python 3.8 or later installed, you will need to do
so. A detailed set of instructions can be found
here.
  2. If you have not installed swprocess previously use pip install swprocess.
If you are not familiar with pip, a useful tutorial can be found
here. If you have
an earlier version and would like to upgrade to the latest version of
swprocess use pip install swprocess --upgrade.
  3. Confirm that swprocess has installed/updated successfully by examining the
last few lines of the text displayed in the console.
Using swprocess
  1. Download the contents of the
examples
directory to any location of your choice.
  2. Start by processing the provided active-source data using the
Jupyter notebook (masw.ipynb). If you have not installed Jupyter,
detailed instructions can be found
here.
  3. Post-process the provided passive-wavefield data using the
Jupyter notebook (mam_fk.ipynb).
  4. Perform interactive trimming and calculate dispersion statistics for the
example data using the Jupyter notebook (stats.ipynb). Compare your results
to those shown in the figure above.
  5. Enjoy!
About
Python package for surface wave processing.
pypi.org/project/swprocess/
Topics
geopsymammaswprocessingsurfacewave
Resources
Readme
License
Activity
Stars
122 stars
Watchers
5 watching
Forks
37 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
swprocess - A Python Package for Surface Wave Processing
Joseph P. Vantassel, jpvantassel.com
DOIPyPI - LicenseCircleCIDocumentation StatusPyPI - Python VersionCodacy Badgecodecov
Table of Contents
About swprocess
swprocess is a Python package for surface wave processing. swprocess was
developed by Joseph P. Vantassel under the
supervision of Professor Brady R. Cox at The University of Texas at Austin.
swprocess continues to be developed and maintained by
Joseph P. Vantassel and his research group at Virginia Tech.
If you use swprocess in your research or consulting, we ask you please cite
the following:
Vantassel, J. P. (2021). jpvantassel/swprocess: latest (Concept). Zenodo.
https://doi.org/10.5281/zenodo.4584128
Vantassel, J. P. & Cox, B. R. (2022). "SWprocess: a workflow for developing robust
estimates of surface wave dispersion uncertainty". Journal of Seismology.
https://doi.org/10.1007/s10950-021-10035-y
Note: For software, version specific citations should be preferred to
general concept citations, such as that listed above. To generate a version
specific citation for swprocess, please use the citation tool on the swprocessarchive.
Why use swprocess
swprocess contains features not currently available in any other open-source
software, including:
  • Multiple pre-processing workflows for active-source [i.e., Multichannel
Analysis of Surface Waves (MASW)] measurements including:

    • time-domain muting,
    • frequency-domain stacking, and
    • time-domain stacking.
  • Multiple wavefield transformations for active-source (i.e., MASW) measurements
including:

    • frequency-wavenumber (Nolet and Panza, 1976),
    • phase-shift (Park, 1998),
    • slant-stack (McMechan and Yedlin, 1981), and
    • frequency domain beamformer (Zywicki 1999).
  • Post-processing of active-source and passive-wavefield [i.e., microtremor
array measurements (MAM)] data from swprocess and Geopsy, respectively.
  • Interactive trimming to remove low quality dispersion data.
  • Rigorous calculation of dispersion statistics to quantify epistemic and
aleatory uncertainty in surface wave measurements.
Examples
Active-source processing
Interactive trimming
Calculation of dispersion statistics
Getting Started
Installing or Upgrading swprocess
  1. If you do not have Python 3.8 or later installed, you will need to do
so. A detailed set of instructions can be found
here.
  2. If you have not installed swprocess previously use pip install swprocess.
If you are not familiar with pip, a useful tutorial can be found
here. If you have
an earlier version and would like to upgrade to the latest version of
swprocess use pip install swprocess --upgrade.
  3. Confirm that swprocess has installed/updated successfully by examining the
last few lines of the text displayed in the console.
Using swprocess
  1. Download the contents of the
examples
directory to any location of your choice.
  2. Start by processing the provided active-source data using the
Jupyter notebook (masw.ipynb). If you have not installed Jupyter,
detailed instructions can be found
here.
  3. Post-process the provided passive-wavefield data using the
Jupyter notebook (mam_fk.ipynb).
  4. Perform interactive trimming and calculate dispersion statistics for the
example data using the Jupyter notebook (stats.ipynb). Compare your results
to those shown in the figure above.
  5. Enjoy!
About
Python package for surface wave processing.
pypi.org/project/swprocess/
Topics
geopsymammaswprocessingsurfacewave
Resources
Readme
License
Activity
Stars
122 stars
Watchers
5 watching
Forks
37 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
swprocess - A Python Package for Surface Wave Processing
Joseph P. Vantassel, jpvantassel.com
DOIPyPI - LicenseCircleCIDocumentation StatusPyPI - Python VersionCodacy Badgecodecov
Table of Contents
About swprocess
swprocess is a Python package for surface wave processing. swprocess was
developed by Joseph P. Vantassel under the
supervision of Professor Brady R. Cox at The University of Texas at Austin.
swprocess continues to be developed and maintained by
Joseph P. Vantassel and his research group at Virginia Tech.
If you use swprocess in your research or consulting, we ask you please cite
the following:
Vantassel, J. P. (2021). jpvantassel/swprocess: latest (Concept). Zenodo.
https://doi.org/10.5281/zenodo.4584128
Vantassel, J. P. & Cox, B. R. (2022). "SWprocess: a workflow for developing robust
estimates of surface wave dispersion uncertainty". Journal of Seismology.
https://doi.org/10.1007/s10950-021-10035-y
Note: For software, version specific citations should be preferred to
general concept citations, such as that listed above. To generate a version
specific citation for swprocess, please use the citation tool on the swprocessarchive.
Why use swprocess
swprocess contains features not currently available in any other open-source
software, including:
  • Multiple pre-processing workflows for active-source [i.e., Multichannel
Analysis of Surface Waves (MASW)] measurements including:

    • time-domain muting,
    • frequency-domain stacking, and
    • time-domain stacking.
  • Multiple wavefield transformations for active-source (i.e., MASW) measurements
including:

    • frequency-wavenumber (Nolet and Panza, 1976),
    • phase-shift (Park, 1998),
    • slant-stack (McMechan and Yedlin, 1981), and
    • frequency domain beamformer (Zywicki 1999).
  • Post-processing of active-source and passive-wavefield [i.e., microtremor
array measurements (MAM)] data from swprocess and Geopsy, respectively.
  • Interactive trimming to remove low quality dispersion data.
  • Rigorous calculation of dispersion statistics to quantify epistemic and
aleatory uncertainty in surface wave measurements.
Examples
Active-source processing
Interactive trimming
Calculation of dispersion statistics
Getting Started
Installing or Upgrading swprocess
  1. If you do not have Python 3.8 or later installed, you will need to do
so. A detailed set of instructions can be found
here.
  2. If you have not installed swprocess previously use pip install swprocess.
If you are not familiar with pip, a useful tutorial can be found
here. If you have
an earlier version and would like to upgrade to the latest version of
swprocess use pip install swprocess --upgrade.
  3. Confirm that swprocess has installed/updated successfully by examining the
last few lines of the text displayed in the console.
Using swprocess
  1. Download the contents of the
examples
directory to any location of your choice.
  2. Start by processing the provided active-source data using the
Jupyter notebook (masw.ipynb). If you have not installed Jupyter,
detailed instructions can be found
here.
  3. Post-process the provided passive-wavefield data using the
Jupyter notebook (mam_fk.ipynb).
  4. Perform interactive trimming and calculate dispersion statistics for the
example data using the Jupyter notebook (stats.ipynb). Compare your results
to those shown in the figure above.
  5. Enjoy!
About
Python package for surface wave processing.
pypi.org/project/swprocess/
Topics
geopsymammaswprocessingsurfacewave
Resources
Readme
License
Activity
Stars
122 stars
Watchers
5 watching
Forks
37 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
swprocess - A Python Package for Surface Wave Processing
Joseph P. Vantassel, jpvantassel.com
DOIPyPI - LicenseCircleCIDocumentation StatusPyPI - Python VersionCodacy Badgecodecov
Table of Contents
About swprocess
swprocess is a Python package for surface wave processing. swprocess was
developed by Joseph P. Vantassel under the
supervision of Professor Brady R. Cox at The University of Texas at Austin.
swprocess continues to be developed and maintained by
Joseph P. Vantassel and his research group at Virginia Tech.
If you use swprocess in your research or consulting, we ask you please cite
the following:
Vantassel, J. P. (2021). jpvantassel/swprocess: latest (Concept). Zenodo.
https://doi.org/10.5281/zenodo.4584128
Vantassel, J. P. & Cox, B. R. (2022). "SWprocess: a workflow for developing robust
estimates of surface wave dispersion uncertainty". Journal of Seismology.
https://doi.org/10.1007/s10950-021-10035-y
Note: For software, version specific citations should be preferred to
general concept citations, such as that listed above. To generate a version
specific citation for swprocess, please use the citation tool on the swprocessarchive.
Why use swprocess
swprocess contains features not currently available in any other open-source
software, including:
  • Multiple pre-processing workflows for active-source [i.e., Multichannel
Analysis of Surface Waves (MASW)] measurements including:

    • time-domain muting,
    • frequency-domain stacking, and
    • time-domain stacking.
  • Multiple wavefield transformations for active-source (i.e., MASW) measurements
including:

    • frequency-wavenumber (Nolet and Panza, 1976),
    • phase-shift (Park, 1998),
    • slant-stack (McMechan and Yedlin, 1981), and
    • frequency domain beamformer (Zywicki 1999).
  • Post-processing of active-source and passive-wavefield [i.e., microtremor
array measurements (MAM)] data from swprocess and Geopsy, respectively.
  • Interactive trimming to remove low quality dispersion data.
  • Rigorous calculation of dispersion statistics to quantify epistemic and
aleatory uncertainty in surface wave measurements.
Examples
Active-source processing
Interactive trimming
Calculation of dispersion statistics
Getting Started
Installing or Upgrading swprocess
  1. If you do not have Python 3.8 or later installed, you will need to do
so. A detailed set of instructions can be found
here.
  2. If you have not installed swprocess previously use pip install swprocess.
If you are not familiar with pip, a useful tutorial can be found
here. If you have
an earlier version and would like to upgrade to the latest version of
swprocess use pip install swprocess --upgrade.
  3. Confirm that swprocess has installed/updated successfully by examining the
last few lines of the text displayed in the console.
Using swprocess
  1. Download the contents of the
examples
directory to any location of your choice.
  2. Start by processing the provided active-source data using the
Jupyter notebook (masw.ipynb). If you have not installed Jupyter,
detailed instructions can be found
here.
  3. Post-process the provided passive-wavefield data using the
Jupyter notebook (mam_fk.ipynb).
  4. Perform interactive trimming and calculate dispersion statistics for the
example data using the Jupyter notebook (stats.ipynb). Compare your results
to those shown in the figure above.
  5. Enjoy!
About
Python package for surface wave processing.
pypi.org/project/swprocess/
Topics
geopsymammaswprocessingsurfacewave
Resources
Readme
License
Activity
Stars
122 stars
Watchers
5 watching
Forks
37 forks
Report repository
Releases
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
swprocess - A Python Package for Surface Wave Processing
Joseph P. Vantassel, jpvantassel.com
DOIPyPI - LicenseCircleCIDocumentation StatusPyPI - Python VersionCodacy Badgecodecov
Table of Contents
About swprocess
swprocess is a Python package for surface wave processing. swprocess was
developed by Joseph P. Vantassel under the
supervision of Professor Brady R. Cox at The University of Texas at Austin.
swprocess continues to be developed and maintained by
Joseph P. Vantassel and his research group at Virginia Tech.
If you use swprocess in your research or consulting, we ask you please cite
the following:
Vantassel, J. P. (2021). jpvantassel/swprocess: latest (Concept). Zenodo.
https://doi.org/10.5281/zenodo.4584128
Vantassel, J. P. & Cox, B. R. (2022). "SWprocess: a workflow for developing robust
estimates of surface wave dispersion uncertainty". Journal of Seismology.
https://doi.org/10.1007/s10950-021-10035-y
Note: For software, version specific citations should be preferred to
general concept citations, such as that listed above. To generate a version
specific citation for swprocess, please use the citation tool on the swprocessarchive.
Why use swprocess
swprocess contains features not currently available in any other open-source
software, including:
  • Multiple pre-processing workflows for active-source [i.e., Multichannel
Analysis of Surface Waves (MASW)] measurements including:

    • time-domain muting,
    • frequency-domain stacking, and
    • time-domain stacking.
  • Multiple wavefield transformations for active-source (i.e., MASW) measurements
including:

    • frequency-wavenumber (Nolet and Panza, 1976),
    • phase-shift (Park, 1998),
    • slant-stack (McMechan and Yedlin, 1981), and
    • frequency domain beamformer (Zywicki 1999).
  • Post-processing of active-source and passive-wavefield [i.e., microtremor
array measurements (MAM)] data from swprocess and Geopsy, respectively.
  • Interactive trimming to remove low quality dispersion data.
  • Rigorous calculation of dispersion statistics to quantify epistemic and
aleatory uncertainty in surface wave measurements.
Examples
Active-source processing
Interactive trimming
Calculation of dispersion statistics
Getting Started
Installing or Upgrading swprocess
  1. If you do not have Python 3.8 or later installed, you will need to do
so. A detailed set of instructions can be found
here.
  2. If you have not installed swprocess previously use pip install swprocess.
If you are not familiar with pip, a useful tutorial can be found
here. If you have
an earlier version and would like to upgrade to the latest version of
swprocess use pip install swprocess --upgrade.
  3. Confirm that swprocess has installed/updated successfully by examining the
last few lines of the text displayed in the console.
Using swprocess
  1. Download the contents of the
examples
directory to any location of your choice.
  2. Start by processing the provided active-source data using the
Jupyter notebook (masw.ipynb). If you have not installed Jupyter,
detailed instructions can be found
here.
  3. Post-process the provided passive-wavefield data using the
Jupyter notebook (mam_fk.ipynb).
  4. Perform interactive trimming and calculate dispersion statistics for the
example data using the Jupyter notebook (stats.ipynb). Compare your results
to those shown in the figure above.
  5. Enjoy!
About
Python package for surface wave processing.
pypi.org/project/swprocess/
Topics
geopsymammaswprocessingsurfacewave
Resources
Readme
License
Activity
Stars
122 stars
Watchers
5 watching
Forks
37 forks
Report repository
Releases
Used by
Contributors
Languages