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A Python package for horizontal-to-vertical spectral ratio processing

Project description

hvsrpy - A Python package for horizontal-to-vertical spectral ratio processing

Joseph P. Vantassel, The University of Texas at Austin

DOI License: GPL v3 CircleCI Documentation Status

Table of Contents


About hvsrpy


hvsrpy is a Python package for performing horizontal-to-vertical spectral ratio (H/V) processing. hvsrpy was developed by Joseph P. Vantassel with contributions from Dana M. Brannon under the supervision of Professor Brady R. Cox at The University of Texas at Austin. The automated frequency-domain window-rejection algorithm and log-normal statistics implemented in hvsrpy were developed by Tianjian Cheng under the supervision of Professor Brady R. Cox at The University of Texas at Austin and detailed in Cox et al. (2020), citation below.

If you use hvsrpy in your research, we ask you please cite the following:

Cox, B. R., Cheng, T., Vantassel, J. P., and Manuel, L. (2020). “A Statistical Representation and Frequency-Domain Window-Rejection Algorithm for Single-Station HVSR Measurements.” Geophysical Journal International. In review.

Joseph Vantassel. (2020). jpvantassel/hvsrpy: latest (Concept). Zenodo. http://doi.org/10.5281/zenodo.3666956

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

Why use hvsrpy


hvsrpy contains features not currently available in any other commercial or open-source software, including:

  • A fully-automated frequency-domain window-rejection algorithm, which allows spurious time windows to be removed in a repeatable and expedient manner.
  • A log-normal distribution for the fundemental site frequency (f0) so the uncertainty in f0 can be represented consistently regardless of whether it is described in terms of frequency or period.
  • Combining the two horizontal components using the geometric mean.
  • Access to the H/V data from each time window, not only the mean/median curve.
  • A performant framework for batch-style processing.

A comparison of hvsrpy with Geopsy


To illustrate that hvsrpy can exactly reproduce the results from the popular open-source software Geopsy two comparisons are shown below. One for a single time window (left) and one for multiple time windows (right). Additional examples and the information necessary to reproduce them are provided at the end of this document.

Getting Started


Installing hvsrpy

  1. If you do not have Python 3.6 or later installed, you will need to do so. A detailed set of instructions can be found here.

  2. pip install hvsrpy. If you are not familiar with pip, a useful tutorial can be found here.

  3. Confirm that hvsrpy has installed succesfully by examining the last few lines of the text displayed in the console.

Using hvsrpy

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

  2. Launch the Jupyter notebook (file with .ipynb extension) in the examples directory for a no-coding-required introduction to the hvsrpy package. If you have not installed Jupyter, detailed instructions can be found here.

  3. Enjoy!

Additional Comparisons between hvsrpy and Geopsy


Multiple Windows

The examples in this section use the same settings applied to different noise records. The settings are provided in the Settings section and the name of each file is provided above the corresponding figure in the Results section. The noise records (i.e., .miniseed files) are provided in the examples directory and also as part of a large published data set (Cox and Vantassel, 2018).

Settings

  • Window Length: 60 seconds
  • Bandpass Filter Boolean: False
  • Cosine Taper Width: 10% (i.e., 5% in Geopsy)
  • Konno and Ohmachi Smoothing Coefficient: 40
  • Resampling:
    • Minimum Frequency: 0.3 Hz
    • Maximum Frequency: 40 Hz
    • Number of Points: 2048
    • Sampling Type: 'log'
  • Method for Combining Horizontal Components: 'squared-average'
  • Distribution for f0 from Time Windows: 'normal'
  • Distribution for Mean Curve: 'log-normal'

Multiple Window Results

File Name: UT.STN11.A2_C50.miniseed

File Name: UT.STN11.A2_C150.miniseed

File Name: UT.STN12.A2_C50.miniseed

File Name: UT.STN12.A2_C150.miniseed

Single Window

The examples in this section apply different settings to the same noise record (UT.STN11.A2_C50.miniseed). For brevity, the default settings are listed in the Default Settings section, with only the variations from these settings noted for each example.

Default Settings

  • Window Length: 60 seconds
  • Bandpass Filter Boolean: False
  • Cosine Taper Width: 10% (i.e., 5% in Geopsy)
  • Konno and Ohmachi Smoothing Coefficient: 40
  • Resampling:
    • Minimum Frequency: 0.3 Hz
    • Maximum Frequency: 40 Hz
    • Number of Points: 2048
    • Sampling Type: 'log'
  • Method for Combining Horizontal Components: 'squared-average'
  • Distribution for f0 from Time Windows: 'normal'
  • Distribution for Mean Curve: 'log-normal'

Single Window Results

Default Case: No variation from those settings listed above.

Window Length: 120 seconds.

Cosine Taper Width: 20 % (i.e., 10 % in Geopsy)

Cosine Taper Width: 0.2 % (i.e., 0.1 % in Geopsy)

Konno and Ohmachi Smoothing Coefficient: 10

Konno and Ohmachi Smoothing Coefficient: 80

Number of Points: 512

Number of Points: 4096

Project details


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