# lts_array

**Repository Path**: liujihao1993/lts_array

## Basic Information

- **Project Name**: lts_array
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-05-12
- **Last Updated**: 2026-05-12

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

lts_array
=========

This package contains a least trimmed squares algorithm written in Python3 and modified for geophysical array processing. An extensive collection
of helper functions is also included. These codes are referenced in

Bishop, J.W., Fee, D., & Szuberla, C. A. L., (2020). *Improved
infrasound array processing with robust estimators*, Geophys. J. Int.,
221(3) p. 2058-2074 doi: https://doi.org/10.1093/gji/ggaa110

Documentation for this package can be found `here <https://uaf-lts-array.readthedocs.io/en/master/index.html#>`__. A broader set of geophysical array processing codes are available
`here <https://github.com/uafgeotools/array_processing>`__, which
utilizes this package as the default (and preferred) array processing
algorithm.

Motivation
-----------------

Infrasonic and seismic array processing often relies on the plane wave
assumption. With this assumption, inter-element travel times can be
regressed over station (co-)array coordinates to determine an optimal
back-azimuth and velocity for waves crossing the array. Station errors
such as digitizer timing issues, reversed polarity, and flat channels
can manifest as apparent deviations from the plane wave assumption as
travel time outliers. Additionally, physical deviations from the plane
wave assumption also appear as travel time outliers. This project
identifies these outliers from infrasound (and seismic) arrays through
the *least trimmed squares* robust regression technique. Our python
implementation uses the FAST_LTS algorithm of *Rousseeuw and Van
Driessen (2006)*. Please see *Bishop et al. (2020)* for processing
examples at arrays from the International Monitoring System and Alaska
Volcano Observatory.

Installation
------------

We recommend using conda and creating a new conda environment such as:

::

   conda create -n uafinfra -c conda-forge python=3 obspy numba

Information on conda environments (and more) is available
`here <https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html>`__.

The package `numba` is a new dependency to pull request `23 <https://github.com/uafgeotools/lts_array/pull/23>`__. If you have a previous `uafinfra` environment, you may need to install the `numba <http://numba.pydata.org>`__  package with

::

    conda install --name uafinfra numba

After setting up the conda environment,
`install <https://pip.pypa.io/en/latest/reference/pip_install/#editable-installs>`__
the package by running the terminal commands:

::

   conda activate uafinfra
   git clone https://github.com/uafgeotools/lts_array
   cd lts_array
   pip install -e .

This set of commands activates the `uafinfra` conda environment. The final command installs the package in “editable” mode, which means that you can update it with a simple ``git pull`` in your local repository. This install command only needs to be run once.

Dependencies
------------

-  `Python3 <https://docs.python.org/3/>`__
-  `ObsPy <http://docs.obspy.org/>`__
-  `Numba <http://numba.pydata.org>`__

and their dependencies.

Usage
-----------

To access the functions in this package, use the following line (for
example):

::

   >> python
   import lts_array as lts_array

Example Processing and Uncertainty Quantification
----------------------------------------------------------------------

See the included ``example.py`` file. The code now automatically calculates uncertainty estimates using the slowness ellipse method of Szuberla and Olson (2004). User notes and more information on uncertainty quantification can be found `here <./docs/_build/html/User_Notes.html#>`__.

References and Credits
----------------------

If you use this code for array processing, we ask that you cite the
following papers:

1. Bishop, J.W., Fee, D., & Szuberla, C. A. L., (2020). Improved
   infrasound array processing with robust estimators, Geophys. J. Int.,
   221(3) p. 2058-2074 doi: https://doi.org/10.1093/gji/ggaa110

2. Rousseeuw, P. J. & Van Driessen, K., 2006. Computing LTS regression
   for large data sets, Data Mining and Knowledge Discovery, 12(1),
   29-45 doi: https://doi.org/10.1007/s10618-005-0024-4

3. Szuberla, C.A.L. & Olson, J.V., 2004. Uncertainties associated with parameter estimation in atmospheric infrasound arrays, J. Acoust. Soc. Am., 115(1), 253–258. doi: https://doi.org/10.1121/1.1635407

License
-------

MIT (c)

Authors and Contributors
------------------------

| Jordan W Bishop
| David Fee
| Curt Szuberla
| Liam Toney

Acknowledgements and Distribution Statement
-------------------------------------------

This work was made possible through support provided by the Defense
Threat Reduction Agency Nuclear Arms Control Technology program under
contract HDTRA1-17-C-0031. Distribution Statement A: Approved for public
release; distribution is unlimited.