Abstract
High-accuracy water level data are needed to resolve the temporal and spatial hydraulic response to pumping in aquifers. However, the water-level data are typically affected by physical process unrelated to the aquifer pumping such as barometric pressure changes, tides, earthquakes, {\etc}. We have developed a computational framework to correct for barometric and Earth tide effects using linear regression deconvolution, as an initial guess for a nonlinear residual minimization to account for pumping effects. The pumping effects are predicted using the Theis equation with initialization by Jacob's method. After minimizing the residuals between the observed data and Theis predicted pumping effects, the algorithm estimates storativity and transmissivity of the aquifer. The algorithm also provides information about the measurement accuracy of the collected water-level data. The computational framework CHiPBETA is developed in Python and demonstrated to analyze synthetic and real data.
- Developers:
-
Banner, Eric [1] ; O'Malley, Daniel [1] ; Vesselinov, Monty [1]
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Release Date:
- 2018-06-28
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Programming Languages:
-
Python
- Licenses:
-
GNU General Public License v3.0
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC52-06NA25396
- Code ID:
- 13527
- Site Accession Number:
- C18076
- Research Org.:
- Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
- Country of Origin:
- United States
Citation Formats
Banner, Eric, O'Malley, Daniel, and Vesselinov, Monty.
CHiPBETA.
Computer Software.
https://gitlab.com/zem/chipbeta.
USDOE.
28 Jun. 2018.
Web.
doi:10.11578/dc.20180710.32.
Banner, Eric, O'Malley, Daniel, & Vesselinov, Monty.
(2018, June 28).
CHiPBETA.
[Computer software].
https://gitlab.com/zem/chipbeta.
https://doi.org/10.11578/dc.20180710.32.
Banner, Eric, O'Malley, Daniel, and Vesselinov, Monty.
"CHiPBETA." Computer software.
June 28, 2018.
https://gitlab.com/zem/chipbeta.
https://doi.org/10.11578/dc.20180710.32.
@misc{
doecode_13527,
title = {CHiPBETA},
author = {Banner, Eric and O'Malley, Daniel and Vesselinov, Monty},
abstractNote = {High-accuracy water level data are needed to resolve the temporal and spatial hydraulic response to pumping in aquifers. However, the water-level data are typically affected by physical process unrelated to the aquifer pumping such as barometric pressure changes, tides, earthquakes, {\etc}. We have developed a computational framework to correct for barometric and Earth tide effects using linear regression deconvolution, as an initial guess for a nonlinear residual minimization to account for pumping effects. The pumping effects are predicted using the Theis equation with initialization by Jacob's method. After minimizing the residuals between the observed data and Theis predicted pumping effects, the algorithm estimates storativity and transmissivity of the aquifer. The algorithm also provides information about the measurement accuracy of the collected water-level data. The computational framework CHiPBETA is developed in Python and demonstrated to analyze synthetic and real data.},
doi = {10.11578/dc.20180710.32},
url = {https://doi.org/10.11578/dc.20180710.32},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20180710.32}},
year = {2018},
month = {jun}
}