---
code_id: 13527
site_ownership_code: "LANL"
open_source: true
repository_link: "https://gitlab.com/zem/chipbeta"
project_type: "OS"
software_type: "S"
official_use_only: {}
developers:
- email: ""
  orcid: ""
  first_name: "Eric"
  last_name: "Banner"
  middle_name: ""
  affiliations:
  - "Los Alamos National Lab. (LANL), Los Alamos, NM (United States)"
- email: ""
  orcid: ""
  first_name: "Daniel"
  last_name: "O'Malley"
  middle_name: ""
  affiliations:
  - "Los Alamos National Lab. (LANL), Los Alamos, NM (United States)"
- email: ""
  orcid: ""
  first_name: "Monty"
  last_name: "Vesselinov"
  middle_name: ""
  affiliations:
  - "Los Alamos National Lab. (LANL), Los Alamos, NM (United States)"
contributors: []
sponsoring_organizations:
- organization_name: "USDOE"
  funding_identifiers: []
  primary_award: "AC52-06NA25396"
  DOE: true
contributing_organizations: []
research_organizations:
- organization_name: "Los Alamos National Laboratory (LANL), Los Alamos, NM (United\
    \ States)"
  DOE: true
related_identifiers: []
release_date: "2018-06-28"
software_title: "CHiPBETA"
doi: "https://doi.org/10.11578/dc.20180710.32"
description: "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."
programming_languages:
- "Python"
country_of_origin: "United States"
licenses:
- "GNU General Public License v3.0"
recipient_org: "LANL, FCI"
site_accession_number: "C18076"
date_record_added: "2018-07-10"
date_record_updated: "2018-07-10"
is_file_certified: false
is_limited: false
links:
- rel: "citation"
  href: "https://www.osti.gov/doecode/biblio/13527"