J
Jeremy T. White
Researcher at United States Geological Survey
Publications - 47
Citations - 892
Jeremy T. White is an academic researcher from United States Geological Survey. The author has contributed to research in topics: Groundwater model & Uncertainty analysis. The author has an hindex of 12, co-authored 40 publications receiving 547 citations. Previous affiliations of Jeremy T. White include GNS Science & University of South Florida.
Papers
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Journal ArticleDOI
Scripting MODFLOW Model Development Using Python and FloPy
Mark Bakker,Vincent E. A. Post,Vincent E. A. Post,Christian D. Langevin,Joseph D. Hughes,Jeremy T. White,J. Jeffrey Starn,Michael N. Fienen +7 more
TL;DR: Scripting model development with the programming language Python is presented here as an alternative approach to facilitate model development and facilitates data exploration, alternative model evaluations, and model analyses that can be difficult to perform with GUIs.
Journal ArticleDOI
A python framework for environmental model uncertainty analysis
TL;DR: The pyEMU framework as mentioned in this paper implements several types of linear (first-order, second-moment (FOSM)) and non-linear uncertainty analyses, which can be used to design objective functions and parameterizations.
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A model-independent iterative ensemble smoother for efficient history-matching and uncertainty quantification in very high dimensions
TL;DR: An open-source, scalable and model-independent (non-intrusive) implementation of an iterative ensemble smoother has been developed to alleviate the computational burden associated with history-matching and uncertainty quantification of real-world-scale environmental models that have very high dimensional parameter spaces.
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Quantifying the predictive consequences of model error with linear subspace analysis
TL;DR: In this article, the authors present a method to identify and quantify the predictive consequences of calibrating a simplified computer model, which is based on linear theory and scales efficiently to the large numbers of parameters and observations characteristic of groundwater and petroleum reservoir models.
OtherDOI
Approaches in highly parameterized inversion—PEST++ Version 3, a Parameter ESTimation and uncertainty analysis software suite optimized for large environmental models
TL;DR: This document summarizes current capabilities, research and operational priorities, and plans for further studies that were established at the 2015 USGS workshop on quantitative hazard assessments of earthquake-triggered landsliding and liquefaction in the Czech Republic.