This project focuses on developing a Graphical User Interface for professionals to use pyPCGA as a tool for decision making in the field along with students and educators who study data science in hydrogeology.
- Module 1: Takes parameters to define the x,y,z dimensions from min to max possible values. Outputs M, the number of unknown values.
- Module 2: Works with the kernel to pass permissions to PCGA algorithm to run groundwater model executable.
- Module 3: Takes file with observation data to be used by PCGA. Calculates and verifies correctness of N.
- Module 3: Takes inversion parameters to use during iterations of the PCGA optimization algorithm.
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1D linear inversion example (from Stanford 362G course)
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1D nonlinear inversion example (from Stanford 362G course)
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MODFLOW-USG/SEAWAT/MODFLOW6/E4D/ADH examples coming soon!
pyPCGA is based on Lee et al. [2016] and currently used for Stanford-USACE ERDC project led by EF Darve and PK Kitanidis and NSF EPSCoR `Ike Wai project.
Code contributors include:
- Jonghyun Harry Lee
- Matthew Farthing
- Ty Hesser (STWAVE example)
FFT-based matvec code is adapted from Arvind Saibaba's work (https://github.com/arvindks/kle).
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J Lee, H Yoon, PK Kitanidis, CJ Werth, AJ Valocchi, "Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging", Water Resources Research 52 (7), 5213-5231
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AK Saibaba, J Lee, PK Kitanidis, Randomized algorithms for generalized Hermitian eigenvalue problems with application to computing Karhunen–Loève expansion, Numerical Linear Algebra with Applications 23 (2), 314-339
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J Lee, PK Kitanidis, "Large‐scale hydraulic tomography and joint inversion of head and tracer data using the Principal Component Geostatistical Approach (PCGA)", WRR 50 (7), 5410-5427
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PK Kitanidis, J Lee, Principal Component Geostatistical Approach for large‐dimensional inverse problems, WRR 50 (7), 5428-5443
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J Lee, H Ghorbanidehno, M Farthing, T. Hesser, EF Darve, and PK Kitanidis, Riverine bathymetry imaging with indirect observations, Water Resources Research, 54(5): 3704-3727, 2018
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J Lee, A Kokkinaki, PK Kitanidis, Fast large-scale joint inversion for deep aquifer characterization using pressure and heat tracer measurements, Transport in Porous Media, 123(3): 533-543, 2018
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PK Kang, J Lee, X Fu, S Lee, PK Kitanidis, J Ruben, Improved Characterization of Heterogeneous Permeability in Saline Aquifers from Transient Pressure Data during Freshwater Injection, Water Resources Research, 53(5): 4444-458, 2017
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S. Fakhreddine, J Lee, PK Kitanidis, S Fendorf, M Rolle, Imaging Geochemical Heterogeneities Using Inverse Reactive Transport Modeling: an Example Relevant for Characterizing Arsenic Mobilization and Distribution, Advances in Water Resources, 88: 186-197, 2016