GemPy in Science
Publications Using GemPy
Below is a selection of scientific publications that have utilized GemPy to advance geological modeling and research.
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Brisson, S., Wellmann, F., Chudalla, N., von Harten, J., & von Hagke, C. (2023). Estimating uncertainties in 3-D models of complex fold-and-thrust belts: A case study of the Eastern Alps triangle zone. Applied Computing and Geosciences, 18, 100115.
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Liang, Z., de la Varga, M., & Wellmann, F. (2023). Kernel method for gravity forward simulation in implicit probabilistic geologic modeling. Geophysics, 88(3), G43-G55.
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Kong, S., Oh, J., Yoon, D., Ryu, D. W., & Kwon, H. S. (2023). Integrating Deep Learning and Deterministic Inversion for Enhancing Fault Detection in Electrical Resistivity Surveys. Applied Sciences, 13(10), 6250.
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Thomas, A. T., Micallef, A., Duan, S., & Zou, Z. (2023). Characteristics and controls of an offshore freshened groundwater system in the Shengsi region, East China Sea. Frontiers in Earth Science, 11, 1198215.
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Haehnel, P., Freund, H., Greskowiak, J. & Massmann, G. (2023). Development of a three-dimensional hydrogeological model for the island of Norderney (Germany) using GemPy. Geoscience Data Journal, 00, 1–17.
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Jüstel, A., de la Varga, M., Chudalla, N., Wagner, J. D., Back, S., & Wellmann, F. (2023). From Maps to Models-Tutorials for structural geological modeling using GemPy and GemGIS. Journal of Open Source Education, 6(66), 185.
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Thomas, A. T., von Harten, J., Jusri, T., Reiche, S., Wellmann, F. (2022). An integrated modeling scheme for characterizing 3D hydrogeological heterogeneity of the New Jersey shelf. Marine Geophysical Research, 43, 11.
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Sehsah, H., Eldosouky, A. M., & Pham, L. T. (2022). Incremental Emplacement of the Sierra Nevada Batholith Constrained by U-Pb Ages and Potential Field Data. The Journal of Geology, 130(5), 381-391.
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von Harten, J., de la Varga, M., Hillier, M., Wellmann, F. (2021). Informed Local Smoothing in 3D Implicit Geological Modeling. Minerals 2021, 11, 1281.
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Schaaf, A., de la Varga, M., Wellmann, F., & Bond, C. E. (2021). Constraining stochastic 3-D structural geological models with topology information using approximate Bayesian computation in GemPy 2.1. Geosci. Model Dev., 14(6), 3899-3913. doi:10.5194/gmd-14-3899-2021
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Güdük, N., de la Varga, M. Kaukolinna, J. and Wellmann, F. (2021). Model-Based Probabilistic Inversion Using Magnetic Data: A Case Study on the Kevitsa Deposit, Geosciences, 11(4):150. https://doi.org/10.3390/geosciences11040150.
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Wu, J., & Sun, B. (2021). Discontinuous mechanical analysis of manifold element strain of rock slope based on open source Gempy. In E3S Web of Conferences (Vol. 248, p. 03084). EDP Sciences.
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Stamm, F. A., de la Varga, M., and Wellmann, F. (2019). Actors, actions, and uncertainties: optimizing decision-making based on 3-D structural geological models, Solid Earth, 10, 2015–2043.
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Wellmann, F., Schaaf, A., de la Varga, M., & von Hagke, C. (2019). From Google Earth to 3D Geology Problem 2: Seeing Below the Surface of the Digital Earth. In Developments in Structural Geology and Tectonics (Vol. 5, pp. 189-204). Elsevier.
Figure from Brisson et al. (2023).
Figure from Kong et al. (2023).
Figure from Jüstel et al. (2023).
Figure from Schaaf et al. (2021).
Figure from Stamm et al. (2019).
Have you used GemPy in your scientific work and would like to see it listed here? Please let us know by writing to gempy@terranigma-solutions.com.