HGS RESEARCH HIGHLIGHT – Hydraulic Tomography Estimates Improved by Zonal Information From the Clustering of Geophysical Survey Data
Wang, C., & Illman, W. A. (2023). Hydraulic Tomography Estimates Improved by Zonal Information From the Clustering of Geophysical Survey Data. Water Resources Research, 59. https://doi.org/10.1029/2023WR035191
“Tracer test simulation is conducted using HydroGeoSphere (HGS) in this study. Tracer injection lasts for 48 hr, and an extra 48 hr of recovery follows, in which only clean water is injected at the same rate”
Fig. 6. Spatial distribution of K tomograms for different cases. Geostatistical inversions are initialized with: (a) Case I: homogeneous K field; zonation models from (b) Case II-a: clustering of EC; (c) Case II-b: clustering of EC and GR; (d) Case III-a: spatially constrained clustering of EC; (e) Case III-b: spatially constrained clustering of EC and GR; (f) Case IV-a: clustering of EC, and improved with local K measurements; (g) Case IV-b: clustering of EC and GR, and improved with local K measurements; (h) Case V-a: spatially constrained clustering of EC, and improved with local K measurements; (i) Case V-b: spatially constrained clustering of EC and GR, and improved with local K measurements; (j) Case VI: true layer structure. (k) Figure shows the true K field for comparison.
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Exploring innovative methods in groundwater characterization, Chenxi Wang and Walter A. Illman present a study on improving Hydraulic Tomography (HT) estimates through the integration of geophysical survey data.
Hydraulic tomography offers valuable insights into subsurface heterogeneity by analyzing multiple pumping tests. However, challenges arise when insufficient observations lead to smooth or inaccurate tomograms. In this study, Wang and Illman investigate the integration of geophysical survey data into HT analysis to address this issue.
Utilizing k-means clustering, the researchers extract zonal information from borehole geophysical logs, enhancing hydrostratigraphic boundaries. Zonation models are then constructed using clustering-based zone geometry and zonal estimates of hydraulic conductivity (K) from pumping data analysis. These models serve as the initial guess for spatial variability in the geostatistical inversion of HT analysis.
HydroGeoSphere (HGS) plays a vital role in simulating tracer test scenarios, providing valuable insights into contaminant plume migration dynamics. By integrating geophysical survey data into HT analysis, particularly when combined with local K measurements, the study demonstrates significant improvements in parameter estimates and subsurface characterization accuracy.
Fig. 10. Relative concentration distributions (C/C0) at 4, 12, 48, and 96 hr for all cases. (a–j) represent C/C0 profiles for Cases I, II-a, II-b, III-a, III-b, IV-a, IV-b, V-a, V-b, and VI. (k) Figure shows the C/C0 distributions based on the spatial distribution of true K field. Influent and effluent ports in each subplot are denoted with downward and upward-pointing triangles, respectively. Contours with C/C0 under 0.1 are removed for clarity
This research highlights the transformative potential of integrating geophysical survey data with Hydraulic Tomography (HT) analysis. By leveraging this combined approach, researchers gain deeper insights into subsurface heterogeneity and hydraulic parameter estimation accuracy. These advancements have the potential to improve simulation accuracy for a wide range of applications including regional groundwater management, solute transport and source zone identification for environmental protection, and real-time operational decision making for site-scale applications. From optimizing resource allocation to mitigating contamination risks, the enhanced characterization afforded by this method empowers stakeholders to make more informed and effective choices in safeguarding water resources and ecosystems.
Abstract:
Hydraulic tomography (HT) has been demonstrated as a robust approach to characterize subsurface heterogeneity through the inverse modeling of multiple pumping data. However, smooth or even erroneous tomograms can result when insufficient observations are involved in the inversion. In this study, the feasibility of integrating geophysical survey data into HT analysis is investigated. First, k-means clustering is utilized to extract zonal information from borehole geophysical logs, and a new type of spatial constraints containing geological knowledge is proposed to obtain improved hydrostratigraphic boundaries along boreholes. Next, zonation models are constructed by applying clustering-based zone geometry and populating zonal estimates of hydraulic conductivity (K) from analyzing pumping data. Afterwards, zonation models are treated as the initial guess of spatial variability in the geostatistical inversion of HT analysis. Additionally, local K measurements can be utilized to further improve HT estimates. Comparative cases of HT analyses are designed for a numerical sandbox experiment to highlight the HT performance integrated with geophysical surveys, in which the geostatistical inversion is initialized with: (a) a homogeneous K field; (b) zonation models built by the clustering of disparate geophysical surveys with/without spatial constraints; and (c) zonation improved by incorporating local K measurements. Based on ln K field comparisons and validation through predictions of drawdowns and tracer plume migration from independent tests not used in the calibration effort, we find that integration of geophysical surveys into HT analysis by clustering with spatial constraints is demonstrated as an effective approach, and local K measurements can further improve HT estimates.
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