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Information-Driven Sequential Inversion for Transient Hydraulic Tomography.

Prem Chand Muraharirao¹, B V N P Kambhammettu¹, Ramdas Pinninti^2,3

¹Department of Civil Engineering, Indian Institute of Technology Hyderabad, Hyderabad, India.

|March 12, 2025

Summary

This study introduces an information-driven approach for Transient Hydraulic Tomography (THT) inversion, outperforming traditional signal-to-noise ratio methods. Ranking pumping data by information content (Kullback-Leibler divergence) improves accuracy and efficiency in hydrogeologic characterization.

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Area of Science:

Hydrogeology
Geophysics
Environmental Science

Background:

Transient Hydraulic Tomography (THT) effectively characterizes subsurface hydraulic and storage properties.
Sequential inversion of THT is computationally efficient but accuracy depends on the selection and order of pumping datasets.
Traditional signal-to-noise ratio (SNR) methods for dataset selection often neglect crucial information content.

Purpose of the Study:

To develop and evaluate an alternative strategy for ranking pumping datasets in THT sequential inversion based on information content.
To improve the accuracy and computational efficiency of THT inversion by prioritizing informative data.

Main Methods:

Utilized a non-parametric Gringorten plotting position to generate cumulative distribution functions (CDFs) for transient datasets.
Employed Kullback-Leibler divergence (KLD) to quantify data variations and rank pumping ports by information content.
Tested the methodology using a laboratory sandbox model, applying Discrete Wavelet Transform (DWT) for denoising and PEST coupled with MODFLOW for inversion.

Main Results:

KLD-assisted inversion demonstrated superior accuracy (RMSE = 0.278 ± 0.177 cm) compared to SNR-assisted inversion (RMSE = 1.075 ± 0.990 cm).
Applying a KLD threshold (>10) reduced THT data by 68% and computational time by 64% with comparable accuracy (RMSE = 0.265 ± 0.121 cm).

Conclusions:

Information-driven dataset selection using KLD significantly enhances the performance of THT sequential inversion.
The proposed method offers a more accurate and computationally efficient alternative to traditional quality-driven approaches, even with reduced data.
This approach is valuable for hydrogeologic characterization in diverse settings.