Geological text descriptions in ill-posed inverse problems: insights from learned hydraulic-conductivity inversion
Read the original on arXiv Machine Learning →The paper investigates how geological text descriptions can aid hydraulic‑conductivity inversion, a problem that remains ill‑posed even with complete head observations. Using a synthetic Darcy‑flow benchmark, the authors show that after training with sparse observations, incorporating textual descriptions can improve reconstruction when structural ambiguity persists, though the benefit varies with observation density, flow direction, and specific content. They find that editing the text shifts reconstructions toward the described geometry, but they do not demonstrate a clear advantage of text over equivalent numerical inputs, and they suggest that these insights could inform joint measurement and knowledge acquisition strategies.
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