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Abstract

Summary

This study presents a deep learning framework for identifying induced polarization (IP)-affected transient electromagnetic (TEM) data. The three-stage framework categorizes data into those that can be modeled under simpler resistivity-only conditions, those requiring the inclusion of IP parameters, and those unsuitable for either approach. The framework was tested on a tTEM dataset from Kigadye, Tanzania, where the results demonstrated strong alignment with manual assessments, showcasing its potential for automating TEM data analysis.

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/content/papers/10.3997/2214-4609.202572056
2025-05-13
2026-02-18
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References

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