  The prediction of formation resistivity plays a crucial role in the
evaluation of oil and gas reservoirs, identification and assessment of
geothermal energy resources, groundwater detection and monitoring, and carbon
capture and storage. However, traditional well logging techniques fail to
measure accurate resistivity in cased boreholes, and the transient
electromagnetic method for cased borehole resistivity logging encounters
challenges of high-frequency disaster (the problem of inadequate learning by
neural networks in high-frequency features) and noise interference, badly
affecting accuracy. To address these challenges, frequency-aware framework and
temporal anti-noise block are proposed to build frequency aware LSTM (FAL). The
frequency-aware framework implements a dual-stream structure through wavelet
transformation, allowing the neural network to simultaneously handle
high-frequency and low-frequency flows of time-series data, thus avoiding
high-frequency disaster. The temporal anti-noise block integrates multiple
attention mechanisms and soft-threshold attention mechanisms, enabling the
model to better distinguish noise from redundant features. Ablation experiments
demonstrate that the frequency-aware framework and temporal anti-noise block
contribute significantly to performance improvement. FAL achieves a 24.3%
improvement in R2 over LSTM, reaching the highest value of 0.91 among all
models. In robustness experiments, the impact of noise on FAL is approximately
1/8 of the baseline, confirming the noise resistance of FAL. The proposed FAL
effectively reduces noise interference in predicting formation resistivity from
cased transient electromagnetic well logging curves, better learns
high-frequency features, and thereby enhances the prediction accuracy and noise
resistance of the neural network model.
