A Dual-Domain Adaptive Mixture-of-Experts-Based Soft Measurement Method for Sonic Transit Time in Deep-Sea Drilling Processes

In deepwater drilling operations, the acquisition of logging parameters such as sonic transit time (DTCO) is associated with high cost and operational constraints, while conventional data-driven soft-sensing methods often exhibit limited generalization capability in cross-well applications. To address these challenges, this study proposes adual-domain adaptive mixture-of-experts-based soft measurement method for sonic transit time in deep-sea drilling processes. This method integrates multi-source drilling data and constructs separate multi-expert soft measurement models for acoustic travel time based on low-frequency migratory trend domains and high-frequency residual domains. Building on this, adaptive mixed weights are introduced to dynamically assess the reliability of the two domains using multi-source unsupervised signals, thereby achieving adaptive fusion of acoustic travel times. Comparative experiments conducted on multiple real drilling datasets demonstrate that the proposed method consistently outperforms benchmark models, including SVR, RF, RNN, LSTM, and Transformer, in terms of cross-well prediction accuracy and stability, achieving an average error reduction of approximately 15%. This method effectively balances prediction accuracy with the stability of cross-well application whilst relying solely on pre-drilling exploration and drilling data, and is suitable for real-time sonic transit time soft measurement in deep-sea drilling environments.