Apparent Velocity Extraction from Quadrupole Acoustic Logging While Drilling: CNN-Based Moveout Regression

In slow formations, dispersive quadrupole guided waves are used in acoustic logging while drilling (LWD), and a global coherence maximum that can switch between competing arrivals is picked by conventional slowness–time coherence (STC) processing. In this study, we train a lightweight convolutional neural network (CNN) on kinematic synthetic gathers, regress the inter-receiver moveout directly from quadrupole array gathers, and convert the estimated moveout to an apparent quadrupole-mode velocity. The conversion uses an empirically set effective spacing of 0.0725 m (nominal spacing of 0.100 m), so independent calibration is required for the absolute velocity scale. Five-fold cross-validation yields a median relative velocity error of 2.51% ± 0.16%, and the held-out test yields 2.11%, with R2 = 0.947. On the same held-out gathers, a lower median error (1.57%) is attained by STC, whereas fewer gross errors (relative error > 20%; 0.50% versus 3.88%) are produced by the CNN; sharp degradation of the CNN is shown by stress tests when secondary events are stronger (As/At ≥ 0.75) or the SNR is lower (≤5 dB) than that in training. On a measured slow-formation interval, numeric and image-inversion median apparent velocities differ by 13.9% before and 0.05% after 2.5–4.5 kHz band-pass filtering (pointwise r = 0.36). A control trained on the measured STC log reaches r = 0.53 on held-out records, but only r = 0.20 under depth-blocked cross-validation. On the same 16-core CPU, the 266 KB model is 3.4× faster than STC (≈21× with the CNN on a desktop GPU, which is not hardware-normalized). The results support kinematic moveout regression with potential edge suitability; absolute formation shear-wave velocity is not independently validated.