"Dataset for ANW"

"The dataset stores the detailed experimental results for paper \"ANW: Adaptive Neighborhood Width Peak Selection for Robust Slowness Extraction in Array Acoustic Logging\".This dataset is designed for validating robust slowness extraction algorithms under challenging conditions, covering typical anomalies that severely degrade conventional Slowness\u2011Time Coherence (STC) peak\u2011picking performance. Dataset 1 presents weak shear\u2011wave arrivals with low coherence, making the shear mode barely distinguishable from background noise. Dataset 2 contains dense false peaks adjacent to the Stoneley wave, which easily mislead traditional amplitude\u2011based methods, and the intervals between each component wave are relativelyl arge. Dataset 3 includes interfering false peaks near the shear wave and a relatively obvious false peak is found on the right side of the ST-wave. Dataset 4 features a prominent pseudo\u2011peak between the shear and Stoneley waves. All data are preprocessed into 1D slowness\u2011coherence curves projected from 2D STC coherence maps, with standardized sampling and slowness units (\u03bcs and \u03bcs\/ft). The dataset enables researchers to verify detection accuracy, pseudo\u2011peak suppression, weak signal recovery, and multi\u2011mode (P\/S\/ST) wave identification. As a hallenging benchmark for acoustic logging signal processing, this dataset contributes to the development of data\u2011driven, unsupervised, and robust signal processing techniques for geophysical and industrial acoustic applications."