<p>Identifying the depth of an acoustic emission (AE) source in plate-like geometries is an open problem with significant practical implications for understanding ply-level damage evolution in composites. Currently, these characterizations are limited to half the plate thickness, as sources symmetric about the plate midline produce similar signals. The challenge arises primarily because AE signals are analyzed using traditional low-dimensional features, such as peak amplitude or peak frequency, which exhibit little distinction across source depths and thereby prevent unique depth identification. In this work, we demonstrate that statistical learning models, trained on high-dimensional feature vectors, can overcome this limitation using two benchmark datasets (1680 waveforms each) of pencil-lead breaks (PLBs) performed at the top, side, and bottom of an aluminum plate. To further simulate realistic conditions, the datasets were designed to isolate sensor coupling and source-to-sensor distance effects, respectively. Models trained on low-dimensional features (&lt;12 parameters) perform poorly at distinguishing top from bottom PLBs. Notably, models derived from the distribution of energy in the frequency spectra achieve 90% accuracy, extrapolating to unseen distances 25.4 mm beyond training locations. A possible solution to the problem of depth identification in plate-like geometries, motivated by this work, is to incorporate data-driven classification as a preprocessing step for a physics-based inversion scheme, in order to reduce the number of possible solutions to the inverse problem.</p>