Passive distributed acoustic sensing (DAS) is an attractive tool for monitoring geological CO₂ storage, but its dense, continuous recordings create a data-volume problem: a multi-month, multi-well archive yields enormous numbers of detector triggers, of which confirmed seismic events form a vanishingly small fraction, and conventional supervised classification is ill-posed when labels remain scarce and the negative class undefined because the non-event population is open-ended and spans noise families that vary over time and between wells. We present Argus, a sparse-label machine-learning workflow that converts continuous DAS recordings into a reproducible, auditable catalogue of event candidates. A deterministic front end reduces the archive to comparable trigger objects, each described by a 67-feature interpretable representation of its two-dimensional time–channel character (e.g., duration and channel span, detector-mask morphology, apparent moveout, inter-channel waveform coherence, and spectral shape); a retrieval-first machine-learning layer then ranks these triggers by their similarity, in this interpretable feature space, to a small seed catalogue of independently confirmed events, within an iterative human-in-the-loop process that introduces local supervised noise-rejection gates only for recurrent artefact families once they have been labelled. Applied to the CO2CRC Otway Stage 4 dataset—120 days of recordings on two wells, comprising roughly 23 TB and 14.14 million raw triggers—the workflow expanded a 39-event seed catalogue into 631 analyst-reviewed events, demonstrating complementarity with an independent template-matching analysis: two additional induced-event candidates were recovered, one within the CRC4 template-matching coverage and one on CRC7 during a CRC4 data gap. The induced-event class itself grew only from four to six candidates, and its counts are reported as a reviewed lower bound rather than a complete census. The result is a provenance-preserving, conservatively interpreted event inventory rather than an opaque classifier output, an outcome aligned with the reproducibility and audit requirements of CO₂ storage assurance.