Over the last decade, distributed acoustic sensing (DAS) has received growing attention in the field of seismic acquisition and monitoring due to its potential high spatial sampling rate, low maintenance cost and high resistance to temperature and pressure. Despite its undeniable advantages, DAS faces some challenges, including a low signal-to-noise ratio, which partly results from the instrument-specific noise generated by DAS interrogators. We present a comparison between two deep learning approaches to address DAS hardware noise and enhance the quality of DAS data. These approaches have the advantage of including real instrumental noise in the neural network training dataset. For the supervised learning (SL) approach, real DAS instrumental noise measured on an acoustically isolated coil is added to synthetic data to generate training pairs of clean/noisy data. For the second method, the Noise2Noise (N2N) approach, the training is performed on noisy/noisy data pairs recorded simultaneously on the downgoing and upgoing parts of a downhole fiber-optic cable. Both approaches allow for the removal of unwanted noise that lies within the same frequency band of the useful signal, a result that cannot be achieved by conventional denoising techniques employing frequency filtering.

Exploring shallow mineral resources requires acquiring denser seismic data, for which Distributed Acoustic Sensing is an effective enabler and relevant to mining operations monitoring. Passive seismic can be of interest in characterizing the subsurface; however, dealing with large amounts of data pushes against the limits of existing computational systems and algorithms, especially for continuous monitoring. Hence, more than ever, novel data analysis methods are needed. In this article, we investigate using synthetic seismic data, paired with real noise recordings, as part of a supervised deep-learning neural network methodology to detect and locate induced seismic sources and explore their potential use to reconstruct subsurface properties. Challenges of this methodology were identified and addressed in the context of induced seismicity applications. Data acquisition and modelling were discussed, preparation workflows were implemented, and the method was demonstrated on synthetic data and tested on relevant seismic monitoring field dataset from the Otway CO₂ injection site. Conducted tests confirmed the effects of time shifts, signal-to-noise ratios, and geometry mismatches on the performance of trained models. Those promising results showed the method’s applicability and paved the way for potential application to more field data, such as seismic while drilling.

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.

Safe and reliable carbon capture and storage (CCS) requires long-term monitoring to verify secure CO₂ containment. Distributed acoustic sensing vertical seismic profiling (DAS-VSP) provides a practical approach for subsurface monitoring due to its permanent installation, dense spatial sampling, and reduced operational costs. However, monitoring programs spanning decades may require hardware upgrades, including interrogator replacements and changes to fibre-optic cable configurations, affecting time-lapse repeatability and interpretability. This field case study evaluates whether the CO₂-induced 4D DAS-VSP response remains interpretable across a combined change in interrogator, fibre type and optical path, whose individual contributions cannot be isolated. The assessment uses data from the CO2CRC Otway Project Stage 4, where approximately 10 kilotons of CO₂-rich reservoir gas were injected into a saline aquifer at approximately 1500 m depth. Baseline and monitor surveys using different DAS configurations are compared at two monitoring wells, with a same-configuration case as a repeatability benchmark. An established 4D DAS-VSP processing workflow, supplemented by system-specific noise attenuation, is applied to mitigate configuration-dependent differences. The results show that, despite higher non-repeatability, the principal CO₂-related time-lapse anomaly remains spatially localised and distinguishable, indicating that the DAS-VSP response can remain interpretable across a combined configuration change under favourable acquisition and signal-quality conditions.