The Large Scale Gas Injection Test (Lasgit) is a field-scale experiment designed to study the impact of gas build-up and subsequent migration through an engineered barrier system (EBS). Lasgit has a substantial experimental dataset containing in excess of 26 million datum points. The dataset is anticipated to contain a wealth of information, ranging from long-term trends and system behaviours to small-scale or 'second-order' features. In order to interrogate the Lasgit dataset, a bespoke computational toolkit, designed to expose and quantify difficult to observe phenomena in large, non-uniform datasets, has been developed and applied. Presented results focus on the investigation and interpretation of second-order events occurring in close proximity (temporally and spatially) to a known macro-scale gas flow event that occurred during the second gas injection test. The similarity of the investigated event to dilatant flow observed in laboratory experiments is noted, as is the evidence for localized flow pathways in the bentonite EBS. The sensitivity of the toolkit's ability to highlight second-order events is also evaluated.

The Large Scale Gas Injection Test (Lasgit) is a field-scale experiment designed to study the impact of gas buildup and subsequent migration through an engineered barrier system. Lasgit has a substantial experimental dataset containing in excess of 21 million datum points. The dataset is anticipated to contain a wealth of information, ranging from long-term trends and system behaviours to small-scale or 'second-order' features. In order to interrogate the Lasgit dataset, a bespoke computational toolkit, designed to expose difficult to observe phenomena, has been developed and applied to the dataset. The preliminary application of the toolkit, presented here, has resulted in a large number of phenomena being indicated/quantified, including highlighting of second-order events (small gas flows, perturbations in stress/pore-water sensors, etc.) and quantification of temperature record frequency content. Localized system behaviour has been shown to occur along with systematic aberrant behaviours that remain unexplained.

A new data analysis toolkit which is suitable for the analysis of large-scale, long-term datasets and the phenomenon/ anomalies they represent is described. The toolkit aims to expose and quantify scientific information in a number of forms contained within a time-series based dataset in a quantitative and rigorous manner, reducing the subjectivity of observations made, thereby supporting the scientific observer. The features contained within the toolkit include the ability to handle non-uniform datasets, time-series component determination, frequency component determination, feature/event detection and characterization/parameterization of local behaviours. An application is presented of a case study dataset arising from the 'Lasgit' experiment.