derivative analysis

Aquifer test analysis is complex, and in many regards, the interpretation resembles an art more than a science. Under the best circumstances, aquifer test analysis is still plagued by ambiguity and uncertainty, compounded by the general lack of information on the subsurface. An approach which has seen widespread adoption in other fields that need to classify time series data is machine learning. A Python script that generates numerical groundwater flow models by interfacing directly with the modelling software produces training data for deep learning.

Test-pumping drawdown curves do not always sufficiently indicate aquifer characteristics and geometry and should never be analysed in isolation. Using derivative analysis and flow dimension theory, inferring the regional geometries and flow characteristics of fractured aquifers that are otherwise unknown or inconclusive is possible. As the drawdown and/or pressure front propagates through the aquifer, it reaches various hydrogeological objects that influence flow regimes and imprints a sequence of signatures in the drawdown derivative curve.