Smart sampling strategy for investigating spatial distribution of subsurface shallow gas pressure in Hangzhou Bay area of China

Abstract

Subsurface shallow gas has been widely discovered in coastal areas across continents at buried depths of approximately 10–100 m. Its presence poses a significant hazard and risk during underground construction, e.g., risk of explosion and fire when using tunnel boring machines for subway construction. Therefore, it is important to determine the spatial distribution of the shallow gas pressure during site investigation for subsequent hazard analysis and mitigation. In engineering practice, the gas pressure is often sparsely measured at a limited number of locations using modified cone penetration tests (CPTs) owing to time, budget, resource, and technical constraints. There are no rational or quantitative methods available for the planning of site investigation of gas pressure. Therefore, it is important to determine ways to properly and efficiently plan a site investigation of the shallow gas pressure during geotechnical site investigation (e.g., how to determine the required number of modified CPTs (i.e., the sample size) and their corresponding optimal locations). A smart sampling strategy is developed for planning of site investigation in this study that uses innovative data analytic methods (e.g., Bayesian compressive sampling, BCS, and information entropy) to automatically determine sample size and optimal sampling location for gas pressure characterization. The proposed smart sampling strategy is illustrated using numerical examples. The results show that the proposed smart sampling strategy performs reasonably well.