Integrated Earth Resistivity Tomography (ERT) and Multilevel Gas Sampling: A Tool to Map Geogenic and Anthropogenic Methane Accumulation on Brownfield Sites

Soil gas emissions of methane and carbon dioxide on brownfield sites are usually attributed to anthropogenic activities; however geogenic sources of soil gas are often not considered during site investigation and risk management strategies. This paper presents a field study at a redeveloped brownfield site on a flood plain to identify accumulations of methane biogas trapped in underlying sediments. The investigation is based on a multidisciplinary approach using direct multi-level sampling measurements and Earth resistivity tomography . Resistivity imaging was applied to evaluate the feasibility of identifying the size and spatial continuity of soil gas accumulations in anthropogenic and naturally occurring deposits. As a result, biogas accumulations are described within both anthropogenic deposits and pristine organic sediments. This result is important to identify the correct approaches to identify and manage risks associated with soil gas emissions on brownfield and pristine sites. The organic-rich sediments in Quaternary fluvial environments of São Paulo Basin in particular the Tietê River, biogas reservoirs can be generated and trapped beneath geogenic and anthropogenic layers, potentially requiring the management of brownfield developments across this region.

Non-stationary variogram models for geostatistical sampling optimisation: An empirical investigation using elevation data

A problem with use of the geostatistical Kriging error for optimal sampling design is that the design does not adapt locally to the character of spatial variation. This is because a stationary variogram or covariance function is a parameter of the geostatistical model. The objective of this paper was to investigate the utility of non-stationary geostatistics for optimal sampling design. First, a contour data set of Wiltshire was split into 25 equal sub-regions and a local variogram was predicted for each. These variograms were fitted with models and the coefficients used in Kriging to select optimal sample spacings for each sub-region. Large differences existed between the designs for the whole region (based on the global variogram) and for the sub-regions (based on the local variograms). Second, a segmentation approach was used to divide a digital terrain model into separate segments. Segment-based variograms were predicted and fitted with models. Optimal sample spacings were then determined for the whole region and for the sub-regions. It was demonstrated that the global design was inadequate, grossly over-sampling some segments while under-sampling others.