Geostatistical inversion of electromagnetic induction data for landfill modelling

Abstract

<p>The characterization and monitoring of landfills has become a major concern, not only for assessing the associated environmental impact (e.g., groundwater contamination) but also for evaluating the potential for recovery of secondary resources, in particular for the production of raw materials and energy. For both objectives, it is crucial to have knowledge of the waste composition and the current landfill conditions (e.g. water saturation level). Near-surface geophysical surveys have been proven effective for the non-invasive investigation of landfills, in which different methods have been used depending on the specific survey targets.  Because of its sensitivity to two subsurface physical properties, electrical conductivity (EC) and magnetic susceptibility (MS), frequency-domain electromagnetic (FDEM) induction has been successfully applied to the qualitative characterization of urban and industrial landfills, including mine tailings. Yet, due to the generally complex composition and strongly heterogeneous spatial distribution of waste deposits, reconstructing a reliable landfill model from surface geophysical measurements remains challenging. Geostatistical inversion emerges as powerful tool to improve the landfill modelling from geophysical data, allowing for a more detailed description of the spatial distribution of the properties of interest and the associated uncertainty. Additionally, it provides a flexible framework for integrating data from geophysical surveys and conventional sampling from drilling or trenching.</p><p>In this work, we present a new geostatistical inversion technique able for the simultaneous inversion of FDEM data for EC and MS, which optimize the landfill modelling procedure and is sensitive towards change on the physical properties of interest. This method is based on an iterative procedure where ensembles of subsurface models of EC and MS are generated with stochastic sequential simulation and co-simulation. These simulated models are conditioned locally by existing borehole data for these properties and by a spatial continuity pattern imposed by a variogram model. Synthetic instrument response data, including both the in-phase and quadrature-phase components of the FDEM response, are generated from each model using a forward model connecting the data domain (FDEM data) with the model domain (subsurface physical properties). The misfit between the observed and forward-modelled FDEM data, weighted according to the depth sensitivity of the FDEM response toward changes in EC and MS, is used to drive the generation of a new set of models in the next iteration. We illustrate the inversion procedure with synthetic landfill example data sets which were created based on real data collected at a mine tailing in Portugal and a municipal solid waste landfill in Belgium.</p>

Geostatistical Joint Inversion of Frequency-Domain Electromagnetic Data and Direct Current Resistivity Data for Modelling Near-Surface Deposits

“…The proposed iterative geostatistical joint FDEM and ERT inversion technique is based on global geostatistical seismic inversion method (Azevedo and Soares, 2017) and an iterative geostatistical FDEM inversion technique (Narciso et al, 2020). It relies on two key main ideas: (1) the perturbation of the model parameter space with direct sequential simulation (DSS) and co-simulation (Soares, 2001), and (2) the convergence is ensured by a global stochastic optimizer driven simultaneously by the misfit between true and synthetic FDEM and ERT data.…”

“…We built a realistic 3-D synthetic data set to be used as benchmark throughout several geostatistical inversion modelling applications developed in the same project. Thus, for comparison purposes, this data set is similar to the one applied in Narciso et al (2020). The data set was modelled based on geological samples collected at a mine tailing in Portugal for which we investigated porosity and particle density and from these a three-dimensional synthetic porosity subsurface model, with a dimension of 150 by 200 by 4 meters with a cell size of 0.5 m by 0.5 m by 0.1 m, was generated using stochastic sequential simulation (Deutsch & Journel, 1998).…”

“…In this work, we present an iterative geostatistical joint inversion technique for FDEM and ERT, based on a previously established iterative geostatistical FDEM inversion technique (Narciso et al, 2020). A geostatistical framework is used to couple both data domains in a consistent spatial model and is capable of modelling the spatial continuity and assess the uncertainty of the modelling procedure.…”