SMART - geoelectrical tomographic monitoring of dynamic systems using adaptive self-optimising data acquisition

Electrical Resistivity Tomography (ERT) is a rapidly evolving imaging technology for scanning the subsurface. It is increasing used for a wide range of geoscientiific problems particularly for the time-lapse monitoring of complex earth systems undergoing dynamic change (e.g: pollution plumes, saline intrusion, chemical interactions). Major advances have been made in recent years with the design of multi-channel, microprocessor-controlled instrumentation systems. BGS has itself designed a world-leading instrument for Automated time-Lapse Electrical Resistivity Tomography (ALERT) which, for the first time, allows the remote, real-time imaging of vulnerable sites 'on demand' using wireless telemetry. Despite these advances, however, the basic resistivity measurement regime remains unchanged. For most conventional subsurface applications the operator measures ground resistance using a pre-determined 4 point electrode arrangement (e.g: Wenner, Schlumberger, Dipole-Dipole) within a larger array. Consequently the data are often poorly sampled, noisy and insensitive to specific regions of interest in the subsurface. Even the best inversion algorithms cannot compensate for the lack of spatial resolution caused by the collection of ill-conditioned field data. Recent advances at BGS and by other international groups suggest that it should be possible to optimise the resolution of the data collected by adaptive sampling. We propose to test the hypothesis that the BGS-designed ALERT system could be programmed to collect SMART (Sensitivity-Modulated Adaptive Resistivity Tomography) data. We will build on earlier work (Wilkinson et al., 2006a; 2006b; 2007) which uses an estimate of the Jacobian model resolution matrix given by R = (GTG+C)-1GTG, where C is the constraint matrix that regularises the inversion. The leading diagonal of R (the model resolution R) will be used to assess how well the existing set of measurements images the subsurface, and which measurements would produce the best improvements to the image resolution if they were to be added. The optimisation procedure then selects several such measurements, recalculates R and iterates the process until the desired total number of measurements is reached. At each iteration, the measurement configurations will comprise a base set (optimised over the whole image region) and an additional set (optimised to enhance resolution in the region of interest). Ways will need to be found to store and retrieve the optimised base set and Jacobian elements rather than rely on recalculation. The algorithm design will be tested up to TRL 3 using synthetic data representing simplified dynamic targets. The results will be compared with those from static configurations (standard and optimised). The effects of noise on each type of monitoring data (standard, optimised and SMART) will also be assessed. Tests of the SMART concept at TRL 4 will be undertaken using the BGS hydrogeophysical test facility. This comprises two test cells, the second of which will be used to monitor the passage of saline tracers with both static and adaptive ERT measurement configurations. The cells have permanent linear 2D and 3D arrays of surface and borehole electrodes which will be used for the SMART tests. Multi-level samplers at 10 cm depth intervals in the simulated boreholes will provide ground-truth to assess the accuracy and resolution of the new SMART algorithm. No existing multi-electrode resistivity survey instrument attempts to improve the quality of the recorded data by achieving this degree of context or target adaptivity by adjusting the applied current distributions to be those most appropriate for a given survey geometry or site surface conditions, or to best cope with the particular (initially unknown) features of the subsurface. If successful, we can anticipate a step change in tomographic image quality.