Tracking tracer motion in a 4-D electrical resistivity tomography experiment

A new framework for automatically tracking subsurface tracers in electrical resistivity tomography (ERT) monitoring images is presented. Using computer vision and Bayesian inference techniques, in the form of a Kalman filter, the trajectory of a subsurface tracer is monitored by predicting and updating a state model representing its movements. Observations for the Kalman filter are gathered using the maximally stable volumes algorithm, which is used to dynamically threshold local regions of an ERT image sequence to detect the tracer at each time step. The application of the framework to the results of 2-D and 3-D tracer monitoring experiments show that the proposed method is effective for detecting and tracking tracer plumes in ERT images in the presence of noise, without intermediate manual intervention

Spatial monitoring of groundwater drawdown and rebound associated with quarry dewatering using automated time-lapse electrical resistivity tomography and distribution guided clustering

Dewatering systems used for mining and quarrying operations often result in highly artificial and complex groundwater conditions, which can be difficult to characterise and monitor using borehole point sampling approaches. Here automated time-lapse electrical resistivity tomography (ALERT) is considered as a means of monitoring subsurface groundwater dynamics associated with changes in the dewatering regime in an operational sand and gravel quarry. We considered two scenarios: the first was unplanned interruption to dewatering due to a pump failure for a period of several days, which involved comparing ALERT monitoring results before and after groundwater rebound; the second involved a planned interruption to pumping over a period of 6 h, for which near-continuous ALERT monitoring of groundwater rebound and drawdown was undertaken. The results of the second test were analysed using distribution guided clustering (DGC) to provide a more quantitative and objective assessment of changes in the subsurface over time. ALERT successfully identified groundwater level changes during both monitoring scenarios. It provided a more useful indication of the rate of water level rise and maximum water levels than piezometer monitoring results. This was due to the piezometers rapidly responding to pressure changes at depth, whilst ALERT/DGC provided information of slower changes associated with the storage and delayed drainage of water within the sediment. By applying DGC we were able to automatically and quantitatively define changes in the resistivity sections, which correlated well with the direct observations of groundwater at site. For ERT monitoring applications that generate numerous time series, the use of DGC could significantly enhance the efficiency of data interpretation, and provide a means of automating groundwater monitoring through assigning alarm thresholds associated with rapid changes in groundwater conditions

Alternative inversion strategies to resistivity data for targets with sharp boundaries

Estimating the geometry and resistivity of archeological structures using resistivity models produced as a result of applying smoothness constraints in most inversion algorithms is difficult, especially when structures are closely spaced. However, such quantification is important to facilitate conservation and to minimize the potential of damage when excavations are undertaken. Alternative inversion approaches more appropriate for imaging such targets require either a priori information about the subsurface (e.g. disconnected inversion) or require two or more geophysical datasets to be collected at the same site (e.g. joint inversion). The research outlined in this dissertation presents three novel approaches to improve resistivity imaging of discrete targets without the need to incorporate a priori information in the inversion. The first approach combines an initial 2D smoothness constraint inversion coupled with a digital image processing technique known as a watershed algorithms and a second inversion step incorporating a disconnect in the regularization based on the output of the watershed algorithm. This approach has improved estimate of the geometries of individual targets, but it was not very effective at predicting the resistivity of the targets or resolving closely spaced targets. The second approach combines an initial 2D smoothness constraint inversion coupled with the watershed algorithm and a trained Artificial Neural Network (ANN). Although this approach has been proven effective for resolving widely and closely spaced archeological targets, the results depend largely on the quality of ANN training and on the accuracy of the watershed algorithm geometry prediction. Finally, the third strategy is an iterative approach that combines an initial 3D smoothness constraint inversion that is used only at the first iteration to recover a resistivity model that is fairly consistent with the measured data, from which an initial target location is estimated using an edge detector method and from which a disconnect in the inversion is identified. The disconnect defining the target outline is then progressively improved following each iteration of the inverse procedure. This approach has been proven more effective for resolving widely and closely spaced archeological targets over other approaches, but it is partially sensitive to artifacts in the initial smoothness constraint model.Ph.D.Includes bibliographical referencesIncludes vitaby Mehrez H. Elwasei

Geophysical constraints on deep weathering and water storage potential in the Southern Sierra Critical Zone Observatory

The conversion of bedrock to regolith marks the inception of critical zone processes, but the factors that regulate it remain poorly understood. Although the thickness and degree of weathering of regolith are widely thought to be important regulators of the development of regolith and its water-storage potential, the functional relationships between regolith properties and the processes that generate it remain poorly documented. This is due in part to the fact that regolith is difficult to characterize by direct observations over the broad scales needed for process-based understanding of the critical zone. Here we use seismic refraction and resistivity imaging techniques to estimate variations in regolith thickness and porosity across a forested slope and swampy meadow in the Southern Sierra Critical Zone Observatory (SSCZO). Inferred seismic velocities and electrical resistivities image a weathering zone ranging in thickness from 10 to 35m (average=23m) along one intensively studied transect. The inferred weathering zone consists of roughly equal thicknesses of saprolite (P-velocity<2kms-1) and moderately weathered bedrock (P-velocity=2-4kms-1). A minimum-porosity model assuming dry pore space shows porosities as high as 50% near the surface, decreasing to near zero at the base of weathered rock. Physical properties of saprolite samples from hand augering and push cores are consistent with our rock physics model when variations in pore saturation are taken into account. Our results indicate that saprolite is a crucial reservoir of water, potentially storing an average of 3m3m-2 of water along a forested slope in the headwaters of the SSCZO. When coupled with published erosion rates from cosmogenic nuclides, our geophysical estimates of weathering zone thickness imply regolith residence times on the order of 105years. Thus, soils at the surface today may integrate weathering over glacial-interglacial fluctuations in climate. © 2013 John Wiley & Sons, Ltd