Jerry M. Harris | USGS Science

USGS2003

Time‐lapse imaging of saline‐tracer transport in fractured rock using difference‐attenuation radar tomography

Accurate characterization of fractured‐rock aquifer heterogeneity remains one of the most challenging and important problems in groundwater hydrology. We demonstrate a promising strategy to identify preferential flow paths in fractured rock using a combination of geophysical monitoring and conventional hydrogeologic tests. Cross‐well difference‐attenuation ground‐penetrating radar was used to monitor saline‐tracer migration in an experiment at the U.S. Geological Survey Fractured Rock Hydrology Research Site in Grafton County, New Hampshire. Radar data sets were collected every 10 min in three adjoining planes for 5 hours during each of 12 tracer tests. An innovative inversion method accounts for data acquisition times and temporal changes in attenuation during data collection. The inverse algorithm minimizes a combination of two functions. The first is the sum of weighted squared data residuals. Second is a measure of solution complexity based on an a priori space‐time covariance function, subject to constraints that limit radar‐attenuation changes to regions of the tomograms traversed by high difference‐attenuation ray paths. The time series of tomograms indicate relative tracer concentrations and tracer arrival times in the image planes; from these we infer the presence and location of a preferential flow path within a previously identified zone of transmissive fractures. These results provide new insights into solute channeling and the nature of aquifer heterogeneity at the site.

New Hampshire↗

USGS2002

Time‐lapse inversion of crosswell radar data

The combination of differential radar tomography with conventional tracer and/or hydraulic tests facilitates high‐resolution characterization of subsurface heterogeneity and enables the identification of preferential flow paths. In dynamic imaging, each tomogram is typically inverted independently, under the assumption that data sets are collected quickly relative to changes in the imaged property (e.g., attenuation or velocity); however, such “snapshot” tomograms may contain large errors if the imaged property changes significantly during data collection. Acquisition of less data over a shorter time interval could ameliorate the problem, but the resulting decrease in ray density and angular coverage could degrade model resolution. To address these problems, we propose a new sequential approach for time‐lapse tomographic inversion. The method uses space‐time parameterization and regularization to combine data collected at multiple times and to account for temporal variation. The inverse algorithm minimizes the sum of weighted squared residuals and a measure of solution complexity based on an a priori space‐time covariance function and a spatiotemporally variable mean. We demonstrate our approach using a synthetic 2‐D time‐lapse ( x , z , t ) data set based loosely on a field experiment in which difference‐attenuation radar tomography was used to monitor the migration of a saline tracer in fractured rock. We quantitatively show the benefits of space‐time inversion by comparing results for snapshot and time‐lapse inversion schemes. Inversion over both space and time results in superior estimation error, model resolution, and data reproduction compared to conventional snapshot inversion. Finally, we suggest strategies to improve time‐lapse cross‐hole inversions using ray‐based inversion constraints and a modified survey design in which different sets of rays are collected in alternating time steps.

Geophysics↗