No abstract

While hydraulic tomography (HT) is a mature aquifer characterization technology, its applications to characterize hydrogeology of kilometer-scale fault and fracture zones are rare. This paper sequentially analyzes datasets from two new pumping tests as well as those from two previous pumping tests analyzed by Illman et al. (2009) at a fractured granite site in Mizunami, Japan. Results of this analysis show that datasets from two previous pumping tests at one side of a fault zone as used in the previous study led to inaccurate mapping of fracture and fault zones. Inclusion of the datasets from the two new pumping tests (one of which was conducted on the other side of the fault) yields locations of the fault zone consistent with those based on geological mapping. The new datasets also produce a detailed image of the irregular fault zone, which is not available from geological investigation alone and the previous study. As a result, we conclude that if prior knowledge about geological structures at a field site is considered during the design of HT surveys, valuable non-redundant datasets about the fracture and fault zones can be collected. Only with these non-redundant data sets, can HT then be a viable and robust tool for delineating fracture and fault distributions over kilometer scales, even when only a limited number of boreholes are available. In essence, this paper proves that HT is a new tool for geologists, geophysicists, and engineers for mapping large-scale fracture and fault zone distributions.

[1] In the 2011 off the Pacific coast of Tohoku Earthquake, groundwater pressure changes were observed in and around the Mizunami Underground Research Laboratory (MIU) in Central Japan, where two vertical shafts and horizontal research galleries are excavated in the granitic rock mass. Coseismic changes of groundwater pressure are believed to correspond to crustal dilation/contraction induced by earthquakes. In this study we calculated volumetric strain changes due to the Tohoku Earthquake based on previously reported fault slip models. The calculation indicates approximately 2 Â 10 À7 of dilational strain around the MIU. Based on the strain sensitivities calculated from tidal responses at the monitoring boreholes, the dilation corresponds to drawdowns of several tens of centimeters, and is almost the same as the drawdown observed in the boreholes at distances greater than 1 km from the MIU. In contrast, rapid elevation of groundwater pressures associated with the earthquake was observed in the boreholes within the 500 m vicinity of the MIU. The anomalous elevation is explained by a temporary recovery of the drawdown due to excavation of the shafts and a unique permeability increase induced by the coseismic dilation of heterogeneous local geological structures such as impervious faults controlling the hydrogeological environment.

Changes in the hydrochemical conditions of groundwater were evaluated following the construction of a large-scale underground facility at the Mizunami Underground Research Laboratory (MIU), Japan. The facility was constructed to a depth of 500 m in sedimentary and granitic rocks. Drawdown of the groundwater level in the range of several tens to hundreds of meters was observed up to hundreds of meters away from the shafts during the first ten years of facility construction and operation. Subsequent changes in groundwater chemistry occurred due to upconing of high-salinity groundwater from the deepest part of the shaft and the infiltration of low-salinity shallow groundwater. We predict that future deep groundwater chemistry in the vicinity of the MIU facility will resemble that of the present-day shallow groundwater. Multivariate statistical analysis provides fundamental insights into such a site. We found that the extent of hydrochemical variability related to MIU construction and operation was dependent on the distance from the facility shafts and galleries and on hydrogeological compartmentalization resulting from lithological boundaries (such as permeable conglomerates vs. more compact lithological units) and other features (such as faults or clay layers). We conclude that hydrochemical impact assessment of groundwater in low-permeability rock is essential prior to the construction of such a facility. This should include characterization of hydrogeological structures and compartments to propose suitable location of shafts and galleries.

Hydraulic tomography (HT) has become a mature aquifer test technology over the last two decades. It collects nonredundant information of aquifer heterogeneity by sequentially stressing the aquifer at different wells and collecting aquifer responses at other wells during each stress. The collected information is then interpreted by inverse models. Among these models, the geostatistical approaches, built upon the Bayesian framework, first conceptualize hydraulic properties to be estimated as random fields, which are characterized by means and covariance functions. They then use the spatial statistics as prior information with the aquifer response data to estimate the spatial distribution of the hydraulic properties at a site. Since the spatial statistics describe the generic spatial structures of the geologic media at the site rather than site‐specific ones (e.g., known spatial distributions of facies, faults, or paleochannels), the estimates are often not optimal. To improve the estimates, we introduce a general statistical framework, which allows the inclusion of site‐specific spatial patterns of geologic features. Subsequently, we test this approach with synthetic numerical experiments. Results show that this approach, using conditional mean and covariance that reflect site‐specific large‐scale geologic features, indeed improves the HT estimates. Afterward, this approach is applied to HT surveys at a kilometer‐scale‐fractured granite field site with a distinct fault zone. We find that by including fault information from outcrops and boreholes for HT analysis, the estimated hydraulic properties are improved. The improved estimates subsequently lead to better prediction of flow during a different pumping test at the site.