Salt water intrusion models are commonly used to support groundwater resource management in coastal aquifers. Concentration data used for model calibration are often sparse and limited in spatial extent. With airborne and ground-based electromagnetic surveys, electrical resistivity models can be obtained to provide high-resolution three-dimensional models of subsurface resistivity variations that can be related to geology and salt concentrations on a regional scale. Several previous studies have calibrated salt water intrusion models with geophysical data, but are typically limited to the use of the inverted electrical resistivity models without considering the measured geophysical data directly. This induces a number of errors related to inconsistent scales between the geophysical and hydrologic models and the applied regularization constraints in the geophysical inversion. To overcome these errors, we perform a coupled hydrogeophysical inversion (CHI) in which we use a salt water intrusion model to interpret the geophysical data and guide the geophysical inversion. We refer to this methodology as a Coupled Hydrogeophysical Inversion-State (CHI-S), in which simulated salt concentrations are transformed to an electrical resistivity model, after which a geophysical forward response is calculated and compared with the measured geophysical data. This approach was applied for a field site in Santa Cruz County, California, where a time-domain electromagnetic (TDEM) dataset was collected. For this location, a simple two-dimensional cross-sectional salt water intrusion model was developed, for which we estimated five uniform aquifer properties, incorporating the porosity that was also part of the employed petrophysical relationship. In addition, one geophysical parameter was estimated. The six parameters could be resolved well by fitting more than 300 apparent resistivities that were comprised by the TDEM dataset. Except for three sounding locations, all the TDEM data could be fitted close to a root-mean-square error of 1. Possible explanations for the poor fit of these soundings are the assumption of spatial uniformity, fixed boundary conditions and the neglecting of 3D effects in the groundwater model and the TDEM forward responses.

[1] We apply an extended Kalman filter (EKF) approach to inversion of time-lapse electrical resistivity imaging (ERI) field data. The EKF is a method of time series signal processing that incorporates both a state evolution model, describing changes in the physical system, and an observation model, incorporating the physics of the electrical resistivity measurement. We test the feasibility of using an EKF approach to inverting ERI data collected with 2-D surface array geometries. As a first test, we invert synthetic data generated using a simulated recharge event and water saturation distributions converted to electrical conductivity values using an Archie's law relationship. In the synthetic example we demonstrate the impact that the noise structure of the state evolution and the regularization weight have on EKF-estimated model parameters and errors. We then apply the method to inversion of field data collected to monitor changes in electrical conductivity beneath a recharge pond that is part of an aquifer storage and recovery project in northern California. Using lines of electrodes buried at a depth of 0.25 m when the base of the pond is dry, we monitor the wetting front associated with the diversion of stormflow runoff to the pond. Using field data, we demonstrate that by oversampling in time, we are able to apply the so-called random walk model for the state evolution and to build the model of observation noise directly from collected data. EKF-estimated values track changes in conductivity associated with both increasing water content in subsurface sediments and changes in the properties of the pore water, showing the method is a feasible approach for inversion of time-lapse ERI field data.Citation: Nenna, V., A. Pidlisecky, and R. Knight (2011), Application of an extended Kalman filter approach to inversion of timelapse electrical resistivity imaging data for monitoring recharge, Water Resour. Res., 47, W10525,

Developing effective resource management strategies to limit or prevent saltwater intrusion as a result of increasing demands on coastal groundwater resources requires reliable information about the geologic structure and hydrologic state of an aquifer system. A common strategy for acquiring such information is to drill sentinel wells near the coast to monitor changes in water salinity with time. However, installation and operation of sentinel wells is costly and provides limited spatial coverage. We studied the use of noninvasive electromagnetic (EM) geophysical methods as an alternative to installation of monitoring wells for characterizing coastal aquifers. We tested the feasibility of using EM methods at a field site in northern California to identify the potential for and/or presence of hydraulic communication between an unconfined saline aquifer and a confined freshwater aquifer. One-dimensional soundings were acquired using the time-domain electromagnetic (TDEM) and audiomagnetotelluric (AMT) methods. We compared inverted resistivity models of TDEM and AMT data obtained from several inversion algorithms. We found that multiple interpretations of inverted models can be supported by the same data set, but that there were consistencies between all data sets and inversion algorithms. Results from all collected data sets suggested that EM methods are capable of reliably identifying a saltwater-saturated zone in the unconfined aquifer. Geophysical data indicated that the impermeable clay between aquifers may be more continuous than is supported by current models.

Electrical resistivity imaging has been successfully used to monitor near‐surface hydrologic processes but use of standard measurement arrays may not provide the greatest data sensitivity to the imaged region. We present a method of experimental design based on the concept of informed imaging for creating an electrical resistivity imaging experiment to monitor flow beneath a recharge pond. Informed imaging is the integration of all available data about a site into the acquisition, inversion and interpretation of electrical resistivity data. Informed experimental design uses all available information to develop an a priori model of the subsurface conductivity structure that guides the selection of measurement arrays for an electrical resistivity imaging experiment given spatial and temporal constraints on the acquisition. Selection of arrays focuses on maximizing the amount of unique information acquired with each source pair. We apply the method to the selection of arrays for imaging the top 5 m of the subsurface beneath a recharge pond in Northern California, which is part of an aquifer storage and recovery project. Decreasing infiltration rates over time reduce the effectiveness of the recharge pond. We seek to monitor infiltration processes at the contact between a fines‐rich sand layer and coarser sand layer in an effort to understand the hydrologic controls on infiltration. The performance of the arrays selected using informed experimental design relative to two standard arrays (Wenner and dipole‐dipole) is validated on two synthetic subsurface conductivity models, which are representative of conductivity structures that may arise during an infiltration event. Performance is evaluated in terms of a singular value decomposition of the sensitivity matrix produced by the three types of arrays, as well as a measure of the region of investigation. Results demonstrate that arrays selected using informed experimental design provide independent information about the imaged region and are robust in the presence of noise, improving the ability to image changes in a conductivity structure that result from infiltration processes.

Effective groundwater management requires that decision makers choose strategies for the allocation and conservation of water resources that satisfy the objectives of, and draw support from, multiple stakeholders with complex and often contradictory value judgments. We demonstrate a value of information (VOI) approach to assess the benefits of acquiring geophysical data as part of a groundwater management strategy in light of data uncertainty. VOI is a method for determining if the acquisition of information improves a decision maker’s ability to select an optimal action given value judgments, risk tolerance, and anticipated consequences of the action. As a practical example we examine the uncertainty associated with time-domain electromagnetic (TDEM) data and evaluate the contribution of these data to management of desalination operations in a coastal aquifer where there is a risk of contaminating freshwater resources. The reliability of TDEM data is evaluated using a forward modeling approach to calculate data sensitivity to parameters of interest in the decision analysis. We evaluate the value added by acquiring uncertain data using a standard VOI approach. The analysis shows additional geophysical information could improve groundwater managers’ ability to make decisions about desalination operations and quantifies the contribution of geophysical data to the management decision. We note several measures that can be taken to improve estimates of the data reliability as well as the valuation of alternative actions.