Time-lapse electrical resistivity tomography ͑ERT͒ has many practical applications to the study of subsurface properties and processes. When inverting time-lapse ERT data, it is useful to proceed beyond straightforward inversion of data differences and take advantage of the time-lapse nature of the data. We assess various approaches for inverting and interpreting time-lapse ERT data and determine that two approaches work well. The first approach is model subtraction after separate inversion of the data from two time periods, and the second approach is to use the inverted model from a base data set as the reference model or prior information for subsequent time periods. We prefer this second approach. Data inversion methodology should be considered when designing data acquisition; i.e., to utilize the second approach, it is important to collect one or more data sets for which the bulk of the subsurface is in a background or relatively unperturbed state. A third and commonly used approach to time-lapse inversion, inverting the difference between two data sets, localizes the regions of the model in which change has occurred; however, varying noise levels between the two data sets can be problematic. To further assess the various time-lapse inversion approaches, we acquired field data from a catchment within the Dry Creek Experimental Watershed near Boise, Idaho, U.S.A. We combined the complimentary information from individual static ERT inversions, time-lapse ERT images, and available hydrologic data in a robust interpretation scheme to aid in quantifying seasonal variations in subsurface moisture content.

Practical decisions are often made based on the subsurface images obtained by inverting geophysical data. Therefore it is important to understand the resolution of the image, which is a function of several factors, including the underlying geophysical experiment, noise in the data, prior information and the ability to model the physics appropriately. An important step towards interpreting the image is to quantify how much of the solution is required to satisfy the data observations and how much exists solely due to the prior information used to stabilize the solution. A procedure to identify the regions that are not constrained by the data would help when interpreting the image. For linear inverse problems this procedure is well established, but for non‐linear problems the procedure is more complicated. In this paper we compare two different approaches to resolution analysis of geophysical images: the region of data influence index and a resolution spread computed using point spread functions. The region of data influence method is a fully non‐linear approach, while the point spread function analysis is a linearized approach. An approximate relationship between the region of data influence and the resolution matrix is derived, which suggests that the region of data influence is connected with the rows of the resolution matrix. The point‐spread‐function spread measure is connected with the columns of the resolution matrix, and therefore the point‐spread‐function spread and the region of data influence are fundamentally different resolution measures. From a practical point of view, if two different approaches indicate similar interpretations on post‐inversion images, the confidence in the interpretation is enhanced. We demonstrate the use of the two approaches on a linear synthetic example and a non‐linear synthetic example, and apply them to a non‐linear electromagnetic field data example.

where G is the sensitivity matrix, δm = m (k+1) − m (k) is the model perturbation, δm 0 = m 0 − m (k) is difference between the reference model and the model estimate at the kth iteration and Q(δm), P(δm)are the higher-order terms, typically neglected in the linearized analysis. When the higher-order

During December 2002 and January 2003, Montana Tech in collaboration with Ain Shams University, Cairo, collected Ground Penetrating Radar (GPR) and seismic data at Saqqara, Egypt. The purpose of this study was to see if GPR and seismic methods could detect manmade structures in the subsurface at Saqqara. In particular, land streamer aided, seismic diving-wave tomography was tested as a method to detect archaeological features. Saqqara was one of the principal necropolises of Memphis, an ancient capital of Egypt. The research site was near the 3rd Dynasty pharaoh Djoser’s Step Pyramid—the first monumental structure built entirely of stone. A preliminary GPR study of our site yielded numerous, possibly manmade features in the subsurface with a [Formula: see text] depth of penetration using [Formula: see text] antennas. A follow-up three-dimensional (3-D) GPR survey over one of the more interesting features showed a broad trench underneath the flat-lying sand that is seen at the surface. This feature is most likely manmade because the horizontally layered limestone rocks of Saqqara are inconsistent with the shape of this feature. Seismic diving-wave tomograms show that this probable manmade feature extends to a depth of [Formula: see text] into the subsurface. Moreover, we were able to complete the seismic survey faster using a land streamer consisting of gimbaled geophones than could be done using conventional planted geophones. This site has potential for further investigation and possible excavation.