Elliot Holtham

Three‐dimensional forward modelling and inversion of Z‐TEM data

Z-TEM data are airborne EM data which record the vertical magnetic field that results from natural sources. Processing the data yields transfer functions which relate the vertical magnetic field to horizontal fields at a reference station. The transfer functions depend upon frequency and provide information about the 3D conductivity structure of the Earth. In this paper we show how these transfer functions can be modelled. This is the crucial step towards any interpretation or inversion. The inversion of Z-TEM data is computationally similar to inversion of MT data. This ability will enhance exploration efforts to find large scale structures buried at depth.

Three‐dimensional inversion of MT and ZTEM data

ZTEM is an airborne electromagnetic survey in which the vertical magnetic field from natural sources is recorded. The data are transfer functions that relate the local vertical field to orthogonal horizontal fields measured at a reference station on the ground. The transfer functions depend on frequency and provide information about the 3D conductivity structure of the Earth. Since a 1D conductivity structure produces no vertical magnetic fields, the ZTEM technique is not very sensitive to the background conductivity. In order to increase sensitivity to the background conductivity, and greatly improve the depth of investigation, MT and ZTEM data can both be collected. The combination of sparse MT data, with the economical and rapid spatial acquisition of airborne ZTEM data, creates a cost effective exploration technique that can map large-scale structures at depths that are difficult to image with other techniques. We develop a Gauss-Newton algorithm to jointly invert ZTEM and MT data. The algorithm is applied to a synthetic model and to a field example from the Reese River geothermal property in Nevada.

3D Multiple Body Parametric Inversion of Time-domain Airborne EM Data

We developed a 3D parametric inversion for time-domain airborne EM data using a skewed ellipsoid representation for multiple conductive or resistive anomalies. The approach aims to simplify the task of imaging thin, potentially highly conductive, anomalies with 3D EM inversion. The algorithm finds the optimal location, shape, size and resistivity of the anomalies in a homogeneous or heterogeneous background by employing a Gauss-Newton style optimization. Our parametric method is tested on a synthetic and field data set. The synthetic model is composed of two narrow dipping conductive anomalies in a resistive background along with a vertical narrow conductor. The survey layout and resistivity structure is based off field data from a greenstone setting. The parametric inversion accurately recovers the spatial extent and dips of the three synthetic anomalies, although the depth extent of the anomalies is exaggerated. In the greenstone field example, the inversion defines the spatial location, extent and dips of three conductive anomalies to provide a new conductivity interpretation of an area where little information is known regarding the true nature of the conductors.