Helicopter borne time domain EM systems historically measure only the Z-component of the secondary field, whereas fixed wing systems often measure all field components. For the latter systems the X-component is often used to map discrete conductors, whereas it finds little use in the mapping of layered settings. Measuring the horizontal X-component with an offset loop helicopter system probes the earth with a complementary sensitivity function that is very different from that of the Z-component, and could potentially be used for improving resolution of layered structures in one dimensional modeling. This area is largely unexplored in terms of quantitative results in the literature, since measuring and inverting X-component data from a helicopter system is not straightforward: The signal strength is low, the noise level is high, the signal is very sensitive to the instrument pitch and the sensitivity function also has a complex lateral behavior.The basis of our study is a state of the art inversion scheme, using a local 1D forward model description, in combination with experiences gathered from extending the SkyTEM system to measure the X component. By means of a 1D sensitivity analysis we motivate that in principle resolution of layered structures can be improved by including an X-component signal in a 1D inversion, given the prerequisite that a low-pass filter of suitably low cut-off frequency can be employed. In presenting our practical experiences with modifying the SkyTEM system we discuss why this prerequisite unfortunately can be very difficult to fulfill in practice. Having discussed instrumental limitations we show what can be obtained in practice using actual field data. Here, we demonstrate how the issue of high sensitivity towards instrument pitch can be overcome by including the pitch angle as an inversion parameter and how joint inversion of the Z- and X-components produces virtually the same model result as for the Z-component alone. We conclude that adding helicopter system X-component to a 1D inversion can be used to facilitate higher confidence in the layered result, as the requirements for fitting the data into a 1D model envelope becomes more stringent and the model result thus less prone to misinterpretation.
- Programområde 2: Vandressourcer