| Summary: | The presence of geometrical survey parameter errors can cause problems when attempting
to invert electromagnetic (EM) data. There are two types of data which are of particular
interest: airborne EM (AEM) and ground based horizontal loop EM (HLEM). When
dealing with AEM data there is a potential for errors in the measurement height. The
presence of measurement height errors can result in distortions in the conductivity models
recovered via inversion. When dealing with HLEM there is a potential for errors in the
coil separation. This can cause the inphase component of the data to be distorted.
Distortions such as these can make it impossible for an inversion algorithm to predict the
inphase data. Examples of these types of errors can be found in the Mt. Milhgan and
Sullivan data sets. The Mt. Milligan data are contaminated with measurement height
errors and the Sullivan data are contaminated with coil separation errors.
In order to ameliorate the problems associated with geophysical survey parameter
errors a regularized inversion methodology is developed through which it is possible to
recover both a function and a parameter. This methodology is applied to the 1-D EM
inverse problem in order to recover both 1-D conductivity structure and a geometrical
survey parameter. The algorithm is tested on synthetic data and is then applied to the
field data sets.
Another problem which is commonly encountered when inverting geophysical data is
the problem of noise estimation. When solving an inverse problem it is necessary to fit
the data to the level of noise present in the data. The common practice is to assign noise
estimates to the data a priori. However, it is difficult to estimate noise by observation
alone and therefore, the assigned errors may be incorrect. Generalized cross validation is a statistical method which can be used to estimate the noise level of a given data set. A
non-linear inversion methodology which utilizes GCV to estimate the noise level within
the data is developed. The methodology is applied to 1-D EM inverse problem. The
algorithm is tested on synthetic examples in order to recover 1-D conductivity and also
to recover 1-D conductivity as well as a geometrical survey parameter. The strengths
and limitations of the algorithm are discussed. === Science, Faculty of === Earth, Ocean and Atmospheric Sciences, Department of === Graduate
|
|---|
