3-D magnetic inversion with data compression and image focusing

Journal ArticleWe develop a method of 3-D magnetic anomaly inversion based on traditional Tikhonov regularization theory. We use a minimum support stabilizing functional to generate a sharp, focused inverse image. An iterative inversion process is constructed in the space of weighted model parameters that accelerates the convergence and robustness of the method. The weighting functions are selected based on sensitivity analysis. To speed up the computations and to decrease the size of memory required, we use a compression technique based on cubic interpolation. Our method is designed for inversion of total magnetic anomalies, assuming the anomalous field is caused by induced magnetization only. The method is applied to synthetic data for typical models of magnetic anomalies and is tested on real airborne data provided by Exxon-Mobil Upstream Research Company

Focusing Inversion of Electroencephalography and Magnetoencephalography Data

Introduction The inverse electroencephalography (EEG) and magnetoencephalography (MEG) problems seek to reconstruct neural sources based on measurements of the electric or magnetic field recorded outside of the head. In this paper, we present a new minimization technique for solving these problems. Our contribution is a novel stabilizing functional that is proven to have minimal support. Motivation Two neuroscience applications that stand to benefit from accurate solutions to the inverse bioelectric and biomagnetic field problems are functional brain studies and clinical diagnosis of neural disease, such as epilepsy. In functional brain studies, sensory signals stimulate the subject, and the brain's electromagnetic activity appears as a response to these stimuli. The signal is measured by a sensor array outside of the head. The location and strength of the neural sources that were activated due to the stimulus can, in theory, be determined by solving the inverse ..