3‐D magnetic inversion with data compression and image focusing

Abstract

We 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.

Subsurface electrical imaging of anisotropic formations affected by a slow active reverse fault, Provence, France

“…For example, smoothness constraint creates smooth images whereas other scheme such as minimum gradient support (e.g. Portniaguine andZhdanov, 2002 or Blasheck, 2006) produce sharp and focused images. This would have a great impact in fault detection in isotropic medium which can be well resolved.…”

3D Inversion of SQUID Magnetic Tensor Data

“…Various methods of compression may be introduced to minimize the storage of A(e.g., [9], [10]). Inversion of equation 4 is ill-posed, and its solution requires regularization [11].…”

Rapid calculation of gravity anomalies based on residual node densities and its GPU implementation

“…One way is to optimize the forward algorithm, for example by the information reduction approach (Portniaguine and Zhdanov, 2002;Li and Oldenburg, 2003;Yao et al, 2003;Davis and Li, 2011;Čuma and Zhdanov, 2014), which has achieved a speedup ratio of several or dozens of times compared to the original method.…”