The Jahamah Platform is a part of a structural depression called the Sirt basin, located in the northern central part of Libya. The Jahamah Platform spans latitude 〖29.95〗^° N to 〖30.55〗^° N and longitudes 〖19.32〗^° E to 〖19.77〗^° E with an estimated area of about 2,187 km2. Libyan Petroleum Institute provided the data of aeromagnetic that was used in this study. The data was used to study the structure beneath the Jahamah Platform by using Oasis montaj software. Various filters from the software have been applied to enhance determining the fault system within the study area. An RTP filter was applied to the magnetic data to construct a reduction to the pole anomaly map. The subsurface structural elements underneath the study area were identified using Total horizontal derivative (THD), CET analysis, and Euler deconvolution. 2-D forward modelling of the area was constructed based on gravity data, and then the basement depth was estimated to range from 2.2 km to 3.1 km based on the model. Based on the interpretation of the constructed maps, the area has a number of faults that trend in NE-SW, NNW-SSE, N-S and NW-SE and faults depth ranging between 790 m to 3102 m.
Intensity inhomogeneity or intensity non-uniformity (INU) is an undesired phenomenon that represents the main obstacle for magnetic resonance (MR) image segmentation and registration methods. Various techniques have been proposed to eliminate or compensate the INU, most of which are embedded into clustering algorithms, and they generally have difficulties when INU reaches high amplitudes. This study proposes a multiple stage fuzzy c-means (FCM) clustering based algorithm for the estimation and compensation of INU, by modelling it as a slowly varying additive or multiplicative noise. The slowly varying behaviour of the bias or gain field is assured by a smoothing filter that performs a context dependent averaging, controlled by a morphological criterion. The segmentation is also supported by a prefiltering technique for Gaussian and impulse noise elimination. The experiments using 2-D synthetic phantoms and real MR images indicate that the proposed method provides accurate segmentation. The resulting segmentation and fuzzy membership values can serve as excellent support for 3-D registration and surface reconstruction techniques.