Recently developed methods for inferring abrupt changes in data series enable such change points in time or space to be identified, and also allow us to estimate noise levels of the observed data. The inferred probability distributions of these parameters provide insights into the capacity of the observed data to constrain the geophysical analysis and hence the magnitudes, and likely sources, o...

A discussion of methodologies for nonlinear geophysical inverse problems is presented. Geophysical inverse problems are often posed as optimization problems in a finitedimensional parameter space. An Earth model is usually described by a set of parameters representing one or more geophysical properties (e.g. the speed with which seismic waves travel through the Earth’s interior). Earth models a...

A stochastic Bayesian approach for combining well logs and geophysical surveys for enhancing subsurface characterization is presented. The main challenge we face is in creating the bridge to link between ambiguously related geophysical surveys and well data. The second challenge is imposed by the disparity between the scale of the geophysical survey and the scale of the well logs. Our approach ...

A Bayesian model coupled with a fuzzy neural network (BFNN) is developed to alleviate the difficulty of using geophysical data in lithology estimation when cross correlation between lithology and geophysical attributes is nonlinear. The prior estimate is inferred from borehole lithology measurements using indicator kriging based on spatial correlation, and the posterior estimate is obtained fro...

Geophysical model domains typically contain irregular, complex fractal-like boundaries and physical processes that act over a wide range of scales. Constructing geographically constrained boundary-conforming spatial discretizations of these domains with flexible use of anisotropically, fully unstructured meshes is a challenge. The problem contains a wide range of scales and a relatively large, ...

This paper presents a path-planning paradigm for distributed control of multiple sensor platforms in a geophysical flow well-approximated by a point-vortex model. We utilize Hamiltonian dynamics to generate control vector fields for vehicle motion in N -vortex flows using the concept of an active singularity whose strength is a tunable control input. We introduce active singularities that are v...

This work presents the application of a geostatistical-based modeling approach for building up electrical properties acquired from a geophysical electrical tomography survey deployed for the purpose of porphyry Cu exploration at the Takht-e-Gonbad deposit, in the central domain of Iran. Electrical data were inverted in 2D along several profiles across the main favorable zones of Cu-bearing mine...

We present a method to calibrate and validate observational models that interrelate remotely sensed energy fluxes to geophysical variables of land and water surfaces. Coincident sets of remote sensing observation of visible and microwave radiations and geophysical data are assembled and subdivided into calibration (Cal) and validation (Val) data sets. Each Cal/Val pair is used to derive the coe...

Shallow depth geophysical data from archaeological sites contain various levels and types of noise that hinters the valuable information of the subsurface architectural relics. Wavelet transform techniques were tested as a method for decomposition of the original geophysical data in order to eliminate the noise levels inherent to the geophysical measurements. In addition to the above, unsupervi...