Kernel Density Derivative Estimation of Euler Solutions

Conventional Euler deconvolution is widely used for interpreting profile, grid, and ungridded potential field data. The Tensor applies additional constraints to the solution using all gravity vectors full gradient tensor. These algorithms use a series of different-sized moving windows yield many solutions that can be employed estimate source location from entire survey area. However, traditional discrimination techniques ignore interrelation among solutions, so they cannot separate adjacent targets. To overcome this difficulty, we introduced multivariate Kernel Density Derivative Estimation (KDDE) as an extension Estimation, which mathematical process probability density function random variable. KDDE was tested on single cube model, cylinder three composite models consisting two cubes with various separations gridded value calculated by discriminate spurious dataset isolate geological sources. method then applied airborne data British Columbia, Canada. Then, results synthetic show proposed successfully locate meaningful