We introduce 2D potential field migration for the interpretation of gravity gradiometry data. This method is based on a direct integral transformation of the observed gravity gradients into subsurface density distributions which can be used for realtime imaging or as an a priori model for subsequent 3D regularized inversion. Potential field migration is very fast and stable with respect to nois...

The methods applied to regularization of the ill-posed problems can be classified under “direct” and “indirect” methods. Practice has shown that the effects of different regularization techniques on an ill-posed problem are not the same, and as such each ill-posed problem requires its own investigation in order to identify its most suitable regularization method. In the geoid computations witho...

to solve the geodetic boundary-value problem the gravity anomalies have to be continued from the earth's surface down onto the geoid surface. helmert’s gravity anomalies (multiplied by the geocentric radius) are harmonic above the geoid. it is thus possible to use them in the poisson solution of the inverse dirichlet boundary value problem to get their values on the geoid. the downward con...

One way of developing a wave-equation approach to seismic imaging is based on the concept of downward continuation of the surface reflection data. Seismic imaging is typically based on the single scattering approximation, and assumes the knowledge of a, smooth, background model in which the mentioned downward continuation is carried out. The downward continued data are subjected, at each depth,...

Downward continuation is a key technique for processing and interpreting gravity anomalies, as it has major role in reducing values to horizontal planes identifying small shallow sources. However, can be unstable inaccurate, particularly when depth increases. While the Milne Adams–Bashforth methods based on numerical solutions of mean-value theorem have partly addressed these problems, more acc...

Downward continuation (DC) of the gravity potential field is an important approach used to understand and interpret density structure boundary anomalous bodies. It widely delineate highlight local shallow sources. However, it well known that direct DC transformation in frequency domain unstable easily affected by high-frequency noise. Recent deep learning applications have led development image...