Estimating depths and dimensions of gravity sources through optimized support vector classifier (SVC)

By researching and applying new methods we will be able to improve significantly estimation of shapes, dimensions and depths of gravity sources. After shapes estimation of gravity sources through support vector classifier (SVC) in our last research [Hekmatian et al. 2015], in this paper SVC is applied for estimating depths and dimensions of gravity sources. These estimations give us logical and complete initial guesses regarding shapes, depths and dimensions of gravity sources which are needed in more precise interpretations and inversions of gravity sources. Also for better application of SVC, we selected more proper features using the technique called feature selection (FS). In this paper, we trained SVC with 320 synthetic gravity profiles for estimation of dimensions and depths of gravity sources. We tested the trained SVC codes by about 200 other synthetic and some real gravity profiles. The depths and dimensions of a well along with two ore bodies (three real gravity sources) are estimated during the testing process.

Abstract licence: CC BY

10 more references, whose wording the paper’s licence keeps private.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.