Novel approach for 1D resistivity: Determining the correct number of layers as input for 1D resistivity inversion is important for constructing a model that well represents the subsurface. In most electrical resistivity inversions, the number of layers is an arbitrary user-defined parameter. Here, I try to provide a method that solves the problem of choosing the correct number of layers. The method follows the two-steps approach suggested by Simms and Morgan to systematically resolve the optimum number of layers. The method utilizes an integrated program that performs the two inversion steps sequentially. It uses correlated recalling and ridge trace regression algorithm in both inversion steps.
A Convolutional Neural Network approach for near-surface seismic tomography: In seismic exploration, first-break picking is the task of determining, given a set of seismic traces, the onsets of the first signal arrivals as accurately as possible. In general, these arrivals are associated with the energy of refracted waves at the base of the weathering layer or to the direct wave that travels directly from the source to the receiver. The accurate determination of the first arrivals onset is needed for calculating the static corrections, a fundamental stage of seismic data processing.
Alali, Ammar, & Morgan, F. (2017) "Novel approach for 1D resistivity inversion using the systematically-determined optimum number of layers," under review, Geophysics.