This paper demonstrates the feasibility of a new approach to system inversion and input signal estimation based on the exploitation of non-minimal state space feedback control system design methods that can be applied to non-minimum phase and unstable systems.
Here, for the first phase at t = k−1, we apply the inversion of input, a ' 0, a ' 1, …, a ' n − 1, to M+ array.
During the model-based inversion, the input data include the stacked seismic data, extracted wavelet, seismic horizon, and the low-frequency initial impedance model.
In general, the complexity of the MUSIC and MVDR algorithms are of the order N3, for Eigen-decomposition and inversion of input correlation matrix, respectively [24 26].
The detection of a fault is followed by input inversion, recomputation, and appropriate output inversion to correct the error and localize the fault.
Design techniques which are based on a stable system inversion result in input trajectories with pre- and/or post-actuation intervals.
a ' 0, a ' 1, …, a ' n − 1 are the inversion of the input vector a 0, a 1, …, a n − 1 which enters the lower M+ array in Fig. 1a.
Algorithm B requires allocation of more than twice the RAM than Algorithm A because it cannot store the results of the inversion in the input matrix.
This estimated PP was next treated as a target log (desired output) and seismic attributes resulting from pre-stack seismic inversion were used as input parameters in the training of a multi-layered feedforward network (MLFN).
In comparison with the inversion results of different input parameters, we found that low Laplace damping constants are essential to excavate the low-frequency background of deeper subsurface structures.
