Cluster Algorithm Integrated with Modification of Gaussian Elimination to Solve a System of Linear Equations
The data accumulation and their inhomogeneous distribution lead to the issue of large and sparse systems solving in various fields: industrials, emergency management, etc. Complex structure in the data error creates additional risk to obtain an adequate solution. To facilitate problem-solving, we describe the technique that is based on intellectual division of data with following application of cluster algorithm and the modification of Gaussian elimination to different portions of data. In this paper, we present results of developed technique that was applied to samples of synthetic and real data. We compare them with outcomes of other algorithms (intelligence and classical) by using of num…
Method to find the Minimum 1D Linear Gradient Model for Seismic Tomography
The changes in the state of a geophysical medium before a strong earthquake can be found by studying of 3D seismic velocity images constructed for consecutive time windows. A preliminary step is to see changes with time in a minimum 1D model. In this paper we develop a method that finds the parameters of the minimum linear gradient model by applying a two-dimensional Taylor series of the observed data for the seismic ray and by performing least-square minimization for all seismic rays. This allows us to obtain the mean value of the discrete observed variable, close to zero value.