To view data-driven methods as an integrated type, we can re-divide fault diagnosis methods into three subclasses, namely analytical model-based methods, qualitative knowledge-based methods, and data-driven based methods (DDBM), where DDBM can be further divided into data transform based methods (DTBM) and data reasoning based methods (DRBM).
We used the model of Case 1 for the initial values of the parameters of the divided fault plane, and the slip and rake angles were then estimated again using the same method.
The best fit to the data was obtained from three and four divided fault segments running along the Nukumi, Neodani, and Umehara faults; although, in past studies, the Gifu-Ichinomiya line has been suggested as a buried fault to explain the ground deformation.
The coal quality parameters of the boreholes along the divided faults were rarely controlled by geological setting.
To estimate the source model, a downhill simplex non-linear inversion method is used to determine the fault rupture geometry, and an automatic fault discretization technique is employed to divide the fault plane to construct the optimal slip model, in which the uncertainties of the fault parameters are assessed by a Monte Carlo method.
The TEP process is the simulation of a chemical plant created by the Eastman Chemical Company to provide a realistic industrial process for evaluating process control and monitoring methods The first step in designing the HANN is to divide the fault patterns space into a few sub-spaces through using fuzzy C-means clustering algorithm.
For discretization in space, we divide the fault plane into 39 in the strike direction and into 19 in the down-dip direction (making a total of 741 subfaults over an area of 12 km × 12 km).
We therefore divided the fault plane for the Umehara fault into four segments, shallow and deep northwest and southeast segments, (see Segment 4 of Case 1), and re-estimated the fault parameters.
The mountains were divided by faults, defined by faults, and framed by them as well: on the near side, the Raymond Fault, the Sierra Madre Fault, the Cucamonga Fault; on the far side, the San Andreas Fault.
The interest of this paper is to improve the performance of single neural network (SNN) by dividing the fault pattern space into a few smaller sub-spaces using Expectation-Maximization (EM) clustering technique and triggering the right local classifier by designing a supervisor agent.
