Real time inversion of array resistivity logging data using dimensional reduction and neural network simulation

2000 ◽  
Author(s):  
Zhiyi Zhang ◽  
Zhiqiang Zhou ◽  
Michael A. Frenkel ◽  
Raghu Chunduru ◽  
Alberto Mezzatesta
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Dimensional Reduction ◽  
Network Simulation ◽  
Time Inversion ◽  
Neural Network Simulation ◽  
Resistivity Logging

Real time quasi–2-D inversion of array resistivity logging data using neural network

Geophysics ◽  
2002 ◽  
Vol 67 (2) ◽  
pp. 517-524 ◽  
Author(s):  
Zhiyi Zhang ◽  
Zhiqiang Zhou
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Forward Modeling ◽  
Time Inversion ◽  
Resistivity Structure ◽  
Inversion Algorithm ◽  
Neural Network Simulation ◽  
Formation Resistivity ◽  
Resistivity Logging ◽  
Fast Inversion

We present a quasi-2-D real-time inversion algorithm for a modern galvanic array tool via dimensional reduction and neural network simulation. Using reciprocity and superposition, we apply a numerical focusing technique to the unfocused data. The numerically focused data are much less subject to 2-D and layering effects and can be approximated as from a cylindrical 1-D earth. We then perform 1-D inversion on the focused data to provide approximate information about the 2-D resistivity structure. A neural network is used to perform forward modeling in the 1-D inversion, which is several hundred times faster than conventional numerical forward solutions. Testing our inversion algorithm on both synthetic and field data shows that this fast inversion algorithm is useful for providing formation resistivity information at a well site.

scholarly journals SNAVA—A real-time multi-FPGA multi-model spiking neural network simulation architecture

2018 ◽  
Vol 97 ◽  
pp. 28-45 ◽  
Author(s):  
Athul Sripad ◽  
Giovanny Sanchez ◽  
Mireya Zapata ◽  
Vito Pirrone ◽  
Taho Dorta ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Simulation ◽  
Spiking Neural Network ◽  
Neural Network Simulation

Autonet: An Application of a Neural Network Simulation as a Tool for Planning Office Layout

1992 ◽  
Vol 36 (7) ◽  
pp. 582-585
Author(s):  
Michael J. O'Neill
Keyword(s):  
Neural Network ◽  
Computer Aided Design ◽  
Network Simulation ◽  
Office Work ◽  
Neural Network Simulation ◽  
Coding Schemes ◽  
Level Of Experience ◽  
Aided Design ◽  
Planning Office ◽  
The Brain

When people have trouble finding their way through office settings, there are costs in terms of poor communication, lost efficiency, time, and stress (Brill, et. al., 1984; O'Neill, 1991; Weisman, 1981; Zimring, 1981). To cope with wayfinding problems, facilities managers often have to resort to partial solutions, like complex signage, color coding schemes, and other methods to guide people. AutoNet is an experimental computer-aided design and planning tool that predicts the paths people will take through a building based on the layout of the space and their level of experience. AutoNet represents environmental information by using an artificial ‘neural network’ simulation. The mechanisms of this simulation are based on the physiology of the brain. Knowledge about the layout of the environment is represented through a network of interconnected processing elements, modeled on the behavior of groups of neurons in the brain. Thus it can create its own rules for predicting worker behavior rather than using predetermined sets of rules that a typical expert system would rely on. This system has great flexibility since there are no rules to rewrite for each setting it evaluates. The predictive validity of this simulation was empirically validated (O'Neill, 1991). This software runs within a popular and commonly available CAD software package in an MS-DOS environment. AutoNet is viewed as a “macro-ergonomic” tool to enhance the office work environment (Hedge & Ellis, 1990).

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