Solution to discrete-valued inverse problems using the fuzzy c-means clustering technique
Author:
Affiliation:
1. Center for Gravity, Electrical, and Magnetics, Colorado School of Mines, Golden, Colorado
Publisher
Society of Exploration Geophysicists
Link
https://library.seg.org/doi/pdf/10.1190/segam2015-5930010.1
Reference20 articles.
1. A comparison of two methods for solving 0–1 integer programs using a general purpose simulated annealing algorithm
2. FCM: The fuzzy c-means clustering algorithm
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4. A simulated annealing approach to the solution of minlp problems
5. Incorporating the FCM–BPN approach with nonlinear programming for internal due date assignment in a wafer fabrication plant
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1. Parameter selection workflow for a discrete-valued gravity inversion with guided fuzzy c-means clustering;International Workshop on Gravity, Electrical & Magnetic Methods and Their Applications, Xi'an, China, 19–22 May 2019;2019-09-28
2. Discrete-valued inversion of IP data using guided FCM clustering;SEG Technical Program Expanded Abstracts 2018;2018-08-27
3. Gravity and Magnetics Complete Session;SEG Technical Program Expanded Abstracts 2016;2016-09-01
4. Application of fuzzy C-means clustering to discrete-valued inversion of gravity gradient data;SEG Technical Program Expanded Abstracts 2016;2016-09-01
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