3D magnetization inversion using fuzzy c-means clustering with application to geology differentiation

Geophysics ◽  
2016 ◽  
Vol 81 (5) ◽  
pp. J61-J78 ◽  
Author(s):  
Yaoguo Li ◽  
Jiajia Sun
Keyword(s):  
Magnetic Anomaly ◽  
Remanent Magnetization ◽  
Data Sets ◽  
Total Field ◽  
Magnetic Interpretation ◽  
New Approach ◽  
Fuzzy C Means ◽  
Magnetic Inversion ◽  
Fuzzy C Means Clustering ◽  
Distribution Of Magnetization

The presence of remanent magnetization has hindered the application of generalized 3D magnetic inversion in exploration geophysics because of the unknown and variable magnetization directions. Although many authors have developed different approaches to deal with this difficulty, it remains a challenge. We have developed a new approach for inverting the total-field magnetic anomaly to recover a 3D distribution of magnetization by using a fuzzy c-means clustering technique. The inversion approximates the variation of magnetization directions with a small number of possible orientations and thereby achieves stability in recovered magnetization directions and improves the spatial imaging of magnetization magnitude. We have also found that the inverted magnetization directions can yield more information than does a standard magnetic susceptibility inversion and provide a new opportunity for magnetic interpretation. The magnitude of magnetization helps to define the configuration and structure of causative bodies in 3D, whereas the magnetization directions can help distinguish between different causative bodies and thereby assist in efforts such as geology differentiation. Thus, 3D magnetization inversion enables the complete use of the magnetic anomaly in the presence of remanent magnetization. We have used synthetic and field data sets to illustrate the algorithm, demonstrate the feasibility of geology differentiation using recovered magnetization directions, and develop a means to quantify the confidence of differentiation results.

Kernel fuzzy C- means clustering with teaching learning based optimization algorithm (TLBO-KFCM)

2021 ◽  
pp. 1-9
Author(s):  
Saumya Singh ◽  
Smriti Srivastava
Keyword(s):  
Genetic Algorithm ◽  
Data Analysis ◽  
Optimization Algorithm ◽  
Clustering Algorithm ◽  
Data Sets ◽  
New Approach ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Teaching Learning Based Optimization ◽  
Teaching Learning

In the field of data analysis clustering is considered to be a major tool. Application of clustering in various field of science, has led to advancement in clustering algorithm. Traditional clustering algorithm have lot of defects, while these defects have been addressed but no clustering algorithm can be considered as superior. A new approach based on Kernel Fuzzy C-means clustering using teaching learning-based optimization algorithm (TLBO-KFCM) is proposed in this paper. Kernel function used in this algorithm improves separation and makes clustering more apprehensive. Teaching learning-based optimization algorithm discussed in the paper helps to improve clustering compactness. Simulation using five data sets are performed and the results are compared with two other optimization algorithms (genetic algorithm GA and particle swam optimization PSO). Results show that the proposed clustering algorithm has better performance. Another simulation on same set of data is also performed, and clustering results of TLBO-KFCM are compared with teaching learning-based optimization algorithm with Fuzzy C- Means Clustering (TLBO-FCM).

scholarly journals A Bi-directional Fuzzy C-Means Clustering Ensemble Algorithm Considering Local Information

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Chunhua Ren ◽  
Linfu Sun
Keyword(s):  
Clustering Algorithms ◽  
Real Data ◽  
Local Information ◽  
Data Sets ◽  
Clustering Ensemble ◽  
K Nearest Neighbors ◽  
Fuzzy C Means ◽  
Clustering Quality ◽  
Fuzzy C Means Clustering ◽  
Fcm Clustering

AbstractThe classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

Metric-Based Semi-Supervised Fuzzy C-Means Clustering

2011 ◽  
Vol 268-270 ◽  
pp. 166-171
Author(s):  
Xue Song Yin ◽  
Qi Huang ◽  
Liang Ming Li
Keyword(s):  
Real World ◽  
Side Information ◽  
Data Sets ◽  
Membership Degree ◽  
Real World Data ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Clustering And Classification ◽  
Classification Tasks ◽  
Fuzzy C Means Algorithm

This paper presents a metric-based semi-supervised fuzzy c-means algorithm called MSFCM. Through using side information and unlabeled data together, MSFCM can be applied to both clustering and classification tasks. The resulting algorithm has the following advantages compared with semi-supervised clustering: firstly, membership degree as side information is used to guide the clustering of the data; secondly, through the metric learned, clustering accuracy can be greatly improved. Experimental results on a collection of real-world data sets demonstrated the effectiveness of the proposed algorithm.

Stepped inversion of magnetic data

Geophysics ◽  
2007 ◽  
Vol 72 (3) ◽  
pp. L21-L30 ◽  
Author(s):  
Soraya Lozada Tuma ◽  
Carlos Alberto Mendonça
Keyword(s):  
Magnetic Anomaly ◽  
Shape Function ◽  
Source Parameters ◽  
Real Data ◽  
Magnetic Data ◽  
Total Field ◽  
Magnetization Direction ◽  
Magnetization Intensity ◽  
Magnetic Inversion ◽  
Total Gradient

We present a three-step magnetic inversion procedure in which invariant quantities with respect to source parameters are inverted sequentially to give (1) shape cross section, (2) magnetization intensity, and (3) magnetization direction for a 2D (elongated) magnetic source. The quantity first inverted (called here the shape function) is obtained from the ratio of the gradient intensity of the total-field anomaly to the intensity of the anomalous vector field. For homogenous sources, the shape function is invariant with source magnetization and allows reconstruction of the source geometry by attributing an arbitrary magnetization to trial solutions. Once determined, the source shape is fixed and magnetization intensity is estimated by fitting the total gradient of the total-field anomaly (equivalent to the amplitude of the analytic signal of magnetic anomaly). Finally, the source shape and magnetization intensity are fixed and the magnetization direction is determined by fitting the magnetic anomaly. As suggested by numerical modeling and real data application, stepped inversion allows checking whether causative sources are homogeneous. This is possible because the shape function from inhomogeneous sources can be fitted by homogeneous models, but a model obtained in this way fits neither the total gradient of the magnetic anomaly nor the magnetic anomaly itself. Such a criterion seems effective in recognizing strongly inhomogeneous sources. Stepped inversion is tested with numerical experiments, and is used to model a magnetic anomaly from intrusive basic rocks from the Paraná Basin, Brazil.

scholarly journals A new approach to the fuzzy c-means clustering algorithm by automatic weights and local clustering

2021 ◽  
Vol 3 (1) ◽  
pp. 1-7
Author(s):  
Yadgar Sirwan Abdulrahman
Keyword(s):  
Clustering Algorithm ◽  
Similarity Criterion ◽  
Real Data ◽  
Well Being ◽  
Classical Solutions ◽  
Data Sets ◽  
Data Set ◽  
New Approach ◽  
Fuzzy C Means Clustering ◽  
Global And Local

Clustering is one of the essential strategies in data analysis. In classical solutions, all features are assumed to contribute equally to the data clustering. Of course, some features are more important than others in real data sets. As a result, essential features will have a more significant impact on identifying optimal clusters than other features. In this article, a fuzzy clustering algorithm with local automatic weighting is presented. The proposed algorithm has many advantages such as: 1) the weights perform features locally, meaning that each cluster's weight is different from the rest. 2) calculating the distance between the samples using a non-euclidian similarity criterion to reduce the noise effect. 3) the weight of the features is obtained comparatively during the learning process. In this study, mathematical analyzes were done to obtain the clustering centers well-being and the features' weights. Experiments were done on the data set range to represent the progressive algorithm's efficiency compared to other proposed algorithms with global and local features

Hybrid revised weighted fuzzy c-means clustering with Nelder-Mead simplex algorithm for generalized multisource Weber problem

2018 ◽  
Vol 31 (6) ◽  
pp. 908-924
Author(s):  
Tarik Kucukdeniz ◽  
Sakir Esnaf
Keyword(s):  
Cost Minimization ◽  
Simplex Algorithm ◽  
Data Sets ◽  
Weber Problem ◽  
Test Results ◽  
Content Type ◽  
Facility Location Problems ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
And Performance

PurposeThe purpose of this paper is to propose hybrid revised weighted fuzzy c-means (RWFCM) clustering and Nelder–Mead (NM) simplex algorithm, called as RWFCM-NM, for generalized multisource Weber problem (MWP).Design/methodology/approachAlthough the RWFCM claims that there is no obligation to sequentially use different methods together, NM’s local search advantage is investigated and performance of the proposed hybrid algorithm for generalized MWP is tested on well-known research data sets.FindingsTest results state the outstanding performance of new hybrid RWFCM and NM simplex algorithm in terms of cost minimization and CPU times.Originality/valueProposed approach achieves better results in continuous facility location problems.

scholarly journals Recognition of Power Quality Disturbances using Fuzzy Expert Systems

2021 ◽  
Vol 16 ◽  
pp. 166-177
Author(s):  
P. Kanirajan ◽  
M. Joly ◽  
T. Eswaran
Keyword(s):  
Power Quality ◽  
Classification Performance ◽  
New Approach ◽  
Fuzzy C Means ◽  
Fitness Value ◽  
The Social ◽  
Fuzzy Expert Systems ◽  
Fuzzy C Means Clustering ◽  
Power Quality Disturbances ◽  
Extensive Computation

This paper presents a new approach to detect and classify power quality disturbances in the power system using Fuzzy C-means clustering, Fuzzy logic (FL) and Radial basis Function Neural Networks (RBFNN). Feature extracted through wavelet is used for training, after training, the obtained weight is used to classify the power quality problems in RBFNN, but it suffers from extensive computation and low convergence speed. Then to detect and classify the events, FL is proposed, the extracted characters are used to find out membership functions and fuzzy rules being determined from the power quality inherence. For the classification,5 types of disturbance are taken in to account. The classification performance of FL is compared with RBFNN.The clustering analysis is used to group the data in to clusters to identifying the class of the data with Fuzzy C-means algorithm. The classification accuracy of FL and Fuzzy C-means clustering is improved with the help of cognitive as well as the social behavior of particles along with fitness value using Particle swarm optimization (PSO),just by determining the ranges of the feature of the membership funtion for each rules to identify each disturbance specifically.The simulation result using Fuzzy C-means clustering possess significant improvements and gives classification results in less than a cycle when compared over other considered approach.

Sign in / Sign up

Export Citation Format

Share Document