Data mining in gravity field by utilizing clustering by self-organizing maps (case study in the southern part of Iran)

The present work is mainly about a method for illustrating the relation between the raw data in the same time; clustering is a key procedure to solve the problem of data division; also illustrating the connection among the elements of the research area simultaneously is important. Therefore, we propose a novel kind of clustering for data mining in the gravity field to reach the presenting connection among all elements in the same time. For this research work, ۸۶۷ gravity surveying points were collected in the southern part of Iran (near diapir of Larestan) with a range of absolute gravity from  ۹۷۸۵۷۹.۶۷۲ to ۹۷۸۹۸۱.۱۸۶. In this paper, clustering by self-organizing- maps, by utilizing scatter plot matrix is utilized for detecting the relation between the easting, northing, elevation, and absolute gravity simultaneously. In the proposed method, the relations between arrays, two by two, are defined, and like matrix, each raw and column has different i and j values, which represent elements of the studied area, instead of number; for example, array A۲۳ is data division between i = ۲ or raw two (in our case northing) and j = ۳ or column, three (in our case elevation). In this algorithm, firstly, by using self-organizing maps, clustering is done, and this processing is generated to all arrays by scatter plot matrix, and in all arrays, three clusters are proposed; the result of this clustering shows that in arrays A۱۲, A۱۳, A۱۴, A۲۱, A۲۳, A۲۴, A۳۱, A۳۲, A۴۱, A۴۲, clustering is performed perfectly, and the relationship between the parameters of the studied area near Larestan salt, diaper, can be useful in notifying the properties of this salt diapir.

کلیدواژه ها:

Clustering ، Gravity ، Self-Organizing maps ، Numerical Analysis ، Artificial intelligence