Investigation of Magneto-/Radio-Metric Behavior in Order to Identify an Estimator Model Using K-Means Clustering and Artificial Neural Network (ANN) (Iron Ore Deposit, Yazd, IRAN); Minerals; Vol. 11, iss. 12
| Parent link: | Minerals Vol. 11, iss. 12.— 2021.— [1304, 18 p.] |
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| Körperschaft: | |
| Weitere Verfasser: | , , , |
| Zusammenfassung: | Title screen The study area is located near Toot village in the Yazd province of Iran, which is considered in terms of its iron mineralization potential. In this area, due to radioactivity, radiometric surveys were performed in a part of the area where magnetometric studies have also been performed. According to geological studies, the presence of magnetic anomalies can have a complex relationship with the intensity of radioactivity of radioactive elements. Using the K-means clustering method, the centers of the clusters were calculated with and without considering the coordinates of radiometric points. Finally, the behavior of the two variables of magnetic field strength and radioactivity of radioactive elements relative to each other was studied, and a mathematical relationship was presented to analyze the behavior of these two variables relative to each other. On the other hand, the increasing and then decreasing behavior of the intensity of the Earth’s magnetic field relative to the intensity of radioactivity of radioactive elements shows that it is possible to generalize the results of magnetometric surveys to radiometry without radiometric re-sampling in this region and neighboring areas. For this purpose, using the general regression neural network and backpropagation neural network (BPNN) methods, radiometric data were estimated with very good accuracy. The general regression neural network (GRNN) method, with more precision in estimation, was used as a model for estimating the radiation intensity of radioactive elements in other neighboring areas. |
| Sprache: | Englisch |
| Veröffentlicht: |
2021
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| Schlagworte: | |
| Online-Zugang: | http://earchive.tpu.ru/handle/11683/71105 https://doi.org/10.3390/min11121304 |
| Format: | Elektronisch Buchkapitel |
| KOHA link: | https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=666412 |
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| 200 | 1 | |a Investigation of Magneto-/Radio-Metric Behavior in Order to Identify an Estimator Model Using K-Means Clustering and Artificial Neural Network (ANN) (Iron Ore Deposit, Yazd, IRAN) |f A. Shirazy, A. Hezarkhani, T. V. Timkin, A. Shirazi | |
| 203 | |a Text |c electronic | ||
| 300 | |a Title screen | ||
| 320 | |a [References: 51 tit.] | ||
| 330 | |a The study area is located near Toot village in the Yazd province of Iran, which is considered in terms of its iron mineralization potential. In this area, due to radioactivity, radiometric surveys were performed in a part of the area where magnetometric studies have also been performed. According to geological studies, the presence of magnetic anomalies can have a complex relationship with the intensity of radioactivity of radioactive elements. Using the K-means clustering method, the centers of the clusters were calculated with and without considering the coordinates of radiometric points. Finally, the behavior of the two variables of magnetic field strength and radioactivity of radioactive elements relative to each other was studied, and a mathematical relationship was presented to analyze the behavior of these two variables relative to each other. On the other hand, the increasing and then decreasing behavior of the intensity of the Earth’s magnetic field relative to the intensity of radioactivity of radioactive elements shows that it is possible to generalize the results of magnetometric surveys to radiometry without radiometric re-sampling in this region and neighboring areas. For this purpose, using the general regression neural network and backpropagation neural network (BPNN) methods, radiometric data were estimated with very good accuracy. The general regression neural network (GRNN) method, with more precision in estimation, was used as a model for estimating the radiation intensity of radioactive elements in other neighboring areas. | ||
| 461 | |t Minerals | ||
| 463 | |t Vol. 11, iss. 12 |v [1304, 18 p.] |d 2021 | ||
| 610 | 1 | |a электронный ресурс | |
| 610 | 1 | |a труды учёных ТПУ | |
| 610 | 1 | |a radiometry | |
| 610 | 1 | |a magnetometry | |
| 610 | 1 | |a iron | |
| 610 | 1 | |a k-means clustering method | |
| 610 | 1 | |a artificial neural network | |
| 610 | 1 | |a GRNN | |
| 610 | 1 | |a Late Cretaceous | |
| 610 | 1 | |a отложения | |
| 610 | 1 | |a аутигенные минералы | |
| 610 | 1 | |a геохимические циклы | |
| 610 | 1 | |a железо | |
| 610 | 1 | |a месторождения | |
| 610 | 1 | |a осадконакопление | |
| 610 | 1 | |a BPNN | |
| 610 | 1 | |a радиометрия | |
| 610 | 1 | |a магнитометрия | |
| 610 | 1 | |a железо | |
| 610 | 1 | |a нейронные сети | |
| 701 | 1 | |a Shirazy |b A. |g Adel | |
| 701 | 1 | |a Hezarkhani |b A. |g Ardeshir | |
| 701 | 1 | |a Timkin |b T. V. |c geologist |c Associate Professor of Tomsk Polytechnic University, Candidate of geological and mineralogical sciences |f 1983- |g Timothy Vasilyevich |3 (RuTPU)RU\TPU\pers\33150 |9 16968 | |
| 701 | 1 | |a Shirazi |b A. |g Aref | |
| 712 | 0 | 2 | |a Национальный исследовательский Томский политехнический университет |b Инженерная школа природных ресурсов |b Отделение геологии |3 (RuTPU)RU\TPU\col\23542 |
| 801 | 2 | |a RU |b 63413507 |c 20220607 |g RCR | |
| 856 | 4 | |u http://earchive.tpu.ru/handle/11683/71105 | |
| 856 | 4 | |u https://doi.org/10.3390/min11121304 | |
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