Strong earthquake-prone areas recognition based on the algorithm with a single pure training class. II. Caucasus, M >= 6.0. Variable EPA method

Dzeboev B. A.; Soloviev A. A.; Dzeranov B. V.; Karapetyan J. K.; Sergeeva N. A.

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Strong earthquake-prone areas recognition based on the algorithm with a single pure training class. II. Caucasus, M >= 6.0. Variable EPA method

机译：基于单一纯训练类的算法强大地震 - 易发的地区识别。 II。高加索，m> = 6.0。可变EPA方法

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Strong earthquake-prone areas recognition (M >= 6.0) in the Caucasus is performed by means of the new "Barrier-3" pattern recognition algorithm. The obtained result is compared with potentially high seismicity zones recognized previously using the "Cora-3" pattern recognition algorithm. It is proposed to define an interpretation of the integral recognition result by the "Barrier-3" and "Cora-3" algorithms as a fuzzy set of recognition objects in the vicinity of which strong earthquakes may occur in the Caucasus.

机译：通过新的“屏障-3”模式识别算法，在高加索中识别强大的地震 - 易于区域识别（M> = 6.0）。将获得的结果与先前使用“Cora-3”模式识别算法认可的潜在高地震区进行比较。建议将“屏障-3”和“Cora-3”算法的积分识别结果的解释为作为一种在高加索中可能发生的强烈地震的模糊识别物体中的积分识别结果。

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来源
《Nature reviews Cancer》 |2019年第6期|共12页
作者
Dzeboev B. A.; Soloviev A. A.; Dzeranov B. V.; Karapetyan J. K.; Sergeeva N. A.;
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作者单位

Geophys Ctr RAS 3 Molodezhnaya St Moscow 119296 Russia;

Inst Earthquake Predict Theory &

Math Geophys RAS Moscow Russia;

Vladikavkaz Sci Ctr RAS Geophys Inst Vladikavkaz Russia;

Inst Geophys &

Engn Seismol Gyumri Armenia;

Geophys Ctr RAS 3 Molodezhnaya St Moscow 119296 Russia;

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原文格式 PDF
正文语种 eng
中图分类肿瘤学;
关键词
Earthquake-prone areas recognition; EPA; Cora-3; Barrier-3; Caucasus; seismic hazard assessment; fuzzy set;

机译：地震易发的地区识别;EPA;科拉3;屏障-3;高加索;地震危险评估;模糊套装;

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1. Strong earthquake-prone areas recognition based on the algorithm with a single pure training class. II. Caucasus, M >= 6.0. Variable EPA method [J] . Dzeboev B. A., Soloviev A. A., Dzeranov B. V., Nature reviews Cancer . 2019,第6期

机译：基于单一纯训练类的算法强大地震 - 易发的地区识别。 II。高加索，m> = 6.0。可变EPA方法
2. New Approach to Recognition of the Strong Earthquake-Prone Areas in the Caucasus [J] . A. D. Gvishiani, B. A. Dzeboev, S. M. Agayan Izvestiya. Physics of the solid earth . 2013,第6期

机译：识别高加索地区强烈地震震源区的新方法
3. Recognition of strong earthquake-prone areas with a single learning class [J] . Gvishiani A. D., Agayan S. M., Dzeboev B. A., Doklady Earth Sciences . 2017,第pta1期

机译：通过一次学习班识别地震多发地区
4. Some new algorithms and software implementation methods for pattern recognition research part II. software implementation methods [C] . Toussaint, G.T., Foulsen, . 1979

机译：模式识别研究的一些新算法和软件实现方法第二部分。软件实施方法
5. Feature extraction method for video based human action recognitions: Extended optical flow algorithm. [D] . Ramadass, Ashok. 2010

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7. A data augmentation methodology for training machine/deep learning gait recognition algorithms [O] . Charalambous, CC, Bharath, AA 2016

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Strong earthquake-prone areas recognition based on the algorithm with a single pure training class. II. Caucasus, M >= 6.0. Variable EPA method

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