scholarly journals Feature Extraction and Analysis of Earthquake Motion Using the Gaussian Mixture Model

2020 ◽  
Vol 20 (1) ◽  
pp. 1_93-1_106
Author(s):  
Masumitsu KUSE ◽  
Nobuoto NOJIMA
2021 ◽  
pp. 1-46
Author(s):  
Donglin Zhu ◽  
Jingbin Cui ◽  
Yan Li ◽  
Zhonghong Wan ◽  
Lei Li

Seismic facies analysis can effectively estimate reservoir properties and seismic waveform clustering is a useful tool for facies analysis. We developed a deep learning-based clustering approach called the modified deep convolutional embedded clustering with adaptive Gaussian mixture model (AGMM-MDCEC) for seismic waveform clustering. Trainable feature extraction and clustering layers in AGMM-MDCEC are implemented using neural networks. The two independent processes of feature extraction and clustering are fused, such that extracted features are modified simultaneously with the results of clustering. A convolutional autoencoder is used in the algorithm for extracting features from seismic data and reduce data redundancy. At the same time, weights of clustering network are fined-tuned through iteration to obtain state-of-the-art clustering results. We apply our new classification algorithm to a data volume acquired in western China to map architectural elements of a complex fluvial depositional system. Our proposed method obtains superior results over those provided by traditional K-means, Gaussian mixture model, and some machine learning methods, and improves the mapping of the extent of the distributary system.


Author(s):  
Ji Ma ◽  
Jinjin Chen ◽  
Liye Chen ◽  
Xingjian Zhou ◽  
Xujia Qin ◽  
...  

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