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基于深度堆栈自编码器的地震相识别

Deep Stacked Auto-Encoder for Seismic Facies Classification
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摘要 地震相是沉积环境分析和储层预测的基础。然而在实际地震勘探中,标注的地震数据难以获取。针对这些问题,本文分别借鉴了深度学习与无监督学习的优点,提出了基于深度堆栈自编码器的地震相识别方法,该模型利用深度学习更能捕捉地震数据语义信息的优点,用深度堆栈自编码器对地震数据进行特征提取,然后用提取的特征进行无监督聚类算法对地震相进行识别。在实际工区的实验结果表明,该模型相较于直接使用聚类算法识别地震相,准确率更高。 Seismic facies play a key role in depicting subsurface geology horizons and predicting. However, large amounts of annotated seismic data are often unavailable. To overcome these challenges, we proposed a deep stacked auto-encoder network for seismic facies classification, which draws on the advantages of deep learning and unsupervised learning. Deep stacked auto-encoder performs bet-ter to exact deep features from seismic data. Then we cluster exacted features to classify seismic fa-cies without the labels for unsupervised learning. The proposed model has been tested successfully on real seismic data fields, which indicates that the model is more effective than clustering directly.
出处 《计算机科学与应用》 2022年第10期2357-2361,共5页 Computer Science and Application
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