Abstract This work proposes a new technique for multiple-point statistics simulation based on a recursive convolutional neural network approach coined RCNN . The work focuses on methodology and implementation rather than performance to demonstrate the potential of deep learning techniques in geosciences. Two and three dimensional case studies are carried out. A sensitivity analysis is presented over the main RCNN structural parameters using a well-known training image of channel structures in two dimensions. The optimum parameters found are applied into image reconstruction problems using two other training images. A three dimensional case is shown using a synthetic lithological surface-based model. The quality of realizations is measured by statistical, spatial and accuracy metrics. The RCNN method is compared to standard MPS techniques and an improving framework is proposed by using the RCNN E -type as secondary information. Strengths and weaknesses of the methodology are discussed by reviewing the theoretical and practical aspects.

中文翻译：

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多点统计框架中的递归卷积神经网络

摘要 这项工作提出了一种基于递归卷积神经网络方法的多点统计模拟新​​技术，创造了 RCNN。这项工作侧重于方法和实施，而不是表现，以展示地球科学中深度学习技术的潜力。进行了二维和三维案例研究。使用众所周知的二维通道结构训练图像对主要 RCNN 结构参数进行敏感性分析。使用两个其他训练图像将找到的最佳参数应用于图像重建问题。使用合成岩性表面模型显示了一个三维案例。实现的质量通过统计、空间和精度指标来衡量。将 RCNN 方法与标准 MPS 技术进行比较，并通过使用 RCNN E 型作为辅助信息提出改进框架。通过回顾理论和实践方面来讨论该方法的优缺点。