9/22/2023 0 Comments Stratigraphy programSecond, for the convenience of seismic experts in providing training labels, we design our workflow applicable to three scenarios, trace-wise, paint-brush, and full-section annotation. First, by initializing the SMB network from the SFSL one, it successfully inherits the prior-knowledge for understanding the target seismic data, and therefore such supervised learning can be efficiently completed by only a small amount of training data. Compared to the convolutional approaches, the proposed workflow is superior in two aspects. While the latter is supervised and of the typical network architecture used in image segmentation, we design the former as unsupervised and requiring no knowledge from domain experts. Specifically, the workflow consists with two components: (a) seismic feature self-learning (SFSL) and (b) stratigraphy model building (SMB), each of which is achieved in a deep CNN. In this paper, we present an innovative workflow for seismic stratigraphy interpretation by utilizing the state-of-the-art deep convolutional neural networks (CNNs). Depicting geologic sequences from three-dimensional seismic surveying is of wide applications to subsurface reservoir exploration.
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