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Integration of Deep Neural Networks into Seismic Workflows for Low-Carbon Energy

Second International Meeting for Applied Geoscience &amp Energy(2022)

Stanford University

Cited 0|Views11
Abstract
The synergistic application of rapidly evolving machine learning technology and modern seismic modeling and imaging algorithms can lead to cost-efficient workflows to provide values to low-carbon energy projects that require cost-effective subsurface characterization and monitoring. In this presentation we will show examples of convolutional neural networks applied to early waring in CCS projects and cost-effective reprocessing of old reflection seismic data.
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要点】:本文探讨了将深度神经网络技术融入地震工作流程,以提高低碳能源项目中的地下特征描述和监测效率,创新性地应用卷积神经网络进行碳捕获与封存项目的预警以及老反射地震数据的成本效益化重处理。

方法】:作者采用了卷积神经网络(CNN)技术,将其集成到地震数据处理和解释的工作流程中。

实验】:文中展示了卷积神经网络在碳捕获与封存(CCS)项目早期预警和老反射地震数据重处理中的应用实例,但未具体提及所使用的数据集名称和实验结果。