发表机构
TNO Netherlands Organisation for Applied Scientific Research; Fraunhofer IEG, Fraunhofer Institution for Energy Infrastructures and Geotechnologies（荷兰应用科学研究组织; 弗劳恩霍夫能源基础设施与地热技术研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究在Horizon Europe项目中开发AI辅助地震数据解译工具包，通过自监督对比学习CNN降噪及半自监督层位断层解译，加速隐式建模，并在荷兰三维地震数据中成功验证。
AI 中文摘要
隐式建模和相对地质时间是地质建模技术，能够实现更高效、更快速、更少偏差且更具可重复性的建模结果。为达到最佳运行效果，这些技术需要大量约束良好的输入数据。在Horizon Europe GO-Forward和MOOI WarmingUP GOO项目的框架内，为加速隐式建模，机器学习（ML）方法已被测试并集成到一个工具包中，用于解释从浅层到深层（约300-3500米）的（陆上）地震数据。其目标是通过高效解释地震数据中的层位和断层，快速刻画该深度域的特征。第一步是通过应用AI技术（如自监督和半监督对比学习卷积神经网络）进行噪声抑制和插值，以改善信号。接下来，通过使用（半）自监督方法，以最少的人工生成训练数据来解译层位和断层。所开发的工具包支持在高效工作流中应用所实现的算法。作为首次演示，已在Leeuwarden和Waalwijk三维地震数据体中解译了荷兰Maassluis组的顶部。总体而言，本研究表明，AI辅助解译工作流已达到成熟水平，可将其整合到应用地质建模和决策中。
英文摘要
Implicit modeling and Relative Geologic Time are geological modeling techniques that enable more efficient, faster, less biased and more reproducible modeling results. For optimal operation, these techniques require many well-constrained input data. In the framework of the Horizon Europe GO-Forward and MOOI WarmingUP GOO projects and to accelerate Implicit modeling, Machine Learning (ML) methods have been tested and implemented in a toolkit for the interpretation of (onshore) seismic data from the shallow to deep range (+- 300 - 3500 m). The goal is to rapidly characterise this depth domain by efficient interpretation of horizons and faults in seismic data. The first step is to improve the signal by applying AI techniques like self-supervised and semi-supervised contrastive learning CNN's for noise reduction and interpolation. Next, horizons and faults are interpreted with minimal use of human-generated training data by using (semi-) self-supervised methods. The resulting developed toolkit supports the application of the implemented algorithms in an efficient workflow. As a first demonstration, the top of the Dutch Maassluis Formation has been interpreted in the Leeuwarden and Waalwijk 3D seismic cubes. Overall, this study demonstrates that AI-assisted interpretation workflows have reached a level of maturity that allows their integration into applied geological modeling and decision-making.
Comments17 page, 19 figures
