多模式数据驱动的基于深度学习的地震阻抗逆转优化优化
Irshad Ali1, Wakeel Ahmad1, Syed M Adnan1
1Department of Computer Science, Faculty of Telecommunication and Information Engineering, University of Engineering and Technology (UET) Taxila, Taxila, Punjab, Pakistan.
PloS one
|September 22, 2025
概括
包括AlexNet在内的深度学习模型增强了地震阻抗逆转,以获得更好的地下成像. 这提高了水库的识别,并减少了在复杂的地质环境下钻探的风险.
科学领域:
- 地质物理学 地质物理学
- 机器学习 机器学习
- 地下表面的表征.
背景情况:
- 地震阻抗逆转对于地下物质分析至关重要.
- 传统的方法与噪音,低分辨率和复杂的地质学扎.
研究的目的:
- 通过深度学习改进地震分辨率和合成地震图生成.
- 为了解决传统的地震逆转技术的局限性.
主要方法:
- 他们使用了深度学习模型 (LeNet,AlexNet,CNNs).
- 连续波形变换 (CWT) 用于特征提取.
- 在合成数据上训练模型,并在真实地震数据上验证模型.
主要成果:
- 亚历克斯网在地震数据重建方面取得了卓越的性能.
- 亚历克斯网展示了最小的平均平方误差 (MSE),根平均平方误差 (RMSE) 和平均绝对误差 (MAE).
- 亚历克斯网获得了最高的R2分数,这表明预测准确度很好.
结论:
- 深度学习,特别是AlexNet,为先进的地球物理分析提供了强大的基准.
- 拟议的技术增强了地下表征,减少了地质风险.
- 改进的地震数据重建导致了更明智的钻井决策.
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