使用基于时刻重建错误约束的生成对抗网络的地震数据重建方法.
Bin Liu1, Xuguang Dong1, Leiliang Xu1
1Sinopec Geophysical Corporation R&D Center, SINOPEC, Nanjing, China.
Science progress
|April 25, 2024
概括
本研究介绍了一种用于地震数据重建的新型生成对抗网络 (GAN),通过使用时刻重建错误约束来改进传统方法. 增强的GAN方法有效地重建低样本的地震数据,并更好地保留振幅.
科学领域:
- 地质物理学 地质物理学
- 地震数据处理 地震数据处理
- 机器学习 机器学习
背景情况:
- 地震数据采集通常会导致空间低采样.
- 高数据规律性对于地震数据处理任务,如多重移除和反转至关重要.
- 传统的地震数据重建方法由于潜在的假设存在局限性.
研究的目的:
- 提出一种新的基于生成对抗网络 (GAN) 的地震数据重建方法.
- 通过避免对数据属性的假设来克服传统重建技术的局限性.
- 改进重建的地震数据的准确性和振幅保存.
主要方法:
- 开发了一种基于GAN的地震数据重建方法,其中包含了时刻重建错误约束.
- 修改了GAN的错误函数,包括加权对抗损失和时刻重建损失.
- 使用理论模型数据和真实地震数据验证了该方法,分析了插值错误.
主要成果:
- 拟议的GAN方法有效地提取深度数据特征非线性,没有事先假设.
- 实验分析表明,与传统方法相比,地震数据重建的性能优于传统方法.
- 时刻重建错误约束方法显示,重建数据中的振幅保存得到了改进.
结论:
- 基于GAN的地震数据重建方法与时刻重建错误约束为低样本的地震数据提供了强大的解决方案.
- 这种方法克服了传统方法的适用性限制.
- 改进的方法提供了优越的重建结果,具有更好的振幅保真度.
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