...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization
【24h】

Data-Driven Seismic Waveform Inversion: A Study on the Robustness and Generalization

机译：数据驱动的地震波形反演：鲁棒性和泛化的研究

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Full-waveform inversion is an important and widely used method to reconstruct subsurface velocity images. Waveform inversion is a typical nonlinear and ill-posed inverse problem. Existing physics-driven computational methods for solving waveform inversion suffer from the cycle-skipping and local-minima issues, and do not mention that solving waveform inversion is computationally expensive. In recent years, data-driven methods become a promising way to solve the waveform-inversion problem. However, most deep-learning frameworks suffer from the generalization and overfitting issue. In this article, we developed a real-time data-driven technique and we call it VelocityGAN, to reconstruct accurately the subsurface velocities. Our VelocityGAN is built on a generative adversarial network (GAN) and trained end to end to learn a mapping function from the raw seismic waveform data to the velocity image. Different from other encoderdecoder-based data-driven seismic waveform-inversion approaches, our VelocityGAN learns regularization from data and further imposes the regularization to the generator so that inversion accuracy is improved. We further develop a transfer-learning strategy based on VelocityGAN to alleviate the generalization issue. A series of experiments is conducted on the synthetic seismic reflection data to evaluate the effectiveness, efficiency, and generalization of VelocityGAN. We not only compare it with the existing physics-driven approaches and data-driven frameworks but also conduct several transfer-learning experiments. The experimental results show that VelocityGAN achieves the state-of-the-art performance among the baselines and can improve the generalization results to some extent.
机译：全波形反转是重建地下速度图像的重要和广泛使用的方法。波形反转是典型的非线性和不良反问题。用于解决波形反演的现有物理驱动的计算方法遭受循环跳过和局部最小问题，并且不提的是求解波形反转是计算昂贵的。近年来，数据驱动方法成为解决波形反转问题的有希望的方法。然而，大多数深度学习框架遭受泛化和过度装备问题。在本文中，我们开发了一个实时数据驱动技术，我们称之为VelocityGan，以准确地重建地下速度。我们的Velocitygn建立在生成的对抗网络（GAN）上，并训练结束，以便从原始地震波形数据到速度图像学习映射函数。与其他基于编码器的数据驱动的地震波形反转方法不同，我们的VelocityGaN从数据中学习正则化，进一步将正则化施加到发电机，以便改善反转精度。我们进一步开发了基于VelocityGan的转移学习策略，以缓解泛化问题。在合成地震反射数据上进行了一系列实验，以评估VelocityaN的有效性，效率和泛化。我们不仅与现有的物理驱动的方法和数据驱动的框架进行比较，而且还进行了几个转移学习实验。实验结果表明，VelocityGaN在基线之间实现了最先进的性能，可以在一定程度上改善普遍化结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号