首页> 外文期刊>Journal of Seismic Exploration >QUANTUM SEISMIC IMAGING: IS IT POSSIBLE?
【24h】

QUANTUM SEISMIC IMAGING: IS IT POSSIBLE?

机译:量子地震成像:可能吗?

获取原文
获取原文并翻译 | 示例
           

摘要

To get an accurate subsurface image from seismic data we need to build a highly accurate velocity model. In most cases this goal is difficult to achieve due to the ill-poseness of the inverse problem. Numerous tomography schemes are suggested and most of them are based on the common image gather flattening. Another scheme, named "full waveform inversion" is connected to data fitting. There are various reasons why exact velocity knowledge is impossible. A fundamental problem in velocity estimation is related to the erroneous measurements and the stochastic nature of the subsurface velocity. In this case the velocity model should be represented by a probability density function, rather than a unique deterministic value and a single velocity model generally does not exist.rnIn this paper we discuss an alternative way to look at seismic imaging using the quantum mechanics concept and path integral idea. The method computes the image by summing contributions of individual signals propagated along all possible paths between the source and observation points. In fact it samples different paths between the source and receiver instead of relying on only one path derived from Fermat's principle. All random ray or wave trajectories between the source and receiver within this volume are, in principle, taken into account. The focusing mechanism is achieved by a weighting function (probability amplitude), which is designed to emphasize contributions from trajectories close to the stationary one and to suppress contributions from unlikely paths. The presented examples demonstrate principles and feasibility of the new concept. There are many issues still needed to be investigated.
机译:为了从地震数据中获得准确的地下图像,我们需要建立一个高精度的速度模型。在大多数情况下,由于反问题的不适性,很难实现这一目标。提出了许多层析成像方案,其中大多数基于共同的图像集展平。名为“全波形反转”的另一种方案连接到数据拟合。不可能有精确的速度知识有多种原因。速度估算中的一个基本问题与地下速度的错误测量和随机性有关。在这种情况下,速度模型应该由概率密度函数表示,而不是由唯一的确定性值表示,并且通常不存在单个速度模型。路径不可或缺的想法。该方法通过求和沿源点和观察点之间所有可能路径传播的各个信号的贡献之和来计算图像。实际上,它对源和接收器之间的不同路径进行采样,而不是仅依赖于费马原理得出的一条路径。原则上,应考虑此体积内源和接收器之间的所有随机射线或波轨迹。聚焦机制是通过加权函数(概率振幅)实现的,该函数旨在强调接近静止轨道的轨迹的贡献,并抑制不太可能的路径的贡献。所提供的示例演示了新概念的原理和可行性。还有许多问题需要调查。

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