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Optimising the value of near-surface seismic reflection data

优化近地表地震反射数据的价值

基本信息

批准号：
1942522
负责人：
金额：
--
依托单位：
University of Southampton
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1942522
关键词：
Optimising value near surface seismic

项目摘要

While seismic methods to investigate depths from several hundred m to km depth beneath the seabed have advanced significantly over the past 20 years, the techniques used to investigate the shallowest 100-200m largely remain very basic. In this project we will adapt techniques from conventional exploration to address the challenges of investigating the shallow subsurface at sub-metre resolution. We will apply a combination of prestack depth migration and post-stack and prestack waveform inversion to multi-offset high-resolution seismic data with three main aims: Deriving high quality structural images of the near surface; estimating physical properties of the near surface and interpreting those properties in terms of parameters that are relevant to engineering within the marine environment; determining error bounds on our estimates of the physical properties. We will do this using a combination of standard industry software such as ProMAX and custom codes to address particular aspects of the problem such as accounting for streamer geometry with sufficient accuracy, waveform inversion, and determining error bounds on the physical property estimates. We will work initially with existing datasets collected in a range of environments and water depths (from 10m to ~200m), although we will expect to collect at least one dedicated dataset with the involvement of the student, probably after ~2 years of the project.

虽然在过去的20年里，用于调查海床下几百米到几千米深度的地震方法取得了显著进展，但用于调查最浅的100-200米深度的技术在很大程度上仍然非常基础。在这个项目中，我们将采用传统勘探技术来解决以亚米分辨率调查浅层地下的挑战。我们将叠前深度偏移与叠后、叠前波形反演相结合，应用于多偏移距高分辨率地震数据，主要有三个目的：获得高质量的近地表结构图像；估计近地表的物理性质，并根据海洋环境中与工程有关的参数解释这些性质；确定我们对物理性质估计的误差范围。我们将使用ProMAX等标准行业软件和自定义代码的组合来解决问题的特定方面，例如以足够的精度计算拖缆几何形状、波形反演以及确定物理性质估计的误差范围。我们最初将使用在一系列环境和水深（从10米到200米）中收集的现有数据集，尽管我们希望在项目进行约2年后，在学生的参与下收集至少一个专用数据集。