DarkSeis: Seismic Imaging Of The Urban Subsurface Using Dark Fibre

DarkSeis：使用暗光纤对城市地下进行地震成像

• 批准号: EP/Y020960/1
• 负责人: James Verdon
• 金额: $ 102.4万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY020960%2F1
• 关键词: DarkSeis; Seismic; Imaging; Urban; Subsurface

Our cities are criss-crossed with fibre-optic telecommunications networks. For system redundancy and after technological improvements have reduced the required bandwidth, much of the installed fibre is unused. This unused fibre is known as "dark fibre". We can use dark fibre to investigate the properties of the ground below our cities. Distributed Acoustic Sensing (DAS) systems fire laser pulses along fibre-optic cables, and record the back-scattered light. If the cable is stretched or compressed then the back-scattered light will arrive slightly later or earlier. By recording and mapping these changes, we can turn any fibre-optic cable into a geophysical sensor, recording any movements or vibrations along the cable with very high resolution. Seismic imaging is a well-established method to map the subsurface. The technique uses seismic sources such as a hammer strike or a special vibrating source to impart seismic energy into the ground. By recording the resulting vibrations that have reflected and/or refracted through the ground, we can build up an image of the subsurface. These images are useful for a broad range of applications, such as siting geothermal developments, understanding groundwater and drainage, assessing the likelihood of landslips, and detecting sinkholes. However, conventional seismic surveys rely on deploying a large array of geophone sensors across the survey area. This is often logistically challenging or impossible in urban areas. As a result, subsurface data under our towns and cities, perhaps the area where this data is needed most, is often lacking.Seismic imaging using DAS provides an alternative with enormous potential. Dark fibre cables are already installed in buried telecommunication networks, meaning our sensor is already in place and available at minimal cost. We can access the fibre-optic network, install a DAS unit in a secure location, and record the resulting seismic data along the length of the cables, without any need to deploy geophone sensors across the area of interest. Hence, DAS seismic acquisition using dark fibre offers the potential to transform how we acquire images of the subsurface in urban areas. To date, the enormous potential of this method is only just being realised. The objective of our research is to investigate the performance of dark fibre DAS for seismic imaging in urban settings, with the aim of working out how best to acquire and process this type of data. We will acquire data using the B-NET telecommunications network, which is a 250 km-long fibre-optic network running across the city of Bristol (owned by Bristol City Council). We will use this data to identify how to produce the best quality DAS seismic images - for example what types of seismic source are best, how best to set up the DAS acquisition unit, and how best to process the resulting data. One of the major challenges of DAS seismic acquisition in urban areas is that background noise levels are likely to be high. This background noise could degrade the quality of our imaging. To address this, we will develop the use of state-of-the-art artificial intelligence algorithms to remove the background noise, improving the quality of our resulting images. All of the data that we acquire and all of the machine learning algorithms that we develop will be posted to publicly available repositories. This will provide an extremely valuable resource for researchers and commercial geophysical companies, both in the UK and globally, who are working on the development of this technology.

我们的城市遍布着纵横交错的光纤电信网络。为了实现系统冗余，并且在技术改进减少了所需带宽之后，大部分已安装的光纤未被使用。这种未使用的纤维被称为“暗纤维”。我们可以使用暗光纤来研究城市下方地面的特性。分布式声学传感 (DAS) 系统沿着光纤电缆发射激光脉冲，并记录反向散射光。如果电缆被拉伸或压缩，则反向散射光将稍微延迟或提前到达。通过记录和绘制这些变化，我们可以将任何光纤电缆变成地球物理传感器，以非常高分辨率记录沿电缆的任何运动或振动。地震成像是一种行之有效的地下测绘方法。该技术使用锤击等震源或特殊振动源将地震能量传递到地面。通过记录通过地面反射和/或折射的振动，我们可以建立地下图像。这些图像可用于广泛的应用，例如定位地热开发、了解地下水和排水、评估山体滑坡的可能性以及检测天坑。然而，传统的地震勘测依赖于在勘测区域部署大量地震检波器传感器。这在城市地区通常在后勤方面具有挑战性或不可能。因此，我们的城镇地下数据（也许是最需要这些数据的区域）常常缺乏。使用 DAS 的地震成像提供了一种具有巨大潜力的替代方案。暗光纤电缆已安装在埋地电信网络中，这意味着我们的传感器已就位并以最低成本提供。我们可以访问光纤网络，在安全位置安装 DAS 单元，并沿着电缆记录所得的地震数据，而无需在感兴趣的区域部署地震检波器传感器。因此，使用暗光纤的 DAS 地震采集有可能改变我们获取城市地区地下图像的方式。迄今为止，这种方法的巨大潜力才刚刚被认识到。我们研究的目的是研究暗光纤 DAS 在城市环境中用于地震成像的性能，旨在找出如何最好地获取和处理此类数据。我们将使用 B-NET 电信网络获取数据，该网络是横跨布里斯托尔市（隶属于布里斯托尔市议会）的 250 公里长光纤网络。我们将使用这些数据来确定如何生成最佳质量的 DAS 地震图像 - 例如，什么类型的震源最好、如何最好地设置 DAS 采集单元以及如何最好地处理结果数据。城市地区 DAS 地震采集的主要挑战之一是背景噪声水平可能很高。这种背景噪音可能会降低我们的成像质量。为了解决这个问题，我们将开发使用最先进的人工智能算法来消除背景噪声，从而提高生成图像的质量。我们获取的所有数据以及我们开发的所有机器学习算法都将发布到公开的存储库中。这将为英国和全球致力于开发这项技术的研究人员和商业地球物理公司提供极其宝贵的资源。