DarkSeis: Seismic Imaging Of The Urban Subsurface Using Dark Fibre

我们的城市被光纤通信网络纵横交错。由于系统冗余和技术改进减少了所需的带宽，许多已安装的光纤是未使用的。这种未使用的纤维被称为“暗纤维”。我们可以使用暗纤维来研究城市地下的性质。分布式声传感（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，那么反向散射光将稍晚或稍早到达。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，以非常高的分辨率记录电缆上的任何运动或振动。地震成像是一种行之有效的地下测绘方法。该技术利用锤击或特殊振动源等震源将地震能量传递到地面。通过记录通过地面反射和/或折射产生的振动，我们可以建立地下的图像。这些图像在很多方面都很有用，比如为地热开发项目选址、了解地下水和排水情况、评估山体滑坡的可能性以及探测天坑。然而，传统的地震调查依赖于在整个调查区域部署大量检波器传感器。在城市地区，这通常在后勤上具有挑战性或不可能。因此，我们的城镇和城市地下的数据，也许是最需要这些数据的地区，往往缺乏。使用DAS进行地震成像提供了一种具有巨大潜力的替代方案。暗光纤电缆已经安装在地下电信网络中，这意味着我们的传感器已经到位，并且可以以最低的成本使用。我们可以访问光纤网络，在安全位置安装DAS单元，并沿着电缆长度记录所得地震数据，而无需在感兴趣的区域部署检波器传感器。因此，使用暗纤维的DAS地震采集有可能改变我们在城市地区获取地下图像的方式。到目前为止，这种方法的巨大潜力才刚刚实现。我们的研究目的是调查暗纤维DAS在城市环境下地震成像的性能，目的是找出如何最好地获取和处理这类数据。我们将使用B-NET电信网络获取数据，这是一个250公里长的光纤网络，横跨布里斯托尔市（由布里斯托尔市议会拥有）。我们将使用这些数据来确定如何产生最优质的DAS地震图像——例如，什么类型的震源是最好的，如何最好地设置DAS采集单元，以及如何最好地处理得到的数据。在城市地区进行DAS地震采集的主要挑战之一是背景噪声水平可能很高。这种背景噪音会降低我们成像的质量。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去除背景噪声，提高我们生成的图像的质量。我们获得的所有数据和我们开发的所有机器学习算法都将发布到公开可用的存储库中。这将为英国和全球致力于这项技术开发的研究人员和商业地球物理公司提供极其宝贵的资源。

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.