We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting the existing terrestrial fiber infrastructure. Employing a waveplate model, we integrate real ground displacement data from seven earthquakes with magnitudes ranging from four to six to simulate the strains within fiber cables and collect a large set of light polarization evolution data. These simulations help to enhance a machine learning model that is trained and validated to detect primary wave arrivals that precede earthquakes’ destructive surface waves. The validation results show that the model achieves over 95% accuracy. The machine learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates via a triangulation method and calculate the fiber-to-epicenter distance. This setup allows early warning generation for municipalities close to the epicenter location, progressing to those further away. The model testing shows a 98% accuracy in detecting primary waves and a one second detection time, affording nearby areas 21 s to take countermeasures, which extends to 57 s in more distant areas.

We propose a low cost implementation of the state-of-polarization monitoring for environmental sensing on terrestrial telecommunication networks’ IM-DD optical signals. Experiment show feasibility of both event’s detection and localization with less than 100 meters accuracy.

We apply transfer learning with an LSTM-attention model, trained on simulated earthquake SOP data and tested on experimentally emulated data over a 38 km deployed fiber link, achieving 98% accuracy in early earthquake detection.

Optical networks for data transmission have become a pervasive infrastructure in the last years in order to cope with the increasing bandwidth request, thus there is a huge potential to be employed as a wide fiber optic sensing network. In the terrestrial scenario such networks are usually arranged on meshed topologies densely covering large areas of hundreths or thousands of kilometers. On the network's nodes, dedicated hardware is used to routed the data traffic between the connections' endpoints. Such nodes are interconnected by optical fiber links of hundreds of kilometers long, repeated every tenths of kilometers using optical amplifiers.To fulfill the modern traffic requirements, optical networks are evolving towards multi-service autonomous, flexible, software defined entities based on a centralized intelligence orchestrating the networking functions and communicating with the network elements using standardized interfaces. This trend opens the perspective of using the optical network for evironmental sensing, such as earthquake detection or anthropic activities monitoring. Indeed, distributed acoustic sensing (DAS) systems based on Rayleigh scattering have demonstrated that optical fibers are excellent sensors of mechanical stress. However, such systems are expensive and pose some limitations on the maximum reach, so they cannot be deployed extensively. In this context, re-using the already deployed optical data infrastructure to support and integrate dedicated system sensing may be highly beneficial. In this work, we propose an optical data network architecture exposing sensing functionalities with minimum or no additional hardware simply by exploiting the pervasiveness of the telecommunication infrastructure and getting data from the physical quantities already monitored for data transmission purposes. Such architecture on a typical terrestrial optical data network is outlined in figure.Modern coherent transceivers based on digital signal processing already track the evolution of the transmitted optical signal phase and polarization to recover the transmitted data at the receiver side. As those quantities are strongly affected by external strain, they already contain environmental information. Furthermore, some polarization-based processing can be implemented on cheaper non-coherent transceivers available at each amplifier site as data-service channel, providing several sensing sources.In addition, further optical devices such as add-drop multiplexer or optical amplifiers typically have several other sensors already embedded (power monitors, temperature sensors) or they can be equipped with some others which can provide environmental data from other physical quantities.The set of all such environmental data streams produced by the network elements constitutes the streaming telemetry fed to a network controller. A post-process agent may be implemented by exploiting the computational power available in typical network elements to perform local data analysis and reduce the amount of data sent to the sensing controller. By cross-processing the data coming from the network elements, a sensing controller is able to detect and localize events making the network act as a smart grid by continuously monitoring large areas and providing early warning signals.To support our proposal, in this work we show the results of an experimental activity aimed at detecting and localizing anthropic activities in the city of Turin using a deployed fiber ring.

We present the use of interconnected optical mesh networks for early earthquake detection and localization, exploiting existing terrestrial fiber infrastructure. Employing a Waveplate model, we integrate real ground displacement data from seven earthquakes, magnitudes ranging from four to six, to simulate the strains within fiber cables and collect large set of light’s polarization evolution data. These simulations help to enhance a Machine-Learning model that is trained and validated to detect Primary waves arrivals that precede earthquakes’ destructive Surface waves. The validation results show that the model achieves over 95% accuracy. The Machine-Learning model is then tested against an M4.3 earthquake, exploiting three interconnected mesh networks as a smart sensing grid. Each network is equipped with a sensing fiber placed to correspond with three distinct seismic stations. The objective is to confirm earthquake detection across the interconnected networks, localize the epicenter coordinates by a triangulation method and calculate fibers to epicenter distance. This setup allows early warning generation to municipalities close to epicenter location, and progressing to those further away. The model testing shows 98% of accuracy in detecting primary wave and one-second of detection time, affording nearby areas 21-seconds to take countermeasures, and an extended 57-seconds in more distant areas.

Telecommunications networks based on optical fiber communication have been vastly deployed in the last years to cope with the increasing traffic demands. They cover wide terrestrial areas with thousands of kilometers of available fiber cables, arranged in meshed, rings or festoon network topologies. Moreover, their operation is becoming more and more software-defined thanks to the definition of open interfaces and data structures, transforming the infrastructure into a crucial commodity able to offer several network services. Recently, the idea of using existing telecommunications fiber networks as a wide smart grid for environmental sensing is gaining momentum, since optical fiber can be used as an excellent mechanical stress sensors, as several physical effects are impacted by external stress. Distributed acoustic sensing (DAS) techniques deliver extremely accurate and spatially resolved measurements which are the state of the art, for example, in earthquake detection. However, its high cost, need for dark fibers and physical limitations prevent its wide deployment in telecom infrastructure. In this context, sensing based on state of polarization (SOP) monitoring of optical signals is an attractive solution. SOP is alredy monitored on optical coherent channel receivers for data recovery, although access to this data is usually closed by transceiver vendors. However, it is potentially accessibile on cheaper intensity modulated optical data channels, still widespread in optical networks, especially in the access segment. Also, it can be monitored using dedicated signals which can be transmitted alongside typical data channels. Moreover, SOP sensing does not require bidirectional transmission onto the same fibers and can extend its reach farther than DAS as it supports optical amplifiers, thus improving the compatibility between data and sensing services. On the downside, SOP sensing loses DAS spatial resolution, as it provides an integrated measuremnts over an entire fiber span and extraction of significant event information is complicated by the randomness of fiber birefringence. However, terrestrial networks can offer several SOP sensing sites which can be implemented with far cheaper equipment with respect to DAS or interferometry. In this work we explore the possibility for wide sensing grids with fiber length scale spatial resolution, which can integrate the information provided by traditional seismic stations networks. In particular, while developed areas may leverage on seismic stations networks, SOP sensing represents a cost effective solutio in emerging economies where telecom infrastructure is already deployed. Another key aspect relates to the development of effective techniques to detect the environemntal events of interest features, such as the earthquakes P/S waves, from the SOP time series. Indeed, especially in the terrestrial networks scenario, anthropic activities act as noise on the monitored SOP evolutions. To this aim, detection based on machine learning techniques is promising, due to the largely vaying characteristic figures of seismic waves. Due to the lack of extensive SOP experimental observations, we have developed simulations tools able to generate SOP synthetic data from realistic strain rates and we show how they can be used to train ML models based on spatially integrated SOP time evolutions.