Abstract
Earthquake early warning (EEW) systems provide seconds to tens of seconds of warning time before potentiallydamaging ground motions are felt. For optimal warning times, seismic sensors should be installed as close as possible to expected earthquake sources. However, while the most hazardous earthquakes on Earth occur underwater, most seismological stations are located onland; precious seconds may go by before these earthquakes are detected. In this work, we harness available optical fiber infrastructure for EEW using the novel approach of distributed acoustic sensing (DAS). DAS strain measurements of earthquakes from different regions are converted to ground motions using a realtime slantstack approach, magnitudes are estimated using a theoretical earthquake source model, and ground shaking intensities are predicted via ground motion prediction equations. The results demonstrate the potential of DASbased EEW and the significant timegains that can be achieved compared to the use of standard sensors, in particular for offshore earthquakes.
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Introduction
While earthquake prediction remains out of reach, continuous seismic monitoring has enabled earthquake early warning (EEW) systems that provide alerts to population centers and critical infrastructure seconds to tens of seconds before intense ground shaking is felt^{1,2,3,4}. Following rupture initiation, warning may be issued by analyzing recorded ground motions in realtime to assess the earthquake’s damage potential. The performance of EEW systems largely depends on the spatial distribution of available seismic sensors^{5}; for fast and robust warning issuance, seismic instruments should be installed in proximity to active faults, where earthquakes are expected to occur. While most of the largest and most hazardous earthquakes on Earth occur offshore in subduction zones, the vast majority of seismic stations are located onland. Thus, valuable time may be lost waiting for seismic waves to reach onland stations^{6}. Current solutions, such as densifying onland seismic networks and installing cabled ocean bottom sensor networks, are implemented in Japan^{7} and Canada^{8}. However, high costs preclude their worldwide implementation. An alternative is to convert existing fiber optic cables into dense seismic networks via the novel technology of distributed acoustic sensing (DAS)^{9,10}. The evergrowing worldwide deployment of optical fiber telecommunication infrastructure, in particular submarine cables, opens opportunities for widespread lowcost implementation of DAS for EEW, circumventing costly oceanbottom deployments and operations. The potential of seafloor DAS for EEW has not been quantitatively demonstrated yet, a gap addressed in this work.
Over the past several years, the unique advantages of DAS have proven valuable for various seismological purposes including earthquake analysis^{11,12,13} and subsurface imaging^{14,15,16,17}. DAS enables the measurement of transient ground deformations (strains or strainrates) along optical fibers (e.g., internet cables), such as those currently traversing most of our planet, both onland and underwater. Unlike pointsensors (e.g., seismometers, accelerometers, GNSS), DAS yields spatially dense longitudinal deformation measurements (every several meters, typically 10) along tensofkilometers long optical fibers with a maximum range between 80 and 150 km, depending on the specific DAS interrogator. This technology allows for continuous monitoring of large regions and provides a more complete picture of the seismic wavefield. Measurements are obtained using an interrogator unit which is placed at one end of the cable and sends laser pulses along the fiber. Due to small heterogeneities within the fiber, a fraction of the transmitted light is backscattered via Rayleigh scattering. When seismic waves perturb the cable, heterogeneities along the fiber change positions, and so does the Rayleigh backscattering pattern. The backscattering phase differences between time samples are then translated into strain or strainrate measurements at spacings of several meters along tensofkilometers long fibers^{18}. This technique allows for the transformation of any optical fiber into a dense array of seismoacoustic sensors, producing measurements with unprecedented spatial and temporal resolutions.
DAS has key features that are ideally suited for the challenges of EEW. It facilitates spatially and temporally continuous seismic measurements at hardtoreach places, such as underwater^{19} and in boreholes^{20}, closer to earthquake hypocenters. The dense spatial sampling facilitates more reliable separation between earthquakes and noise^{21} compared to pointsensors. Furthermore, the DAS interrogator is sensing the whole fiber from one of its ends, nullifying power and telemetry considerations to distant fiber segments. Thus, the use of optical fibers as dense seismic networks could be decisive in the performance of EEW systems, significantly improving earthquake warning times and allowing for better preparedness for intense shaking.
While the advantages of DAS for EEW are appealing, this novel technology suffers several drawbacks that need to be addressed. For DAS strain measurements to represent ground deformations, fibers should be adequately coupled to the ground. However, since many fibers were not deployed with seismological applications in mind, coupling can vary, and be insufficient along specific sections, for reliable measurements. Stateoftheart DAS interrogator units can sense fibers of up to ~ 150 km (as demonstrated by the earthquake recording performed by an Alcatel OptoDAS interrogator unit in Supplementary Fig. 1) or up to the first repeater, such that more than one system and fiber may be needed to cover vast regions. DAS records strains or strainrates: these measurements are very sensitive to the local velocity structure beneath the fiber^{13} and to lateral subsurface heterogeneities^{12,22}. This property is troublesome to both earthquake magnitude estimation, which typically relies on ground motion (i.e., displacements, velocities or accelerations) measurements^{23}, and for earthquake location efforts, where sensitivity to the local subsurface structure may complicate earthquake sources’ locations^{12}. In addition, DAS measures strains along the fiber’s axis, such that Pwaves recorded by horizontal fibers typically induce low amplitudes that may even be below instrumental noise levels, potentially hindering earthquake location capabilities. Furthermore, while EEW is intended for large earthquakes at short distances, where damages are expected to occur, such DAS observations are currently unavailable. Thus, the effects of DAS amplitude saturation and cableground coupling behavior during intense deformations are insufficiently reported and studied. In the following sections we tackle several of the mentioned disadvantages, yet additional work is needed to address all issues to reliably use DAS for EEW.
In this work, we propose the first quantitative realtime schemes that will be part of an operational DASbased EEW system. Early warning is typically achieved by (1) detecting an earthquake, (2) determining its location, (3) resolving the earthquake source parameters (magnitude and stress drop^{24}), and (4) predicting ground shaking intensities, typically peak ground velocities (PGV) and peak ground accelerations (PGA)^{25}. To the best of our knowledge, these four realtime objectives are yet to be addressed using DAS. Realtime earthquake detection and location may be achieved using either wellestablished pointsensorbased approaches^{26,27,28,29} applied to single or multiple DAS channels, or array processing techniques such as beamforming^{12,30,31}. While detection can be achieved with relative ease even with pointsensor based algorithms^{27,29}, earthquake location poses several challenges that are unique to DAS data^{12,22}. The recorded strain wavefield may not be coherent enough for reliable earthquake location or may be dominated by scattered waves. In addition, the geographical locations of measurements along the fiber need to be calibrated to reduce earthquake location errors. These issues will be considered when devising realtime earthquake location schemes, a subject of subsequent manuscripts. Here, we address the last two objectives: realtime magnitude estimation and shaking intensity prediction.
Most operational EEW systems rely on empirical relations for both magnitude estimation and ground motion prediction^{32,33}. The robustness of these relations largely relies on the quality, quantity, and magnitude range of available earthquake observations^{34}. Since DAS is a relatively new seismic measurement technology^{9}, current earthquake DAS datasets are insufficient in all aspects to devise robust empirical methods, and a physicsbased approach that does not rely on data availability should be developed^{24,35,36}. Recently, a holistic physicsbased approach for realtime earthquake source parameter (magnitude and stress drop) estimation and ground motion prediction has been proposed^{24}. A similar method, adapted to DAS data, is developed here by deriving a theoretical expression for realtime magnitude estimation using the rootmeansquares (rms) of ground accelerations.
Since DAS measures strains (or strainrates) and earthquake magnitude is directly related to ground motions (displacements, velocities and accelerations)^{23}, DAS measurements first need to be converted to ground motions^{37}. This objective is typically achieved by using the apparent slowness (reciprocal of velocity), \({p}_{x}\), measured along the fiber^{38}:
where ϵ(t) and D(t) are the timeseries of strains and ground displacements, respectively, and n equals 0, 1 or 2 corresponds to conversions to ground displacements, velocities or accelerations, respectively. This relation assumes perfect coupling between the fiber and the Earth, an assumption that was found to hold well for different fibers and installations^{11,13,37,37,38,39}. Slowness has been observed to change rapidly both in time and space (along the fiber): temporal variations are due to velocity differences among recorded seismic phases (i.e., P, S, surfacewaves)^{37} and spatial variations are a result of lateral subsurfacevelocity heterogeneities, that may be significant and abrupt^{12,16,22,37}. Accurate conversion of strainrates to ground accelerations requires that slowness be resolved as a function of both time and space. Recently, a slantstack based strains to ground motions conversion method has been proposed^{37}, and is modified and adapted here for realtime processing.
The approaches presented in this manuscript build on the abovementioned advancements in physicsbased EEW^{24} and DAS earthquake data processing^{37}. The potential of the modified strains to ground motions conversion and the new magnitude expression for EEW are examined in conjunction with a theoretical ground motion prediction equation (GMPE)^{40}. In the following sections, we present and validate a computationally efficient realtime protocol that relies on straightforward analytical formulations for the analysis of DAS recorded earthquakes. Strainrates are converted to ground accelerations using a realtime adapted slantstack approach. Then, earthquake magnitudes are estimated via an analytical expression derived using the Omegasquared source spectra model^{41,42}. Subject to the theoretical model, this magnitude expression is applicable to all body wave (P and Swaves) farfield ground motion recordings. This scheme is applied to several wellcoupled fiber segments along different oceanbottom fibers. Finally, the magnitude is used to predict PGV and PGA away from the hypocenter using a GMPE^{24,40}, derived using the same Omegasquared source model. The fact that both magnitude estimation and ground motion prediction are derived from the same theoretical model contributes to the stability and consistency of the estimates, as shown in the next sections. Magnitude and peak ground shaking predictions are continuously updated and modified as new seismic signals are recorded. We demonstrate the robustness of these realtime approaches for a wide magnitude range and show that using wellcoupled offshore fibers for EEW can significantly improve warning times compared to those expected from standard pointsensorbased EEW systems.
To demonstrate the merits of the proposed DASbased EEW schemes, we compiled a DAS earthquake dataset from different tectonic environments. Data were recorded by four different oceanbottom fibers: two offshore Greece^{13,16,37,43}, one offshore France^{13,19,37,44} and one offshore Chile (Supplementary Fig. 2). The measurements in Greece, France and Chile were conducted using three different interrogator units: a Febus A1 DAS interrogator, an Aragon Photonics hDAS interrogator and an ASN OptoDAS interrogator, respectively (See “Earthquake dataset” in Methods). We analyzed a total of 53 DAS recorded earthquakes that range from magnitude 2 to 5.7 (Supplementary Fig. 2) at hypocentral distances of 17 to 365 km (Supplementary Fig. 3). Earthquake metadata are provided in Supplementary Table 1.
Results
Using DAS data for magnitude estimation
Ideally, moment magnitude should be estimated using seismic recordings of ground displacements, D, rather than ground velocities, V, or accelerations, A, and the signals should include as much of their lowfrequency portion as possible to avoid magnitude saturation^{23,35}. Ground displacements can be obtained from wellcoupled DAS measurements by integrating strain measurements in time (or double integration of strainrates) and dividing them by the apparent slowness (n = 0 in Eq. 1)^{11,37,38,39}. This conversion approach has been demonstrated by previous studies that considered DAS instrument response^{11} and coupling^{45}. However, the use of DAS converted ground displacements is challenging given the inherently high instrumental noise levels, especially at large distances along long fibers^{13,46,47}. The behavior of DAS instrumental noise is demonstrated in Fig. (1) for an earthquake of magnitude 3.6 recorded at a distance of 135 km from an optical fiber offshore southeastern Greece^{13,16,37,43} (See map in Supplementary Fig. 2). At low frequencies, the instrumental noise of the timeintegral of strains (∝ D, Fig. 1a,b), strains (∝ V, Fig. 1c,d) and strainrates (∝ A, Fig. 1e,f) is proportional to f ^{−2}, f ^{−1} and independent of frequency, f, respectively. As a result, strainsintegral (∝ D, Fig. 1a) and strains (∝ V, Fig. 1c) timeseries are contaminated by lowfrequency noise, and their use may lead to magnitude overestimation and false alarms. Thus, we only use strainrates (∝ A) for realtime magnitude estimation even though they present a weaker correlation with earthquake magnitude compared to strainsintegral (∝ D) and strains (∝ V) (See “The relation between earthquake source parameters and ground motions” in Methods). Since strainrates’ instrumental noise increases as f at high frequencies (Fig. 1f), a lowpass filter is needed. This filter will not bias magnitude estimations because larger earthquakes produce lower frequency radiation.
Strainrates to ground accelerations conversion
The performance of the conversion algorithm (See “Realtime strainrates to ground accelerations conversion” in Methods) is demonstrated for a magnitude 3.8 earthquake recorded offshore Chile at a hypocentral distance of 60 km (See map in Supplementary Fig. 2) for a single DAS channel at a distance of 103 km along the fiber (Fig. 2). Note that direct Pwaves are not visible, although Pwave induced scatteredwaves are clearly seen (1–6 s in Fig. 2a,b). The same analysis for the largest earthquake in the dataset, a magnitude 5.7, is shown in Supplementary Fig. 4; for this earthquake, strainrate amplitudes exhibit some saturation (See Discussion). The realtime slantstack approach resolves the apparent velocities of the different seismic phases: ~ 4.2 km/s for direct Swaves (6–9 s in Fig. 2a,b) and ~ 1.8 km/s for surfacewaves (e.g., 1–6 s and 10–13 s in Fig. 2a,b). Owing to these velocity variations, ground accelerations are somewhat different from strainrates: accelerations (blue curve in Fig. 2c) exhibit a noticeable amplitude difference between fast Swaves and slow surfacewaves, while strainrates (black curve in Fig. 2c) display similar amplitudes for both phases. A comparison between the performance of the realtime slantstack conversion and the previously presented approach^{37} indicates that the realtime adaptations do not decrease the conversion quality (Supplementary Fig. 5).
The effect of stress drop variability
Stress drop, Δτ, is a fundamental earthquake source parameter that strongly affects ground motion intensities^{40,48,49,50} (See “The relation between earthquake source parameters and ground motions” in Methods). For optimal ground motion prediction, both magnitude and stress drop should be determined, as demonstrated by recent studies^{3,36,40}. Since in this framework we only use one ground motion metric, i.e., ground accelerations rms, A_{rms}, we may only estimate the magnitude (see “Magnitude estimation from bandlimited ground accelerations” in Methods) while the stress drop needs to be set a priori. Because A_{rms} are highly affected by the stress drop, and because its a priori value may deviate from its earthquakespecific real value^{40,51}, it is useful to examine the effect of stress drop variability on magnitude estimation and intense shaking prediction. To this end, we synthesized A_{rms} using an ideal lowpass Butterworth filter, and PGV_{synt} and PGA_{synt} for different magnitudes using Δτ = 10 MPa at a hypocentral distance of 50 km (See “Synthetic ground motions” in Methods). We then used the synthetic A_{rms} to estimate the magnitudes, using different a priori stress drops of 1, 10 and 100 MPa (Eq. 7). The estimated magnitude and a priori stress drop were then used to predict PGV_{pred} and PGA_{pred} (Eq. 10), assuming that the distance is known (Fig. 3). When using Δτ = 10 MPa in Eqs. (7) and (10), magnitude, PGV, and PGA discrepancies are small (panels b, d and f of Fig. 3, respectively) and mostly attributed to the approximations made in deriving Eq. (7) (See Supplementary Note 1). When the stress drop in Eq. (7) is underestimated (Δτ = 1 MPa) and overestimated (Δτ = 100 MPa), magnitudes are overestimated and underestimated, respectively, by as much as 1.33 magnitude units for large earthquakes (Fig. 3a). When these biased magnitudes and stress drops are used to predict PGV, and PGA, they result in reasonable predictions: the standard deviations of the residuals are limited to ~ 0.43 log_{10}(PGV) and log_{10}(PGA) units (solid curves in Fig. 3c,e, respectively). This behavior is explained by inspecting Eq. (10): to first order^{40}, \(PGV\propto {M}_{0}^{1/2}\Delta {\tau }^{1/2}\) and \(PGA\propto {M}_{0}^{1/3}\Delta {\tau }^{2/3}\), so using underestimated stress drops along with overestimated seismic moments (and viceversa), as is the case here, will result in relatively small PGV and PGA discrepancies; Magnitude and stress drop biases reduce each other’s effect on ground motion predictions. In contrast, if synthetic magnitudes are used in conjunction with the over and underestimated stress drops, PGV and PGA discrepancies would be significantly higher (dashed curves in Fig. 3c,e). Further explanations on the shape of the residual plots are provided in Supplementary Note 2.
When implementing the proposed methods to different fibers in different tectonic settings, a priori stress drop may be estimated using available earthquake observations^{40,52,53} or taken from previous studies, if available. However, the results in Fig. 3 show that while the discrepancies between the synthetic earthquake stress drop and that used in Eq. (7) may have a significant impact on magnitude estimation, the effect on ground motion prediction is minimized, and the approach may be reliably used even with a biased stress drop. The effect of stress drop variability will be further examined using recorded earthquakes in the following section.
Realtime magnitude estimation and peak ground shaking prediction
The performance of the realtime strainrates to ground accelerations conversion, magnitude estimation, and ground motion prediction are demonstrated using a composite earthquake catalog of 53 DAS and pointsensor (seismometer and accelerometer) recorded earthquakes from Greece, France, and Chile (See “Earthquake dataset” in Methods, earthquake catalog in Supplementary Table 1, and maps showing the locations of earthquakes, fibers, and pointsensors in Supplementary Fig. 2). These earthquakes range from magnitude 2 to 5.7 (Supplementary Fig. 3) and were recorded by four different offshore fibers using three different DAS interrogators. DAS records are converted to ground accelerations and used to estimate the magnitude, while pointsensor records are used to compare observed and predicted PGV and PGA. Earthquake locations (and hypocentral distances) and P and Swave arrival times are assumed to be known: the former are extracted from available earthquake catalogs and the latter are manually picked. In practice, earthquake location and phase picking will be achieved in realtime via additional modules, whose development is beyond the scope of this manuscript. Thus, the uncertainties and discrepancies reported in this section are expected to be larger when earthquake detection and location are also implemented in realtime.
As previously stated, the goal of an EEW system is to produce robust ground motion predictions, while magnitude estimations are merely a byproduct. In addition, while we estimate moment magnitudes, most catalogs report local magnitudes, whose values may significantly differ^{54}. Thus, in subsequent analysis, we focus on the discrepancies between predicted and observed PGV and PGA as a measure for the algorithms’ performance, and provide less attention to the agreement between realtime and catalog reported magnitudes.
Magnitude is estimated using several wellcoupled fiber segments for each cable. Coupling quality is evaluated by inspecting earthquakes’ seismic wavefield along the fiber and identifying sections that display continuous seismic wavefronts and small amplitude variabilities of less than 4 dB^{37}. DAS data are converted to ground accelerations and an initial magnitude estimate is obtained two seconds following Pwave detection at the first fiber segment, and is continuously updated with increasing data intervals and as the earthquake is recorded at additional locations along the fiber. The analysis uses all available phases including direct P and Sarrival, scattered waves and surface waves. For Pwaves, which are seldom undetected by horizontal DAS arrays, scattered and later arriving Pphases are used for the analysis. In this work, phases were identified and picked manually, while in realtime it will be achieved via automatic algorithms^{21,31}. Realtime magnitude estimation is demonstrated for a catalog magnitude 3.8 earthquake using a single fiber segment in Fig. 2d. Magnitude increases with time, starting at the scattered Pwaves (2–7 s), followed by a significant increase with the Swave arrivals (7–9.5 s). As theoretically predicted (Fig. 3), magnitude estimates vary for different a priori stress drops, with magnitudes of 5.8 and 4.6 for 1 and 10 MPa, respectively, at 9.5 s from Pwave detection. Similar behavior is observed for the catalog Magnitude 5.7 earthquake shown in Supplementary Fig. 4d. Magnitude estimates improve with time as seen in Fig. 4a–c where realtime and catalog magnitudes are compared at 4, 10 and 15 s from the first Pwave detection, for the entire dataset.
A comparison between predicted (Eq. 10) and observed (See “Earthquake dataset” in Methods) PGV and PGA at 15 s indicates that the residuals are independent of hypocentral distance (Fig. 4d,e and Supplementary Fig. 6d, e) and catalog magnitude (Supplementary Fig. 7d, e), and that their standard deviations are relatively small, only slightly higher than the optimal values, i.e., withinevent variabilities reported in the caption of Fig. 4. The latter result suggests that peak ground motion residuals are mainly caused by different site and path conditions that may be accounted for in future implementations, subject to additional research. While magnitude estimates are highly sensitive to the a priori stress drop, PGV and PGA residuals exhibit low sensitivity (Fig. 4 and Supplementary Fig. 6 for 10 and 1 MPa, respectively). This behavior is further demonstrated by examining the average magnitude, and PGV and PGA residuals for the largest available earthquake (Supplementary Fig. 8): average residuals show little sensitivity to stress drop and similar trends to those theoretically predicted (Fig. 3), i.e., PGV residuals are higher for stress drop underestimation, and PGA residuals are lower for stress drop underestimation.
Discussion
The results presented in this manuscript demonstrate that DAS can be reliably used for realtime magnitude estimation and ground motion prediction, two critical components of an operational EEW system. The use of DAS for EEW presents several significant advantages compared to the use of standard pointsensors, especially in the timegain for offshore earthquakes. This latter advantage is illustrated in Fig. 5 using the fiber deployed offshore Chile, where oceanbottom earthquakes pose a significant seismic hazard. For the offshore earthquakes shown in Fig. 5a, by the time Swaves are expected to reach the Chilean coastline, realtime magnitude estimates are typically within half a magnitude unit of catalog values, allowing for robust alert issuance before intense ground shaking is felt onland, and well before earthquakes are recorded by the available seismic network (Fig. 5b). The timegain achieved by using the offshore Chile fiber compared to the current pointsensor network is defined here as the difference between the Pwave arrival at the closest fiber segment and at the fourth seismic station, as commonly required by EEW systems^{55}. This timegain may be as large as 25 s for earthquakes that occur near the fiber and may even result in early detection and alert issuance for onland earthquakes where pointsensor coverage is sparse (Fig. 5b). These precious seconds can have a decisive impact on mitigating the risk posed by potentially catastrophic offshore earthquakes.
Together with the timegain for offshore earthquakes, DASbased EEW can potentially outperform pointsensorbased EEW for several reasons. When implemented on wellcoupled fiber segments, magnitude estimates are more reliable since data from many closely spaced DAS channels are averaged, reducing the impact of outliers and smoothing local effects. DAS facilitates robust differentiation between earthquakes and noise since earthquakes’ seismic wavefield is nearinstantaneously recorded on hundredsofmeters long fiber segments. As a result, false detections will be reduced and one fiber segment may be sufficient to issue early warning, subject to earthquake location capabilities.
While direct Swaves are detected by horizontally installed fibers, direct Pwaves are usually not (Fig. 2a), a result of their fast velocities and the angle between the waves’ polarization and the fiber’s axis^{13,56}. In contrast, Pwave induced scatteredwaves are well recorded (2–6 s in Fig. 2a,b). Direct Pwaves, if available, and Swaves, as well as scattered P and Swaves are all used for magnitude estimation. While scattering results from heterogeneities of Earth’s media and varies from one region to another, the use of the waves’ apparent velocities to convert strainrates to ground accelerations reduces the effect of this local phenomena on magnitude estimations. The dominance of these scattered waves will pose difficulties for earthquake location, since using scattered Pwaves instead of direct Pwaves will likely point to the scatterers’ locations rather than the earthquake’s source. Because Pwave based magnitudes are typically underestimated (Fig. 2d), they are not expected to cause false alarms, yet they may be sufficient to surpass predefined alert thresholds. Since for EEW, sensors should be placed at proximity to expected epicenters, the closer the fiber is to earthquake locations, the sooner the high signaltonoise Swaves are detected and used. For large earthquakes, the lower sensitivity of DAS to Pwaves is an advantage because it will limit the saturation of direct and scattered Pwaves.
Since DAS is an emerging technology, available datasets do not contain sufficient earthquakes whose damage potential is of interest for EEW. As a result, several technical aspects of the proposed schemes such as amplitude saturation and fiberground coupling during strong shaking cannot be fully evaluated. However, unlike commonly used empirical EEW approaches^{32,33,34}, the proposed scheme is theoretical and relies on a wellestablished source model^{41} that was found to adequately describe the farfield radiation of a large range of earthquakes. Thus, showing that the proposed methods work for the current earthquake dataset is sufficient to demonstrate their validity. In addition, considering small magnitude earthquakes, as we do here, demonstrates the robustness of the system to false alarms. Analyzing near field records is a troublesome issue for EEW; since a complete theoretical framework is yet to be developed^{57}, other approaches such as resolving line sources^{58} or extrapolating peak ground motions away from the earthquake source^{59} may need to be adapted to DAS data in order to address this gap.
The derivation of the presented physicsbased magnitude estimation approach did not require any earthquake observations, a significant advantage since the scarcity of DAS earthquake observations hinders the derivation of empirical methods. Because no earthquakes were required, the approach is geographically independent and readily applicable in any tectonic setting using both offshore and onland fibers and different DAS interrogator units, as demonstrated here using earthquakes from Greece, France and Chile. Earthquake observations are only required to map wellcoupled fiber segments, although this objective may also be achieved using ambient noise^{16}. Using segments that are not wellcoupled may lead to either magnitude underestimation, if strain amplitudes are weak, or overestimation, if a segment is suspended and experiences strong vibrations due to cablewaves^{60}. The approach allows for continuous update of magnitude and ground motion predictions, key for analyzing large earthquakes with long durations. In addition, using a holistic magnitude estimation and ground motion prediction that are derived from the same earthquake model reduces the impact of stress drop related magnitude biases on ground motion predictions and enhances the overall robustness of the system.
The computational costs of the presented approaches are low. While DAS acquisitions typically provide very large data volumes, for EEW, data can be largely downsampled in both time and space, limiting both the volume of data and processing times. For instance, to obtain timely and robust magnitude estimates, it is sufficient to use preselected wellcoupled fiber portions at spacings of several kilometers. For the purpose of this study, we analyzed 180 s long recordings of preselected DAS fiber segments (33 channels), downsampled to ~ 20 Hz, in ~ 136 s using a Python code on an Intel Core i7 laptop with 32 GB RAM using a single thread. These computation times indicate that the method is valid for realtime. For future implementation as part of an operational EEW system, several aspects of the code can be optimized and run in parallel. For example, slantstack computations, which are the most timeconsuming (~ 4 s per channel), can be parallelized, in addition to computations for different fiber segments. The latter can also increase the number of fiber segments used for magnitude estimation.
For few earthquakes, strain amplitudes exhibited a small degree of saturation. Nevertheless, magnitude estimations still allow for reliable ground motion predictions (Fig. 4). This phenomenon is insufficiently reported and investigated in existing literature and needs to be quantified and addressed as it may affect the ability to analyze higher strain amplitudes and provide reliable warnings for larger earthquakes. DAS saturation needs to be studied along with DAS manufacturers to devise preprocessing and postprocessing methods in order to fully demonstrate the viability of DAS for EEW.
The framework presented in this study demonstrates the great potential of using DAS for EEW. The approaches presented here allow for easy, robust, and fast implementation of EEW using both offshore and onland optical fibers in any tectonic setting. Specifically, using existing oceanbottom optical fibers, which are almost ubiquitous along subduction zones worldwide, provide a cheap and readily available EEW solution, especially for exposed developing countries, that will significantly enhance earthquake hazard mitigation capabilities.
Methods
The relation between earthquake source parameters and ground motions
For large earthquakes , i.e., when highfrequency attenuation is negligible, recorded in the farfield, ground displacements rootmeansquares (rms), D_{rms}, and peak ground displacements (PGD) are mostly a function of the seismic moment, M_{0}: \({D}_{rms}\propto PGD\propto {M}_{0}^{5/6}\Delta {\tau }^{1/6}\) while ground velocities rms, V_{rms}, and PGV, and accelerations rms, A_{rms}, and PGA, are also strongly influenced by the stress drop, Δτ: \({V}_{rms}\propto PGV\propto {M}_{0}^{1/2}\Delta {\tau }^{1/2}\) and \({A}_{rms}\propto PGA\propto {M}_{0}^{1/3}\Delta {\tau }^{2/3}\) ^{24,40}. The proportionality between rms and peak ground motions stems from statistical theories^{61} and was observed by previous studies^{40}. Note the different powers associated with M_{0} and Δτ. Thus, ground displacements serve as a better magnitude predictor compared to velocities or accelerations^{32,35,40}.
Realtime strainrates to ground accelerations conversion
The slantstack^{62} based strains to ground motions conversion scheme^{37} accounts for apparent phase velocity variations in both time and space. The conversion is applied for each DAS channel along the fiber using short, approximately linear, fiber segments. Here, this recently presented approach^{37} is modified and optimized for realtime performance. The semblance (coherency) as a function of apparent slowness p_{x} and time t, for a DAS channel located at x_{0} along the fiber, can be written as:
where L is the number of DAS channels used for slowness estimation, g(t) is the DAS strainrates timeseries, and x_{j}—x_{0} is the distance between station j and the reference channel (at x_{0}). Equation (2) can be regarded as the causal slantstack, where only data samples of g(t) that have already been recorded are considered.
The conversion procedure is performed as follows. For computational efficiency, recorded strainrates are downsampled to 20 Hz (or slightly higher, depending on the original signals’ samplingrate). Data is lowpass filtered at 5 Hz using a 4pole Butterworth filter to diminish high frequency instrumental noise. The applied downsampling and filtering did not decrease the robustness of the conversion and subsequent magnitude estimation. The local slantstack transform is applied using fiber segments of ~ 380 m length^{37}, with channel spacings of ~ 20 m, skipping several channels for densely spaced measurements. The used fiber segments are long enough to resolve long seismic wavelengths with fast velocities of several km/s, and short enough so that seismic waves are coherent and fiber sections are approximately linear^{37}. Semblance is calculated using 50 predefined slowness values, equally spaced between − 5 s/km and 5 s/km. At each t, the wavefield’s slowness is determined as the one with highest semblance. The produced slowness timeseries is then smoothed by applying a causal movingmean filter of 1 s to its absolute value. Strainrates timeseries are then divided by the slowness timeseries to obtain ground accelerations, followed by an additional 5 Hz lowpass filter. Because we are eventually interested in the converted strainrates’ rms, the slowness’ sign may be discarded (See “Magnitude estimation from bandlimited ground accelerations” in Methods).
Magnitude estimation from bandlimited ground accelerations
We derive an expression for the rms of the ground accelerations using the commonly used Omegasquared source model^{41} describing the farfield body wave spectra (grey dashed curve in Supplementary Fig. 9). This derivation procedure follows that used by several previous studies^{24,35,40,63,64,65,66}. The acceleration omegasquared model^{41} subject to high frequency attenuation^{67} (grey dotted curve in Supplementary Fig. 9) reads as:
where f_{0} is the source corner frequency, Ω_{0} is the displacement low frequency spectral plateau, and κ is an attenuation parameter. Since strainrates are lowpass filtered at 5 Hz, acceleration rms are calculated using Eq. (3) as \({A}_{rms}=\sqrt{\frac{2}{T}{\int }_{f=0}^{f=5}{\left\ddot{\Omega }\left(f\right)\right}^{2}df}\) (black dashed curve in Supplementary Fig. 9), where T is the data interval. The integral is solved and an analytic approximation is obtained (See Supplementary Note 1). The spectral parameters Ω_{0} and f_{0} are substituted with the seismic moment^{23} and stress drop^{68}, respectively, via:
where ρ is the density at the source, C is the wave velocity at the source (C_{P} and C_{S} for P and Swaves, respectively), R is the hypocentral distance, U_{φθ} is the average radiation pattern, F_{S} is the freesurface correction, and k is a phasespecific constant which also depends on the source model and rupture speed^{42}. Equation (4b) is valid for a circular crack embedded in a homogeneous medium^{68}. The resulting expression is:
where the superscript approx signifies approximate rms, \({A}_{1}=\frac{{U}_{\varphi \theta }{F}_{s}\sqrt{\pi }}{\rho {C}^{3}}{\left(\frac{16}{7}\right)}^\frac{2}{3}{\left(k{C}_{S}\right)}^{2}\), \({A}_{2}=\pi {\left(\frac{16}{7}\right)}^{1/3}k{C}_{S}\), \(h\left({\alpha }_{m}\right)={e}^{{\alpha }_{m}}\sqrt{\frac{1}{2}\left[36{\alpha }_{m}6{\alpha }_{m}^{2}4{\alpha }_{m}^{3}2{\alpha }_{m}^{4}+3{e}^{2{\alpha }_{m}}\right]}\) and \({\alpha }_{m}=5\pi \kappa\).
Equation (5) can be analytically solved for the seismic moment:
where \({a}_{1}={A}_{1}{\Delta \tau }^\frac{2}{3}\sqrt{1{e}^{2{\alpha }_{m}}}\frac{1}{R\sqrt{\kappa T}}\), \({a}_{2}={A}_{rms}\), \({a}_{3}=\frac{{A}_{rms}{A}_{2}^{2}\Delta {\tau }^\frac{2}{3}{\kappa }^{2}\sqrt{1{e}^{2{\alpha }_{m}}}}{h\left({\alpha }_{m}\right)}\) and \({a}_{4}={\left(3\sqrt{3\left(27{a}_{1}^{4}{a}_{3}^{2}+4{a}_{1}^{2}{a}_{2}^{3}{a}_{3}\right)}+27{a}_{1}^{2}{a}_{3}+2{a}_{2}^{3}\right)}^\frac{1}{3}\). The moment magnitude can then be written as:
where M_{0} is expressed in Nm.
While the coefficients \({a}_{1}\), \({a}_{2}\), \({a}_{3}\) and \({a}_{4}\) contain many parameters, only few are updated in realtime: A_{rms} is continuously updated as new data is recorded, the available data interval T begins at the Pwave arrival and increases with time, and R is updated as earthquake location improves. The parameters used are^{24}: F_{S} = 2, ρ = 2600 kg/m^{3}, C_{S} = 3.2 km/s, C_{P} = 5.3 km/s, κ = 0.025 s, U_{φθ} equals 0.52 and 0.63 for P and Swaves, respectively^{23}, and k equals 0.32 and 0.21 for Pand Swaves, respectively^{42}. For data intervals that contain both P and Swaves, the phase specific constants need to be averaged based on the relative intervals of each phase^{24}:
where const stands for U_{φθ}, C or k for P or Swaves, and T_{SP} is the SP data interval. Using these parameters, \({a}_{1}\) and \({a}_{3}\) may be written as:
where phasespecific terms are written in parentheses.
In this application, the magnitude is estimated using several manually identified wellcoupled fiber segments of ~ 600 m as follows. Strainrates within each fiber segment are converted to ground accelerations (See “Realtime strainrates to ground accelerations conversion” in Methods). A_{rms} is calculated per DAS channel starting at the Pwave arrival, and is then logarithmically averaged per fiber segment at every timestep to minimize the impact of outliers. Since DAS can only measure the wavefield inline with the fiber, A_{rms} is multiplied by \(\sqrt{2}\) to compensate for the missing orthogonal component. The averaged A_{rms} at time T is then input to Eq. (7) along with Δτ and R to estimate the magnitude. Magnitude estimates are continuously updated until either averaged A_{rms} reaches its maximum value, or T = 60 seconds^{24}. Magnitude estimates from different fiber segments are weightaveraged by the available data interval to obtain an event specific estimate.
Ground motion prediction
For PGV and PGA prediction, we use a set of physicsbased GMPEs^{24,40}, derived using the same source model^{41} (Eq. 3) used to obtain the realtime magnitude expression (Eq. 7) (See “Magnitude estimation from bandlimited ground accelerations” in Methods). The GMPEs for PGV and PGA are:
where \({\beta }_{V}=\frac{2\pi {U}_{\phi \theta }Fs\sqrt{\frac{16}{7}} {\left(k{C}_{S}\right)}^\frac{3}{2}}{(\sqrt{2\pi }4\rho {C}_{S}^{3})}\) and \({\beta }_{A}=\frac{4\pi {U}_{\phi \theta }Fs{\left(\frac{16}{7}\right)}^{2/3}{\left(k{C}_{S}\right)}^{2}}{\left(\sqrt{\pi }4\rho {C}_{S}^{3}\right)}\). These theoretical GMPEs are readily applicable in any seismic region. Using the parameter tuning for Swaves (See “Magnitude estimation from bandlimited ground accelerations” in Methods), β_{V} = 2.44 × 10^{–10} m^{1.5}s^{1.5}/kg and β_{A} = 2.05 × 10^{–8} m^{2}s/kg.
Synthetic ground motions
The GMPEs in Eq. (10) are used to generate synthetic PGV and PGA for different seismic moments, stress drops and hypocentral distances. Synthetic A_{rms} are generated by calculating the rms of the acceleration spectra (Supplementary Fig. 9). These spectra are produced for a specific seismic moment, stress drop and hypocentral distance using Eq. (3) and (4), subject to a lowpass filter. The filter is modeled in two manners: as a clean cutoff (dashed black curve in Supplementary Fig. 9) as used for the model derivation (See “Magnitude estimation from bandlimited ground accelerations” in Methods), or as an ideal 4pole Butterworth filter (solid black curve in Supplementary Fig. 9), similar to that used for DAS signal processing.
Earthquake dataset
DAS measurements in Greece were conducted using a Febus A1 DAS interrogator between 18–19 and 19–25 April 2019 on 13.2 km and 26.2 km long fibers, sampled at 6 ms and 5 ms, respectively. Gauge length and spatial sampling were both set to 19.2 m for the two fibers. DAS measurements in France were conducted using an Aragon Photonics hDAS interrogator between 11–31 July 2019 on a 44.8 km long fiber, sampled at 10 ms and 2 ms for the first and last 10 days, respectively. Gauge length and spatial sampling were both set to 10 m. DAS measurements in Chile were conducted using an ASN OptoDAS interrogator between 27 October and 3 December 2021 on a 204 km long fiber, sampled at 8 ms. Gauge length and spatial sampling were both set to 4.085 m. The Febus and OptoDAS interrogators record strainrates while the Aragon instrument records stains; the latter were differentiated to strainrates before the conversion to ground accelerations.
Seismometer and accelerometer recordings were used to calculate PGV and PGA for the different earthquakes as follows. Data for Greece, France and Chile were obtained from the National Observatory of Athens, the RESIF repository and IRIS, respectively. The two horizontal components of pointsensors were demeaned and highpass filtered at 1 Hz using a 4pole Butterworth filter, followed by a simple gain correction. Velocitymeter signals were differentiated to obtain ground accelerations and accelerometer records were integrated to obtain ground velocities. An additional highpass filter was applied after differentiations and integrations. PGV (PGA) were then calculated as the geometric mean of the maximum of the absolute value of the two velocity (acceleration) components. PGV and PGA that are smaller than 5 times the standard deviation of the associated timeseries are discarded as they may be biased by noise.
Data availability
Samples of DAS earthquakes are available on https://osf.io/4bjph/.
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Acknowledgements
This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 101041092), from the UCA^{JEDI} Investments in the Future project managed by the National Research Agency (ANR) with the reference number ANR15IDEX01, and from the Observatoire de la Côte d’Azur. We thank GTD Grupo SA who provided access to the infrastructure, and el Centro Sismologico Nacional staff who helped in the logistics.
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I.L. designed the presented algorithms, performed the analysis and wrote the initial draft. D.R., J.P.A. and A.S. contributed to the discussion, methodology, interpretation and presentation of the results. D.R., J.P.A., A.S., S.B., R.S.O., G.A.V.O. and J.A.B.P. took part in performing DAS measurements.
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Lior, I., Rivet, D., Ampuero, JP. et al. Magnitude estimation and ground motion prediction to harness fiber optic distributed acoustic sensing for earthquake early warning. Sci Rep 13, 424 (2023). https://doi.org/10.1038/s41598023274443
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DOI: https://doi.org/10.1038/s41598023274443
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