Research ArticleGEOPHYSICS

Connecting a broad spectrum of transient slip on the San Andreas fault

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Science Advances  14 Aug 2020:
Vol. 6, no. 33, eabb2489
DOI: 10.1126/sciadv.abb2489

Abstract

Strain accumulated on the deep extension of some faults is episodically released during transient slow-slip events, which can subsequently load the shallow seismogenic region. At the San Andreas fault, the characteristics of slow-slip events are difficult to constrain geodetically due to their small deformation signal. Slow-slip events (SSEs) are often accompanied by coincident tremor bursts composed of many low-frequency earthquakes. Here, we probabilistically estimate the spatiotemporal clustering properties of low-frequency earthquakes detected along the central San Andreas fault. We find that tremor bursts follow a power-law spatial and temporal decay similar to earthquake aftershock sequences. The low-frequency earthquake clusters reveal that the underlying slow-slip events have two modes of rupture velocity. Compared to regular earthquakes, these slow-slip events have smaller stress drop and rupture velocity but follow similar magnitude-frequency, moment-area, and moment-duration scaling. Our results connect a broad spectrum of transient fault slip that spans several orders of magnitude in rupture velocity.

INTRODUCTION

The establishment of continuous global positioning system (GPS) measurements led to the discovery of slow-slip events (SSEs) down dip of the seismogenically locked region of the Nankai (1) and Cascadia (2) subduction zones. Subsequently, tectonic tremors that correlate spatially and temporally with SSEs were found (3, 4). These long-duration tremors have been inferred to be the superposition of many low-frequency earthquakes (LFEs) that represent asperities that are repeatedly driven to failure by surrounding aseismic slip (5). This interpretation that LFEs are markers of SSEs is supported by recent studies that managed to extract slow-slip deformation signal by using the timing of increased LFE rate as a guide to stack GPS time series (6, 7).

Surface geodetic measurements can typically only detect SSEs above moment magnitude (Mw) ∼ 6 (8). Along the central San Andreas fault, tremors and LFEs have long been observed (9, 10), but it was only recently that the deformation signature of Mw 4.9 SSEs were detected after stacking 20 such events using the timing of increased LFE rate (7). Since these SSEs in the deeper part of the fault might be episodically loading the shallow region that last ruptured in the 1857 magnitude 7.9 Fort Tejon earthquake (11), it is important that we have a more comprehensive catalog of these events. The bias toward only detecting the largest SSEs also limits our ability to robustly characterize their scaling properties. For instance, it is still debated whether the moment (M0) of SSEs is proportional to their duration (T) (12) or follows the T3 scaling observed for regular earthquakes (13, 14). A M0T scaling relationship for SSEs could reflect fundamentally different underlying dynamics compared to regular earthquakes (12) or simply be the by-product of only cataloging the largest “bounded” events with aspect ratios >1 (15).

Previous studies have used tremors and LFEs to characterize SSEs invisible to geodetic measurements. Along the central San Andreas fault, Shelly (10) cataloged 88 LFE families (Fig. 1) that represent groups of LFEs with similar waveforms and, hence, similar source mechanisms and locations. Thomas et al. (16) clustered the LFEs of each individual family based on their recurrence intervals using empirically derived separation time scales, while Lengliné et al. (17) proposed a stochastic model that successfully reproduced the temporal clustering behavior of the LFEs. However, these studies did not account for interactions between LFE families and, hence, could not directly estimate the spatial properties of the underlying SSEs from the LFE clusters, even though the occurrence patterns of different families have been shown to correlate (Fig. 1C) (18, 19). Clustering of tremors and LFEs that takes into account their spatial relationships has been attempted to identify secondary slip fronts at subduction zones but relied on rather ad hoc definitions of what constitutes a cluster (20, 21).

Fig. 1 Tectonic context and LFEs’ spatiotemporal behavior.

(A) Map view. Black dots show LFE locations. Magenta dots show 10 LFE families that Rousset et al. (7) used as guide to stack GPS time series and extract slow-slip deformation signal. Red dot shows LFE family previously suggested to be isolated (26). Gray dots show regular earthquakes in the region from 1984 to 2011 (44). Inset shows geographical location of Parkfield. (B) Along-fault cross section. Yellow star shows hypocenter of the 2004 Mw 6.0 Parkfield earthquake. (C) Space-time plot of the LFEs. Black dots show all events over an 8-day period. Colored dots show a few example clusters during this time period; events of the same color belong to the same cluster.

Here, we estimate the spatiotemporal clustering properties of LFEs detected along the central San Andreas fault (10) probabilistically with minimal model assumptions and a priori parameterization. We then use the extracted LFE clusters to estimate the area, average slip, moment, duration, stress drop, and rupture velocity of the underlying SSEs and explore their scaling properties.

RESULTS

Clustering properties of LFEs

We analyze the catalog of 88 LFE families that span ∼150 km along the central San Andreas fault (Fig. 1) (10), which includes more than 1 million events from 2001 to 2016. We limit our analysis to between 2006 and 2016 to minimize the impact of the 2004 Mw 6 Parkfield earthquake. This leaves us with a catalog of ∼750,000 LFEs. The most populous family has ∼22,000 events, while the least populous family has ∼2500 events over this time period. We model the LFE rate at time t asλ(t)=x=1D(μx+y=1D tjy<tKxyg(ttjy))(1)where D is the number of LFE families, μx is the uniform background rate of family x, Kxy encodes the excitation strength, i.e., the number of events in family x “excited” by an event in family y on average, g is the normalized time-dependent excitation kernel, and tjy is the occurrence time of LFE j from family y. Therefore, we are only assuming that (i) the LFE clustering behavior is linear, i.e., the contribution of different LFEs can be added up; (ii) a mean-field response to the occurrence of an LFE, i.e., two LFEs from the same family are modeled similarly; and (iii) the excitation kernel g(t) does not vary between different families, although we obtain similar results when relaxing this assumption (see Materials and Methods). We do not make an a priori assumption about the shape of g(t) but instead discretize it as piece-wise constant. We then adopt an expectation-maximization approach (22, 23) to estimate the parameters μx, Kxy, and g(t) (see Materials and Methods). We verified the algorithm’s ability to correctly estimate the model parameters using a synthetic catalog (see Materials and Methods).

g(t) characterizes the temporal clustering property of the LFEs. We find that g follows a power-law decay with time up to 10 days, with a plateau between 0.02 and 0.2 days (Fig. 2A). We fit the decay in the range of [2 × 10−4 to 2 × 10−2] days and between 0.2 and 10 days separately and obtain gt−1.8 for both time ranges. The shape of g(t) and the power-law exponent that we obtain are similar to what Lengliné et al. (17) obtained from applying a comparable stochastic model to one LFE family at a time, as well as the stacked interevent time density of the different LFE families (fig. S1). Therefore, the power-law decay of g, which is similar to the temporal evolution of earthquake aftershock sequences often referred to as the Omori-Utsu law (24), is a robust feature of the LFE catalog. Lengliné et al. (17) concluded that this feature is unlikely to have arose from direct triggering of LFEs by the stress change due to a preceding LFE since the number of excited LFEs is not correlated with the amplitude of “mainshock” LFEs in their analysis. Therefore, the power-law decay of g likely reflects the triggering of LFEs by changes in stress or loading rate due to underlying SSE. The plateau of g between 0.02 and 0.2 days (30 min and 5 hours) is rather perplexing but indicates that there are two clustering time scales. When we allow g(t) to vary between LFE families (see Materials and Methods and fig. S2A), we find that g(t) has such a plateau only for LFE families that have short-duration bursts occurring within long-duration bursts, i.e., a trimodal interevent time distribution (fig. S3) (16), similar to secondary slip fronts observed within SSEs at the Cascadia and Nankai subduction zones (21, 25).

Fig. 2 LFE clustering properties.

(A) g as a function of time. Red dashed line shows least-squares fit giving gt−1.8. (B) K as a function of distance. Since the absolute K value is affected by the LFE family size, we account for this effect by multiplying Kxy by nynx, where nx is the number of events from the excited family x and ny is the number of events from the exciting family y, before calculating the mean K value for different interfamily distance bins. Red dashed lines show least-squares fits giving Kd−2.8 and Kd−2.5 for along-strike and along-dip distances, respectively.

Kxy characterizes the excitation strength between LFE families and thus the spatial clustering property of the LFEs. We find that a family that was previously suggested to be isolated (Fig. 1) (26) has the second smallest interfamily K value, lending confidence that K indeed captures the interaction between LFE families. On average, K decays with interfamily distance (Fig. 2B), even though interfamily distance is not present in our model (Eq. 1). The decay of K with distance is consistent with previous observations that the occurrence pattern of nearby LFE families is correlated (18, 19). For along-strike distance, the decay of K is well approximated by a power law up to 16 km, above which the value of K saturates possibly due to the values being too small to be resolvable (Fig. 2B). We fit the power-law decay between 1 and 16 km and obtain Kd−2.8. For along-dip distance, we obtain Kd−2.5. The fast decay of K with distance is consistent with Trugman et al. (19) obtaining groups that typically span <15 km when grouping LFE families based on the similarity of their long–time scale occurrence pattern. The power-law exponent of ∼3 is similar to the expected earthquake aftershock density decay with distance from the mainshock if aftershocks are triggered by static stress change. However, a model where LFEs are triggered by static stress change due to SSEs is inconsistent with interfamily excitation in the along-strike direction being 10 times stronger compared to the along-dip direction (Fig. 2B). Therefore, the decay of K with distance probably reflects the likelihood of the underlying SSEs to grow spatially, with there being a greater tendency to propagate along strike than along dip. This favors the model where LFEs are small asperities driven to failure by surrounding aseismic slip. Lastly, we find that the decay of K is similar for both southeast (the excited family is located southeast of the exciting family) and northwest excitation (Fig. 2B). This suggests that the underlying SSEs are equally likely to propagate in either direction, even though previous studies have suggested that earthquakes along the Parkfield segment of the San Andreas fault preferentially rupture to the southeast [e.g., (27)].

Estimating properties of underlying SSEs

Now that we have estimated μ, K, and g, which govern the LFE rate at any given time, for each LFE i, we can calculate the probability that it is a background event and the probability that it was excited by each preceding LFE j (see Materials and Methods). Using the probabilities associated with each LFE, we perform stochastic clustering (28) of the events to isolate individual LFE bursts. Each cluster includes one background event and the events it directly and indirectly “excites.” We interpret this background event to mark the initiation of an SSE, while the excited events reflect the subsequent evolution of the SSE. Stochastic clustering is not unique, and each catalog that it produces represents a sampling of the underlying structure. Here, we present a catalog from one iteration, which includes 16,327 LFE clusters that involved ≥2 LFE families, but have verified that the presented statistics remain stable between different iterations as expected. For each of these clusters, we calculate their along-strike extent (L) and depth extent (W). We then infer the underlying SSE to have a rupture area A = LW (fig. S4). The time difference between the first and last LFEs in a cluster is taken as the SSE duration (T). We then estimate the average rupture velocity Vr=LT, which assumes unilateral propagation. For a rupture that starts at the middle and propagates bilaterally, Vr=L2T, so our estimated rupture velocity is a proxy up to a factor of 2. While LFEs are often interpreted as asperities that are repeatedly driven to failure by surrounding aseismic slip (5) and our observed clustering properties of LFEs appear to support this model, our estimating these SSE properties using LFE clusters only requires that they are spatially and temporally coincident (7) and is valid regardless of the physical mechanism behind their coincidence.

Thomas et al. (16) have shown that LFEs on the San Andreas fault can be used to meter slip. The long-term slip rate of the 150-km-long creeping segment of the San Andreas fault is approximately 34 mm/year [e.g., (29)]. For each LFE family x, we estimate the slip per LFE (dx) by multiplying the long-term slip rate by the catalog duration (10 years) before dividing by the total number of LFEs in that family. For each LFE cluster, we can then estimate the total slip of each LFE family x by multiplying dx by the number of LFEs of that family that is part of the cluster. We assume the total slip of each LFE family represents a point sample of the slip distribution over the rupture area of the underlying SSE, i.e., there is slip throughout the area delineated by each LFE cluster, but we only have an estimate of the slip amount where the LFE families are located. For each LFE cluster that involved ≥2 LFE families, we then take the mean slip of all the activated LFE families as the average slip (D¯) of the underlying SSE (fig. S4) and calculate seismic moment Mo=μAD¯, taking shear modulus μ = 30 GPa. While we are assuming that there is no aseismic slip on the LFE patches, if aseismic slip is a constant factor relative to seismic slip, it will just scale our Mo estimates by a constant factor and would not affect its inferred scaling relationship with other SSE properties. We calculate Mw=23(log Mo9.1) and find that the 20 largest SSEs have a mean Mw of 5.1. This is just slightly larger than the geodetically determined average Mw of 4.9 for the SSEs associated with the 20 largest bursts of 10 LFE families on the northwest Parkfield segment of the San Andreas fault (7), suggesting that our derived SSE moments are reasonable.

SSE scaling properties

Earthquakes follow a power-law magnitude-frequency distribution given as log10(N) = abM, where N is the number of earthquakes of magnitude ≥ M, and a and b are constants (30). We find that the SSEs also approximately follow a power-law magnitude-frequency distribution (Fig. 3A). We obtain a b value of 1.61 ± 0.04 (31, 32) when taking a magnitude of completeness Mc = 3.9. Nonvolcanic tremors that are approximately spatially coincident with our LFE families in the southeast have been suggested to also follow a power-law magnitude-frequency distribution with a b value of 2.50 (33). The same study found that regular earthquakes on the creeping section of the San Andreas fault, which are on the shallower portion of the fault above our LFE families in the northwest, have 0.8 < b < 2.0. SSEs at the Cascadia subduction zone have also been suggested to follow a power-law magnitude-frequency distribution with b values between 0.8 and 1.0 (14, 34). The differences in b values might reflect differences in stress condition (35, 36) and fault roughness (37).

Fig. 3 SSE scaling properties.

(A) Noncumulative magnitude-frequency distribution of the SSEs. Black circles show the number of SSEs in different Mw bins. Red dashed line shows maximum-likelihood fit giving a b value of 1.61. (B) Relationship between seismic moment (Mo) and rupture area (A) of SSEs. Black circles show the median A for different Mo bins. Red dashed line shows least-squares fit giving MoA1.5. Blue dashed lines show the MoA1.5 theoretical scaling relationships for circular cracks with different constant stress drops (38). (C) Histogram of SSE duration. Blue vertical line marks the local minimum used to separate the two populations of SSEs. (D) Relationship between Mo and duration (T) of SSEs. Blue horizontal line is the same duration boundary as in (C) that is used to separate the SSEs into two populations. For each SSE population, black circles show the median T for different Mo bins. Red dashed lines show least-squares fits giving MoT3.1 and MoT2.8 for the shorter- and longer-duration SSE populations, respectively.

We find that the SSE moment and rupture area approximately follow the MoA1.5 scaling of regular earthquakes (Fig. 3B). Since we estimated Mo from A and D¯, this is a direct result of D¯A (fig. S5A). There is deviation from this relationship at approximately Mo < 1013.5 (Fig. 3B), probably due to the poorer rupture area estimates for these smaller events since their length and width are comparable to the LFE location uncertainties of 1 to 2 km (10). For bins of Mo > 1013.5 N · m, we calculate the median rupture area. Fitting these median values, we obtain MoA1.5 (Fig. 3B). This is consistent with our SSEs being mostly “unbounded” events (15) where both the length and width are still growing as the rupture propagates (fig. S5B), although these events show a greater tendency to rupture in the along-strike direction (Fig. 2B) with the median SSE aspect ratio LW=2. For the largest events, W eventually saturates while L and D¯ continue to grow (fig. S5B). This maximum W of ∼13 km (fig. S5B) could reflect the maximum channel width of SSEs at the central San Andreas fault, as determined by rheological change with depth, or simply a coincidence of asperities’ distribution that resulted in the LFEs being located in a narrow 13-km band (Fig. 1B) even though SSEs can rupture beyond this zone.

The SSE stress drops are mostly within the range of 1 to 10 kPa (Fig. 3B), with a median value of 6 kPa based on a circular crack model (38). This is a few orders of magnitude smaller than the stress drop of regular earthquakes on the San Andreas fault, which are estimated to be on the order of 0.1 to 100 MPa (39). The SSEs have a median stress drop of 2 kPa when assuming strike-slip faulting on a rectangular fault instead (40). Our results are comparable to the estimated stress drop of ∼10 kPa from spectral analysis of two LFE families on the San Andreas fault (41) and is consistent with the low stress drops (0.1 to 10 kPa) typically obtained for slow earthquake phenomena [e.g., (12, 14, 21, 34, 42)].

The logarithm of the SSE duration has a bimodal distribution (Fig. 3C) with a clear separation at approximately 103.5 s (84 min), which is in the time range when g has a plateau (Fig. 2A). This translates to a bimodal distribution in the rupture velocity, with two modes at 6 and 708 km/day (fig. S6). These velocities are within the range of tremor migration velocities previously observed at the San Andreas fault (18), as well as at the Nankai (5) and Cascadia subduction zones (21). We use T = 103.5 s as a boundary to separate the SSEs into two populations. For the shorter-duration population, we calculate the median T for Mo bins in the range of [1011 to 1015] N · m. For the longer-duration population, we calculate the median T for Mo bins in the range of [1012.5 to 1016.5] N · m. Fitting these median values, we obtain MoT3.1 and MoT2.8 for the shorter- and longer-duration SSE populations, respectively (Fig. 3D). Recently, Michel et al. (14) suggested that 40 Mw 5.3 to 6.8 SSEs cataloged by inverting GPS measurements at the Cascadia subduction zone likely follow a MoT3 relationship. However, their best-fit value is MoT5, and for these large SSEs that rupture the entire down-dip width of the slip zone, a linear scaling is expected instead (15). Therefore, questions remain regarding how well constrained the scaling relationship is given the small sample size, as well as whether the observed scaling relationship is unique to the Cascadia subduction zone. At the Mexican subduction zone, Frank and Brodsky (13) calibrated daily median S wave amplitudes of LFEs from 58 families to GPS-measured moment rate of 12 SSEs. They then use the calibrated seismic to geodetic moment rates scaling to estimate daily moment release from the LFE catalog and find that the underlying SSEs follow a MoT3 relationship. Since we obtain the same scaling relationship for SSEs at a transform fault using a fundamentally different method, it is likely a universal property that unbounded SSEs follow a MoT3 scaling (15) similar to regular earthquakes (38). We also show that there are two populations of SSEs at the San Andreas fault with vastly different rupture velocities, with each population approximately following a cubic moment-duration scaling (Figs. 3D and 4). Therefore, the previously apparent MoT scaling suggested to be a unique characteristic of slow earthquakes (12) is possibly an artifact of fitting the relationship across different populations of slow-slip phenomena spanning a wide range of rupture velocities.

CONCLUSIONS

Fig. 4 Comparison between slow events on the San Andreas fault and proposed moment-duration scaling for regular (green bar) and slow (blue bar) earthquakes (12).

Green circles, two LFE families (41); magenta circle, episodic creep events (45); blue circle, slow earthquake (46); red circle, average of 20 SSEs (7); black circles, SSEs in this study (same as in Fig. 3D).

We have shown that LFEs can be used to catalog the large number of SSEs that are episodically loading the shallow seismogenic zone. We find that the LFEs’ clustering properties are similar to earthquake aftershock sequences but with interfamily excitation in the along-strike direction being 10 times stronger than in the along-dip direction. The underlying SSEs have two modes of rupture velocity with stress drop and rupture velocity a few orders of magnitude smaller than regular earthquakes. However, the SSEs follow similar magnitude-frequency, moment-area, and moment-duration scaling as regular earthquakes, suggesting that transient fault slip with velocities spanning many orders of magnitude may be governed by the same dynamics. Our observations provide important constraints on the relationships between LFEs, SSEs, and regular earthquakes.

MATERIALS AND METHODS

Modeling LFE rate

For the most generalized model, the LFE rate at time t can be modeled asλ(t)=x=1D(μx+y=1D tjy<tgxy(ttjy))(2)where D is the number of LFE families, μx is the uniform background rate of LFE family x, gxy is the time-dependent excitation kernel that encodes the influence of an LFE from family y on the future rate of family x, and tjy is the occurrence time of LFE j from family y. g(t) can be discretized as piece-wise constantgm=g(Tm<t<Tm+1)(3)where Tm are the discretization times and m ∈ [1 : M] with T1 = 0 and TM = 10 days. For M = 20 and D = 88, we would have to solve for ∼155,000 parameters. Therefore, we simplify the model by assuming that g(t) does not vary between different families such thatλ(t)=x=1D(μx+y=1D tjy<tKxyg(ttjy))(4)where Kxy encodes the excitation strength, i.e., the number of events in family x excited by an event in family y on average, and g(t) is normalized such that it represents a probability density functionm=1M gm δtm=1(5)where δtm is the discretization time step of gm, i.e., δtm = Tm + 1Tm. With this model, we only have to solve for ∼8000 parameters. ∑x, yKxy has to be <D for the model to be stable; ∑x, yKxy > D would result in an infinite number of events within a finite time period, i.e., λ growing to infinity. We adopt an expectation-maximization approach to estimate μx, Kxy, and gm (22, 23). The expectation step involves computing the probability that event i from family x was excited by event j from family ypijxy=Kxyg(titj)μx+z=1D tlz<tiKxzg(titlz)(6)where {tlz<ti} are all events occurring before ti, and the probability that event i from family x was a background event (not excited by any previous events)pi0x=μxμx+z=1D tlz<tiKxzg(titlz)(7)such thatpi0x+j,y pijxy=1(8)

The log-likelihood function associated with Eq. 4 is thusL=x=1D [i=1N pi0xlog μxμxT+y=1D m=1M[i,jAmxypijxylog KxygmnyKxygmδtm]](9)where T is the duration of the time series, Amxy is the set of pairs of events such that (tixtjy)(Tm+1Tm), and ny is the number of events from family y.

The maximization step then involves updating the background rates asLμx=0  μx=1Ti=1N pi0x(10)the excitation kernel asLgm=0  gm=x=1D y=1D i,jAmxy pijxyδtmx=1D y=1D nyKxy(11)and the excitation strengths asLKxy=0  Kxy=i,j pijxynym=1M gmδtm=i,j pijxyny(12)as a result of normalizing g following Eq. 5. We start with initial guesses of μx (uniform values of 1 for all families), gm (random values between 0 and 1), and Kxy (random values between 0 and 1). We then iterate through the expectation-maximization steps until the difference of the estimated parameters between two successive iterations is smaller than a certain threshold. We obtain ∑x, yKxy = 81.6. We generate a 10-year synthetic catalog with the g, μ, and Kxy that we obtained for the San Andreas LFE catalog, with Kxy first multiplied by 0.8 to test whether we can resolve parameters from a catalog with weaker interevent excitation. We find that we can correctly estimate the model parameters from the synthetic catalog using the proposed expectation-maximization algorithm (fig. S7).

Since it has been observed that different LFE families have different interevent time distribution (16), we explored having one g(t) per family such thatλ(t)=x=1D(μx+y=1D tjy<t Kxygx(ttjy))(13)where gx is the time-dependent excitation kernel that encodes the influence of an LFE on the future rate of family x. With this model, we have to solve for ∼9500 parameters and obtain ∑x, yKxy = 83.3. While gx(t)’s shape varies depending on the LFE family’s interevent time distribution (fig. S3), we obtain similar scaling properties (figs. S2B and S8) as when using Eq. 4, except that we now obtain a b value of 1.35 (fig. S8A) and MoT4.2 for the shorter-duration SSE population (fig. S8D).

SUPPLEMENTARY MATERIALS

Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/6/33/eabb2489/DC1

https://creativecommons.org/licenses/by-nc/4.0/

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.

REFERENCES AND NOTES

Acknowledgments: We thank B. Yuan, C. Cattania, D. M. Saffer, G. C. Beroza, P. Segall, W. L. Ellsworth, and W. B. Frank for fruitful discussions. We also thank B. Rousset, C. H. Scholz, and H. Houston for providing helpful feedback on the manuscript. Funding: Y.J.T. acknowledges support by the Chateaubriand fellowship and the “Make Our Planet Great Again” initiative. Author contributions: Y.J.T. and D.M. designed the study. Y.J.T. performed the data analysis, made the figures, and wrote the initial draft of the manuscript. All authors contributed to the interpretation of the results and writing of the manuscript. Competing interest: The authors declare that they have no competing interests. Data and materials availability: The LFE catalog is available from the supplementary materials of Shelly (10). The statistical modeling was performed using a modified version of an open-source software (43) that can be downloaded from https://x-datainitiative.github.io/tick/. All data needed to evaluate the conclusions in the paper are present in the paper and/or the Supplementary Materials. Additional data related to this paper can be requested from the authors.
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