# Combined logarithmic and exponential function model for fitting postseismic GNSS time series after 2011 Tohoku-Oki earthquake

- Mikio Tobita
^{1}Email author

**Received: **22 December 2015

**Accepted: **3 March 2016

**Published: **12 March 2016

## Abstract

The time series of a postseismic deformation is commonly fitted by a logarithmic or exponential decay function. However, the high-quality postseismic Global Navigation Satellite System (GNSS) time series of the 2011 Mw 9 Tohoku-Oki earthquake indicates that a single decay function cannot be used to represent the postseismic behaviour. We therefore combined the logarithmic (log) and exponential (exp) decay functions and developed methods for obtaining global solutions using nonlinear least squares calculations for such complex functions. Our models significantly improved the fitting performance of the postseismic time series and the prediction performance of the evolution of postseismic deformation. The solutions obtained by the proposed models and methods enabled distinction between the contributions of the log and exp functions, and explanation of characteristic phenomena such as the subsidence that occurs immediately after an earthquake is reversed to an uplift. The analysis of the solutions may suggest that there has been a continuous increase in the contribution of viscoelastic relaxation to postseismic deformation in eastern Japan, whereas the contribution of afterslip has rapidly decreased. The short-term prediction performance and the universal applicability of the proposed models to the Tohoku-Oki earthquake have contributed to the detection of a slow-slip event in the Tokai region. Rather than the existence of a unique single relaxation time for each surface site, our results suggest a unique single relaxation time for each postseismic deformation mechanism at a given subsurface location. Although the predictions were highly dependent on the assigned steady velocities and the long-term relaxation time constants, they indicate that the coseismic subsidence of the Yamoto station in Miyagi prefecture will recover around the year 2020. The estimated relaxation time constants of the present models appeared to be uniform throughout eastern Japan.

## Keywords

## Background

Since the 2011 off the Pacific coast of Tohoku Earthquake (Tohoku-Oki earthquake), crustal deformation has continued to occur in eastern Japan (Ozawa et al. 2012; GSI 2015a). The primary mechanisms of the postseismic deformations are commonly recognized as afterslip and viscoelastic relaxations. However, the relative contributions of the afterslip and viscoelastic relaxations remain unclear (Sun and Wang 2015). An understanding of the relevant mechanisms will contribute to the assessment of earthquake potential in the Japan Trench region and neighbouring regions along the Nankai Trough. For example, the postseismic deformation caused by the Tohoku-Oki earthquake is still being observed in 2015, including in the Tokai region, which is located on the eastern part of the Nankai Trough. The deformation has been a noise source in the monitoring of aseismic slips that might be related to the expected Tokai earthquake. The monitoring is part of the monthly assessments of the Earthquake Assessment Committee (EAC) of Japan, and the elimination of the noise is urgently required.

Attempts have been made to use function fitting regression analysis to investigate postseismic deformation (Takahashi et al. 2004; Hashimoto et al. 2006; Nishimura 2014). The time series of a postseismic deformation is commonly fitted by a logarithmic or exponential decay function. However, the postseismic time series of the 2011 Tohoku-Oki earthquake observed by the GNSS Earth Observation Network System (GEONET) indicates that a single decay function cannot be used to represent the postseismic behaviour (GSI 2014; Tobita and Akashi 2015; Fig. 2).

Global Navigation Satellite System (GNSS) time series data with a high signal-to-noise ratio (S/N) may be used to select an appropriate function fitting model. Hence, in the present study, we derived a fitting function for better explanation of the GNSS time series and prediction of the short-term evolution of postseismic deformation. We utilized summation of decay functions and developed methods for obtaining global solutions by nonlinear least squares calculations using complex functions. We evaluated the fitting and prediction performances of the combined function models, as well as their limitations. Moreover, we demonstrated the universality of the determined relaxation time in the Tohoku-Oki earthquake.

## Methods

The original GNSS time series contained offsets caused by earthquakes, and the offsets hampered the derivation of the fitting functions. We therefore removed those caused by the M 6.0 23 March 2011 Fukushimaken-Hamadori earthquake, M 7.1 7 April 2011 Miyagiken-Oki earthquake, M 7.0 11 April 2011 Fukushimaken-Hamadori earthquake, M 6.4 12 April 2011 Chibaken-Toho-Oki earthquake, M 6.9 23 June 2011 Iwateken-Oki earthquake, M 5.4 29 September 2011 Fukushimaken-Hamadori earthquake, M 6.1 14 March 2012 Chibaken-Toho-Oki earthquake, M 6.9 17 February 2015 Sanriku-Oki earthquake, and M 6.8 13 May 2015 Miyagiken-Oki earthquake.

The Yamoto, Choshi, Miyako, and Minase stations [(a)–(d) in Fig. 1] were used for the main modelling for two reasons. Firstly, among all the GEONET stations, these four stations have acquired extremely high-quality GNSS data from the viewpoint of low noise and seasonal variations. Secondly, although the four stations had all coseismically subsided at the time of the 2011 Tohoku-Oki earthquake, the progresses of their vertical postseismic deformations have been different, as represented by the black and grey observed positions in Fig. 3. The Yamoto station (a) in Miyagi prefecture uplifted rapidly after the earthquake, and the uplift has continued at a rate of ~5 cm/yr since 2013. The Choshi station (b) in Chiba prefecture uplifted rapidly immediately after the earthquake, but the vertical movement slowed down and slightly subsided in 2015. The Miyako station (c) in Iwate prefecture subsided rapidly after the earthquake, but this was reversed to a slow uplift around 2012. The Minase station (d) in Akita prefecture experienced rapid subsidence after the earthquake, and the subsidence was still observed in 2015. These four stations exhibit representative regional crustal deformations and thus provided strong constraints for our nonlinear least squares calculations, as described below.

*D*(

*t*) denotes a displacement component (east, north, or up);

*t*is the time in days relative to the occurrence of the main shock (11 March 2011);

*b*,

*e*, and

*g*denote the timescale parameters of the logarithmic or exponential decays (relaxation times); and

*V*is the steady velocity. Here, we refer to the model represented by Eq. (1) as Model 1 or log + exp model, that represented by Eq. (2) as Model 2 or log + log + exp model, and that represented by Eq. (3) as Model 3 or log + exp + exp model. Whereas the parameters

*a*,

*b*,

*c*,

*d*,

*e*,

*f*, and

*g*were estimated by the nonlinear least squares method, the steady velocity

*V*at each station was assigned the corresponding value of the interseismic velocity between 1 April 1997 and 31 March 2000. The reason for this and the consequent limitations are discussed below in the subsection titled ‘Limitations of Combined log and exp Models’ under ‘Discussion’.

To evaluate the fitting and prediction performances of these models, we used the Akaike information criteria (AIC) (Akaike 1974). We assigned 731 days (2 years) after the 2011 Tohoku-Oki earthquake as the ‘fitting period’, and the number of days between the 732nd and 1681th days (~2 years 7 months) as the ‘prediction period’. One of the reasons why exact 2.00 years (731 days) was used as the fitting period was to minimize the effect of seasonal noise in the GNSS data on the fitting. The prediction performance was also evaluated by computing the deviation of the latest observed GNSS data from the value predicted (calculated) by the fitted model function.

Generally, it is difficult to obtain a global solution in nonlinear least squares calculations, and obtaining a global solution of a more complex function requires greater effort. We thus addressed the problem using the following procedure. Firstly, we assumed that the relaxation times *b*, *e*, and *g* of the three components (east, north, and up, respectively) of the displacements were the same. This was because the parameters represented the characteristic relaxation times of the corresponding source mechanisms (afterslip and viscoelastic relaxation). Secondly, we assumed that the four stations had the same relaxation times (*b*, *e*, and *g*). The basis and validity of this assumption are presented under ‘Discussion’. Thirdly, we used only high-S/N data obtained from the four high-quality stations, which had different vertical movement progressions as noted above. Fourthly, we used multiple starting points, which means that the computations started with different initial values. This adopted approach effectively improved the computability of the global solutions of the nonlinear least squares calculations, as well as the stability of the estimated values of the relaxation time.

## Results

^{8}days for a 1681-day fitting period. The exp + exp model with its two relaxation time constants is better than both the log and exp models, but is obviously poorer than Models 1, 2, and 3. Its prediction performance is particularly poorer. Moreover, while the log + log model is superior to the exp + exp model, it is not superior to Models 1, 2, and 3 in terms of the fitting and prediction performances.

Results of fitting to data for two years using Models 1, 2, and 3

Model number | Standard deviation (mm) | AIC | Relaxation time (days) | |||
---|---|---|---|---|---|---|

Log1 | Log2 | Exp1 | Exp2 | |||

1 | 4.86 | −93387 | 3.9 | N/A | 1110.0 | N/A |

2 | 4.72 | −93865 | 1.4 | 82.0 | N/A | 3217.0 |

3 | 4.71 | −93893 | 2.4 | N/A | 213.3 | 2100.0 |

Here, we attempt to predict future postseismic displacements using Model 2 (Fig. 4). The 50-cm coseismic subsidence of the Yamoto station (a) is predicted to recover around the year 2020. Although the Choshi station (b) was rapidly uplifted immediately after the main shock, the vertical movement is predicted to gradually reverse to subsidence. Furthermore, whereas the subsidence of the Miyako station (c) immediately after the main shock has been reversed to an uplift, the uplift is predicted to slow down. The subsidence of the Minase station (d) is predicted to continue, although it is expected to slow down. The contribution of the exponential term possibly viscoelastic relaxation (see ‘“Relationship between postseismic deformation mechanisms and decay functions’ under ‘Discussion’ section) has gradually increased, while the relative contribution of the logarithmic term possibly mainly afterslip (see ‘Relationship between postseismic deformation mechanisms and decay functions’ under ‘Discussion’ section) has rapidly decreased.

## Discussion

As observed above, we found that a combination of the relaxation times estimated by the simultaneous estimation of the three components of the four stations could be universally applied to GNSS stations in the region. Our results do not suggest a unique single relaxation time for each surface site, but that each postseismic deformation mechanism at a given subsurface location has a unique single relaxation time. Because the fitting and prediction performances of our combined logarithmic and exponential model were reasonably high, we confirmed that each station had observed a surface displacement that is a superposition of the contributions caused by multiple postseismic deformation mechanisms with different timescales, with differing contribution ratios of each mechanism at the different stations. We observed that multiple postseismic deformation mechanisms with different timescales had been continuously active in eastern Japan.

The universality of the relaxation time affords some significant advantages for certain applications of our model. For example, the estimations of the parameters *a*, *d*, and *f* in Eq. (4) is done by linear least squares calculations, while those in Eq. (3) are estimated by nonlinear least squares calculations. This means that there would be no difficulty in obtaining global solutions of Eq. (4). Once the relaxation time has been estimated, users would not have to further solve nonlinear least square equations. One of such possible applications is the simple modelling of nonlinear motions of the International Terrestrial Reference Frame (ITRF) (IAG 2015), which requires a short-term prediction performance.

The relaxation time constants of each of the above equations were estimated by nonlinear least squares calculations using the latest data obtained between 12 March 2011 and 17 October 2015. Because of the possible universal application of these equations over a wide area in Japan, they may be used to fit the GNSS time series of many stations in eastern Japan. The use of these equations to perform linear least squares calculations would enable easier calculation of postseismic displacements. Users can test each model and choose the one most suitable for their purpose.

In conventional approaches, there are cases in which the three components (east, north, and up) of a displacement have very different relaxation times, or different stations have very different relaxations times, or the estimated relaxation times are highly dependent on the fitting period. The findings of the present study suggest that such dependencies imply inappropriate selection of the fitting functions. Specifically, we found that a single set of relaxation times could be used to better explain the postseismic time series of the three components of all the considered stations. However, although there was an improvement in the stability of the relaxation times of our model with respect to the fitting period, the relaxation times still depended on the fitting period. This was probably because the three relaxation time constants still represented multiple numbers, or a broad spectrum, of relaxation times. This is one of the limitations of our model. Moreover, there may be better fitting functions than the functions proposed in this work. These issues would be addressed in a future work of ours.

As an application of our model, we considered the elimination of noise, namely the postseismic deformation of the Tohoku-Oki earthquake in the Tokai region. In a related study, by analysis of the velocity change of the GPS time series, Ozawa et al. (2002) detected a long-term slow-slip event (L-SSE) in the Tokai region between 2000 and 2005 at the boundary between the Eurasian and Philippine Sea plates. Although a similar velocity change on a smaller scale has been observed since 2013, the S/N has been low mainly because of postseismic noise. Thus, the relationship between the small velocity change and the L-SSE was uncertain. The elimination of the noise has thus been an urgent need. The GSI applied Model 1 to their analysis of the recent velocity change of the GPS time series in the Tokai region and successfully eliminated the noise due to postseismic deformation, after which the L-SSE was detected (GSI 2015b). Although Model 1 has the poorest fitting and prediction performance among the three models presented in this paper, its simplicity affords two advantages under these conditions. Firstly, it enables modelling using low-S/N data observed far from the source region of the Tohoku-Oki earthquake, such as in the Tokai region. Secondly, it could be used for a shorter fitting period in the early postseismic stage because the global solutions can be more easily obtained compared to when the other two models used.

### Relationship between postseismic deformation mechanisms and decay functions

The determination of the relative contributions of the afterslip and viscoelastic relaxations to the postseismic deformation of the 2011 Tohoku-Oki earthquake was the primary objective of this study. Although this is very difficult to achieve, we attempted to determine the possible relationships between the respective mechanisms of the afterslip and viscoelastic relaxations and the exp and log decay functions of our model. Scholz (1990), Marone et al. (1991), and Scholz (1998) all proposed that the afterslip increases logarithmically with time. Freed et al. (2007) and Gahalaut et al. (2008) also used a logarithmic function to fit GPS displacement time series. Furthermore, the postseismic surface deformations determined by Maxwell viscoelastic rheologies exhibited an exponential-like relaxation phase (Hetland and Hager 2006). Moreover, postseismic displacements calculated by models with biviscous Burgers rheologies are often described by logarithmic functions (Hetland and Hager 2006). Hoechner et al. (2011) also proposed that the response of a Burgers body consists of two superposed exponential decays.

Thus, the exponential term may represent the viscoelastic relaxation, while the logarithmic term probably includes the contribution of the afterslip and possibly that of the viscoelastic relaxation. Our results therefore may indicate that the contribution of the viscoelastic relaxation has been on an increase, while that of the afterslip has rapidly decreased. The superiority of Models 2 and 3 over Model 1 suggests a contribution of short-term viscoelastic relaxation, corresponding to the second term in Eqs. (2) and (3). This is consistent with the findings of Sun et al. (2014).

Hu et al. (2014) investigated the contributions of poroelastic rebound to the postseismic deformation of the Tohoku-Oki earthquake. Their test results indicate that the poroelastic contribution to the surface deformation is limited to the vicinity of the rupture area. Their model predicts 0–5 cm uplifts and horizontal displacements less than 2 cm on land. These minor contributions of poroelastic rebound are probably included in the GNSS time series as a minor factor.

### Function fitting of sum of multiple decay functions

The degree of complexity of the fitting function may depend on the magnitude of the earthquake because of the following two reasons. Firstly, the decay time of the subsurface viscoelastic relaxation depends on the subsurface local conditions such as the viscosity and thickness of the material. Secondly, a larger earthquake generally has a wider source area with various subsurface local conditions. Thus, the postseismic surface deformation (produced by a combination of effects resulting from multiple subsurface deformation mechanisms) of a larger earthquake may have a broader spectrum of timescales, and more decay function terms with different timescales may therefore be required to fit the time series of the postseismic deformation. In this case, for the same number of relaxation time constants, a logarithmic function will be more advantageous for the fitting and prediction of the postseismic deformation compared to an exponential function.

### Limitations of combined log and exp models

We assumed that the velocity *V* in Eqs. (1)–(3) does not change, and we therefore used the average velocity between 1 April 1997 and 31 March 2000 as the steady velocity at each station. The reason why we did this rather than estimate *V* was because the evolution of the exponential decay term (indicated by the light blue curves in Figs. 4, 5) over a long relaxation time is similar to that of the velocity term (indicated by the green curves in Figs. 4, 5), and the correlation between the two is very high. This makes it very difficult to obtain a stable global solution. The period just before the Tohoku-Oki earthquake was not selected because Suito et al. (2011) suggested that large transient seaward displacements were observed at Iwate, Miyagi, Fukushima, and Ibaraki several years prior to the Tohoku-Oki earthquake, and we could therefore not obtain steady velocities for this period.

As shown in Figs. 3, 4, and 5, the future velocities will approach the assigned steady velocities. The long-term predictions of our models are thus highly dependent on the steady velocities and the long-term relaxation time constants. The assigned steady velocity is thus very important, especially for predictions. Future observations will either validate or invalidate the assumptions and predictions of the present work. In this context, our models may be the key to resolving the question of whether *V* is constant throughout a seismic cycle.

To investigate this question, we conducted a test simulation to attempt an estimation of the steady velocity component. Actually, no reasonable stable global solution could be obtained by this procedure. Nevertheless, it was imperative for us to report the tendency of the estimated steady velocity for reference. Firstly, the estimated east component of the steady velocity of each of the four stations was negative (i.e. westward). Secondly, the estimated vertical component of the steady velocity in each of the stations was positive (indicating uplift), while the estimated long-term exp component was negative (indicating subsidence).

As we mentioned above, although the long-term prediction is highly dependent on the steady velocity *V*, it could not be estimated. This is the main prediction limitation of log and exp models. Other limitations constitute factors that may cause the actual future time series of the postseismic deformations to deviate from our predictions. They are as follows: (1) the limitation of the function fitting, (2) status change of the postseismic deformation mechanisms, (3) variable (inconstant) interseismic velocity, (4) future coseismic deformations, and (5) observation errors. The actual postseismic deformations could deviate from the predictions due to only some of the above factors. Hence, rather than to make predictions, the primary objective of this study was to develop new models for predicting postseismic deformations, demonstrate the capability of the proposed models, and provide fundamental information for better understanding of crustal activities.

## Conclusions

- 1.
We proposed combined logarithmic and exponential function models for fitting high-S/N GNSS postseismic deformation data of the 2011 Tohoku-Oki earthquake.

- 2.
By simultaneous estimation of the common relaxation time constants of three-component GNSS data obtained from four stations with different vertical evolutions, we presented an improved method for obtaining global nonlinear least squares solutions.

- 3.
The log + log + exp model (Model 2; Fig. 4) and log + exp + exp model (Model 3; Fig. 5) have significantly better fitting and short-term prediction performances than the conventional decay function models (e.g. Fig. 2).

- 4.
Our findings suggest that multiple postseismic deformation mechanisms with different timescales have been active in eastern Japan.

- 5.
Our results may indicate that the effect of viscoelastic relaxation on land GNSS stations has been on a gradual increase, while that of afterslip has rapidly decreased.

- 6.
Limitations and potential: The predictions of our model were significantly dependent on the assigned velocities. Future observations are expected to determine the validity of the assigned values and predictions.

- 7.
The proposed models, which were developed using relaxation time constants that were estimated only once, appear to be universally applicable, and this means that linear rather than nonlinear least squares calculations can be used for various applications.

- 8.
As part of its monthly assessments, the Earthquake Assessment Committee in Japan used our model to detect the Tokai L-SSE, which has started in 2013.

## Declarations

### Acknowledgements

We would like to thank Reiko Kai for preparing the GEONET data, Hisashi Suito for his valuable discussions, and Teruyuki Kato for his useful advice on the application of our model.

### Competing interests

The author declares that he has no competing interests.

**Open Access**This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

## Authors’ Affiliations

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