Connection between linear surface displacements and the seismic fault
The depth of the main earthquake’s hypocenter (obtained from the seismic wave) was ~ 35 km (Earthquake Research Committee 2018). Furthermore, the seismic source fault model obtained from the crustal deformation showed that the depth of the fault plane’s top was ~ 16 km and it had a dip angle of 74° (Kobayashi et al. 2019). Based on the surface displacements shown in Figs. 5 and 6, the linear surface displacement features represent the movement of the reverse fault along its low dip angle. In addition, the horizontal distance from the linear surface displacements to the epicenter was ~ 10 km (Fig. 8). The known active faults on the north side of the linear surface displacements had low dip angles at 3 km depth and almost zero at 2 km depth (Earthquake Research Committee 2010; Ikeda et al. 2002; Yokokura et al. 2014).
Given these facts, if the linear surface displacements are considered to be directly connected to the hypocenter, the fault’s low dip angle at shallow depth must change to a high dip angle at around 30 km depth. Therefore, it is unlikely that the linear surface displacements directly connect with the focal seismic fault. Furthermore, since few aftershocks were observed at depths < 10 km (Earthquake Research Committee 2018), even if the linear surface displacements were structurally connected to the hypocenter, it is almost impossible that the motion of the linear surface displacements was directly linked to the fault movement at the hypocenter.
Nature of linear surface displacements
Similar linear surface displacements induced by other large earthquakes were first presented by Sangawa et al. (1985) with respect to faults on the western coast along the southern part of Suruga Bay in central Japan; these had not caused a large earthquake independently but had moved as a result of other large earthquakes. Recently, Fujiwara et al. (2016) identified more than 200 linear surface displacements associated with the 2016 Kumamoto Earthquake that shared some common features with those in this study:
The typical length was several kilometers or more, with linear or gentle curvilinear shapes, and displacements of a few centimeters to several tens of centimeters.
Many were far from the seismogenic fault, making it unlikely that they were directly connected.
There is no evidence that they generated strong seismic waves at the time of the main earthquake.
They likely moved passively and are considered results of, not the cause of, the main earthquake.
Given the correlation between topography and displacement, some have been recognized as active faults from their topographical features. Therefore, there is a possibility that similar movements have occurred in the past.
Their strike directions and displacement patterns are consistent with the surrounding stress field over a wide region.
Furthermore, Kobayashi et al. (2019) pointed out that a ΔCFF estimate suggests that the static stress change due to the 2018 Hokkaido Eastern Iburi Earthquake could have encouraged reverse slip in the area around the linear surface displacements of several dozen kilopascals. Given this context, the linear surface displacements were likely produced by secondary non-seismogenic fault motions passively generated by the 2018 Hokkaido Eastern Iburi Earthquake.
As for the independent horizontal movement in area D (Figs. 5, 6), other independent movements were found in the Mukawa area (Fig. 9). Because the latter occurred in flat coastal areas, they can be considered as lateral spread related to liquefaction. Although the movement of area D was similar to that in Mukawa, the former was situated on a slightly elevated area and seems not to have moved by lateral spreading. Togo (2000) pointed out that the surface expression of a reverse fault tends to move forward (the lower side of the fault), generating a new fault plane, and so area D may be a fault expression shaped by erosion.
Alternatively, area D could have remained in place while the surrounding area moved east (similar to lateral spreading). In this scenario, since the whole area should have shifted fairly uniformly to the east, a uniform horizontal slip must have occurred at a certain depth over a wide area of ~ 15 km N–S and ~ 5 km E–W. Fully understanding the complex movements of area D requires further study.
Use of linear surface displacements to recognize active faults
Although there is no evidence that the linear features considered in this study generated strong seismic waves at the time of the main earthquake, there is a possibility that these represent surface traces of active faults and could cause large earthquakes in the future. At present, we can provide no concrete answer to this question.
As shown in Fig. 8, the linear surface displacements appear to be geographical extensions of the known active faults. In addition, their strikes and displacements match surrounding geological features and fault-like structures were found just beneath them by a seismic reflection survey (Yokokura et al. 2014). Such circumstantial evidence sufficiently indicates that these features are most likely the traces of active faults.
Some of the linear surface displacements recognized after the 2016 Kumamoto Earthquake included those that had been identified as active faults by their topography, but the number was limited (Fujiwara et al. 2016). However, the linear surface displacements detected after the 2018 Hokkaido Eastern Iburi Earthquake had not been identified as active faults. One of the possible reasons why previous studies could not map these lineaments as ‘active faults’ is that the lineaments were not straight lines owing to the low dip angle. Even in this situation, our results support the existence of hidden faults, of which the linear surface displacements are most likely surface traces. On this basis, the detection of linear surface displacements can be used for active fault identification in the future.
However, it is also likely that those previously recognized ‘active faults’ from their topographical features did not generate seismic waves, either. Therefore, it is important to recognize the diversity of ‘active faults’ for earthquake hazard assessment.
Coherence analysis in mountainous and urban areas
Figures 10b and 12c, d show normalized coherence-change maps obtained by the same method. In Fig. 10b, fluctuations associated with liquefaction in urban areas can be clearly detected. Although Fig. 12c, d clearly shows the landslide distribution in the mountains, it is possible that these features were excessively extracted (see also Additional file 1: Fig. S4). There are two possible reasons for this: the sensitivity of the normalized coherence change could be too high, or the original coherence was too low in the mountains. In urban areas, the presence of buildings and pavements means that changes in the surface itself are relatively small, but in mountainous areas sediment moves more freely, and thus surface conditions can change more significantly. Although normalized coherence change is capable of detecting minute deformations, it does have a tendency toward saturation. Thus, in mountainous landslide-prone areas, it is possible that small displacements or changes occurring near actual landslides could be detected and classified as actual landslides.
Although the normalized coherence-change method is inaccurate in areas with low coherence, it can be corrected by masking those areas using a coherence filter for the pre-earthquake image (Watanabe et al. 2016). We used masks of 0.5 or less in the urban area (Fig. 10b) and of 0.2 in the mountainous area (Fig. 12c, d). In the latter, adopting a mask of 0.5 or less could improve the accuracy, but since the coherence was extremely low, values could not be obtained in most places using the coherence filter. Therefore, it was difficult to detect coherence changes in mountainous areas, and other methods such as multi-polarimetry (Watanabe et al. 2016), should be considered.
On the contrary, in areas masked using the normalized phase noise deviation (Figs. 11, 12a, and Additional file 1: Fig. S4b), the degradation area (large phase variation) of the SAR interferogram roughly agreed with the landslide area interpreted by the aerial photographs. Consequently, the normalized phase noise deviation can be used to roughly estimate the spatial distribution of landslide concentration areas for rapid response or mitigation.