We report horizontal sliding of the kilometre-scale geologic block under the Aso hot springs (Uchinomaki area) caused by vibrations from the 2016 Kumamoto earthquake (Mw 7.0). Direct borehole observations demonstrate the sliding along the horizontal geological formation at ~50 m depth, which is where the shallowest hydrothermal reservoir developed. Owing to >1 m northwest movement of the geologic block, as shown by differential interferometric synthetic aperture radar (DInSAR), extensional open fissures were generated at the southeastern edge of the horizontal sliding block, and compressional deformation and spontaneous fluid emission from wells were observed at the northwestern edge of the block. The temporal and spatial variation of the hot spring supply during the earthquake can be explained by the horizontal sliding and borehole failures. Because there was no strain accumulation around the hot spring area prior to the earthquake and gravitational instability could be ignored, the horizontal sliding along the low-frictional formation was likely caused by seismic forces from the remote earthquake. The insights derived from our field-scale observations may assist further research into geologic block sliding in horizontal geological formations.

We have identified areas of soil liquefaction by the analysis of surface changes caused by the 2011 Tohoku earthquake, using synthetic aperture radar (SAR) interferometry in the Kanto region of Japan. Changes in surface scattering properties were evaluated using phase-corrected coherence, computed from the reflective intensity (amplitude) of SAR data. Often, the loss of coherence (decorrelation) is simply considered to represent areas damaged from the disaster. However, temporal decorrelation could also be induced by ordinal surface cover change in addition to disaster damage. Therefore, we use a coherence change threshold to discriminate significant decorrelation caused by soil liquefaction from that produced by ordinal surface cover changes. Moreover, local surface displacements are estimated using phase information from the SAR data. Our results compare favorably with those from surveys of sand boils and aerial photography, showing that surface changes derived from SAR data are associated with soil liquefaction. Our results demonstrate that soil liquefaction occurred mainly near the waterfront along Tokyo Bay and the Tone River, and ground subsidence was widely distributed.

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We estimated recent surface displacements around Bangkok by means of persistent scatterer interferometry with ALOS/PALSAR images acquired from November 2007 to December 2010. Land subsidence due to excessive groundwater pumping has been reported in this region. However, we detected ground surface uplift around the mega-city, along with seasonal surface displacement, with high spatial resolution. We then discriminated long-term natural rebound and seasonal displacement by fitting exponential and sinusoidal functions to displacement time-series, and mapped their spatial distributions. This mapping allowed us to infer that the second and third shallowest aquifers are laterally continuous, whereas the shallowest aquifer has lateral discontinuities. The temporal decay rate of the long-term rebound might reflect spatial changes of the Chao Phraya River watershed or the magnitude of the preceding groundwater extraction. We demonstrated that our method of decomposing the displacement time series into different spatial and temporal patterns is useful for understanding aquifer connectivity and the elastic response pattern in an aquifer system.

Ground-penetrating radar allows the acquisition of many images for investigation of the pavement interior and shallow geological structures. Accordingly, an efficient methodology of detecting objects, such as pipes, reinforcing steel bars, and internal voids, in ground-penetrating radar images is an emerging technology. In this paper, we propose using a deep convolutional neural network to detect characteristic hyperbolic signatures from embedded objects. As a first step, we developed a migration-based method to collect many training data and created 53510 categorized images. We then examined the accuracy of the deep convolutional neural network in detecting the signatures. The accuracy of the classification was 0.945 (94.5%)–0.979 (97.9%) when using several thousands of training images and was much better than the accuracy of the conventional neural network approach. Our results demonstrate the effectiveness of the deep convolutional neural network in detecting characteristic events in ground-penetrating radar images.