Automated near-field deformation detection from mobile laser scanning for the 2014 Mw 6.0 South Napa earthquake

Quantifying off-fault deformation in the near field remains a challenge for earthquake monitoring using geodetic observations. We propose an automated change detection strategy using geometric primitives generated using a deep neural network, random sample consensus and least squares adjustment. Using mobile laser scanning point clouds of vineyards acquired after the magnitude 6.0 2014 South Napa earthquake, our results reveal centimeter-level horizontal ground deformation over three kilometers along a segment of the West Napa Fault. A fault trace is detected from rows of vineyards modeled as planar primitives from the accumulated coseismic response, and the postseismic surface displacement field is revealed by tracking displacements of vineyard posts modeled as cylindrical primitives. Interpreted from the detected changes, we summarized distributions of deformation versus off-fault distances and found evidence of off-fault deformation. The proposed framework using geometric primitives is shown to be accurate and practical for detection of near-field off-fault deformation.

The influence of off-fault deformation zones on the near-fault distribution of coseismic landslides

Coseismic landslides are observed in higher concentrations around surface-rupturing faults. This observation has been attributed to a combination of stronger ground motions and increased rock mass damage closer to faults. Past work has shown it is difficult to separate the influences of rock mass damage from strong ground motions on landslide occurrence. We measured coseismic off-fault deformation (OFD) zone widths (treating them as a proxy for areas of more intense rock mass damage) using high-resolution, three-dimensional surface displacements from the 2016 Mw 7.8 Kaikōura earthquake in New Zealand. OFD zones vary in width from ~50 m to 1500 m over the ~180 km length of ruptures analyzed. Using landslide densities from a database of 29,557 Kaikōura landslides, we demonstrate that our OFD zone captures a higher density of coseismic landslide incidence than generic “distance to fault rupture” within ~650 m of surface fault ruptures. This result suggests that the effects of rock mass damage within OFD zones (including ground motions from trapped and amplified seismic waves) may contribute to near-fault coseismic landslide occurrence in addition to the influence of regional ground motions, which attenuate with distance from the fault. The OFD zone represents a new path toward understanding, and planning for, the distribution of coseismic landslides around surface fault ruptures. Inclusion of estimates of fault zone width may improve landslide susceptibility models and decrease landslide risk.

Supplemental Material: The influence of off-fault deformation zones on the near-fault distribution of coseismic landslides

Off-fault deformation (OFD) data, additional methodology, and analysis.<br>

Supplemental Material: The influence of off-fault deformation zones on the near-fault distribution of coseismic landslides

Off-fault deformation (OFD) data, additional methodology, and analysis.<br>

Optimal design of the Sermo Fault deformation monitoring network using sensitivity criteria based on geological information

The Sermo Reservoir is located in Kulon Progo District, Special Region of Yogyakarta, Indonesia. It plays a vital role in providing water to its surrounding communities. According to the geological structure of this area, a fault exists near the Ngrancah River and passes through the reservoir’s inundation. At present, a network consists of 15 Global Navigation Satellite System (GNSS) monitoring stations spread around the Sermo Fault to monitor its deformation. However, this network was designed without consideration of geological parameters. This study aimed to design an optimal deformation monitoring network of the Sermo Fault by taking into account geological information and evaluate the existing network, specifically, the optimal distance for the station to the fault plane. Geological surveys were carried out to obtain information regarding the type and characteristics of the fault. This information was used as one of the parameters in the optimization design, using sensitivity criteria, of a network to monitor for the optimal distance between the observation station and fault. Using the strike-slip fault model, the optimal distance obtained was from 4.5 to 8.5 km from the fault plane. The existing stations of the Sermo Fault deformation monitoring network are about 0.07–3.3 km away from the fault. Therefore, the network is insufficiently sensitive and needs to be developed by adding stations that are more than 4.5 km away from the fault. This study designed an alternative network by rearranging the stations’ location to obtain a better configuration and sensitivity.

The slow self-arresting nature of low-frequency earthquakes

Low-frequency earthquakes are a series of recurring small earthquakes that are thought to compose tectonic tremors. Compared with regular earthquakes of the same magnitude, low-frequency earthquakes have longer source durations and smaller stress drops and slip rates. The mechanism that drives their unusual type of stress accumulation and release processes is unknown. Here, we use phase diagrams of rupture dynamics to explore the connection between low-frequency earthquakes and regular earthquakes. By comparing the source parameters of low-frequency earthquakes from 2001 to 2016 in Parkfield, on the San Andreas Fault, with those from numerical simulations, we conclude that low-frequency earthquakes are earthquakes that self-arrest within the rupture patch without any introduced interference. We also explain the scaling property of low-frequency earthquakes. Our findings suggest a framework for fault deformation in which nucleation asperities can release stress through slow self-arrest processes.