Modeling of the 1783 Tsunami Event in Scilla Generated by Landslide

In 1783, an event that has gone down in history as the great seismic crisis in Calabria began, during which two major earthquakes occurred, affecting the Calabrian ridge from the Strait to the north. Between 6 and 7 February in Scilla a tsunami occurred that caused the greatest number of victims in Italy: 1500 people. The mechanism that triggered the tsunami was the detachment of a ridge of rock due to a violent earthquake that affected the area; this detachment caused a subaerial landslide which, by sliding, then deposited the rock on the seabed a few kilometers from the coast, immediately generating the tsunami event. The objective of this study is to perform numerical simulations for tsunami events that occurred in history and use models that perform the propagation of a tsunami, using the best possible bathymetric and topographic data and the historical data to compare the validity of the results. In this way, one can obtain the validation of a model that can be used to simulate possible events of this magnitude on the Calabrian coasts and therefore be able to develop a reliable early warning tsunami system; it also has the advantage of perfectly combining computational burdens and the validity of results.

Using Remote Sensing and Machine Learning to Locate Groundwater Discharge to Salmon-Bearing Streams

We hypothesized topographic features alone could be used to locate groundwater discharge, but only where diagnostic topographic signatures could first be identified through the use of limited field observations and geologic data. We built a geodatabase from geologic and topographic data, with the geologic data only covering ~40% of the study area and topographic data derived from airborne LiDAR covering the entire study area. We identified two types of groundwater discharge: shallow hillslope groundwater discharge, commonly manifested as diffuse seeps, and aquifer-outcrop groundwater discharge, commonly manifested as springs. We developed multistep manual procedures that allowed us to accurately predict the locations of both types of groundwater discharge in 93% of cases, though only where geologic data were available. However, field verification suggested that both types of groundwater discharge could be identified by specific combinations of topographic variables alone. We then applied maximum entropy modeling, a machine learning technique, to predict the prevalence of both types of groundwater discharge using six topographic variables: profile curvature range, with a permutation importance of 43.2%, followed by distance to flowlines, elevation, topographic roughness index, flow-weighted slope, and planform curvature, with permutation importance of 20.8%, 18.5%, 15.2%, 1.8%, and 0.5%, respectively. The AUC values for the model were 0.95 for training data and 0.91 for testing data, indicating outstanding model performance.

Precision Evaluation and Fusion of Topographic Data Based on UAVs and TLS Surveys of a Loess Landslide

In recent years, low-cost unmanned aerial vehicles (UAVs) photogrammetry and terrestrial laser scanner (TLS) techniques have become very important non-contact measurement methods for obtaining topographic data about landslides. However, owing to the differences in the types of UAVs and whether the ground control points (GCPs) are set in the measurement, the obtained topographic data for landslides often have large precision differences. In this study, two types of UAVs (DJI Mavic Pro and DJI Phantom 4 RTK) with and without GCPs were used to survey a loess landslide. UAVs point clouds and digital surface model (DSM) data for the landslide were obtained. Based on this, we used the Geomorphic Change Detection software (GCD 7.0) and the Multiscale Model-To-Model Cloud Comparison (M3C2) algorithm in the Cloud Compare software for comparative analysis and accuracy evaluation of the different point clouds and DSM data obtained using the same and different UAVs. The experimental results show that the DJI Phantom 4 RTK obtained the highest accuracy landslide terrain data when the GCPs were set. In addition, we also used the Maptek I-Site 8,820 terrestrial laser scanner to obtain higher precision topographic point cloud data for the Beiguo landslide. However, owing to the terrain limitations, some of the point cloud data were missing in the blind area of the TLS measurement. To make up for the scanning defect of the TLS, we used the iterative closest point (ICP) algorithm in the Cloud Compare software to conduct data fusion between the point clouds obtained using the DJI Phantom 4 RTK with GCPs and the point clouds obtained using TLS. The results demonstrate that after the data fusion, the point clouds not only retained the high-precision characteristics of the original point clouds of the TLS, but also filled in the blind area of the TLS data. This study introduces a novel perspective and technical scheme for the precision evaluation of UAVs surveys and the fusion of point clouds data based on different sensors in geological hazard surveys.

Multimodal Data and Multiscale Kernel-Based Multistream CNN for Fine Classification of a Complex Surface-Mined Area

Fine land cover classification (FLCC) of complex landscapes is a popular and challenging task in the remote sensing community. In complex surface-mined areas (CSMAs), researchers have conducted FLCC using traditional machine learning methods and deep learning algorithms. However, convolutional neural network (CNN) algorithms that may be useful for FLCC of CSMAs have not been fully investigated. This study proposes a multimodal remote sensing data and multiscale kernel-based multistream CNN (3M-CNN) model. Experiments based on two ZiYuan-3 (ZY-3) satellite imageries of different times and seasons were conducted in Wuhan, China. The 3M-CNN model had three main features: (1) multimodal data-based multistream CNNs, i.e., using ZY-3 imagery-derived true color, false color, and digital elevation model data to form three CNNs; (2) multisize neighbors, i.e., using different neighbors of optical and topographic data as inputs; and (3) multiscale convolution flows revised from an inception module for optical and topographic data. Results showed that the proposed 3M-CNN model achieved excellent overall accuracies on two different images, and outperformed other comparative models. In particular, the 3M-CNN model yielded obvious better visual performances. In general, the proposed process was beneficial for the FLCC of complex landscape areas.

Trachyandesite of Kennedy Table, its vent complex, and post−9.3 Ma uplift of the central Sierra Nevada: Reply

We thank Emmanuel Gabet for his interest in our work on the Trachyandesite of Kennedy Table and for the opportunity to more fully explain our methods. We (Hildreth et al., 2021) claimed that various lines of evidence from the lava flow strongly support ∼1° of tilting of the central Sierra Nevada since 9.3 Ma. Gabet (2021) stated, “However, their analyses rely on inferences and assumptions that are not supported by field evidence.” First, he addressed the issue of whether the sinuous lava-flow remnants east of Millerton Lake are fortuitously shaped erosional remnants of a planar lava flow, or whether they are fossilized meanders of the paleo−San Joaquin River, which he terms to be our “interpretation.” We strongly disagree with this characterization. In Hildreth et al. (2021), we treated the meander question as a hypothesis to be tested using topographic data.

Surface Slip Distribution and Earthquake Rupture Model of the Fuyun Fault, China, Based on High-Resolution Topographic Data

The characteristics of earthquake surface ruptures, such as geometry, slip distribution, and coseismic deformation, contain important information about the earthquake rupture process, and so investigations and analyses of earthquake surface rupture have played a crucial role in modern earthquake hazard studies, especially with the increasing availability of high-resolution topographic and imagery data for tectono-geomorphic interpretation. In this study, we use Structure from Motion (SfM) photogrammetry to build a 1 m resolution digital elevation model (DEM) of the fault and combine this with filed observations to map the surface ruptures of the 1931 M8.0 Fuyun earthquake, China. These high-resolution topographic data enable to identify and measure the displaced gullies, and so the rupture locations and along-strike slip distribution are obtained in detail. Four paleoearthquake events are identified through the offset cluster characteristics. The coseismic offset of the 1931 Fuyun earthquake is extracted from the offset distribution, which shows four continuous undulations along the fault strike, corresponding to the four segments of surface rupture. Moreover, a high offset gradient is observed in the step area connected by the rupture segment. These findings, combined with the width and bending angle of the step area at the joint of the rupture segment, indicate that the 1931 Fuyun earthquake was a cascade rupture formed by four rupture segments. This study expands the available offset measurement data of Fuyun fault and confirms the applicability of high-resolution topographic data to active tectonic research.