Deep Learning Hydrodynamic Forecasting for Flooded Region Assessment in Near-Real-Time (DL Hydro-FRAN)

Hydrodynamic flood modeling improves hydrologic and hydraulic prediction of storm events. However, the computationally intensive numerical solutions required for high-resolution hydrodynamics have historically prevented their implementation in near-real-time flood forecasting. This study examines whether several Deep Neural Network (DNN) architectures are suitable for optimizing hydrodynamic flood models. Several pluvial flooding events were simulated in a low-relief high-resolution urban environment using a 2D HEC-RAS hydrodynamic model. These simulations were assembled into a training set for the DNNs, which were then used to forecast flooding depths and velocities. The DNNs' forecasts were compared to the hydrodynamic flood models, and showed good agreement, with a median RMSE of around 2 mm for cell flooding depths in the study area. The DNNs also improved forecast computation time significantly, with the DNNs providing forecasts between 34.2 and 72.4 times faster than conventional hydrodynamic models. The study area showed little change between HEC-RAS' Full Momentum Equations and Diffusion Equations, however, important numerical stability considerations were discovered that impact equation selection and DNN architecture configuration. Overall, the results from this study show that DNNs can greatly optimize hydrodynamic flood modeling, and enable near-real-time hydrodynamic flood forecasting.Comment: 21 pages, 8 figure

Buried shallow fault slip from the South Napa earthquake revealed by near-field geodesy.

Earthquake-related fault slip in the upper hundreds of meters of Earths surface has remained largely unstudied because of challenges measuring deformation in the near field of a fault rupture. We analyze centimeter-scale accuracy mobile laser scanning (MLS) data of deformed vine rows within ±300 m of the principal surface expression of the M (magnitude) 6.0 2014 South Napa earthquake. Rather than assuming surface displacement equivalence to fault slip, we invert the near-field data with a model that allows for, but does not require, the fault to be buried below the surface. The inversion maps the position on a preexisting fault plane of a slip front that terminates ~3 to 25 m below the surface coseismically and within a few hours postseismically. The lack of surface-breaching fault slip is verified by two trenches. We estimate near-surface slip ranging from ~0.5 to 1.25 m. Surface displacement can underestimate fault slip by as much as 30%. This implies that similar biases could be present in short-term geologic slip rates used in seismic hazard analyses. Along strike and downdip, we find deficits in slip: The along-strike deficit is erased after ~1 month by afterslip. We find no evidence of off-fault deformation and conclude that the downdip shallow slip deficit for this event is likely an artifact. As near-field geodetic data rapidly proliferate and will become commonplace, we suggest that analyses of near-surface fault rupture should also use more sophisticated mechanical models and subsurface geomechanical tests

Crowdsourced earthquake early warning

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an M_w (moment magnitude) 7 earthquake on California’s Hayward fault, and real data from the M_w 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing

An analysis of airborne gravity by strapdown INS/DGPS

Bibliography: p. 116-121

Temporal Stability of the Velodyne HDL-64E S2 Scanner for High Accuracy Scanning Applications

The temporal stability and static calibration and analysis of the Velodyne HDL‑64E S2 scanning LiDAR system is discussed and analyzed. The mathematical model for measurements for the HDL-64E S2 scanner is updated to include misalignments between the angular encoder and scanner axis of rotation, which are found to be a marginally significant source of error. It is reported that the horizontal and vertical laser offsets cannot reliably be obtained with the current calibration model due to their high correlation with the horizontal and vertical offsets. By analyzing observations from two separate HDL-64E S2 scanners it was found that the temporal stability of the horizontal angle offset is near the quantization level of the encoder, but the vertical angular offset, distance offset and distance scale are slightly larger than expected. This is felt to be due to long term variations in the scanner range, whose root cause is as of yet unidentified. Nevertheless, a temporally averaged calibration dataset for each of the scanners resulted in a 25% improvement in the 3D planar misclosure residual RMSE over the standard factory calibration model

Analyzing Glacier Surface Motion Using LiDAR Data

Understanding glacier motion is key to understanding how glaciers are growing, shrinking, and responding to changing environmental conditions. In situ observations are often difficult to collect and offer an analysis of glacier surface motion only at a few discrete points. Using light detection and ranging (LiDAR) data collected from surveys over six glaciers in Greenland and Antarctica, particle image velocimetry (PIV) was applied to temporally-spaced point clouds to detect and measure surface motion. The type and distribution of surface features, surface roughness, and spatial and temporal resolution of the data were all found to be important factors, which limited the use of PIV to four of the original six glaciers. The PIV results were found to be in good agreement with other, widely accepted, measurement techniques, including manual tracking and GPS, and offered a comprehensive distribution of velocity data points across glacier surfaces. For three glaciers in Taylor Valley, Antarctica, average velocities ranged from 0.8–2.1 m/year. For one glacier in Greenland, the average velocity was 22.1 m/day (8067 m/year)

Weak Echo Detection from Single Photon Lidar Data Using a Rigorous Adaptive Ellipsoid Searching Algorithm

Single photon lidar (SPL) systems have great potential to be an effective tool for mapping due to their high data collection efficiency. However, the large number of false returns in SPL point clouds represents a huge challenge for the extraction of weak signal targets with low reflectivity or small cross sections. Numerous filtering methods have been proposed that attempt to effectively remove these noise points from the final point cloud model. However, weak signal points have similar characteristics to noise returns, and thus can be incorrectly eliminated as noise points during the filtering process. Herein, a novel voxel-spherical adaptive ellipsoid searching (VSAES) method is proposed, by which weak signal returns can be successfully retained while still removing a majority of the noise points. By employing this voxel-spherical (VS) model, our proposed method can simultaneously process a combined SPL dataset containing multiple flightlines, in which the noise density is unevenly distributed throughout the whole dataset. In addition, an improved adaptive ellipsoid searching (AES) method based on hypothesis testing is able to remove noise points more robustly than the originally described version. The experimental results show that the proposed method retains 89.1% of the weak signal point returns from electric power lines, which is a significant improvement over the performance of either to the original AES method (25.9%) or a histogram filtering based method (13.4%)

Terrestrial Hyperspectral Image Shadow Restoration through Lidar Fusion

Acquisition of hyperspectral imagery (HSI) from cameras mounted on terrestrial platforms is a relatively recent development that enables spectral analysis of dominantly vertical structures. Although solar shadowing is prevalent in terrestrial HSI due to the vertical scene geometry, automated shadow detection and restoration algorithms have not yet been applied to this capture modality. We investigate the fusion of terrestrial laser scanning (TLS) spatial information with terrestrial HSI for geometric shadow detection on a rough vertical surface and examine the contribution of radiometrically calibrated TLS intensity, which is resistant to the influence of solar shadowing, to HSI shadow restoration. Qualitative assessment of the shadow detection results indicates pixel level accuracy, which is indirectly validated by shadow restoration improvements when sub-pixel shadow detection is used in lieu of single pixel detection. The inclusion of TLS intensity in existing shadow restoration algorithms that use regions of matching material in sun and shade exposures was found to have a marginal positive influence on restoring shadow spectrum shape, while a proposed combination of TLS intensity with passive HSI spectra boosts restored shadow spectrum magnitude precision by 40% and band correlation with respect to a truth image by 45% compared to existing restoration methods