Non-planarity, scale-dependent roughness and kinematic properties of the Pidima active normal fault scarp (Messinia, Greece) using high-resolution terrestrial LiDAR data

2020 ◽  
Vol 136 ◽  
pp. 104065 ◽  
Author(s):  
Ioannis Karamitros ◽  
Athanassios Ganas ◽  
Alexandros Chatzipetros ◽  
Sotirios Valkaniotis
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Magali Riesner ◽  
Laurent Bollinger ◽  
Judith Hubbard ◽  
Cyrielle Guérin ◽  
Marthe Lefèvre ◽  
...  

AbstractThe largest (M8+) known earthquakes in the Himalaya have ruptured the upper locked section of the Main Himalayan Thrust zone, offsetting the ground surface along the Main Frontal Thrust at the range front. However, out-of-sequence active structures have received less attention. One of the most impressive examples of such faults is the active fault that generally follows the surface trace of the Main Boundary Thrust (MBT). This fault has generated a clear geomorphological signature of recent deformation in eastern and western Nepal, as well as further west in India. We focus on western Nepal, between the municipalities of Surkhet and Gorahi where this fault is well expressed. Although the fault system as a whole is accommodating contraction, across most of its length, this particular fault appears geomorphologically as a normal fault, indicating crustal extension in the hanging wall of the MHT. We focus this study on the reactivation of the MBT along the Surkhet-Gorahi segment of the surface trace of the newly named Reactivated Boundary Fault, which is ~ 120 km long. We first generate a high-resolution Digital Elevation Model from triplets of high-resolution Pleiades images and use this to map the fault scarp and its geomorphological lateral variation. For most of its length, normal motion slip is observed with a dip varying between 20° and 60° and a maximum cumulative vertical offset of 27 m. We then present evidence for recent normal faulting in a trench located in the village of Sukhetal. Radiocarbon dating of detrital charcoals sampled in the hanging wall of the fault, including the main colluvial wedge and overlying sedimentary layers, suggest that the last event occurred in the early sixteenth century. This period saw the devastating 1505 earthquake, which produced ~ 23 m of slip on the Main Frontal Thrust. Linked or not, the ruptures on the MFT and MBT happened within a short time period compared to the centuries of quiescence of the faults that followed. We suggest that episodic normal-sense activity of the MBT could be related to large earthquakes rupturing the MFT, given its proximity, the sense of motion, and the large distance that separates the MBT from the downdip end of the locked fault zone of the MHT fault system. We discuss these results and their implications for the frontal Himalayan thrust system.


2020 ◽  
Vol 10 (21) ◽  
pp. 7409 ◽  
Author(s):  
Waldemar Kociuba

High resolution terrestrial laser scanning data (TLS; terrestrial LiDAR) provide an excellent background for quantitative resource estimation through the comparative analysis of topographic surface changes. However, unlike airborne LiDAR data, which is usually provided as classified and contains a class of ground points, raw TLS data include all of the points of the scanned space within the specified scanner range. In effect, utilizing the latter data to estimate the volume of the resource by the differential analysis of digital elevation models (DEMs) requires the data to be specially prepared, i.e., separating from the point cloud only the data that represent the relevant class. In the case of natural resources, e.g., mineral resources, the class is represented by ground points. This paper presents the results that were obtained by differential analysis of high resolution DEMs (DEM of difference (DoD) method) using TLS data that were processed both manually (operator noise removal) and with the use of the automatic Cloth Simulation Filter (CSF) algorithm. Three different time pairs of DoD data were analyzed for a potential gravel-cobble deposit area of 45,444 m2, which was located at the bottom of the mouth section of the Scott River in south-east Svalbard. It was found that the applied method of ground point classification had very little influence on the errors in the range of estimating volumetric parameters of the mineral resources and measurement uncertainty. Moreover, it was shown that the point cloud density had an influence on the CSF filtering efficiency and spatial distribution of errors.


Author(s):  
J. S. Park ◽  
K. S. Lee ◽  
S. Kim

<p><strong>Abstract.</strong> The pothole is called a road breakage where the surface of road is locally recessed. The conventional vehicle-mounted pothole detection sensor acquires data only when the vehicle directly contacts the pothole and moreover, the acquired data is limited to show the position information of the pothole based on the low-precision GPS. We have, therefore, studied the assessment methods for the road condition using the terrestrial LiDAR data to complement and improve the current methodology. In this study we subtract the low-resolution raster data from the high-resolution raster one that are taken from the terrestrial LiDAR, which is similar to the trend analysis. We assessed the proposed method using the manholes, which we assumed as potholes. It also showed that our method can improve a variety of perspectives such as time consuming, space extension and quantitative interpretation. As a result, we could expect the proposed method will be effectively used in the field of road management.</p>


2011 ◽  
Vol 36 (7-8) ◽  
pp. 281-291 ◽  
Author(s):  
Timothy J. Fewtrell ◽  
Alastair Duncan ◽  
Christopher C. Sampson ◽  
Jeffrey C. Neal ◽  
Paul D. Bates

2021 ◽  
pp. 103613
Author(s):  
Ehsan Sadighrad ◽  
Bettina A. Fach ◽  
Sinan S. Arkin ◽  
Baris Salihoğlu ◽  
Sinan Hüsrevoğlu

2021 ◽  
Vol 13 (13) ◽  
pp. 2473
Author(s):  
Qinglie Yuan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Aidi Hizami Alias ◽  
Shaiful Jahari Hashim

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.


Author(s):  
Mohammad Pashaei ◽  
Michael J. Starek ◽  
Philippe Tissot ◽  
Jacob Berryhill

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