High-resolution seismic study of modern fine-grained deposits: Inner shelf off the southeastern coast of Korea

1990 ◽  
Vol 10 (3) ◽  
pp. 145-149 ◽  
Author(s):  
S. C. Park ◽  
K. M. Jang ◽  
S. D. Lee
Keyword(s):  
High Resolution ◽  
Inner Shelf ◽  
Southeastern Coast ◽  
Fine Grained

High Resolution Low Loss Microscopy of Crystalline Surfaces

1980 ◽  
Vol 38 ◽  
pp. 162-163
Author(s):  
William Krakow ◽  
Alec N. Broers
Keyword(s):  
Electron Microscopy ◽  
High Resolution ◽  
Surface Topography ◽  
Secondary Electron ◽  
Objective Lens ◽  
Surface Imaging ◽  
Low Loss ◽  
Fine Grained ◽  
Scanning Electron ◽  
Crystalline Surfaces

Low-loss scanning electron microscopy can be used to investigate the surface topography of solid specimens and provides enhanced image contrast over secondary electron images. A high resolution-condenser objective lens has allowed the low-loss technique to resolve separations of Au nucleii of 50Å and smaller dimensions of 25Å in samples coated with a fine grained carbon-Au-palladium layer. An estimate of the surface topography of fine grained vapor deposited materials (20 - 100Å) and the surface topography of underlying single crystal Si in the 1000 - 2000Å range has also been investigated. Surface imaging has also been performed on single crystals using diffracted electrons scattered through 10−2 rad in a conventional TEM. However, severe tilting of the specimen is required which degrades the resolution 15 to 100 fold due to image forshortening.

scholarly journals Neutron Imaging Using a Fine-Grained Nuclear Emulsion

2021 ◽  
Vol 7 (1) ◽  
pp. 4
Author(s):  
Katsuya Hirota ◽  
Tomoko Ariga ◽  
Masahiro Hino ◽  
Go Ichikawa ◽  
Shinsuke Kawasaki ◽  
...  
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Neutron Detector ◽  
Nuclear Emulsion ◽  
Neutron Imaging ◽  
The Other ◽  
Crystal Oscillator ◽  
Fine Grained ◽  
Cold Neutrons ◽  
Track Analysis

A neutron detector using a fine-grained nuclear emulsion has a sub-micron spatial resolution and thus has potential to be applied as high-resolution neutron imaging. In this paper, we present two approaches to applying the emulsion detectors for neutron imaging. One is using a track analysis to derive the reaction points for high resolution. From an image obtained with a 9 μm pitch Gd grating with cold neutrons, periodic peak with a standard deviation of 1.3 μm was observed. The other is an approach without a track analysis for high-density irradiation. An internal structure of a crystal oscillator chip, with a scale of approximately 30 μm, was able to be observed after an image analysis.

scholarly journals Text to Realistic Image Generation with Attentional Concatenation Generative Adversarial Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Linyan Li ◽  
Yu Sun ◽  
Fuyuan Hu ◽  
Tao Zhou ◽  
Xuefeng Xi ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Synthesis ◽  
Semantic Space ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
Fine Grained ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Cascade Structure ◽  
High Resolution Images

In this paper, we propose an Attentional Concatenation Generative Adversarial Network (ACGAN) aiming at generating 1024 × 1024 high-resolution images. First, we propose a multilevel cascade structure, for text-to-image synthesis. During training progress, we gradually add new layers and, at the same time, use the results and word vectors from the previous layer as inputs to the next layer to generate high-resolution images with photo-realistic details. Second, the deep attentional multimodal similarity model is introduced into the network, and we match word vectors with images in a common semantic space to compute a fine-grained matching loss for training the generator. In this way, we can pay attention to the fine-grained information of the word level in the semantics. Finally, the measure of diversity is added to the discriminator, which enables the generator to obtain more diverse gradient directions and improve the diversity of generated samples. The experimental results show that the inception scores of the proposed model on the CUB and Oxford-102 datasets have reached 4.48 and 4.16, improved by 2.75% and 6.42% compared to Attentional Generative Adversarial Networks (AttenGAN). The ACGAN model has a better effect on text-generated images, and the resulting image is closer to the real image.

scholarly journals Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Teja Kattenborn ◽  
Jana Eichel ◽  
Fabian Ewald Fassnacht
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Low Cost ◽  
Training Data ◽  
Observation Data ◽  
Visual Interpretation ◽  
Fine Grained ◽  
Wide Range ◽  
Vegetation Species

AbstractRecent technological advances in remote sensing sensors and platforms, such as high-resolution satellite imagers or unmanned aerial vehicles (UAV), facilitate the availability of fine-grained earth observation data. Such data reveal vegetation canopies in high spatial detail. Efficient methods are needed to fully harness this unpreceded source of information for vegetation mapping. Deep learning algorithms such as Convolutional Neural Networks (CNN) are currently paving new avenues in the field of image analysis and computer vision. Using multiple datasets, we test a CNN-based segmentation approach (U-net) in combination with training data directly derived from visual interpretation of UAV-based high-resolution RGB imagery for fine-grained mapping of vegetation species and communities. We demonstrate that this approach indeed accurately segments and maps vegetation species and communities (at least 84% accuracy). The fact that we only used RGB imagery suggests that plant identification at very high spatial resolutions is facilitated through spatial patterns rather than spectral information. Accordingly, the presented approach is compatible with low-cost UAV systems that are easy to operate and thus applicable to a wide range of users.

scholarly journals A seismically induced onshore surge deposit at the KPg boundary, North Dakota

2019 ◽  
Vol 116 (17) ◽  
pp. 8190-8199 ◽  
Author(s):  
Robert A. DePalma ◽  
Jan Smit ◽  
David A. Burnham ◽  
Klaudia Kuiper ◽  
Phillip L. Manning ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Waves ◽  
Complex Dynamics ◽  
High Energy ◽  
Global Scale ◽  
River Channel ◽  
Impact Event ◽  
Fine Grained ◽  
Hell Creek Formation ◽  
Chicxulub Impact

The most immediate effects of the terminal-Cretaceous Chicxulub impact, essential to understanding the global-scale environmental and biotic collapses that mark the Cretaceous–Paleogene extinction, are poorly resolved despite extensive previous work. Here, we help to resolve this by describing a rapidly emplaced, high-energy onshore surge deposit from the terrestrial Hell Creek Formation in Montana. Associated ejecta and a cap of iridium-rich impactite reveal that its emplacement coincided with the Chicxulub event. Acipenseriform fish, densely packed in the deposit, contain ejecta spherules in their gills and were buried by an inland-directed surge that inundated a deeply incised river channel before accretion of the fine-grained impactite. Although this deposit displays all of the physical characteristics of a tsunami runup, the timing (<1 hour postimpact) is instead consistent with the arrival of strong seismic waves from the magnitude Mw∼10 to 11 earthquake generated by the Chicxulub impact, identifying a seismically coupled seiche inundation as the likely cause. Our findings present high-resolution chronology of the immediate aftereffects of the Chicxulub impact event in the Western Interior, and report an impact-triggered onshore mix of marine and terrestrial sedimentation—potentially a significant advancement for eventually resolving both the complex dynamics of debris ejection and the full nature and extent of biotic disruptions that took place in the first moments postimpact.

Use of novel high-resolution 3D marine seismic technology to evaluate Quaternary fluvial valley development and geologic controls on shallow gas distribution, inner shelf, Gulf of Mexico

2016 ◽  
Vol 4 (1) ◽  
pp. SC35-SC49 ◽  
Author(s):  
Timothy A. Meckel ◽  
Francis J. Mulcahy
Keyword(s):  
High Resolution ◽  
Gulf Of Mexico ◽  
Peak Frequency ◽  
High Amplitude ◽  
Structural Features ◽  
Inner Shelf ◽  
Sediment Delivery ◽  
Seismic Attribute ◽  
Meandering Channel ◽  
Attribute Analysis

The first deployment of the P-Cable™ high-resolution 3D (HR3D) seismic acquisition system in the Gulf of Mexico has provided unprecedented resolution of depositional, architectural, and structural features related to relative sea-level change recorded in the Quaternary stratigraphy. These details are typically beyond conventional 3D seismic resolution and/or excluded from commercial surveys, which are generally optimized for deeper targets. Such HR3D data are valuable for detailed studies of reservoir analogs, sediment delivery systems, fluid-migration systems, and geotechnical hazard assessment (i.e., drilling and infrastructure). The HR3D survey ([Formula: see text]) collected on the inner shelf ([Formula: see text] water depth) offshore San Luis Pass, Texas, imaged the upper 500 m of stratigraphy using peak frequency of 150 Hz and [Formula: see text] bin size. These data provided an exceptionally well-imaged example of shallow subsurface depositional system and stratigraphic architecture development during a lowstand period. The system evolved from a meandering channel with isolated point-bar deposits to a transgressive estuary characterized by dendritic erosional features that were eventually flooded. In addition, HR3D data have identified a previously unidentified seismically discontinuous zone interpreted to be a gas chimney system emanating from a tested (drilled) nonproductive, three-way structure in the lower Miocene (1.5 km depth). Within the shallowest intervals ([Formula: see text]) and at the top of the chimney zone, seismic attribute analysis revealed several high-amplitude anomalies up to [Formula: see text]. The anomalies were interpreted as reaccumulated thermogenic gas, and their distribution conforms to the stratigraphy and structure of the Quaternary interval, in that they occupy local fault-bounded footwall highs within remnant coarser-grained interfluvial zones, which are overlain by finer grained, transgressive deposits.

scholarly journals ORIENTED VEHICLE DETECTION IN HIGH-RESOLUTION REMOTE SENSING IMAGES BASED ON FEATURE AMPLIFICATION AND CATEGORY BALANCE BY OVERSAMPLING DATA AUGMENTATION

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 153-159
Author(s):  
N. Mo ◽  
L. Yan
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Vehicle Detection ◽  
Training Dataset ◽  
Remote Sensing Images ◽  
Feature Maps ◽  
Fine Grained ◽  
Bounding Boxes ◽  
The Impact ◽  
Oriented Bounding Boxes

Abstract. Vehicles usually lack detailed information and are difficult to be trained on the high-resolution remote sensing images because of small size. In addition, vehicles contain multiple fine-grained categories that are slightly different, randomly located and oriented. Therefore, it is difficult to locate and identify these fine categories of vehicles. Considering the above problems in high-resolution remote sensing images, this paper proposes an oriented vehicle detection approach. First of all, we propose an oversampling and stitching method to augment the training dataset by increasing the frequency of objects with fewer training samples in order to balance the number of objects in each fine-grained vehicle category. Then considering the effect of the pooling operations on representing small objects, we propose to improve the resolution of feature maps so that detailed information hidden in feature maps can be enriched and they can better distinguish the fine-grained vehicle categories. Finally, we design a joint training loss function for horizontal and oriented bounding boxes with center loss, to decrease the impact of small between-class diversity on vehicle detection. Experimental verification is performed on the VEDAI dataset consisting of 9 fine-grained vehicle categories so as to evaluate the proposed framework. The experimental results show that the proposed framework performs better than most of competitive approaches in terms of a mean average precision of 60.7% and 60.4% in detecting horizontal and oriented bounding boxes respectively.

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