Radar Imaging of Fractures and Voids behind the Walls of an Underground Mine

Geophysics ◽  
2021 ◽  
pp. 1-65
Author(s):  
Amin Abbasi Baghbadorani ◽  
John A. Hole ◽  
Jonathan Baggett ◽  
Nino Ripepi
Keyword(s):  
Water Inrush ◽  
Radar Data ◽  
Image Resolution ◽  
Fracture Aperture ◽  
Flood Hazards ◽  
Depth Of Penetration ◽  
Fracture Geometry ◽  
Radar Images ◽  
Mining Hazards ◽  
Fracture Fluid

2-D and 3-D rock-penetrating radar data were acquired on the wall of a pillar in an underground limestone mine. The objective was to test the ability of radar to image fractures and karst voids and to characterize their geometry, aperture, and fluid content, with the goal of mitigating mining hazards. Strong radar reflections in the field data correlate with fractures and a cave exposed on the pillar walls. Large pillar wall topography was included in the steep-dip Kirchhoff migration algorithm because standard elevation corrections are inaccurate. The depth-migrated 250 MHz radar images illuminate fractures, karst voids, and the far wall of the pillar up to ~25 m depth into the rock, with a spatial resolution of lt;0.5 m. Higher-frequency radar improved image resolution and aided the interpretation, but at the cost of shallower depth of penetration and extra acquisition effort. Due to the strong contrast in physical properties between rock and fracture fluid, fractures with apertures as thin as a fiftieth of a radar wavelength were imaged. Water-filled fractures with mm-scale aperture and air-filled fractures with cm-scale aperture produce strong reflections at 250 MHz. Strong variation in reflection amplitude along each fracture is interpreted to represent both variable fracture aperture and non-planar fracture structure. Fracture apertures were quantitively measured, but distinguishing water- from air-filled the fractures was not possible due to the complex radar wavelet and fracture geometry. Two conjugate fracture sets were imaged. One of these fracture sets dominates rock mass stability and water inrush challenges throughout the mine. All of the detected voids and a large cave are at the intersection of two fractures, indicating preferential water flow and dissolution along conjugate fracture intersections. Detecting, locating, and characterizing fractures and voids prior to excavation can enable miners to mitigate potential collapse and flood hazards before they occur.

Fusion of rain radar images and wind forecasts in adeep learning model applied to rain nowcasting

2021 ◽  
Author(s):  
Anastase Charantonis ◽  
Vincent Bouget ◽  
Dominique Béréziat ◽  
Julien Brajard ◽  
Arthur Filoche
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Weather Forecast ◽  
Forecast Model ◽  
Radar Data ◽  
Learning Model ◽  
Rainfall Forecasting ◽  
Radar Images ◽  
Deep Learning Model ◽  
Rain Radar

<p>Short or mid-term rainfall forecasting is a major task with several environmental applications such as agricultural management or flood risks monitoring. Existing data-driven approaches, especially deep learning models, have shown significant skill at this task, using only rainfall radar images as inputs. In order to determine whether using other meteorological parameters such as wind would improve forecasts, we trained a deep learning model on a fusion of rainfall radar images and wind velocity produced by a weather forecast model. The network was compared to a similar architecture trained only on radar data, to a basic persistence model and to an approach based on optical flow. Our network outperforms by 8% the F1-score calculated for the optical flow on moderate and higher rain events for forecasts at a horizon time of 30 minutes. Furthermore, it outperforms by 7% the same architecture trained using only rainfall radar images. Merging rain and wind data has also proven to stabilize the training process and enabled significant improvement especially on the difficult-to-predict high precipitation rainfalls. These results can also be found in Bouget, V., Béréziat, D., Brajard, J., Charantonis, A., & Filoche, A. (2020). Fusion of rain radar images and wind forecasts in a deep learning model applied to rain nowcasting. arXiv preprint arXiv:2012.05015</p>

scholarly journals Method for transforming symbolic radar marks of low-noticeable moving objects based on the Talbot effect

Radiotekhnika ◽  
2021 ◽  
pp. 129-137
Author(s):  
V. Zhyrnov ◽  
S. Solonskaya
Keyword(s):  
Moving Objects ◽  
Radar Data ◽  
Pulse Frequency ◽  
Asymptotic Equality ◽  
Talbot Effect ◽  
Radar Images ◽  
Frequency Code ◽  
Semantic Dependencies ◽  
Symbolic Images ◽  
Synchronous Pulse

In this paper a method to transform radar images of moving aerial objects with scintillating inter-period fluctuations, sometimes resulting to complete signal fading, using the Talbot effect is considered. These transformations are reduced to the establishment of a certain correspondence of the asymptotic equality of perception of visual images, arbitrarily changing in time and space, in the statement about the conditions of simple equality of perception of images of radar marks that have different frequencies of fluctuations. It is shown how this approach can be used to analyze radar data by transforming and smoothing scintillating signal fluctuations, invisible in the presence of interference, into visible symbolic images. First, to detect and recognize the aerial objects from the analysis of relations and functional (semantic) dependencies between attributes, second, to make a decision based on semantic components of symbolic radar images. The possibility of using such transformation to generate pulse-frequency code of fluctuations of the symbolic radar angel-echo images as an important characteristic for their recognition has been experimentally verified. Algorithms for generating symbolic images in asynchronous and synchronous pulse-frequency code are formulated. The symbolic image represented by such a code is considered as an additional feature for recognizing and filtering out natural interferences such as angel-echoes.

scholarly journals MULTI-DATE SENTINEL1 SAR IMAGE TEXTURES DISCRIMINATE PERENNIAL AGROFORESTS IN A TROPICAL FOREST-SAVANNAH TRANSITION LANDSCAPE

2018 ◽  
Vol XLII-1 ◽  
pp. 339-346 ◽  
Author(s):  
F. N. Numbisi ◽  
F. Van Coillie ◽  
R. De Wulf
Keyword(s):  
Land Cover ◽  
Vegetation Indices ◽  
Image Resolution ◽  
Optical Image ◽  
Congo Basin ◽  
Sar Image ◽  
Radar Images ◽  
Tropical Conditions ◽  
Cocoa Agroforestry ◽  
Occurrence Matrix

<p><strong>Abstract.</strong> Synthetic Aperture Radar (SAR) provides consistent information on target land features; especially in tropical conditions that restrain penetration of optical imaging sensors. Because radar response signal is influenced by geometric and di-electrical properties of surface features’, the different land cover may appear similar in radar images. For discriminating perennial cocoa agroforestry land cover, we compare a multi-spectral optical image from RapidEye, acquired in the dry season, and multi-seasonal C-band SAR of Sentinel 1: A final set of 10 (out of 50) images that represent six dry and four wet seasons from 2015 to 2017. We ran eight RF models for different input band combinations; multi-spectral reflectance, vegetation indices, co-(VV) and cross-(VH) polarised SAR intensity and Grey Level Co-occurrence Matrix (GLCM) texture measures. Following a pixel-based image analysis, we evaluated accuracy metrics and uncertainty Shannon entropy. The model comprising co- and cross-polarised texture bands had the highest accuracy of 88.07<span class="thinspace"></span>% (95<span class="thinspace"></span>% CI: 85.52&amp;ndash;90.31) and kappa of 85.37; and the low class uncertainty for perennial agroforests and transition forests. The optical image had low classification uncertainty for the entire image; but, it performed better in discriminating non-vegetated areas. The measured uncertainty provides reliable validation for comparing class discrimination from different image resolution. The GLCM texture measures that are crucial in delineating vegetation cover differed for the season and polarization of SAR image. Given the high accuracies of mapping, our approach has value for landscape monitoring; and, an improved valuation of agroforestry contribution to REDD+ strategies in the Congo basin sub-region.</p>

scholarly journals A 5-yr 40-km-Resolution Global Climatology of Superrefraction for Ground-Based Weather Radars

2009 ◽  
Vol 48 (1) ◽  
pp. 89-110 ◽  
Author(s):  
Philippe Lopez
Keyword(s):  
United States ◽  
Electromagnetic Waves ◽  
Vertical Gradient ◽  
Radar Data ◽  
The United States ◽  
Horizontal Resolution ◽  
Trade Wind ◽  
Late Afternoon ◽  
The Balkans ◽  
Radar Images

Abstract The propagation of electromagnetic waves emitted from ground-based meteorological radars is determined by the stratification of the atmosphere. In extreme superrefractive situations characterized by strong temperature inversions or strong vertical gradients of moisture, the radar beam can be deflected toward the ground (ducting or trapping). This phenomenon often results in spurious returned echoes and misinterpretation of radar images such as erroneous precipitation detection. In this work, a 5-yr global climatology of the frequency of superrefractive and ducting conditions and of trapping-layer base height has been produced using refractivity computations from ECMWF temperature, moisture, and pressure analyses at a 40-km horizontal resolution. The aim of this climatology is to better document how frequent such events are, which is a prerequisite for fully benefiting from radar data information for the multiple purposes of model validation, precipitation analysis, and data assimilation. First, the main climatological features are summarized for the whole globe: high- and midlatitude oceans seldom experience superrefraction or ducting whereas tropical oceans are strongly affected, especially in regions where the trade wind inversion is intense and lying near the surface. Over land, seasonal averages of superrefraction (ducting) frequencies reach 80% (40%) over tropical moist areas year-round but remain below 40% (15%) in most other regions. A particular focus is then laid on Europe and the United States, where extensive precipitation radar networks already exist. Seasonal statistics exhibit a pronounced diurnal cycle of ducting occurrences, with averaged frequencies peaking at 60% in summer late afternoon over the eastern half of the United States, the Balkans, and the Po Valley but no ducts by midday. Similarly high ducting frequencies are found over the southwestern coast of the United States at night. A potentially strong reduction of ducting occurrences with increased radar height (especially in midlatitude summer late afternoon) is evidenced by initiating refractivity vertical gradient computations from either the lowest or the second lowest model level. However, installing radar on tall towers also brings other problems, such as a possible amplification of sidelobe clutter echoes.

scholarly journals Detecting and Tracking Communal Bird Roosts in Weather Radar Data

2020 ◽  
Vol 34 (01) ◽  
pp. 378-385
Author(s):  
Zezhou Cheng ◽  
Saadia Gabriel ◽  
Pankaj Bhambhani ◽  
Daniel Sheldon ◽  
Subhransu Maji ◽  
...  
Keyword(s):  
Latent Variable ◽  
Weather Radar ◽  
Radar Data ◽  
Learning System ◽  
Variable Model ◽  
Detection Model ◽  
Radar Images ◽  
Biological Phenomena ◽  
The Us ◽  
Spatio Temporal

The US weather radar archive holds detailed information about biological phenomena in the atmosphere over the last 20 years. Communally roosting birds congregate in large numbers at nighttime roosting locations, and their morning exodus from the roost is often visible as a distinctive pattern in radar images. This paper describes a machine learning system to detect and track roost signatures in weather radar data. A significant challenge is that labels were collected opportunistically from previous research studies and there are systematic differences in labeling style. We contribute a latent-variable model and EM algorithm to learn a detection model together with models of labeling styles for individual annotators. By properly accounting for these variations we learn a significantly more accurate detector. The resulting system detects previously unknown roosting locations and provides comprehensive spatio-temporal data about roosts across the US. This data will provide biologists important information about the poorly understood phenomena of broad-scale habitat use and movements of communally roosting birds during the non-breeding season.

Navigation by Computer Processing of Marine Radar Images

1985 ◽  
Vol 38 (3) ◽  
pp. 375-383 ◽  
Author(s):  
G. L. Austin ◽  
A. Bellon ◽  
M. Riley ◽  
E. Ballantyne
Keyword(s):  
Radar Data ◽  
Computer Processing ◽  
Pattern Recognition Technique ◽  
Radar Images ◽  
Well Yield ◽  
Radar Imagery ◽  
On Line ◽  
Marine Radar ◽  
Recognition Technique ◽  
Better Than

The advantages of being able to process marine radar imagery in an on-line computer system have been illustrated by study of some navigational problems. The experiments suggest that accuracies of the order of 100 metres may be obtained in navigation in coastal regions using map overlays with marine radar data. A similar technique using different radar imagery of the same location suggests that the pattern-recognition technique may well yield a position-keeping ability of better than 10 metres.

scholarly journals NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency

Geosciences ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 290 ◽  
Author(s):  
Rubel ◽  
Lukin ◽  
Rubel ◽  
Egiazarian
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Real Life ◽  
Remote Sensing Data ◽  
Radar Data ◽  
Quality Metrics ◽  
Radar Images ◽  
Filtering Efficiency ◽  
Trained Neural Network ◽  
Synthetic Aperture Radars

Images acquired by synthetic aperture radars are degraded by speckle that prevents efficient extraction of useful information from radar remote sensing data. Filtering or despeckling is a tool often used to improve image quality. However, depending upon image and noise properties, the quality of improvement can vary. Besides, a quality can be characterized by different criteria or metrics, where visual quality metrics can be of value. For the case study of discrete cosine transform (DCT)based filtering, we show that improvement of radar image quality due to denoising can be predicted in a simple and fast way, especially if one deals with particular type of radar data such as images acquired by Sentinel-1. Our approach is based on application of a trained neural network that, in general, might have a different number of inputs (features). We propose a set of features describing image and noise statistics from different viewpoints. From this set, that contains 28 features, we analyze different subsets and show that a subset of the 13 most important and informative features leads to a very accurate prediction. Test image generation and network training peculiarities are discussed. The trained neural network is then tested using different verification strategies. The results of the network application to test and real-life radar images are presented, demonstrating good performance for a wide set of quality metrics.

scholarly journals Satellite monitoring of flods by C-band radar data

2018 ◽  
pp. 4-12
Author(s):  
Dmytro Mozgovoy
Keyword(s):  
Image Processing ◽  
Spatial Resolution ◽  
Radar Data ◽  
Satellite Monitoring ◽  
Radar Images ◽  
Automated Image Processing ◽  
Monitoring Area ◽  
Flooded Areas ◽  
Sentinel 2

Automated image processing methodology is proposed for all-weather satellite monitoring of floods based on C-band radar data, which allows to determine the boundaries and areas of flooded areas when assessing the magnitude, dynamics and consequences of floods. Processing results comparison of medium spatial resolution scanner and radar images from Sentinel-1 and Sentinel-2 satellites is made. The advantages of a radar survey with cloudiness in the monitoring area are shown.

scholarly journals Fusion of SAR Interferometry and Polarimetry Methods for Landslide Reactivation Study, the Bureya River (Russia) Event Case Study

2021 ◽  
Vol 13 (24) ◽  
pp. 5136
Author(s):  
Valery Bondur ◽  
Tumen Chimitdorzhiev ◽  
Aleksey Dmitriev ◽  
Pavel Dagurov
Keyword(s):  
Synthetic Aperture Radar ◽  
Radar Data ◽  
Sar Interferometry ◽  
Synthetic Aperture ◽  
Line Of Sight ◽  
Scattering Mechanisms ◽  
Radar Images ◽  
Landslide Reactivation ◽  
Sbas Insar ◽  
Aperture Radar

In this paper, we demonstrate the estimation capabilities of landslide reactivation based on various SAR (Synthetic Aperture Radar) methods: Cloude-Pottier decomposition of Sentinel-1 dual polarimetry data, MT-InSAR (Multi-temporal Interferometric Synthetic Aperture Radar) techniques, and cloud computing of backscattering time series. The object of the study is the landslide in the east of Russia that took place on 11 December 2018 on the Bureya River. H-α-A polarimetric decomposition of C-band radar images not detected significant transformations of scattering mechanisms for the surface of the rupture, whereas L-band radar data show changes in scattering mechanisms before and after the main landslide. The assessment of ground displacements along the surface of the rupture in the 2019–2021 snowless periods was carried out using MT-InSAR methods. These displacements were 40 mm/year along the line of sight. The SBAS-InSAR results have allowed us to reveal displacements of great area in 2020 and 2021 snowless periods that were 30–40 mm/year along the line-of-sight. In general, the results obtained by MT-InSAR methods showed, on the one hand, the continuation of displacements along the surface of the rupture and on the other hand, some stabilization of the rate of landslide processes.

A Novel Approach of the Modelling of Dynamics of the Ice Cover Applying Microsatellites Data

2021 ◽  
Author(s):  
Magdalena Łukosz ◽  
Wojciech Witkowski
Keyword(s):  
High Resolution ◽  
Ice Cover ◽  
Image Resolution ◽  
Global Ocean ◽  
Future Research ◽  
Polar Regions ◽  
Radar Images ◽  
Novel Approach ◽  
Glacier Velocity ◽  
Offset Tracking

&lt;p&gt;Keywords: ice cover; glacier dynamics; microsatellites; offset-tracking; climate changes&lt;/p&gt;&lt;p&gt;Radar images acquired by SAR satellites allow scientists to monitor the movements of glaciers in polar regions. Observation of these areas is significant as it provides information on the process of global warming. It also makes it possible to assess the amount of ice mass that is melting and, as a result, increasing the mean level of the global ocean. Due to high speeds and loss of consistency in glacial areas, the optimal technique for estimating glacier velocity is Offset-Tracking. Its accuracy depends on the size of the terrain pixel and can therefore increase the accuracy of the results obtained by using high-resolution images. Microsatellites open up new possibilities through high resolution imagery and short revisit time.&lt;/p&gt;&lt;p&gt;The study uses ICEYE products. The aim of the research was to investigate the influence of SAR image resolution on the accuracy of calculated movements in the Offset-Tracking method. Additionally, a comparison of obtained results with previous studies allowed to analyze changes in the dynamics of chosen areas. The research was carried out for 2 glaciers: Jakobshavn in Greenland and Thwaites in Antarctica. It made it possible to compare the quality of results in areas that are located in various parts of the world and moving at different dynamics. Additionally, calculations were made for Sentinel-1 SAR images for comparative analysis.&amp;#160;&lt;/p&gt;&lt;p&gt;As a result of research, velocities of glaciers and their directions in periods of several days were obtained. For Thwaites glacier, daily changes in dynamics were also analyzed. Moreover, by comparing results to earlier researches which were carried out in these areas, it was possible to estimate changes in ice cover during longer timespans. In the last step, the quality and accuracy of products obtained from ICEYE and Sentinel-1 satellites were compared.&amp;#160;&lt;/p&gt;&lt;p&gt;This research assesses the utility of microsatellite images for monitoring glacier movements and shows possibilities of their usage in future research.&lt;/p&gt;

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