Detecting Surface Changes from an Underground Explosion in Granite Using Unmanned Aerial System Photogrammetry

Efficient detection and high-fidelity quantification of surface changes resulting from underground activities are important national and global security efforts. In this investigation, a team performed field-based topographic characterization by gathering high-quality photographs at very low altitudes from an unmanned aerial system (UAS)-borne camera platform. The data collection occurred shortly before and after a controlled underground chemical explosion as part of the United States Department of Energy’s Source Physics Experiments (SPE-5) series. The high-resolution overlapping photographs were used to create 3D photogrammetric models of the site, which then served to map changes in the landscape down to 1-cm-scale. Separate models were created for two areas, herein referred to as the test table grid region and the nearfield grid region. The test table grid includes the region within ~40 m from surface ground zero, with photographs collected at a flight altitude of 8.5 m above ground level (AGL). The near-field grid area covered a broader area, 90–130 m from surface ground zero, and collected at a flight altitude of 22 m AGL. The photographs, processed using Agisoft Photoscan® in conjunction with 125 surveyed ground control point targets, yielded a 6-mm pixel-size digital elevation model (DEM) for the test table grid region. This provided the ≤3 cm resolution in the topographic data to map in fine detail a suite of features related to the underground explosion: uplift, subsidence, surface fractures, and morphological change detection. The near-field grid region data collection resulted in a 2-cm pixel-size DEM, enabling mapping of a broader range of features related to the explosion, including: uplift and subsidence, rock fall, and slope sloughing. This study represents one of the first works to constrain, both temporally and spatially, explosion-related surface damage using a UAS photogrammetric platform; these data will help to advance the science of underground explosion detection.

[1]  M. James,et al.  Exceptional mobility of an advancing rhyolitic obsidian flow at Cordón Caulle volcano in Chile , 2013, Nature Communications.

[2]  M. Brook.  Structure from motion in the geosciences , 2017 .

[3]  Natan Micheletti,et al.  Investigating the geomorphological potential of freely available and accessible structure‐from‐motion photogrammetry using a smartphone , 2015 .

[4]  Mark A. Fonstad,et al.  Topographic structure from motion: a new development in photogrammetric measurement , 2013 .

[5]  MEASUREMENT OF SURFACE DISPLACEMENT CAUSED BY UNDERGROUND NUCLEAR EXPLOSIONS BY DIFFERENTIAL SAR INTERFEROMETRY , 2007 .

[6]  J. Warrick,et al.  New Techniques to Measure Cliff Change from Historical Oblique Aerial Photographs and Structure-from-Motion Photogrammetry , 2016, Journal of Coastal Research.

[7]  M. R. James,et al.  Identification of structural controls in an active lava dome with high resolution DEMs: Volcán de Colima, Mexico , 2012 .

[8]  J. Travelletti,et al.  UAV-BASED REMOTE SENSING OF LANDSLIDES , 2010 .

[9]  Srikanth Saripalli,et al.  Rapid mapping of ultrafine fault zone topography with structure from motion , 2014 .

[10]  Yuriy Reshetyuk,et al.  Generation of Highly Accurate Digital Elevation Models with Unmanned Aerial Vehicles , 2016 .

[11]  S. Robson,et al.  Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application , 2012 .

[12]  M. Westoby,et al.  ‘Structure-from-Motion’ photogrammetry: A low-cost, effective tool for geoscience applications , 2012 .

[13]  Jon D. Pelletier,et al.  How do sediment yields from post‐wildfire debris‐laden flows depend on terrain slope, soil burn severity class, and drainage basin area? Insights from airborne‐LiDAR change detection , 2014 .

[14]  Yong Yuan,et al.  Evaluation of influence of vibrations generated by blasting construction on an existing tunnel in soft soils , 2014 .

[15]  Samuel T. Thiele,et al.  Ground-based and UAV-Based photogrammetry: A multi-scale, high-resolution mapping tool for structural geology and paleoseismology , 2014 .

[16]  K. McGwire,et al.  Assessing the performance of structure‐from‐motion photogrammetry and terrestrial LiDAR for reconstructing soil surface microtopography of naturally vegetated plots , 2016 .

[18]  S. Robson,et al.  Mitigating systematic error in topographic models derived from UAV and ground‐based image networks , 2014 .

[19]  M. Piras,et al.  Detailed geological mapping in mountain areas using an unmanned aerial vehicle: application to the Rodoretto Valley, NW Italian Alps , 2017 .

[20]  Florian Maldonado,et al.  Summary of the geology and physical properties of the Climax Stock, Nevada Test Site , 1977 .

[21]  D. J. Chadwick,et al.  Analysis of LiDAR-derived topographic information for characterizing and differentiating landslide morphology and activity , 2006 .

[22]  Watson W. Gregg,et al.  Interannual Variation in Phytoplankton Primary Production at A Global Scale , 2013, Remote. Sens..

[23]  John R. Andrews,et al.  PRECISE AIRBORNE LIDAR SURVEYING FOR COASTAL RESEARCH AND GEOHAZARDS APPLICATIONS , 2001 .

[24]  David W. Steedman,et al.  Coupling hydrodynamic and wave propagation modeling for waveform modeling of SPE. , 2015 .

[25]  Patrice E. Carbonneau,et al.  Cost‐effective non‐metric photogrammetry from consumer‐grade sUAS: implications for direct georeferencing of structure from motion photogrammetry , 2017 .

[26]  E. Swanson,et al.  Multi-scale fracture damage associated with underground chemical explosions , 2018 .

[27]  G. Jóźków,et al.  Georeferencing experiments with UAS imagery , 2014 .

[28]  Massimiliano Molinari,et al.  Vertical Measurements in Oblique Aerial Imagery , 2014, ISPRS Int. J. Geo Inf..

[29]  Catherine Mary Snelson,et al.  Chemical Explosion Experiments to Improve Nuclear Test Monitoring , 2013 .

[30]  Howard J. Patton.  New Insights into the Explosion Source from SPE , 2015 .

[31]  Laura Longoni,et al.  Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives , 2014, Remote. Sens..

[32]  J. Avouac,et al.  Monitoring Earth Surface Dynamics With Optical Imagery , 2007 .

[33]  C. Hugenholtz,et al.  Geomorphological mapping with a small unmanned aircraft system (sUAS): Feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model , 2013 .

[34]  H. Patton,et al.  Rg excitation by underground explosions: insights from source modelling the 1997 Kazakhstan depth-of-burial experiment , 2005 .