14 AprEarth Science and Engineering Graduate SeminarZOOM WEBINAR: Semi-universal Geo-Crack Detection by Machine Learning
ZOOM WEBINAR: Semi-universal Geo-Crack Detection by Machine Learning
  • Prof. Gerard Schuster
  • King Abdullah University of Science and Technology
  • Wednesday, April 14, 2021
  • 04:45 PM - 05:45 PM
2021-04-14T16:452021-04-14T17:45Asia/RiyadhZOOM WEBINAR: Semi-universal Geo-Crack Detection by Machine LearningKAUST, WEBINAR VIA ZOOMProf. Daniel Peterdaniel.peter@kaust.edu.sa


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Abstract: Cracks are an outcrop’s stress fingerprint etched by a region’s tectonic forces and weathering patterns. To deduce the stress history, the cracks of outcrops must be detected and catalogued over a wide area and then interpreted by the structural geologist. Tens of thousands of high-resolution photographs can now be conveniently obtained by inexpensive drone surveys so that cracks to a resolution of centimeters can be recorded. Here, we will use the term crack to indicate any discontinuity on the surface of the rock face, whether it is a bedding surface, a shearing surface, a fracture, or a joint where the rock face separates due to cooling. The problem with detecting cracks in petabytes of digital images is that it demands a highly accurate and fast computerized method for accurate detection in the presence of noise such as shadows, vegetation, sedimentary interfaces, and linear discolorations due to weathering. In the presence of such noise, deterministic edge detectors will typically fail. To overcome this problem we develop a modified U-Net architecture with transfer learning to detect cracks with an accuracy greater than 97.5% in our examples. We train this crack detector on labeled drone photos from a drone survey over large sandstone massifs in Saudi Arabia. We also found that this detector with transfer training could detect cracks in photos of a Martian floodplain taken by a Mars orbiter. We believe our semi-universal sandstone U-Net model with transfer training can accurately detect cracks in photos of many different geological environments. We also show that a machine learning algorithm can also separate one type of geo-signal from another to give a more interpretable image.

Biography: Gerard Schuster is a Professor of Geophysics at King Abdullah University of Science and Technology (KAUST) and an adjunct Professor of Geophysics at the University of Utah, where he was a professor from 1985 to 2009. He was the founder and director of the Utah Tomography and Modeling / Migration consortium from 1987 to 2009 and has extensive experience in developing innovative migration and inversion methods for both exploration and earthquake seismology. Jerry, as his friends call him, helped pioneer seismic interferometry and its practical applications in applied geophysics, through his active research program and extensive publications, including his books "Seismic Interferometry" in  2009, "Seismic Inversion" in 2017, and "Machine Learning in Geosciences" to be published in 2021. He received a number of teaching and research awards while at the University of Utah. He was editor of GEOPHYSICS from 2004-2005 and was awarded SEG’s Virgil Kauffman gold medal in 2010 for his work in seismic interferometry.


  • Prof. Daniel Peter
  • daniel.peter@kaust.edu.sa