Geophysics and natural risks: instruments and principles of data analysis

Section outline

• Select section Introduction

  Introduction

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  Department of Earth Sciences, University of Pisa, Via Santa Maria, 53, 56126 Pisa PI - The link is temporarily unavailable

  27 January 2025

  28 January 2025

  29 January 2025

  30 January 2025

   20 (in person)

  Ilaria Catapano (CNR-IREA), Stefano Perna (University of Partheope, Napoli), Vincenzo Serlenga (CNR-IMAA), Sebastian Uhlemann (British Geological Survay)

  

  Learning the basic concepts about the use of airborne synthetic aperture radar (SAR) systems and introduction to strategies for acquiring, processing and integrating data acquired by in situ geophysical instrumentations, with a special focus on ground penetrating radar (GPR), electrical resistivity, electromagnetic systems and passive seismic methods, and hydrogeophysical monitoring methodologies.

  The students will learn notions regarding airborne based SAR imaging and interferometry, microwave tomography enhanced GPR, as well as gain an overview on the use of ground-based geophysical characterization and monitoring techniques to image subsurface properties and their evolution with regards to natural hazards.

  Therefore, the students will gather skills about the interpretation of the images provided by remote and in situ electromagnetic and seismic sensors in the frame of natural risks assessment.
• Select section Syllabus

  Syllabus

  • Microwave Tomography enhanced GPR
  • Airborne SAR interferometry
  • Petrophysical relationships linking geophysical and hydrogeomechanical parameters
  • Ground and airborne geophysical characterization
  • Geophysical monitoring: principles and applications
  • Machine Learning for exploiting and assimilating environmental data
  • The Ambient Seismic Noise for subsurface characterization
• Select section MODULE 1 - Microwave Tomography enhanced GPR

  MODULE 1 - Microwave Tomography enhanced GPR

  Microwave  Tomography  enhanced  GPR:  Mathematical  models  of  signal  propagation  in homogeneous  and  two‐layered  media;  Forward  and  Inverse  Scattering  problems,  The  Born Approximation;  the  truncated  Singular  Value  Decomposition  method,  analysis  of  the  reconstruction capabilities; examples of data processing.

  • Select activity Microwave Tomography enhanced

    

    Microwave Tomography enhanced File PDF
• Select section MODULE 2 - Airborne SAR interferometry

  MODULE 2 - Airborne SAR interferometry

  Airborne  SAR  interferometry:  Acquisition  geometry  and  operational  issues;  raw  data  formation; 
  SAR  transfer  function;  geometric  distortions  in  SAR  images;  SAR  interferometry;  differential  SAR 
  Interferometry; SAR Polarimetry; effects of uncompensated motion errors; time domain focusing; real‐
  world examples.

  • Select activity AirborneSARinterferometry

    

    AirborneSARinterferometry File PDF
• Select section MODULE 3 - Petrophysical relationships linking geophysical and hydrogeomechanical parameters

  MODULE 3 - Petrophysical relationships linking geophysical and hydrogeomechanical parameters

  Petrophysical  relationships  linking  geophysical  and  hydrogeomechanical  properties

  • Select activity PETROPHYSICAL RELATIONSHIPS

    

    PETROPHYSICAL RELATIONSHIPS File PDF
• Select section MODULE 4 - Ground and airborne geophysical characterization

  MODULE 4 - Ground and airborne geophysical characterization

  Ground and airborne geophysical characterization.

  • Select activity Ground and airborne geophysical

    

    Ground and airborne geophysical File PDF
  • Select activity ResIPy Example

    

    ResIPy Example File ZIP
• Select section MODULE 5 - Geophysical monitoring: principles and applications

  MODULE 5 - Geophysical monitoring: principles and applications

  Geophysical monitoring: principles and applications

  • Select activity Geophysical monitoring

    

    Geophysical monitoring File PDF
• Select section MODULE 6 - Machine Learning for exploiting and assimilating environmental data

  MODULE 6 - Machine Learning for exploiting and assimilating environmental data

  Machine Learning for exploiting and assimilating environmental data

  • Select activity Machine Learning

    

    Machine Learning File PDF
• Select section MODULE 7 - The Ambient Seismic Noise for subsurface characterization

  MODULE 7 - The Ambient Seismic Noise for subsurface characterization

  The Ambient Seismic Noise for subsurface characterization

  • Select activity Ambient Seismic Noise

    

    Ambient Seismic Noise File PDF