Imaging seismic waves from space Rémi Michel 1,2, Sébastien Leprince 1, Serge Primet 3, S. Somala 1, Jean-Paul Ampuero 1, Nadia Lapusta 1, Jean-Philippe.

Slides:



Advertisements
Similar presentations
Seismic energy radiation from dynamic faulting Raúl Madariaga Ecole Normale Supérieure Laboratoire de Géologie (from Aochi and Madariaga, BSSA 2003)
Advertisements

An estimate of post-seismic gravity change caused by the 1960 Chile earthquake and comparison with GRACE gravity fields Y. Tanaka 1, 2, V. Klemann 2, K.
Copyright, © Qiming Zhou GEOG1150. Cartography Sources of Data.
Lecture 12 Content LIDAR 4/15/2017 GEM 3366.
Uncertainty in Cloud Aerosol Transport System (CATS) Products and Measurements Presented by Patrick Selmer Goddard advisor: Dr. Matthew McGill Assisted.
Some Basic Concepts of Remote Sensing
The Advanced Earth Surface Observation Project (AESOP): New Developments for Optical Imagery Sébastien Leprince, Francois Ayoub, Jiao Lin, Jean-Philippe.
A DECADE OF PROGRESS : FROM EARTHQUAKE KINEMATICS TO DYNAMICS Raúl Madariaga Laboratoire de Géologie, Ecole Normale Supérieure.
Specular reflectorquasi-specular reflector quasi-Lambert reflector Lambert reflector Limiting Forms of Reflection and Scatter from a Surface.
Earthquake dynamics at the crossroads between seismology, mechanics and geometry Raúl Madariaga, Mokhtar Adda-Bedia ENS Paris, Jean-Paul Ampuero, ETH Zürich,
Lecture-15 1 Lecture #15- Seismic Wave Overview. Lecture-15 2 Seismograms F Seismograms are records of Earth’s motion as a function of time.
Introduction to Remote Sensing The Electromagnetic (EM) Spectrum.
STATISTICS and ENERGETICS of the SOLID EARTH PROCESSES: RELIEF, PLATES, EARTH PROCESSES, VOLCANOES G.S. Golitsyn A.M. Obukhov Institute of Atmospheric.
DETECTION OF UPPER LEVEL TURBULENCE VIA GPS OCCULTATION METHODS Larry Cornman National Center for Atmospheric Research USA.
Active Microwave and LIDAR. Three models for remote sensing 1. Passive-Reflective: Sensors that rely on EM energy emitted by the sun to illuminate the.
Initial 3D isotropic fractal field An initial fractal cloud-like field can be generated by essentially performing an inverse 3D Fourier Transform on the.
Earthquakes recorded in the landscape: Using digital topography to investigate earthquake faulting Christopher Crosby GEON / Arizona State University SDSC.
5: EARTHQUAKES WAVEFORM MODELING S&W SOMETIMES FIRST MOTIONS DON’T CONSTRAIN FOCAL MECHANISM Especially likely when - Few nearby stations, as.
Doppler Radar From Josh Wurman Radar Meteorology M. D. Eastin.
Proposed Capabilities ASIS – Pulse-Coherent Sonars (RaDyO/SO GasEx) – Bubble-Size Distribution (Duck) Ship-Based – WaMoS II (SO GasEx) – Scanning LIDAR.
Turbulence layers detection from ground-based Rayleigh lidar Alain Hauchecorne 1, and the MMEDTAC Team Charles Cot 1, Francis Dalaudier 1, Jacques Porteneuve.
The Remote Sensing of Winds Student: Paul Behrens Placement and monitoring of wind turbines Supervisor: Stuart Bradley.
ElectroScience Lab IGARSS 2011 Vancouver Jul 26th, 2011 Chun-Sik Chae and Joel T. Johnson ElectroScience Laboratory Department of Electrical and Computer.
Application of a High-Pulse-Rate, Low-Pulse-Energy Doppler Lidar for Airborne Pollution Transport Measurement Mike Hardesty 1,4, Sara Tucker 4*,Guy Pearson.
Remote Sensing and Active Tectonics Barry Parsons and Richard Walker Michaelmas Term 2011 Lecture 4.
B. Gentry/GSFCSLWG 06/29/05 Scaling Ground-Based Molecular Direct Detection Doppler Lidar Measurements to Space Using Wind Profile Measurements from GLOW.
Active Microwave and LIDAR. Three models for remote sensing 1. Passive-Reflective: Sensors that rely on EM energy emitted by the sun to illuminate the.
Problems and Future Directions in Remote Sensing of the Ocean and Troposphere Dahai Jeong AMP.
Astronomy 1020-H Stellar Astronomy Spring_2015 Day-22.
School of Earth and Environment Institute of Geophysics and Tectonics Robust corrections for topographically- correlated atmospheric noise in InSAR data.
Study Design and Summary Atmospheric boundary layer (ABL) observations were conducted in Sapporo, Japan from April 2005 to July Three-dimensional.
B. Gentry/GSFCGTWS 2/26/01 Doppler Wind Lidar Measurement Principles Bruce Gentry NASA / Goddard Space Flight Center based on a presentation made to the.
GIFTS - The Precursor Geostationary Satellite Component of a Future Earth Observing System GIFTS - The Precursor Geostationary Satellite Component of a.
InSAR and LIDAR Lecture Interferometric Synthetic Aperture Radar (InSAR or IFSAR)  Is a process whereby radar images of the same location on the.
Set-up of a ground-based Rayleigh lidar to detect clear air turbulence Alain Hauchecorne 1, Charles Cot 1, Francis Dalaudier 1, Jacques Porteneuve 1, Thierry.
Wind power Part 2: Resource Assesment San Jose State University FX Rongère February 2009.
Observation of diffuse seismic waves at teleseismic distances
Spaceborne 3D Imaging Lidar John J. Degnan Geoscience Technology Office, Code Code 900 Instrument and Mission Initiative Review March 13, 2002.
InSAR and LIDAR Lecture 8 Oct 13, 2004.
NASA ESTO ATIP Laser Sounder for Remotely Measuring Atmospheric CO 2 Concentrations 12/12/01 NASA Goddard - Laser Remote Sensing Branch 1 James B. Abshire,
Surface Layer SLODAR J. Osborn, R. Wilson and T. Butterley A prototype of a new SLODAR instrument has been developed at Durham CfAI and tested at the Paranal.
How does InSAR work? Gareth Funning University of California, Riverside.
Program 2 Internal structure and deformation of volcanic edifices.
Introduction to Interferometric Synthetic Aperture Radar - InSAR
California Institute of Technology
GWOLF and VALIDAR Comparisons M. Kavaya & G. Koch NASA/LaRC D. Emmitt & S. Wood SWA Lidar Working Group Meeting Sedona, AZ January 2004.
Lenschow - ATD retreat - 23 Jan 04 GEOPHYSICAL TURBULENCE Scientific and observational needs Geophysics − The physics of the earth and its environment,
Summary Remote Sensing Seminar Summary Remote Sensing Seminar Lectures in Maratea Paul Menzel NOAA/NESDIS/ORA May 2003.
Active Remote Sensing for Elevation Mapping
Quick Review - Fronts. Quick Review - Clouds Using Satellite and Radar Imagery to Find Weather Features.
SATELLITE AND SENSORS.  Satellite Characteristics Satellite Power sources Satellite Orientation Orbits and Swaths  Sensors Active Sensors Passive Sensors.
Shaking and Flooding by the Tohoku-Oki earthquake Shengji Wei*, Rob Graves**, Don Helmberger*, Jean-Philippe Avouac* and Junle Jiang* * Seismological Lab,
Early VALIDAR Case Study Results Rod Frehlich: RAL/NCAR Grady Koch: NASA Langley.
Lab 2-4 Surveying Bear Lake Shorelines
Lidar winds from GEO: The Photons to Winds Conversion Efficiency
Susan L. Beck George Zandt Kevin M. Ward Jonathan R. Delph.
Active Remote Sensing for Elevation Mapping
Basic Concepts of Remote Sensing
California Institute of Technology
Doppler Radar Basics Pulsed radar
Kinematic GNSS Systems Units 2, 2. 1, and 2
Université Joseph Fourier Grenoble, France
Instrument Considerations
Douglas Dreger, Gabriel Hurtado, and Anil Chopra
Douglas Dreger, Gabriel Hurtado, and Anil Chopra
Acoustic Reflection 2 (distance) = (velocity) (time) *
The radar band is loosely taken to extend from approximately 0
Validation of airborne 1
Weather Analysis.
National Institute for Environmental Studies, Tsukuba, Japan
Presentation transcript:

Imaging seismic waves from space Rémi Michel 1,2, Sébastien Leprince 1, Serge Primet 3, S. Somala 1, Jean-Paul Ampuero 1, Nadia Lapusta 1, Jean-Philippe Avouac 1 1 California Institute of Technology, Pasadena, USA 2 Commissariat a l’Energie Atomique, Saclay, France 3 Institut d’Optique, Palaiseau, France

Motivation Source Model of the 1999, Mw 7.1 Duzce Earthquake. Data: SPOT offsets, inSAR, GPS, Seismograms (Konca et al, BSSA, 2010)

Motivation Source Model of the 1999, Mw 7.1 Duzce Earthquake: An initial crack-like rupture which evolve into a super-shear pulse (Konca et al, BSSA, 2010)

Simulation of a seismic rupture: horizontal strike-parallel component of the velocity field), computed for a theoretical Mw 7.0 earthquake simulated using a rate&state friction law and the Spectral Element Method (e.g., Kaneko, Lapusta and Ampuero, 2009) Motivation 50km t=15s

The determination of a kinematic source model from the inversion of static ground displacement and seismograms measured at a sparse set of seismic stations is an ill-posed problem. …but such models are needed to investigate Earthquake physics. Motivation

RequirementsComments Field of view# 550X250 kmMain Shocks, California SamplingSpatial# 100 mOk for Mw > 5.5 Poor for Mw < 5.5 ? Temporal# 1 Hz Accuracy# 1cm.s -1 Similar to accelerometers Requirements S -1 Mw7Mw7 Mw6Mw6 50km

Airborne/Space Systems InstrumentComments Drones Field Of View Airplanes FOV, temporal sampling ? Balloons Stability, FOV Earth Orbiter Low, Medium Temporal Sampling FOV # 1000km : Huge Constellations Geostationary Instrumentation?  {Radar GESS Geosynch Radar Study }  Optics Passive Imagery Limited to Clear Sky, Daylight ? A Large Geostationary Optical Telescope?

Photometry Essentials L:radiance,  : reflectance, S : atmosphere spherical reflectance Movie [  m] Wavelength (  m) Sun RadianceAtmosphere Transmittance Wavelength (  m) Ground Reflectance Wavelength (  m) Atmospheric Turbulence

SignalsTechniqueGeostationary Issues Horizontal Deformation Optical flow, Correlation  Telescope size (Resolution)  Detection  Data flow  1/100 pixel Slope (Radiance) VariationPhotoclinometry  Telescope size (Photometry)   I/I=  Detection  Stability Topographic LiDAR Differential topography  Laser Energy!  #100 photons=5cm Doppler Shift (atmosphere) Rayleigh=>Acoustic Wind Velocimeters  Small Doppler  10 cm.s -1  Complex Signal Measurable quantities with Optical Systems

Sun Poisson Incidence Atmosphere Steering (geometry) Kolmogorov Shift < few centimeters<6cm Blur < few centimeters<6cm Dancing (geometry) Scintillation (photometry) Rytov Ground Bidirectional Reflectance Distribution Function Natural, 1-100m Urban TBD (high level), lights, damages, etc. Vegetation TBD Shakened ground TBD Natural Light Fluctuations [10Hz-1Hz]

Horizontal offsets Measured by Correlation or Optical Flow 1/100 of the pixel size. SNR 1000 (reflectance 0.15, back-illuminated CCD) : 0.03 s per frame for  4m. Stability is an issue for data management and not for accuracy.

Photoclinometry Accuracy possible up to [ ] (number of detected photons [10 10 – ])  =0.15, pixel size : 100m, integration time 0.03s, most unfavorable incidence angle # 11 degrees

Simulations-Horizontal offsets Synthetic Telescope  4.0m Telescope  10.m Mw 7.1, t=15s Mw 6.0 t=4.22s Mw 6.0 t=4.22s Mw 6.0 t=4.22s Model (Mw 7.1, 30s, 2Hz, pixel:100m) and 2 critical telescope diameters (1Hz, pixel:100m)

Telescope  10.0m S -1 Accuracy 1/50 th pixel Telescope  4.0m Simulations- Horizontal offsets

Modele  7.0m  4.0m  10.0m

Simulations-Photoclinometry Synthetic Simulation (4m) rad.s -1 (Mw7)

Conclusions  Optics-Geostationary : a solution to measure seismic waves and other transients  40% of Earthquakes in Southern California, #1.2 per year with Mw 6 and above  Telescope size is critical, large field of view (up to 4 degrees?)  Less than 10% light budget, more applications possible  Payload sub-critical  Further investigations required Atmosphere, aliasing, moonlight, weather, wide field up to 4 degrees telescope design, geostationary environment, laser, etc.  Will require new analysis technique in seismology. Conclusions

 4.0m Ariane V Compatible  7.0m Challenging  10.0m Challenging  4.0m  7.0m  10.0m Horizontal Velocities [-0.4,+1.9] (m.s-1) ModelA A’ A Transect  Model of strike slip fault (Mw 7.1), Field of view 60*24km, 1Hz, ground resolution 100m Accuracy of Optical Correlation = f(  telescope, geostationary)