Coronal seismology aia hmi and image processing best wishes
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Coronal seismology, AIA/HMI and image processing (-: Best wishes :-). JF Hochedez, E Robbrecht, O Podladchikova, A Zhukov, D Berghmans SIDC @ ROB Solar Influences Data analysis Center Royal Observatory of Belgium. Mandate of this presentation. AIA. Coronal Seismology. Image Processing.

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Coronal seismology, AIA/HMI and image processing (-: Best wishes :-)

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Coronal seismology, AIA/HMI and image processing(-: Best wishes :-)

JF Hochedez, E Robbrecht, O Podladchikova, A Zhukov, D Berghmans


Solar Influences Data analysis Center

Royal Observatory of Belgium

Mandate of this presentation






EUV imaging observations and seismology(1) in [simple] flux tube magnetic structures

Optical Flow

Motion & brightness changetracking

  • Loop recognition andCactus-like approach

  • x-t diagrams,

  • Hough transform,

  • clustering

EUV imaging observations and seismology(2) in [other] coronal structures

EIT wave detector

Flare detector and Podladchikova et al (submitted)

Presentation sections

  • When Optical Flow will detect fast modes in flux tubes

  • Loop recognition and Hough transform applied to slow waves

  • What EIT waves can tell us about the corona

  • [Prospective] sympathetic flares. How do they communicate?

  • Conclusions

Optical Flow

& its application to fast modes

Remaining problems with kink oscillations

  • Damping

    • Test competing explanations

      • phase mixing

      • resonant absorption (Goosens et al 2002)

      • leakage at footpoints, others…

    • Too many parameters

      • stratification (estimated by Andries et al 2005)

      • Curvature

      • variable cross-section

         More statistics needed

  • Exciter(s)

    • Their nature? From below? From side?

  • Why so few ?

    • Damping or lack of exciters?

Hopes from AIA-HMI (1/2)

  • 8 bandpasses

    • Longitudinal density profile (DEM tools)

    • Heating profile

  • Spatial resolution

    • Radial density profiles: concentric shells, threads?

      • 0.6”probably still too low

    • Overtones (Verwichte et al 2004)

    • 3D geometry with Secchi

      • Loop length

      • vertical vs swaying (Wang et Solanki 2004), etc.

  • Full Sun FOV

    • 2 pressure scale heights

      • long loops with good SNR

    • With temporal coverage: statistics

Hopes from AIA-HMI (2/2)

  • 2s Cadence

    • time aliasing repressed

    • SNR  Time rebinning

    • exposure time ~0.1s

      • Less kinetic blurring

      • Stroboscopy

    • Observe fast sausage waves, fast sausage oscillations, fast propagating kink waves!

  • Effective area (44x [email protected], 61x @194)

    • See smaller disturbances.

  • Presence of HMI

    • Independent estimate of B (cf. too many parameters)

      • Compatible with seismology? (NLFF and dynamics)

AIA trade-off TBD

VELOCIRAPTORVELOCIty & bRightness vAriations maPs construcTOR

Gissot & Hochedez, 2006

Quantify motiontogether withintrinsic brightness variationin EIT image sequences

Hochedez & Gissot

Inputs& outputs



  • Similarity fieldbetweenIn(x,y) (warped)and In+1(x,y)

  • Local “texture”

  • Residuals

Image In(x,y)

e.g. EIT “CME Watch”

Image In+1(x,y)




Differential rotation recovered from a couple of EIT images

(No BV estimation)

BV map of the May 12, 1997 event

Velocity map of the May 12, 1997 event

(No BV estimation)

14 July 1998 12:50:16

Presence of texture in 2 orthogonal directions

Presence of texture at least in one direction

Zoom of the previous representation

Velocity field produced by Velociraptor

Average displacement ~0.3 pixel

→ LCT not appropriate (a posteriori justification)

Velocity field corrected for global shift

Loop displacement ~0.15 pixel

Question: What are the anticipated artifacts for AIA?

OF & fast magneto-sonic waves:Conclusions and outlook

  • Velociraptor can measuresausage and kink waves

    • Precisely, all along the loops, systematically, Outliers?

    • Challenging development

    • Being fully calibrated

    • 2 main problems understood and being corrected:

      • Strong BV  fictive motion

      • Some spurious sliding remains along loops

  • Post-processing of the fields needed in order to identify waves autonomously (1D wavelets?)

  • AIA + OF  great prospect

    • Sausage modes by EUV imaging?

    • Flows from steady reconnections?

    • Mode coupling?

Slow waves

Wave or plasma motion? (no Doppler measurements)

Sound speed if pattern seen in several BPs

cf. Robbrecht et al. 2001 EIT vs TRACE

Klimchuk et al 2004:

Their study validates classical thermal conduction damping

But “TRACE loops are inconsistent with static equilibrium and steady flow”

“Observed damping times of slow mode oscillations might be a lower limit to effective damping times, which can only be corrected if the cooling time is known from multi-filter data.”

Seismology is complementary to DEM

Good overall understandingbut …

Useful image processingfor slow waves (1)

  • Loop extraction (ridge detection)

Useful image processingfor slow waves (2)

  • Analysis of X-T diagrams

    • Hough Transform

    • Clustering

    • Cf “CACTUS” applied to [faint] CME detection

      • in LASCO C2 & C3

Computer Aided CME Tracking -CACTus

11 November2003








EIT waves

EIT waves for coronal seismology

  • EIT waves: bright fronts propagating from eruption sites observed in EUV (SOHO/EIT, TRACE, CORONAS-F/SPIRIT, 195 Å, 171 Å, 284 Å bandpasses).

  • Sometimes EIT waves propagate nearly isotropically and often – globally.

  • EIT wave speeds are usually about 150–400 km/s, typically around 250 km/s.

  • Association with chromospheric Moreton waves, waves in He I and waves in SXR?



Wang (2000)

Wu et al. (2001)

Fast magnetosonic wave speed around 250 km/s means b ~ 1 or b > 1 in the “quiet Sun” corona

Force-free approximation is not valid!

If EIT waves are fast magnetosonic waves…

Courtesy A Zhukov 2006

a quantitative investigation

Podladchikova & Berghmans, 2005

  • DIMMING & EIT wave extraction from EUV image

  • Brightness distribution (histogram) analysis

    • study of higher moments

  • EIT wave radial and polar analysis

  • Ring Analysis

    • radial velocities in the EIT wave

  • Angular-Ring Analysis

    • potential angular features

Skewness & Kurtosis of PDF of difference image versus time

Simultaneous peaks

+ dimming area criteria→ EIT Waves!

Courtesy of Podladchikova & Berghmans

12 May 1997




Both quadratic

Distances vs Time

Integrated signals vs Time

Courtesy of Podladchikova & Berghmans


  • Anisotropy even without obstacles. Correlation with associated dimming;

  • Dimming contiguous to wave front in all directions

  • Width of the front grows ~quadratically in time;

  • Integrated intensity of wave front grows during > 1/2 hrThe front intensity of linear magnetosonic waves would decrease

  • Integrated intensity of frontbalances integrated intensity of the dimmings (in early life of wave)

    EIT wave = MHD wave?

Sympathetic flaring

Consecutive occurrence of flares in different AR

Perturbation velocity from flare to flare “to set the fire”

Vchar ~ 110 km/s

t < 5h.

Velocity [km/s]

3225 flares registered with coordinates since 01/01/2004. Statistically complete series.

Result does not depend on time interval


  • significant number of events where one flare “sets fire”triggering another distant flare in a separate active region.

  • Propagation velocities for such perturbations around 110 km/s.

B2X flare detector

Method:Wavelet spectrum (scale measure) analysis

Hochedez et al ’02 Solspa2 Proc., Delouille et al SoPh ’05

Result:Small flares automatic detection

Relevance:Sympathetic flaring studies

At flare peak

½ log(μ(scale))

Just before

the flare begins


Beauty spotter

Method: Extraction in scale space by Lipschitz coefficient

Hochedez et al 2002, Soho11 WS Proc.,

Hochedez et al 2003 Soho13 WS


BPs, brightenings and

Cosmic Ray Hits extracted


Oscillations in point-like structures


  • The easy things about waves have been found. Intelligent techniques can invigorate future research

    • Prospect for eruption precursors?

  • Image processing = binding agent between theory and observation

    • Like an additional "telescope" for small scale physics

      • improve resolution

      • separate different processes (mutually and from noise)

      • extract waves or reconnection events

      • part intensity from velocity variations

    • Like a new "microscope" for large scale physics

      • Describe of important events

      • "in situ sensor“, identifying the nature of events

      • Uncover unexpected regularities

  • For all these reasons, all detected waves should go in the SDO catalogs

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