|
|
The SEEDS team at George Mason University is:
Jie Zhang (team leader),
Oscar Olmedo (leading developer),
Harry Wechsler,
Art Poland,
Kirk Borne
|
The Solar Eruptive Event Detection System (SEEDS) is a software
package under development to automatically detect,
characterize and classify
transient/eruptive solar events, including CMEs, coronal dimmings, and
flares, using image processing, machine learning and data mining
technique.
As the first stage of development an algorithm was
implemented that detects and tracks CMEs, as seen in LASCO C2 images and soon with
STEREO COR2 images.
Refer to the publication for details
Olmedo, O., Zhang, J., Wechsler, H., Poland, A., Borne, K.: 2008, Solar Phys. 248, 485.
"Automatic Detection and Tracking of Coronal Mass Ejections in Coronagraph Time Series"
The advantage of this algorithm is that detections are made directly on
running-difference images and many parameters are generated that can be used to
classify CME events. The most important of these patameters are the
velocity, acceleration, and position angle, these are seen in the
columns of the monthly detection tables in the SEEDS monthly catalog.
Comparing the SEEDS CME detections to the monthly catalogs prepared by
a visual manual method (the CDAW catalog), approximatly 75% of the CMEs
are recovered. As is the case with other automated detection methods
(CACTUS), an excessive number of events are seen, where most of these
are related to, either pre or post, outflows of CMEs and a few are real
events, but were unreported in the visual catalogs.
There are three main stages in the algorithm, pre-processing,
detection, and tracking. The pre-processing stage begins with
transforming the Coronograph image into polar coordinates where the x
axis represents angle and the y axis the height. A running difference
is made of the data and each running difference image is normalized
such that its mean is zero. The next step is the detection, first a
projection of the transformed image is taken in the x-axis (Fig. 1),
with thresholds, outstanding angles are found (Fig. 2). Region Growing and Thresholding
segentation determine the angular width
of the cme as well as an approximate outline of the CME (Fig. 3). From here
the CME can be tracked in subsequent images.
|
Fig. 1 |
Fig. 2 |
Fig. 3 |
Fig. 4 |
Fig. 5 |
Fig. 8 |
Fig. 9 |
Fig. 6 |
Fig. 7 |
Fig. 10 |
Fig. 11 |
|
|
Curators: Jie Zhang (jzhang7 AT gmu.edu); Suman Dhakal (sdhakal2 AT gmu.edu);
Last updated:
2020/04
|
