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CATSEYES: Categorizing Seismic structures with tessellated scattering wavelet networks

Citation Author(s):
Yash BHALGAT, Jean CHARLETY
Submitted by:
LAURENT DUVAL
Last updated:
13 April 2018 - 9:50am
Document Type:
Poster
Document Year:
2018
Event:
Presenters Name:
Laurent DUVAL
Paper Code:
1949

Abstract 

Abstract: 

As field seismic data sizes are dramatically increasing toward exabytes, automating the labeling of ``structural monads'' --- corresponding to geological patterns and yielding subsurface interpretation --- in a huge amount of available information would drastically reduce interpretation time. Since customary designed features may not account for gradual deformations observable in seismic data, we propose to adapt the wavelet-based scattering network methodology with a tessellation of geophysical images. Its invariances are expected to be able to thwart the effect of the tectonics. The sparse structure of extracted feature vectors suggest to resort to dimension reduction methods before classification. The most promising one is based on a tessellated version of scattering decompositions, combined with a standard affine PCA classifier. Extensive comparative results on a four-class seismic database show the effectiveness of the proposed method in terms of seismic data labeling and object retrieval, in affordable computational time.

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2 users have voted: LAURENT DUVAL, Yash Bhalgat

Dataset Files

Supervised seismic structure classification clustering with wavelet scattering networks

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