It’s a common phenomenon that the weak signal is masked by the strong low-frequency signal in seismic section of thin interbedded reservoir, which impacts the interpretation of the weak geologic structural information. In this abstract, we introduce the multi-scaled morphology to solve this problem. By decomposing the original seismic data into several morphology scales, we extract the reconstruction factors adaptively from each scale based on the theory of variance norm, and reconstruct the seismic data with those reconstruction factors to feature the weak signal relatively. The examples of synthetic data and real seismic data demonstrates the applicability of multi-scaled morphology in weak signal detection of thin interbedded reservoir.


Article metrics loading...

Loading full text...

Full text loading...


  1. X.Q.Chen, R.Q.Wang, H.J.Li, C.G.Lu
    [2015] Application of Multi-scaled Morphology in Microseismic Weak Signal Detection. 77th EAGE Conference & Exhibition, Madrid, Spain.
    [Google Scholar]
  2. Matheron, G.
    [1975] Random sets and integral geometry. New York: Wiley, (Vol. 1).
    [Google Scholar]
  3. Serra, J.
    [1982] Image analysis and mathematical morphology. Academic press, (Vol. 1).
    [Google Scholar]
  4. [1988] Image Analysis and Mathematical Morphology Theoretical adcances. Academic press, (Vol. 1).
    [Google Scholar]
  5. Sinha, D. and Dougherty, E.R.
    [1992] Fuzzy mathematical morphology. Journal of Visual Communication and Image Representation, 3(3), 286–302 .
    [Google Scholar]
  6. Wang, R., Li, Q. and Zhang, M.
    [2008] Application of multi-scaled morphology in denoising seismic data. Applied Geophysics, 5(3), 197–203.
    [Google Scholar]

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error