Automatic event picking in pre-stack migrated gathers using a probabilistic neural network
- Lawrence Livermore National Lab., CA (United States)
- Shell E&P Technology Co. (United States)
- California Univ., Davis, CA (United States)
We describe algorithms for automating the process of picking seismic events in pre-stack migrated gathers. The approach uses supervised learning and statistical classification algorithms along with advanced signal-image processing algorithms. We train a probabilistic neural network (PNN) for pixel classification using event times and offsets (ground truth information) picked manually by expert interpreters. The key to success is in using effective features that capture the important behavior of the measured signals. We use a variety of features calculated in a local neighborhood about the pixel under analysis. Feature selection algorithms are used to ensure that we use only the features that maximize class separability. The novelty of the work lies in (a) the use of pre-stack migrated gathers rather than stacked data, (b) the use of two-dimensional statistical and wavelet features, and (c) the use of a PNN for classification. 8 refs., 3 figs
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE, Washington, DC (United States)
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 394450
- Report Number(s):
- UCRL-JC-124022; CONF-961134-1; ON: TI96010645
- Resource Relation:
- Other Information: PBD: Apr 1996
- Country of Publication:
- United States
- Language:
- English
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