Abstract
We present an artificial neural network based approach for robust event detection from low S/N waveforms. We use a feed-forward network with a single hidden layer that is tuned on a training dataset and later applied on the entire example dataset for event detection. The input features used include the average of absolute amplitudes, variance, energy-ratio and polarization rectilinearity. These features are calculated in a moving-window of same length for the entire waveform. The output is set as a user-specified relative probability curve, which provides a robust way of distinguishing between weak and strong events. An optimal network is selected by studying the weight-based saliency and effect of number of neurons on the predicted results. Using synthetic data examples, we demonstrate that this approach is effective in detecting weaker events and reduces the number of false positives.
| Original language | English (US) |
|---|---|
| Title of host publication | SEG Technical Program Expanded Abstracts 2017 |
| Publisher | Society of Exploration Geophysicists |
| DOIs | |
| State | Published - Aug 17 2017 |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledgements: The research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST). For computer time, this research used the resources of the Supercomputing Laboratory, Information Technology Division and Extreme Computing Research Center at King Abdullah University of Science and Technology (KAUST) in Thuwal, Saudi Arabia.