A robust neural network-based approach for microseismic event detection
Type
Conference PaperKAUST Department
Earth Science and Engineering ProgramExtreme Computing Research Center
Physical Science and Engineering (PSE) Division
Date
2017-08-17Online Publication Date
2017-08-17Print Publication Date
2017-08-17Permanent link to this record
http://hdl.handle.net/10754/625376
Metadata
Show full item recordAbstract
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.Citation
Akram J, Ovcharenko O, Peter D (2017) A robust neural network-based approach for microseismic event detection. SEG Technical Program Expanded Abstracts 2017. Available: http://dx.doi.org/10.1190/segam2017-17761195.1.Sponsors
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.Publisher
Society of Exploration GeophysicistsAdditional Links
http://library.seg.org/doi/10.1190/segam2017-17761195.1ae974a485f413a2113503eed53cd6c53
10.1190/segam2017-17761195.1