Application of neural networks modeling for interpretation of acoustic logging traces

M. Lazarenko, O. Gerasimenko

Abstract


The neural networks are proposed for application as a method for automatic P- and S-waves onset time-picking on sonic logging. The neural network models of acoustic emission preceding phase onset are trained and used to discriminate noise and desired signal, the last one being packets of  longitudinal  and transversal waves. The given algorithm is easily adapted to existing systems and is able to provide both processing of logging tracks in online regime and high productivity of archive materials interpretation.


Keywords


neural network modelling; acoustic logging; wave arrival time; adaptive threshold level; longitudinal and transversal waves

References


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Wong J., Han L., Stewart R. R., Bancro J. C., 2009. Geophysical well logs from a shallow test well and automatic time-picking on full-waveform sonic logs. CSEG Recorder 34 (4), 20—29.




DOI: https://doi.org/10.24028/gzh.0203-3100.v37i5.2015.111160

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