Classification of seismic windows using artificial neural networks

Steve Diersen, En Jui Lee, Diana Spears, Po Chen, Liqiang Wang

Research output: Contribution to journalConference articlepeer-review

29 Citations (Scopus)


We examine the plausibility of using an Artificial Neural Network (ANN) and an Importance-Aided Neural Network (IANN) for the refinement of the structural model used to create full-wave tomography images. Specifically, we apply the machine learning techniques to classifying segments of observed data wave seismograms and synthetic data wave seismograms as either usable for iteratively refining the structural model or not usable for refinement. Segments of observed and synthetic seismograms are considered usable if they are not too different, a heuristic observation made by a human expert, which is considered a match. The use of the ANN and the IANN for classification of the data wave segments removes the human computational cost of the classification process and removes the need for an expert to oversee all such classifications. Our experiments on the seismic data for Southern California have shown this technique to be promising for both classification accuracy and the reduction of the time required to compute the classification of observed data wave segment and synthetic data wave segment matches.

Original languageEnglish
Pages (from-to)1572-1581
Number of pages10
JournalProcedia Computer Science
Publication statusPublished - 2011
Event11th International Conference on Computational Science, ICCS 2011 - Singapore, Singapore
Duration: 2011 Jun 12011 Jun 3

All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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