Classification of seismic windows using artificial neural networks

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

研究成果: Conference article同行評審

45 引文 斯高帕斯(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.

頁(從 - 到)1572-1581
期刊Procedia Computer Science
出版狀態Published - 2011
事件11th International Conference on Computational Science, ICCS 2011 - Singapore, Singapore
持續時間: 2011 6月 12011 6月 3

All Science Journal Classification (ASJC) codes

  • 電腦科學(全部)


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