SEG Technical Program Expanded Abstracts 2012 2012
DOI: 10.1190/segam2012-1222.1
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Anomaly detection using dynamic Neural Networks, classification of prestack data

Abstract: Automatic seismic facies classification is now common practice in the oil and gas industry. Unfortunately unsupervised seismic classification is often not optimal. The main criticism of unsupervised classification is the a priori nature of the seismic data set organization and the poor description of seismic due to data redundancy. Data reduction, such as Principal Component Analysis (PCA) is often used in association to reveal the principal characteristics of the geological system. The new clustering describe… Show more

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“…Based on these characteristics in modeling real problems, the use of the DNN model is more rational than the SNN model. Several studies related to the application of the DNN model include prediction of weather data [11], prediction of Zika virus risk [12], detection of seismic data anomalies [13], prediction of temperature at tube surface [14], segmentation and gesture recognition [15], and prediction of radio signal loss [16].…”
Section: Introductionmentioning
confidence: 99%
“…Based on these characteristics in modeling real problems, the use of the DNN model is more rational than the SNN model. Several studies related to the application of the DNN model include prediction of weather data [11], prediction of Zika virus risk [12], detection of seismic data anomalies [13], prediction of temperature at tube surface [14], segmentation and gesture recognition [15], and prediction of radio signal loss [16].…”
Section: Introductionmentioning
confidence: 99%