Download or read book Neural Networks in Seismic Discrimination written by and published by . This book was released on 1995 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Seismic Discrimination Using Neural Networks written by Sean F. Walden and published by . This book was released on 1992 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Application of Neural Networks to Seismic Signal Discrimination Research Findings written by James A. Cercone and published by . This book was released on 1994 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research focused on identification and collection of a suitable database, identification of parametric representation of the time series seismic waveforms, and the training and testing of neural networks for seismic event classification. It was necessary to utilize seismic events that had a high degree of reliability for accurate training of the neural networks. The seismic waveforms were obtained from the Center for Seismic Studies and were organized into smaller databases for training and classification purposes. Unprocessed seismograms were not well suited for presentation to a neural network because of the large number of data points required to represent a seismic event in the time domain. The parametric representation of the seismic events in some cases provided adequate information for accurate event classification, while significantly reducing the minimum size of the neural network. Various networks have achieved classification rates ranging from 88 percent classification of three class problem to 75 percent for the 5 class problem. The results vary dependent on the number of classes and the method of parametric transformations utilized. Multiple tests were performed in order to statistically average the training and classification rates. Test summaries presented and individual test results are given in the appendix.
Download or read book Application of Neural Networks to Seismic Signal Discrimination written by James A. Cercone and published by . This book was released on 1993 with total page 66 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Seismic Event Discrimination Using Neural Networks written by Joseph E. Bitto and published by . This book was released on 1989 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Test and Evaluation of Neural Network Applications for Seismic Signal Discrimination written by and published by . This book was released on 1992 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study describes operational test and evaluation of two neural network applications that were integrated into the Intelligent Monitoring System (IMS) for automated processing and interpretation of regional seismic data. Also reported is the result of a preliminary study on the application of neural networks to regional seismic event identification. The first application is for initial identification of seismic phases (P or S) recorded by 3-component stations based on polarization data and context. This neural network performed 3-6% better than the current rule-based system when tested on data obtained from the 3-component IRIS stations in the former Soviet Union. This resulted in an improved event bulletin which showed that the number of analyst-verified events that were missed by the automated processing decreased by more than a factor of 2 (about 10 events/week). The second operational test was conducted on the neural network developed by MIT/Lincoln Laboratory for regional final phase identification (e.g., Pn, Pg, Sn, Lg, and Rg). This neural network performed 3. 3% better than the rule-based system in IMS station processing. However, for the final phase identifications obtained after network processing (where data from all stations are combined), the gain dropped to about 1.0%. It is likely that this could be regained by using the neural network phase identification confidence factors in the network processing. Finally, our preliminary study on the application of neural networks to identify regional seismic events on the basis of coda shape gave about 80% accuracy on data recorded at GERESS. In general, the neural network classifier utilized the coda decay rate which was lower for the earthquakes than it was for the explosions, although there was substantial overlap.
Download or read book Application of a Recurrent Neural Network to Seismic Event Discrimination written by Steven Craig Sandven and published by . This book was released on 1991 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Network Traffic Discrimination Through Seismic Signature Examination written by Victor Shane Foster and published by . This book was released on 1993 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Application of Neutral Networks to Seismic Signal Discrimination written by and published by . This book was released on 1993 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first Annual Technical Summary of the West Virginia Institute of Technology Applications of Neural Networks to Seismic Classification project. The first year of research focused on identification and collection of a suitable database, identification of parametric representation of the time series seismic waveforms, and the initial training and testing of neural networks for seismic event classification. It was necessary to utilize seismic events that had a high degree of reliability for accurate training of the neural networks. The seismic waveforms were obtained from the Center for Seismic Studies and were organized into three smaller databases for training and classification purposes. Unprocessed seismograms are not well suited for presentation to a neural network because of the large number of data points required to represent a seismic event in the time domain. Parametric representation of the seismic waveform numerically extracts those features of the waveform that enable accurate event classification. Sonograms and moment feature extraction are two of the several transformations investigated for parametric representation of a seismic event. This parametric representation of the seismic events provides adequate information. Neural networks, Data points, Signal discrimination, Parametric representation, Seismic events.
Download or read book Data to Test and Evaluate the Performance of Neural Network Architectures for Seismic Signal Discrimination written by Sereno, Jr. (Thomas J.) and published by . This book was released on 1991 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report describes a data set that was developed to test and evaluate the performance of neural networks for automated processing and interprepation of seismic data. This data set may also be valuable for many other studies related to seismic monitoring of nuclear explosion testing at regional distance. It includes waveform and parametric data from 241 regional events recorded by the short-period elements of the NORESS and ARCESS arrays in Norway (33 channels/array). The waveform data are stored in SAC binary format, and the parametric data are stored in ASCII files. The event epicentral distances are 200-1800 km, and the event Lg magnitudes are approximately 1.5-3. 2. Most of the events are mining explosions in western USSR, Sweden, and Finland. However, 18 of the events are earthquakes, and 22 are presumed underwater explosions. Detailed documentation has been developed for each event, and is included in eight separate database reports.
Download or read book Data to Test and Evaluate the Performance of Neural Network Architectures for Seismic Signal Discrimination Data Sets 2 3 Volume 1 written by and published by . This book was released on 1992 with total page 80 pages. Available in PDF, EPUB and Kindle. Book excerpt: This study describes a data set that was developed to test and evaluate the performance of neural networks for automated processing and interpretation of regional seismic data. This data set may also be valuable for other applications related to seismic monitoring at regional distances, and it is available at the Center for Seismic Studies (CSS) in an Oracle database or in UNIX tar format on exabyte tapes. It consists of waveform and parametric data from>500 regional events recorded by the short-period elements of the NORESS and ARCESS arrays in Norway, and the GERESS array in Germany (the Oracle database at CSS also included data from the FINESA array in Finland and a 3- component station in Poland called KSP). The epicentral distances are primarily 50-2000 km, and the magnitudes are primarily 1.0-5.0. Most of the events are mining explosions in the western part of the CIS, Sweden, Finland, Poland, and Germany. Also included are 22 presumed underwater explosions, and 51 earthquakes in Fennoscandia that were identified in a regional bulletin produced by the University of Helsinki. Other presumed earthquakes (for which independent bulletin information was not available) include events in the Alps and Mediterranean region that were recorded by GERESS. The Oracle database is in CSS 3.0 format, and the exabyte tapes include waveforms in SAC binary format and parametric data in ASCII tables. Detailed documentation has been developed for each event, and is included in a 13-volume report at the CSS.
Download or read book Test and Evaluation of Neural Network Application for Seismic Signal Discrimination written by Gagan B. Patnaik and published by . This book was released on 1992 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data to Test and Evaluate the Performance of Neural Network Architectures for Seismic Signal Discrimination written by Thomas J. Sereno and published by . This book was released on 1992 with total page 71 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Neural Nets WIRN10 written by Bruno Apolloni and published by IOS Press. This book was released on 2011 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book EPSCoR Supplemental Grant for an Application of Neural Networks to Seismic Signal Discrimination written by James A. Cercone and published by . This book was released on 1994 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Data to Test and Evaluate the Performance of Neural Network Architectures for Seismic Signal Discrimination Volume 2 Neural Computing for Seismic Phase Identification written by and published by . This book was released on 1991 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report describes the application of a neural computing approach for automated initial identification of seismic phases (P or S) recorded by 3- component stations. We use a 3-layer back-propagation neural network to identify phases on the basis of their polarization attributes. This approach is much easier to develop than a more traditional rule-based system because of the high-dimensionality of the input (8-10 polarization attributes), and because the data are station-dependent. The neural network approach also performs 3-7% better than a linear multivariate method. Most of the gain is for signals with low signal-to-noise ratio since the non-linear neural network classifier is less sensitive to outliers (or noisy data) than the linear multivariate method. Another advantage of the neural network approach is that it is easily adapted to data recorded by new stations. For example, we find that we achieve 75-80% identification accuracy for a new station without system retraining (e.g., using a network derived from data from a different station). The data required for retraining can be accumulated in about two weeks of continuous operation of the new station, and training takes less than one hour on a Sun4 Sparc station. After this retraining, the identification accuracy increases to> 90%. We have recently added context (e.g., the number of arrivals before and after the arrival under consideration) to the input of the neural network, and we have found that this further improves the identification accuracy by 3-5%. This neural network approach performs better than competing technologies for automated initial phase identification, and it is amenable to machine-learning techniques to automate the process of acquiring new knowledge.
Download or read book Data to Test and Evaluate the Performance of Neural Network Architectures for Seismic Signal Discrimination written by Gagan B. Patnaik and published by . This book was released on 1991 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt:
