(22th-February-2021)
• Getting the right signal
• A neural network was trained on a set of traces selected from a representative set of seismic records, each of which had their first break signals highlighted by an expert.
• The vast quantities of seismic data involved are cluttered with noise and are highly dependent on the location being investigated. Classical statistical analysis techniques lose their effectiveness when the data is noisy and comes from an environment not previously encountered. Even a small improvement in correctly identifying first break signals could result in a considerable return on investment.
• The neural network achieves better than 95 % accuracy, easily outperforming existing manual and computer-based methods. As well as being more accurate, the system also achieves an 88% improvement in the time taken to identify first break signals. Considerable cost savings have been made as a result.
• Analysing signals buried in background noise
• Defence radar and sonar analysis
• Medical scanner analysis
• Radio astronomy signal analysis
Commentaires