Benefits of using convolutional neural networks for seismic data quality analysis
Abstract. Seismic data represent an excellent source of information and can be used to investigate several phenomena such as earthquake nature, faults geometry, tomography etc. These data are affected by several types of noise that are often grouped into two main classes: anthropogenic and environmental ones. Nevertheless instrumental noise or malfunctioning stations detection is also a relevant step in terms of data quality control and in the efficiency of the seismic network. As we will show, visual inspection of seismic spectral diagrams allows us to detect problems that can compromise data quality, for example invalidating subsequent calculations, such as Magnitude or Peak Ground Acceleration (PGA). However, such visual inspection requires human experience (due to the complexity of the diagrams), time demanding and effort as there are too many stations to be checked. That’s why, in this paper, we have explored the possibility of “transferring” such human experience into an artificial intelligence system in order to automatically and quickly perform such detection. The results have been very encouraging as the automatic system we have set up shows a detection accuracy of over 90 % on a set of 840 noise spectral diagrams obtained from seismic station records.
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