Automatic Digitization of JMA Strong-Motion Seismograms Recorded on Smoked Paper: An Attempt Using Deep Learning

Abstract

Abstract Using deep learning, an attempt was made to automatically digitize waveforms in scanned Japan Meteorological Agency (JMA) analog seismograms of strong-motion seismographs recorded on smoked paper. We manually traced the waveforms in the scanned images to be used as supervised data for a convolutional neural network (CNN) model using these images. In the analog recording system, ground shaking was recorded on paper with a needle attached to the end of an arm. Because the other end of the arm is fixed on a pivot, the needle moves in an arc around the pivot for large shaking compared to the arm length. We carefully considered the effects of the finite arm length in the analog system and trained the CNNs. To validate the learning results, the trained CNN model was applied to images that were not used for the training. The automatic digitization by our method works fairly well, except for seismograms rich in high-frequency components and those with long-period large amplitudes. Even these images can be well digitized by resizing the image height by, for example, 1/2. To further improve the accuracy of automatic digitization, it would be effective to prepare many additional input and supervised data, and retrain the model. Other training is also necessary for other types of seismographs that have features and characteristics different from strong-motion seismograms.