A Novel Approach to Automatically Digitize Analog Seismograms

Abstract Before the widespread adoption of the digital seismographs, seismic records were stored in analog form on paper and manually read by analysts. These analog seismograms contained various useful information and were crucial for seismic research. To meet the demands of the modern computational analysis, researchers must digitize historical analog seismograms and extract information. In this article, we present a novel approach to automatically digitize analog seismograms. Initially, Otsu threshold segmentation was applied to the analog seismograms to remove underlying noise and improve their clarity. Subsequently, a novel dynamic distributed seismic waveform onset-point-search algorithm was implemented, which automatically locates the onset point of each seismic waveform baseline in analog seismograms and accurately determines the total number of seismic waveform curves. To address the complexity and diversity of seismic waveforms, we implemented an innovative seismic waveform classification algorithm that can distinguish between complex waveforms and smooth waveforms, and further implemented a new smooth waveform removal method to eliminate interference from smooth waveforms during complex waveform extraction. Then, we used a YOLOv9s-based model to identify time markers within the seismic waveforms for removal. In addition, in the seismic waveform digitization extraction and reconstruction phase, we implemented a novel method for extracting significant seismic waveform features and geometric restoration for peak and trough feature extraction and geometric restoration, as well as vertical feature extraction of seismic waveforms. Finally, we implemented a new waveform sequence integration and time mapping model, which can effectively reconstruct seismic waveform data based on the extracted features and map arrival times to each waveform point. Experiments have verified the significant superiority and stability of the methods implemented in this article for digitizing analog seismograms.

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Publication Details

Journal
Seismological Research Letters
Published
2024-08-30
DOI
https://doi.org/10.1785/0220240220
Citations
2
Primary Topic
Seismic Imaging and Inversion Techniques
Type
article
Field-Weighted Citation Impact
1.07
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article

A Novel Approach to Automatically Digitize Analog Seismograms

Fengshan Yang, Jun Jiang, Xin Liao, Ke Gao et al.
2 citations
Seismological Research Letters
Seismic Imaging and Inversion Techniques
1.07
article

A Novel Approach to Automatically Digitize Analog Seismograms

Fengshan Yang, Jun Jiang, Xin Liao, Ke Gao, Bin Wang, Lu Zhang, Bingchen Yan, Maofa Wang, Wenheng Guo, Yanlin Xu, Quan Wan
article en
2 citations

Abstract

Abstract Before the widespread adoption of the digital seismographs, seismic records were stored in analog form on paper and manually read by analysts. These analog seismograms contained various useful information and were crucial for seismic research. To meet the demands of the modern computational analysis, researchers must digitize historical analog seismograms and extract information. In this article, we present a novel approach to automatically digitize analog seismograms. Initially, Otsu threshold segmentation was applied to the analog seismograms to remove underlying noise and improve their clarity. Subsequently, a novel dynamic distributed seismic waveform onset-point-search algorithm was implemented, which automatically locates the onset point of each seismic waveform baseline in analog seismograms and accurately determines the total number of seismic waveform curves. To address the complexity and diversity of seismic waveforms, we implemented an innovative seismic waveform classification algorithm that can distinguish between complex waveforms and smooth waveforms, and further implemented a new smooth waveform removal method to eliminate interference from smooth waveforms during complex waveform extraction. Then, we used a YOLOv9s-based model to identify time markers within the seismic waveforms for removal. In addition, in the seismic waveform digitization extraction and reconstruction phase, we implemented a novel method for extracting significant seismic waveform features and geometric restoration for peak and trough feature extraction and geometric restoration, as well as vertical feature extraction of seismic waveforms. Finally, we implemented a new waveform sequence integration and time mapping model, which can effectively reconstruct seismic waveform data based on the extracted features and map arrival times to each waveform point. Experiments have verified the significant superiority and stability of the methods implemented in this article for digitizing analog seismograms.

Seismological Research LettersVol. 96(1)
China Geological Survey (CN), Southern University of Science and Technology (CN), Institute of Disaster Prevention (CN), Guilin University of Electronic Technology (CN)
Openalex Percentile: Top 25%
Seismic Imaging and Inversion Techniques
1.07
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