Estimation site effect from the seismogram

Yuliatmoko, Rahmat Setyo; Kurniawan, Telly; Vita, Aprilia Nur; Rohadi, Supriyanto; Riama, Nelly Florida; Gunawan, Indra; Karnawati, Dwi Korita

doi:10.1063/5.0037541

Cited by 1 publication

(1 citation statement)

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“…Processing these datasets is important for practical applications in the fields of seismology, geology, tectonics and physics of the Earth. The wide use of information retrieved from the seismograms [ 20 ] includes such domains as geophysics [ 21 ], ocean climate modelling [ 22 ], subsurface exploration, global and planetary tectonics [ 23 , 24 , 25 ], risk assessment in civil engineering [ 26 , 27 , 28 ], earthquakerisk analysis with associated features (dynamic rupture, seismic tomography and wave forms) [ 29 , 30 , 31 , 32 , 33 ], and volcanology [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium

Lemenkova

Plaen

Lecocq

et al. 2022

Sensors

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Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research.

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