Vectorising analog seismograms by techniques of machine learning for automated discriminating of seismic signal traces
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The document is a presentation outlining a project on vectorizing historical seismograms using machine learning to automate the digitization process, aimed at preserving archived seismic data stored at the Royal Observatory of Belgium. It discusses the challenges of digitizing old paper records, the multi-disciplinary approach incorporating fields like image processing and geophysics, and the methodologies employed, including the use of software like DigitSeis and Python for data analysis. The project's goals include rapid and accurate processing of seismic data to enhance the understanding of earthquakes and ground motion.
Vectorising analog seismograms by techniques of machine learning for automated discriminating of seismic signal traces
- 1. Polina Lemenkova PRESENTATION PLACE DATE PRESENTER 10.XII.2021 Prof. Dr. Olivier DEBEIR (ULB) and Dr. Thomas LECOCQ (Royal Observatory of Belgium, Department of Seismology and Gravimetry, co-promoteur) Université Libre de Bruxelles, École polytechnique de Bruxelles (Brussels Faculty of Engineering), Laboratory of Image Synthesis and Analysis (LISA). 1 Vectorising analog seismograms by techniques of machine learning for automated discriminating of seismic signal traces SUPERVISORS
- 2. Part 1. Introduction. Key Facts on Seismograms. Project Objectives and Goals. Data and Instruments. Multi-Disciplinary Approaches. 2
- 3. • Study object => old historical scanned seismograms in TIFF format from the archives of Royal Observatory of Belgium (ROB), Department of Seismology & Gravimetry. • Study area: Uccle station (see map). • Study problem => to digitise large archive of the old paper-based seismograms from ROB quickly, accurately and automatically. 3 Research Object and Problem
- 4. Data and Instrument There are various types of seismometers used in geophysics. In this study we used archived seismograms recorded in 1954 by the Galitzine seismometer in Uccle station. Currently dataset included a collection of 145 images from 1 January 1954 to 12 March 1954 The period will be gradually enlarged as soon as other seismograms are scanned to cover 70 last years. Now images are monochrome (B/W). Other images might be scanned in colour. Some of the images are well preserved, some have distortions and defects visible on the aged paper 4 Instrument used for data capture in 1954: Horizontal Galitzine seismometer located in UCC. Image source: courtesy of ROB. Photo: Raphaël S. M. De Plaen
- 6. 6 PROBLEMS ARE CAUSED BY TECHNIQUES OF OLD SEISMOGRAM RECORDING + TIME (SPOTS, BLURS, BROKEN PAPER, ETC.) Examples of the raw data: paper-based seismograms Empty records between the lines of seismic traces with enlarged fragment of seismogram. Here: UCC19540106Gal_N_0811.TIFF Partially spotted image caused by storage, with enlarged fragment of seismogram. Here: UCC19540107Gal_N_0815.TIFF Continuous noise dark background with blurred traces => lack of contrast for image recognition. Here: UCC19540108Gal_N_0815.TIFF Overlapped traces => problems for recognition of trace direction during vectorising. Here: UCC19540112Gal_E_0750.TIFF
- 7. •Manual digitising cannot provide accurate and rapid data processing for developing digitised big dataset of archived seismograms •Seismic data cannot be processed manually and require automatization and programming approaches •We need to process big archives of seismic data from ROB effectively and quickly but accurately and precisely •We need to analyse data with minimised human labour to derive information on earthquakes and ground motion 7 Actuality, Importance and Research Tasks Text Example of the digitised seismograms using DigitSeis So far there are no existing integrated studies of digitising seismograms in big data volumes by ML methods. Only selected software exist (e.g. DigitSeis, SKATE, Teseo)
- 8. 8 Interdisciplinary Nature of Project • Complexity of geophysical data processing requires integrated multi-disciplinary approaches • Applying ML to digitising seismograms brings new possibilities and bene fi ts in seismology. • Opportunities of ML =>> accurate and rapid digitising of the scanned images, rapid processing of historical seismograms, improved techniques of automated recognition of signals and data interpreting. =>> • We need to handle seismic data by ML techniques and advanced software. • Therefore, our project presents a multi- disciplinary approach of ML applications to seismic data processing
- 9. 9 Project Motivation, Strengths and Challenges Old scanned raster seismogram (TIFF fi le) Fragment of the vectorised output (DigitSeis)
- 10. 10 Various Approaches in One Study: Overlapping Disciplines Our project presents an interdisciplinary research combining overlapping scienti fi c clusters and engineering disciplines (image processing, geophysics, ML and data science). A multi-disciplinary project integrates 3 major scienti fi c clusters and several disciplines as sub- sections for vectorising seismograms: 1. Image Processing, Pattern Recognition, Computer Science, Programming, ML 2. Earth Observation data (ROB, Uccle archive), Geophysics and Seismology, Geology, Earthquake Engineering 3. Data Science, Data Analysis, Signal Processing Algorithms of Digitising & Vectorising
- 11. 11 Goals and Objectives of my PhD Project
- 12. Activities Towards Achieving Project Goals 12
- 13. Part 2. Application of DigitSeis Software for Vectorising Seismograms. 13
- 14. 14 Work fl ow of DigitSeis Software for Vectorising
- 15. 15 Examples of marking time gaps (minutes/hours) on seismograms in Cytomine Seismogram processed by DigitSeis Examples of the annotation classes on the raw data: scanned analog seismograms from the Uccle station. Fragment of the resulting digitised output (enlarged) showing seismic traces (horizontal curve lines) and 1-minute time gaps (small vertical dashed lines)
- 16. 16 Examples of the identi fi ed time gaps on the raw TIFF images • Enlarged fragment of image • Time gaps indicating minutes breaking the trace line • Zoomed segment separating the trace line between each other (tiny white gaps breaking traces) Original scanned seismogram (UCC19540116Gal_E_0820.tif)
- 17. 17 Identifying time gaps on seismogram using DigitSeis • Identifying time marks on seismograms by measuring time gap between records. Here: UCC19540119Gal_N_0825.tif Indicating time marks on seismograms as -22 and preparing image for classi fi cation
- 18. 18 Identifying noise and annotations on seismogram using DigitSeis • Results of the classi fi ed seismogram with shown identi fi ed object categories. • Traces are vector white lines while noise is red-coloured objects, automatically recognised (here: handwritten annotations) Small region analysis used for de fi ning a smaller area of interest for closer examination of a border region of the seismogram
- 19. 19 Digitised segments of the trace lines in DigitSeis • Results of the classi fi ed image with shown yellow segments of the identi fi ed trace (enlarged fragment). • Here: example of the fi le UCC19540109Gal_E_0812.tif • Classi fi ed seismogram with traces saved in binary format 0-1. • Here: example of fi le UCC19540109Gal_E_0812.tif (January 9, 1954.)
- 20. 20 Digitised traces after classi fi cation in DigitSeis • Some time gaps (upper left part of the image) were not identi fi ed and not recognised automatically between the trace and dark background. • In these cases, gaps required manual correction to identify time intervals. • Enlarged view of the automatically recognised digitised traces displayed by lines of various colours, • Zero-lines for each trace are visualised as cyan-coloured dashed lines, numbered from top to bottom. • Vertical yellow dashes are time gaps
- 21. 21 Identi fi ed traces for selective correction and re-digitising using Correct Trace mode • Identi fi ed wrong vector direction of line crossing individual traces • Detected misclassi fi cations caused erroneous digitising. • The gaps on the zero-lines (small yellow boxes) show the gaps that existed in the old paper in the original image itself.
- 22. 22 Identi fi ed traces for selective correction and re-digitising using Correct Trace mode • Overlap of line traces unrecognised during digitising: one segment of trace went steeply downwards and merged with another trace • Enlarged view of the manually corrected entangled traces. Correcting misclassi fi ed traces with wrong direction based on colour and geometric pixel’s characteristics.
- 23. 23 Identi fi ed traces for selective correction and re-digitising using Correct Trace mode • Merging the trace initially broken into the three separate parts (three small yellow boxes) • Reclassi fi cation of the selected segment and digitising the centroid of the trace line (purple-coloured). Correcting trace for the selected segments
- 24. 24 Seismogram image with adjusted timing. Here: UCC19540311Gal_E_0727.mat • Timing setup using time display increment • Yellow vertical small dash lines - minute marks • Time markers at 1-minute intervals on each 30-minute trace.
- 25. 25 Example of the digitised image with minute time gaps Here: fragment of UCC19540311Gal_E_0727.mat)
- 26. 26 Validating Results of MATLAB File in Python: Post-Processing Controlling digitising results using Python (Matplotlib library). Blue dots shown the starting position of the hours segments. Green dots show the minute marks. Red dots show the noise and edge dots. Correctly identi fi ed time gaps controlled by Python’s Matplotlib Quality control for time gaps: missed marks in unrecognised segments.
- 27. 27 Statistical Plotting of Data Frequency in .mat File processed by Python
- 28. 28 Research Approach of DigitSeis: Major Steps of Seismic Data Processing
- 29. •Machine Learning (ML) and Deep Learning (DL) in vectorising analog seismograms • ML & DL: Automatic and intelligent data analysis: detecting trace lines using threshold parameters • Image processing: segmentation, classi fi cation of seismograms (separating lines from noise) • Data visualisation and plotting • Data analysis and interpretation •Advanced methods => solve problem of ef fi cient processing of big massifs of old scanned fi les (TIFFs) for geophysical modelling and data interpretation for seismology research •Developing new advanced ML algorithms to digitise seismograms and convert them in vector format automatically 29 Methodology of Project
- 30. Part 3. Using Cytomine Workspace for Storing, Viewing and Analysing Data. 30
- 31. 31 Why using Cytomine for Processing Seismostorm Project ?
- 32. 32 Cytomine for data storage, sharing and analysis View of the Seismostorm project and fi le browsing system Content of fi les in the Seismostorm project in Cytomine • The workspace containing seismic dataset is shared by users (collaborators of Seismostorm) • Navigating in Cytomine =>> paths and hierarchical structure of the project Cytomine is an image analysis workspace to contain, organise, visualise, annotate and analyse images. • Data were placed on the Cytomine environment (Cytomine), developed by the ULiège team. • We uploaded our TIFF images into our project. • Originally designed as a tool for biomedical image processing, Cytomine is adopted in this study for geophysical data processing using seismograms. • The dataset contains 145 fi les recorded in 1954 by Galitzine seismometer.
- 33. 33 Creating ontologies in Cytomine for objects recognition View of the Seismostorm project and fi le browsing system Hour ticks, minute ticks and various categories detected as object classes on the images Examples of the detected and annotated object classes on the scanned seismograms • Ontologies generated in Seismostorm project in Cytomine enable to class shapes for automated recognition • Segments, start hours ticks and fl ares detected as object classes on the scanned images
- 34. 34 Examples of detecting cases in seismograms in Cytomine Hour ticks on the seismograms recorded by the seismometer drum Examples of the detected and annotated object classes on the scanned seismograms Segments separated as fragments on the trace lines
- 35. 35 Examples of marking time gaps (minutes/hours) on seismograms in Cytomine Manual ticks for the start hours on the partially spotted image Examples of the annotation classes on the raw data: scanned analog seismograms from the Uccle station. Manual hour marks for handwritten annotations on the old scanned image
- 36. 36 Examples of marking time gaps (minutes/hours) on seismograms in Cytomine Flares detected on the old scanned raster images of the analog seismograms Examples of the annotation classes on the raw data: scanned analog seismograms from the Uccle station. Minute marks detected, recognised and classi fi ed using ‘ontologies’ of Cytomine on the TIFF fi les
- 37. Part 4. Using Python for Automatic Data Processing 37
- 38. Why Python in Vectorising Seismograms? 38
- 39. ML for Vectorising Seismograms: a Work fl ow in Python 39 The work fl ow for digitising seismograms in Python includes several steps: • De fi ning Region of Interest (ROI) • Selecting threshold parameters (radius of pixels, percentage of contrast) • Sampling several approaches with varied parameters • Processing full ROI after testing parameters and selecting the best and optimal parameters (e.g. pixel size 30, radius 85%) • Vectorising (executing Python script) • Exporting the results to the HDFS format
- 40. 40 Python-based digitising of raster image (1) Automated vectorising of seismograms was performed using several work steps. First, the low-resolution images were grabbed by Python script from the Cytomine and used in script. Work fl ow for vectorising in Python, Matplotlib library (slide 1/10) Enlarged fragment of the vectorised segments of the trace lines
- 41. 41 Text Work fl ow for vectorising in Python, Matplotlib library (slide 2/10) Python-based digitising of raster image (2) Second, the hour gaps have been detected using the indication of the repeatability of gaps (double gaps, close located next to the fi rst minute of this hour). Above: view of the seismogram with indicated hour gaps. Right: enlarged fragment.
- 42. 42 Text Third, the line with double vectorisation (overlapping time periods) were processed. Work fl ow for vectorising in Python, Matplotlib library (slide 3/10) Python-based digitising of raster image (3)
- 43. 43 Work fl ow for vectorising in Python, Matplotlib library (slide 4/10) Python-based digitising of raster image (4) Left: Example of the digitised traces in Python. Above: Example of the misclassi fi ed line, which was vectorised several times as belonging to ‘neighbor’ hours segments (e.g. hour 1 and hour 2).
- 44. 44 Region of Interest: Automatic Detection (slide 1/2) ROI detection was performed using setup of threshold for contrasting pixels on the images. As a result, the mask only included ROI between the red dashed lines (upper left image). The histograms show the value of pixels excluded from the ROI (those above the red line on the graphs). It is possible to process images in Python both in horizontal and in vertical orientation (image on the right) Work fl ow for vectorising in Python, Matplotlib library (slide 5/10)
- 45. 45 Region of Interest: Automatic Detection (slide 2/2) De fi ning ROI (between the red dashed lines) and enlarged fragment. Below: 2 histograms showing the distribution of pixels and those deleted (above the red dashed line). Right: enlarged fragment of the digitised seismogram. Work fl ow for vectorising in Python, Matplotlib library (slide 6/10).
- 46. 46 De fi ning optimal parameters for the line thickness and radius of pixels (1) The thickness of the trace lines was de fi ned by a series of trial tests with varied parameters. Radius of 30 pixels was de fi ned as the optimal for the given image (it may vary through in other cases). Above: image with tested line thickness from 17 to 34 and radius of 50. Below: image with tested thickness of the trace line from 14 to 26 pixels (upper row) and 20 to 38 pixels (lower row) and radius of 40, 50 and 60 for each corresponding row (downwards). Changed thickness of line is visible in all trial cases (yellow-coloured horizontal lines). Work fl ow for vectorising in Python, Matplotlib library (slide 7/10)
- 47. 47 Continue testing the parameters for the line with spots and seismogram with blurred contrast of lines against the background Work fl ow for vectorising in Python, Matplotlib library (slide 8/10) De fi ning optimal parameters for the line thickness and radius of pixels (2)
- 48. 48 Buffering minute intervals for the one- minute gaps completed for the whole seismogram Work fl ow for vectorising in Python, Matplotlib library (slide 9/10) Buffering parameters for the time gaps Buffering minute intervals for the one-minute gaps completed for the whole seismogram; Buffering of missing data: minute and hour gaps
- 49. 49 Work fl ow for vectorising in Python, Matplotlib library (slide 10/10) Seismogram vectorised by Python overlain on the original image and uploaded in Cytomine Example of the vectorised trace segments (red lines) overlaid on the spotted image Enlarged fragment with visible distinct traces; Enlarged fragment with visible time gaps
- 50. 50 Summary of Project Milestones and Approaches
- 51. 51 Conclusion: Research Connections and Structure Data Challenge of big data in seismic studies: massif volumes of historical seismograms from ROB exist and present a source of information. These archive old data must be processed, digitised and ‘revitalised’. Methods Our project focuses on developing automated methods of vectorising seismograms with minimised human interaction and maximised ML approach in trace vectorisation People Human interaction is necessary for the whole work fl ow: archiving and processing data, organising project, developing algorithms, executing scripts, visualising graphics, testing methods and trials, interpreting results
- 52. 52 Thank you for attention ! Looking forward to your questions !