Abstract: History paper seismograms are very important information for earthquake monitoring and prediction, and the vectorization of paper seismograms is a very import problem to be resolved. In our study, a new tracing algorithm for simulated seismogram curves based on visual filed feature is presented. We also give out the technological process to vectorizing simulated seismograms, and an analog seismic record vectorization system has been accomplished independently. Using it, we can precisely and speedy vectorize analog seismic records (need professionals to participate interactively)

Jilin Feng

Maofa Wang

Qigang Jiang

Shichen Feng

CiteSeerX

Sensors & Transducers, Vol. 178, Issue 9, September 2014, pp. 209-213 
 209
  
Sensors & Transducers
© 2014 by IFSA Publishing, S. L. 
http://www.sensorsportal.com 
 
 
 
 
 
Researches on Key Algorithms in Analogue Seismogram 
Records Vectorization 
 
1, 2 Maofa WANG, 1 Qigang JIANG, 2 Jilin FENG, 2 Shichen FENG 
1 College of Geoexploration Science and Technology, Jilin University, Jilin, Changchun,  
China, 130026 
2 Department of Disaster Information Engineering, Institute of Disaster Prevention,  
Hebei, Sanhe, China, 065201 
E-mail: wangmaofa2008@126.com 
 
 
Received: 22 June 2014   /Accepted: 29 August 2014   /Published: 30 September 2014 
 
 
Abstract: History paper seismograms are very important information for earthquake monitoring and prediction, 
and the vectorization of paper seismograms is a very import problem to be resolved. In our study, a new tracing 
algorithm for simulated seismogram curves based on visual filed feature is presented. We also give out the 
technological process to vectorizing simulated seismograms, and an analog seismic record vectorization system 
has been accomplished independently. Using it, we can precisely and speedy vectorize analog seismic records 
(need professionals to participate interactively). Copyright © 2014 IFSA Publishing, S. L. 
 
Keywords: Analog seismic record, Vectorization, Digital image processing, Visual field feature. 
 
 
 
1. Introduction 
 
Historical paper earthquake records are important 
information for earthquake monitoring and prediction 
[1-2]. Although the construction of digital seismic 
stations has been undertaken in many countries at 
present, those massive saved simulated seismogram 
records over the past decades still have a great value, 
which is important original information associated 
with the earthquake-generating process for analyzing 
and studying earthquake. 
As is known to all, with the development of 
computer technology, a large number of digital 
seismic records across time and space are need in 
seismic research. Unfortunately, in a data explosion 
era, analog seismic records still cannot be processed 
quickly by computer, although they are an important 
part of human seismic records. This means that the 
analog seismic record will lose its precious historical 
value without digitized and vectorized.  
In Harvard University, a seismograph station 
located at the Adam Dziewonski Observatory (Oak 
Ridge, MA) was deployed in 1933 which is one of 
the oldest seismograph stations in the USA. The 
recordings of this station after 1972 are digital and 
accessible online through IRIS. However, the earlier 
seismograms were not digitized or cataloged in a 
systematic manner. Starting around mid 2012, a 
named HRV seismogram Archival Project is 
established, and a significant amount of resources are 
devoted to clean and digitize the old seismograms 
from the HRV station [3]. After the processed, 
simulated earthquake records are stored and released 
in digital image form, which is more convenient to 
store and read, but still cannot be processed such as 
seismic data mining and extraction easily and quickly 
by computer without further vectorizing. 
In China, many seismic stations such as Chengde 
seismic station have a great deal of analog seismic 
records, and a lot of recording drawings are piled up 
http://www.sensorsportal.com/HTML/DIGEST/P_2379.htm
Sensors & Transducers, Vol. 178, Issue 9, September 2014, pp. 209-213 
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in warehouses, long-term suffering from rat biting, 
air mildew, erosion of various natural phenomena. In 
today's digital world, simply digitization such as 
taking pictures by camera and scanning by scanner, 
also just to prolong its storage period, and no further 
reflect or extend its using value, can say is alive but 
no significance. 
So it is an important and urgent task for 
vectorizing paper analog seismic records. In this 
paper, we present a new tracing algorithm for 
simulated seismogram curves based on visual filed 
feature(VFF), give out the whole technological 
process to vectorizing simulated seismograms, and 
an analog seismic records vectorization system has 
been accomplished independently. Using it, we can 
precisely and speedy vectorize analog seismic 
records (some time need professionals to participate 
interactively). 
 
 
2. Key Algorithms Analysis and 
Discussion 
 
2.1. The Whole Technological Process  
to Vectorizing Simulated Seismograms 
 
We want to vectorize paper simulated 
seismograms with digital image processing technique. 
Fig. 1 is the whole technological process to 
vectorizing simulated seismograms system, 
throughout it, there are six approaches: loading 
images, preprocessing, vectorizing image, waveform 
and time coordinate mosaic, Warehousing waveform 
and time coordinate, Waveform inversion. In the 
paper, we give the detailed discuss and depict of the 
above key approaches. 
 
 
 
 
Fig. 1. Technological process to vectorizing  
simulated seismograms. 
2.2. Image Preprocessing 
 
Firstly, all the simulated seismogram drawings 
are digitally scanned using industrial scanner, and 
different formats of scanning images are converted 
into PNG format in loading image stage. 
To reduce the noise of the subsequent 
vectorization, image binarization is in need. 
Binarization is a digital image processing technique, 
by which one image can be divided into two parts: 
object part and background part, and here Otsu 
algorithm is used [4-6]. Fig. 2 (a) is an original image, 
and Fig. 2 (b) is the last binarization processing 
result. 
In order to reduce the effective waveform 
information (entropy) damage because of 
pretreatment to a minimum, we do not adopt further 
preprocessing methods, especially line thinning. And 
all the subsequent processing steps are based on 
binary images. 
 
 
 
 
 
 
Fig. 2. (a) An original image of simulated seismogram;  
(b) The last binarization processing result. 
 
 
2.3. Image Vectorizing Technological Process 
 
For every one curve, firstly we need to acquire its 
starting point. Then we start to trace one curve 
waveform from the starting point, and record all the 
key points on it which will be stored in the database. 
The whole tracing process is like a man climbs a lot 
of mountains, sometimes the mountains (waveforms) 
are gentler, and sometimes the mountains 
(waveforms) are very much steep. For different 
waveforms, we use different tracing algorithms 
which will be discussed in following paragraphs. 
When the tracing result of one curve is not so 
satisfactory, we can choose a back-roll point. And 
from the back-roll point, we can pick out key points 
Sensors & Transducers, Vol. 178, Issue 9, September 2014, pp. 209-213 
 211
manually or automatically tracing points within in a 
smaller local area. When the tracing precision meets 
requirement, automatically tracking is continuing 
again. A technological process to vectorizing 
seismogram curves is shown in Fig. 3. 
 
 
 
 
Fig. 3. Technological process to vectorizing  
seismogram curves. 
 
 
2.4. A Tracking Algorithm for Smooth 
Waveforms of one Seismogram Curve 
 
According to the characteristics of seismogram 
curves, the starting point C ( )0 0,x y  of one curve is 
taken as its first key point, and the whole searching 
direction is horizontal right. Horizontal searching 
step length is xstep , and vertical searching width is 
ystep  
which is the average width of adjacent curves 
and is set dynamically. Fig. 4 is the sketch map of 
tracking algorithm for smooth waveforms. The 
specific algorithm is as follows: 
Horizontal direction 
 
 
1 0 xx x step= + ,  
 
 
2 1 xx x step= + ,  
 
 
1n n xx x step−= + , (1) 
 
Vertical direction 
If ( ),n n nC x y is one known key point, we start 
to judge whether point ( )' ,n n x nC x step y+  is 
black, if it is white, turn to complex waveform 
searching which will discussed in following 
paragraph. If point ( )' ,n n x nC x step y+  is black, 
we search black pixels in two directions to within the 
range ,n y n yy step y step − +   of point 
( )' ,n n x nC x step y+  up and down, until the 
emergence of discoloration, and get two discolor 
points: ( ),L n x nLC x step y+ and 
( ),H n x nHC x step y+ . 
 
 
1 2
nH nL
n
y y
y +
+=
, 
(2) 
 
That is to obtain ( )1 1 1, :n n nC x y+ + +  
 
 ( )1 0
1
1
2
n n x x
nH nL
n
x x step x n step
y y
y
+
+
= + = + + ∗

 += , 
(3) 
 
 
 
 
Fig. 4. Sketch map of tracking algorithm  
for smooth waveforms. 
 
 
Fig. 5(a) is an original image simulated 
seismogram, and Fig. 5 (b) is the last smooth 
waveforms processing result. 
 
 
2.5. A Tracking Algorithm for Complicated 
Waveforms of one Seismogram Curve 
 
In our research, we have tried many times using 
different ways to trace complicated waveforms, 
including of tracking algorithm based on sparse pixel 
[7], tracing method with anti-noise analysis [8], 
homotopic method following solution curve [9], 
searching negative curvature points [10], and the like. 
But all the results do not look so good. Through a lot 
of analysis, we found that some waveform segment 
of one seismogram curve may be so complicated 
which includes many steep wave crests, irregular 
cross sections, smudge, breakpoints and so on. So it 
is very difficult to find a way to deal with all the 
interference factors for tracing complicated 
waveforms.  
Sensors & Transducers, Vol. 178, Issue 9, September 2014, pp. 209-213 
 212
 
 
 
 
Fig. 5. (a) One original seismogram curve image,  
(b) The last smooth waveforms processing result. 
 
 
On further analysis, however, it is noticed that 
seismic wave energy will increase and decrease with 
periodic and has equilibrium position of its own. If 
we will shift an observation point (a starting position 
for searching next one key point) slightly further 
from next one key point, not nearby in next one key 
point, and relatively fixed such as wave energy 
equilibrium position, errors and noise will be isolated 
in a very small range, and will not stack and 
influence each other. Based on the above analysis 
and thinking, finally we propose a tracking algorithm 
for complicated waveforms based on visual filed 
features (VFF). It takes the steps as follows: 
1) Suppose ( ),n n nC x y  is a known key point, 
we can judge whether Point ( )' ,n n x nC x step y+  
is black, if it is black, turn to smooth waveforms 
which has been discussed in the above paragraph.  
2) If ( )' ,n n x nC x step y+ is white, we search 
black pixels in two directions to within the 
range [ ]0 0,y step y step− +  of Point 
( )'' 0,n n xC x step y+  up and down, and Point 
( )'' 0,n n xC x step y+  is an energy equilibrium 
position. Then saving y coordinate values of all the 
lower boundary points of black connected area 
searched above into set [ ](i 0,1,..., m)downa i = , 
and y coordinate values of all the Upper bound points 
of black connected area searched above into 
set [ ](i 1,....)upa i m= + ,. So that I can get a points 
set [ ]( )+1 1' , (i 0,1,m,m 1...)n nC x a i+ = + . 
3) We can establish many groups of four points: 
( ), yn nx − Δ , ( ), yn nx + Δ , [ ]( )1 ,nx a i+ − Δ , 
and [ ]( )1 ,nx a i+ + Δ (i 0,1,...)= where Δ is an 
experience value decided by dynamically. For each a 
candidate key point [ ]( )1 1' ,n nC x a i+ + , we can get a 
parallelogram as connected monitoring area with the 
four points, and whose area can be expressed       
as Eq. (4). 
 
 [ ]i nS a i y= Δ ∗ − , (4) 
 
4) Then we calculate out the number of black 
pixels in each parallelogram cited above denoted 
by in , and the ratio of in  to the area of the 
Corresponding parallelogram denoted by iη  can be 
computed by Eq. (5). 
 
 
i
i
i
n
S
η =
, 
(5) 
 
The larger the value of iη  corresponding to one 
candidate Point [ ]( )+1 1' ,n nC x a i+ is, the better the 
connectivity between it and the previous key point 
( ),n n nC x y  becomes. So point 
[ ]( )+1 1' ,n nC x a i+  which owns the maximum value 
of iη  is fixed as the next key point.  
5) If ( )1 1 1,n n nC x y+ + + is one new key point 
searched by the above steps, we start to judge 
whether point ( )1 1 1' ,n n nC x l y+ + ++  ( )l < Δ is 
black, if it is white, turn to smooth  waveform 
searching discussed on the above paragraph. If point 
( )1 1 1' ,n n nC x l y+ + ++  is white, Point 
( )''1 1 0,n nC x l y+ + +  is taken as one new energy 
equilibrium position. And start searching from which 
as by steps (2-4), lastly we can get the next key point 
( )2 2 2,n n nC x y+ + + . 
6) Repeat  steps (1-5), until all the key points of 
the whole curve are detected out. 
Fig. 6 is the sketch map of tracking algorithm for 
complicated waveforms based on VFF. 
Sensors & Transducers, Vol. 178, Issue 9, September 2014, pp. 209-213 
 213
 
 
Fig. 6. Sketch map of tracking algorithm for complicated 
waveforms based on visual field features (VFF). 
 
 
Fig. 7(a) is one complicated seismogram curve 
image; (b) is the detected result of one corresponding 
complicated waveform; (c) is the overall detected 
result. 
 
 
 
 
 
 
 
 
Fig. 7. (a) One complicated waveforms image;  
(b) The detected result of one corresponding complicated 
waveform; (c) The overall detected result. 
 
 
3. Summary 
 
The paper focuses on key algorithms in analogue 
seismogram records vectorization. From the detected 
results of smooth and complicated waveforms as 
shown in Fig. 5 and Fig. 7 we can see that the 
topology of the tracing result is consistent with o 
original curves. 
We also write the correlative software with c# 
codes, which applying the above digital image 
processing algorithms. The analogue seismogram 
records which come from Chengde seismic station  
in 1991 and excess of 10 G have been vectorized 
using the software.  
And Mosaic Algorithm of waveforms and time 
coordinates by vectorized will be studied and 
discussed on in a subsequent article. 
 
 
Acknowledgment 
 
Two of Authors (Maofa Wang, Jilin Feng) thank 
the financial support from Special Fund of 
Fundamental Scientific Research Business Expense 
for Higher School of Central Government (Projects 
for creation teams)( Research on the Acquisition and 
rapid Processing of earthquake disaster information 
ZY20120104). 
 
 
References 
 
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[2]. Zhian Pan, Jilin Feng, Maofa Wang, A base-point 
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http://www.seismology.harvard.edu/resources.html#
HRV 
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(http://www.sensorsportal.com) 
 


Researches on Key Algorithms in Analogue Seismogram Records Vectorization

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Researches on Key Algorithms in Analogue Seismogram Records Vectorization

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