Ishii and Ishii ﻿Progress in Earth and Planetary Science(2022) 9:50
https://doi.org/10.1186/s40645-022-00508-0

Progress in Earth andPlanetary Science

Open Access

REVIEW

DigitSeis: software to extract time series
from analogue seismograms
Miaki Ishii* and Hiromi Ishii

Abstract
A vast amount of analogue seismograms recorded between the end of the nineteenth century and late twentieth century are often inaccessible for seismological research since they are not available as digital time series. This
manuscript describes freely available software, DigitSeis, that takes a digital image of an analogue seismic recordand returns waveforms either as a function of their x-y position on the image or as time–amplitude information. The
overall structure and approach of the software are provided along with how they have evolved over different versions.
The effectiveness of the software is demonstrated with three examples. The first example is a long-period east–west
seismogram recorded at the Harvard Seismographic Observatory on photographic paper in May of 1938, which contains signals associated with a magnitude 7.7 earthquake that occurred off the coast of Northern Ibaraki, Japan. The
second example is an analysis of a 35-mm microfilm copy of the short-period vertical seismogram recorded at Tucson,
Arizona, on July 16, 1945, that shows blast signals from the first nuclear bomb detonation. The final example uses a
70 mm microfiche image of a long-period north–south seismogram recorded at College, Alaska in December of 1966,
which shows a pair of earthquakes with nearly identical waveforms. The software is, by no means, perfect, and discussion of its limitations such as the compatibility with pen- and Develocorder-type seismograms is included, as well as
comments about challenges of incorporating machine learning into the digitization process.
Keywords: Analogue seismograms, Digitization, DigitSeis software
1 Introduction
Recordings of ground motion using seismometers began
in the late 1800s, and these valuable data capture any
phenomenon that transfers energy into the Earth. Consequently, they can be used to study unusual earthquakes
or nuclear explosions. In addition, recent developments
in seismology show a trend in utilizing data beyond traditional event-based waveforms. What used to be “noise”
is now becoming “signal” (e.g., Shapiro et al. 2005), andif the analogue data can be made digitally accessible, thedatabase will expand significantly in time (by a factor of
2 to 4, depending upon how one defines the “digital” eraand the start time of the analogue collection). These data
will therefore open the possibility of studying phenomena*Correspondence: ishii@eps.harvard.edu
Department of Earth and Planetary Sciences, Harvard University, 20 Oxford
Street, Cambridge, MA 02138, USA

that evolve over time. Geophysical processes have timescales that are much longer than the combined periodsof digital and analogue recordings, but for some relatively rapid processes, having a longer time window may
provide substantial insight into the mechanisms. For
example, monitoring subsurface evolution before andafter volcanic activity, examining the rate of inner-coresuper-rotation, or examining effects due to global warming would benefit greatly from having a much longer time
window than just those from the digital era.
However, much of the analogue data, the predominant format up until about the mid to late 1980s, areincompatible with modern analyses that require digitized time series. The length of the analogue recording
era also means that there is a staggering amount of data
to be digitized. Additionally, the sheer physical volumeof the analogue recordings poses a storage challenge,
with increasing rent putting many analogue seismogram© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

Ishii and Ishii P﻿ rogress in Earth and Planetary Science(2022) 9:50

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collections in danger of being discarded. Finally, working with the analogue seismograms is becoming moredifficult as time passes. People with knowledge of theserecordings are retiring, and the medium on which they
are stored (e.g., photographic paper and microfilm) is
deteriorating.
Despite the immense value the analogue seismograms
represent to the Earth Sciences community, there has
been little progress in reclaiming these data for modern
applications. The two main reasons are the lack of efficient methods to digitize the images and the substantial
volume (and hence required labor) of the collections.
Digitization programs have been written (e.g., Bromirski
and Chuang 2003; Pintore et al. 2005), but they are difficult, complex, and time-consuming to use and, thus, havenot gained general traction. Some images of significant
earthquakes have been digitized by individual researchers (e.g., Kanamori and Cipar 1974; Okal and Stein 1987;
Song and Richards 1994), but these are exceptions andmost extant seismograms remain unused in storage. In
the last decade, the Harvard Seismology group has beenworking to preserve and convert analogue seismograms
that were recorded at the Harvard Seismographic Station.
One part of this effort is the development of software thattakes a digital image of a seismogram and generates timeseries. In this manuscript, we review the evolution andcurrent status of this digitization software and describe
some challenges and future directions.

two codes tested, SeisDig (Bromirski and Chuang 2003)
and Teseo2 (Pintore et al. 2005), are well-known, freely
available software for digitizing analogue seismograms.
The third code, NeuraLog (NeuraLog 2013), is widelyused digitization software in the oil and gas industry forwell-logs. Even though all three software automaticallytraced some parts of the scanned seismograms, they eachrequired a significant amount of manual interaction. For
example, every software had issues identifying and tracing minute marks, which are vertically offset from themain trace, and the sampling rates caused issues withreproducing intricate features of seismograms. It became
quickly evident that it would be very difficult and timeconsuming to digitize the Harvard seismograms using
any of the three available software.
Given such challenges with the existing tools, new software has been developed. The first version of this software, DigitSeis, was publicly released in 2016 (Bogiatzis
and Ishii 2016). There have been three subsequent
releases, versions 1.1 (2017), 1.3 (2018), and 1.5 (2020),
and every version is available on Harvard Seismology
Web page. We provide a brief description of the softwareand how it has evolved over versions in the following section as well as some examples of DigitSeis output. The
software is by no means complete, and we outline someof the major issues that will be addressed in the near
future.

2 Review
Harvard University operated analogue seismometers
between 1908 and 1933 at the main University campus in
Cambridge, Massachusetts, in the USA. These facilities
were moved to a quieter location at Harvard, Massachusetts in 1933 (Leet 1934) and were operated until 1954,
and many of the seismograms survived to the twenty-first
century. Working with historical photograph specialists
from the Weissman Preservation Center at Harvard University, the Harvard collection of analogue galvanometerstyle seismograms was cleaned and scanned (Ishii et al.
2015). The seismograms were then placed into 75 boxes
designed and made specifically for this purpose by staff at
the Weissman Preservation Center, and transferred to thecare of Harvard Archives in 2016. They are now stored
in a temperature- and humidity-controlled storage space,
reducing further deterioration, and are made available tothe public through the Harvard Library system. Images(tif and jpg files) of all 11,339 seismograms are also available online at the Harvard Seismology Web page.
The ultimate goal of this data rescue project is to convert the analogue seismograms to digital format for use
in research. We initially experimented with three digitization software that were available in 2011. The first

DigitSeis takes advantage of the extensive library ofimage processing routines that are both built into MATLAB and publicly shared by various programmers. The
software is not designed to be fully automatic, i.e., it
requires the presence of a human analyst. This approachallowed for the simplification of some steps compared
to previous software. For example, time marks are difficult to handle in other software that tries to automatically trace everything, and the user is typically forced
to manually trace individual time marks. On the other
hand, in DigitSeis, by requiring user input for some key
parameters, time marks can be treated semiautomatically, significantly reducing the amount of work and timerequired. DigitSeis also aims to obtain the most accurate
time series possible, and considers often-ignored features
such as distortion of the images; some of these considerations are highlighted below.
One of the main motivations for the development of
DigitSeis was the challenge of working with seismograms
where vertical offsets are introduced for a short timeto indicate the beginning of each minute or hour (timemarks). In order to effectively treat the time marks, theDigitSeis software has a classification step where different
objects within the image are separated into time marks

2.1 Digitization software: DigitSeis

Ishii and Ishii ﻿Progress in Earth and Planetary Science(2022) 9:50

and main (albeit disconnected) traces (Fig. 1). This is performed semiautomatically by considering the user-specified horizontal lengths of individual objects along withuser identification of poorly defined or strange objects(e.g., stains and handwritten notes). Once the classification is finished, the time mark objects are amalgamatedwith the main trace with a vertical offset that produces
as smooth a connection as possible (Fig. 2). This offset is obtained by using the derivative of the combined
trace (if the vertical correction is insufficient, the derivative will be large). Because the software works under theassumption that these vertically offset segments exist,
the time marks are no longer an issue in the digitization
procedure.
One property of the seismogram images that came asa surprise is the level of distortion they contained. On
hindsight, this should have been expected considering
the significant amount of curvature and deformation that

Fig. 1 An example of classification. The input image is processed todefine objects that are categorized as main trace (white), time marks(cyan), or noise (red)Page 3 of 16

exist in the original analogue seismograms. Some of these
distortions/deformations come from the poor conditions in which the seismograms were stored, but a major
contribution is the difference in materials that make up
the photographic paper. The trace side, with a photographic surface, has a gelatin emulsion that contracts
as the surface dries over time, leading to curling of thepaper inward (since there is no contraction on the backside made up of plain paper). In addition, the process ofacquiring digital images of these seismograms produces
distortion such as the lens effect on images taken with adigital camera (even with a wide-angle lens). Regardlessof the source of distortion, it is often significant enoughto jeopardize the tracing exercise by obscuring the zeroamplitude position and complicating the conversion ofhorizontal position to time. With these complexities,
time cannot be assigned to the trace simply by use of thehorizontal position, and the existence of the time marks
turns into an asset. DigitSeis characterizes and corrects
for image distortion, takes the positions of the timemarks, determines the time associated with each mark,and interpolates between them to assign time to a corrected “horizontal” location (Bogiatzis and Ishii 2016).
The above description of procedures summarizes thebasic approach with which DigitSeis has been developed.
There have been significant modifications in terms of
how exactly each step is taken, and they are reflected in
the multiple version releases. Just to illustrate the magnitude of the work that went in over the last several years,
the software that was initially released (v0.53) consistedof about 4,000 lines of code. Nearly 100% of this code
has been either modified, replaced, or removed, and withnew features that improve accuracy, robustness, stability,

Fig. 2 An example of digitization result. Comparison of the original (top) and digitized (bottom, snapshot of the SAC window view) long-period
north–south seismogram containing seismic wave arrivals. Note that the x- and y-axes do not quite match for the two plots, although the SAC
display has been stretched/shrunk to mimic the original image. The numbers “45” and “50” on the original image correspond to 21:45 and 21:50 on
September 21, 1939. The seismic wave arrivals are associated with a magnitude 5.8 earthquake in the Gulf of California (International SeismologicalCentre 2022)

Ishii and Ishii P﻿ rogress in Earth and Planetary Science(2022) 9:50

and usability, the current released version (v1.5) contains
nearly 50,000 lines of code. Some of the improvementsfor versions 1.3 and 1.5 are described below.
2.1.1 DigitSeis version 1.3

DigitSeis version 1.3 was released in 2018 and contained
significant improvements over the initial version.
Stand-alone processing The initial version of software
required an image that was nearly perfectly clean for processing and included a handful of tools to achieve this(e.g., remove stains, adjust contrast). All of these imagecleaning functionalities were applied to the entire image
rather than to specific areas. Analogue seismograms
often have regions of exposure and condition that differ
in quality, that result in heterogeneities in the image, i.e.,
full-image processing is not ideal. Prior to version 1.3, thesolution was to clean the image using another software(e.g., Photoshop) before importing it into DigitSeis. Thiswas changed in two ways. The classification and digitization algorithms were modified so as not to require thecleanest possible images to work (in fact, version 1.3
uses the original image, including stains and handwritten
notes, for the definition of objects and calculation of digital traces), obviating the need for third-party software.
Another improvement was the introduction of automatic
image cleanup, and tools that allow the user to process
only small portions of the image when the automatedresults are not sufficient. These significantly reduced thework load and sped up the digitizing process. Furthermore, some images that were undigitizable with earlier
versions could be processed with version 1.3.
Efficiency Many of the algorithms were rewritten to
improve the efficiency of the program. Two examples arein object classification and digitization (two of the mostimportant components of the software). The classification process used to be very cumbersome, with even a
simple operation such as changing an object type (e.g., tomake an object that has been misclassified as noise into atime mark) easily taking 20–30 seconds, and in the worst
case, about a minute. From the user’s point of view, this is
frustrating; after clicking twice, the user needs to wait foran unreasonable length of time before the next object canbe processed. With version 1.3, the change became nearly
instantaneous. Other features needed in object classification (e.g., region removal, undo) were sped up in a similarmanner. Another example of efficiency gains is in the digitization process. For simple images, it used to take about
15 minutes, and for complicated ones, it took up to anhour. With the different approach and newer algorithms,
even the most complicated images could now finish inless than a minute.
Automation With the vast amount of seismograms
available to be digitized, any automation is desirable.

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In developing version 1.3, the code was scrutinized forany tasks that could be automated, and when possible,
this was done, for example, the timing calculation used
to require the user to manually select the starting andending positions, as well as the positions of the first andlast time marks for each trace. All of these tasks wereautomated (with the option for the user to make modifications if automation resulted in imperfect location
determinations) so that it could be done with a single
button click.
Robustness The original version of DigitSeis had quite
a number of issues, in terms of both the setup and algorithms. For example, the user could inadvertently open
multiple analyses and have actions applied to wrong
analysis, or if there were multiple open windows of asingle analysis, the user could mistakenly click on thewrong window (e.g., the main window while working
in the Classification window) leading to the next figure
appearing in the wrong place. These issues were corrected in version 1.3. Another major problem with previous versions was that some algorithms resulted inproblems that were either fatal, slowed the processing
down, or produced numerous error messages (despite
not having functional problems). All these known bugs
were fixed, and practically no unintended error messages were produced, and the user is no longer forced
to terminate the program. The third way in which thecode was improved was by providing more flexibility. For
example, the parameters that controlled the calculationof trace-zero lines (line tracing zero-amplitude position)
were hard-wired in the previous version of the code, andif trace-zero lines were incorrectly determined, the user
needed to make changes in the source code. These types
of issues were changed in two ways. First, by providing
tools so that the user can modify default settings (morebelow). Second, by taking advantage of the known issues
and their causes, and improving the algorithms to automatically address them. The final type of modificationthat improved robustness of the code was the conversion
into more mistake-friendly code, such that even when theuser makes mistakes, in most cases they can easily correct it. In the old version, it often resulted in needing toredo major parts of the analysis.
Accuracy Accuracy of the digitization process was
improved through three approaches. One was to implement algorithms that were more accurate. For example, the trace-zero lines were originally calculated using
rough estimates based upon image intensity distribution.
Version 1.3 takes advantage of some of the processing
already done and uses the trace objects to determine thetrace-zero line. This approach required reorganizationof various processes, but produced more reliable results.
The second way in which version 1.3 improved accuracy

Ishii and Ishii ﻿Progress in Earth and Planetary Science(2022) 9:50

was by providing the user with check tools. For example, nearly 3000 objects exist on a single analysis, and it
is difficult to make sure that every single object is classified correctly (e.g., no noise object is mistakenly classified as time marks). Consequently, the ability to display
locations of the time marks so that the user can easily
check visually if any objects have been misclassified wasintroduced. Similarly, a check algorithm that allows user
to see if any section of the trace has been missed was also
included. Finally, additional tools that allow the user tocorrect results of automatic calculations were implemented. For example, in the old version, once the location of each minute position was determined by the time
calculation algorithm, it was impossible to change themeven if the code produced less than satisfactory positions.
Version 1.3, gave the user the ability to move the minuteposition bars so that they can be updated easily.
Compactness The method for saving the analysis wascompletely rewritten to improve the data storage structure, and reduce file size (and hence time needed to save).
For example, a complicated analysis with hundreds ofsegments requiring manual correction (e.g., the amplitudes are such that traces are touching those above andbelow), resulted in files that were more than 6 GB using
the original algorithm and took tens of minutes to save.
Using version 1.3, the same analysis could be saved in afile that is about 700 MB, with the significant reduction
in size leading to quicker save and load times.
User control Many of the features and default setups
were hard-wired into the original version of DigitSeis,and the user had no control over them. For example, thedefault color scheme for the classified objects (i.e., white
for main trace, green for time marks, and red for noise
objects) in v0.53 did not work for color blind people.
The default color scheme was changed to take this into
account, and the user was also given an option to set
their favorite colors and save them as the default setup.
Similarly, the user was given control over other default
setups (e.g., size of the window for correcting traces) thesetting for which could also be easily accessed and saved.
Clearer work flow The layout of the original version
of DigitSeis (Fig. 3) was such that the user had to know
exactly which buttons needed to be employed next. In
version 1.3 this considerably changed in two ways. One
was organization of the buttons so that they need to bepressed from top to bottom as the user made progress.
The other was activation and inactivation of these buttons. Many of the functions are not made available until
the user completes the previous step, and some of thefunctionalities are turned off after the user completes it.
With this arrangement, the time spent learning the program was greatly reduced.

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Simplification The DigitSeis window in version 1.3 was
simplified in appearance (Fig. 3). Unnecessary plots, messages, and control buttons were removed while someof the new and old functionalities that are typically notneeded were moved and hidden from the main window. Furthermore, improved programming reduced theamount of interaction with the user. For example, selecting and inputting positoin of a point used to require theuser to click on the cursor button to activate cursor, clickon the screen, find out the x and y positions of the cursor
location, manually type in the position values into a window, click on a button to store the information, and thenmanually remove the data cursor symbol that remainedon the figure window. In version 1.3, the need for thisinteraction was mostly removed (through automation),
but if necessary, the user has the option to click on abutton to activate a point selection algorithm, click on
the screen close to the desired location, move the pointaround until satisfied, and then double-click on the symbol to finalize the position and to have it removed fromthe screen. These updates were not necessarily improvements to the digitization process itself, but nonethelesssignificantly reduced the amount of time the user needs
to spend on a single trace.
Saved information Even though the previous version of
DigitSeis generated files that were several factors larger
in size, some important information was not saved (whilesome unnecessary data were). This made reproducing
some processes difficult, if not impossible, and required
redundant actions if the analysis was saved and closed.
For example, association of each object with a specifictrace was not saved, so whenever the user needed to digitize again, the association needed to be recalculated. Furthermore, manually corrected trace information was also
not saved, so running automatic digitization again meant
the manual correction got lost. Version 1.3 allowed more
flexibility by saving carefully selected data that allows theuser to start exactly from where they finished and to have
all necessary information for future analysis.
2.1.2 DigitSeis version 1.5

The latest version of DigitSeis (v1.5) was released in September 2020 and, in addition to fixing bugs, it includes
substantial improvements over version 1.3, some ofwhich are described below (a full list of changes from one
version to another is provided with DigitSeis packages).
Languages Based upon inquiries and our work withJapanese high-school students, language options havebeen implemented. The user can choose to run DigitSeis
in English, Japanese, or Spanish. This required substantialchanges throughout the DigitSeis codes, but now, a new
language can be added quite simply by adding phrases ina single file.

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Fig. 3 Screenshots of DigitSeis. a Screenshot of DigitSeis v0.53. The image in the main field of the screen shows digitized (but not timed) traces
of a long-period north–south recording from January 22–24, 1938. The arrivals in the middle of the window are associated with a magnitude 6.6
earthquake in Maui, Hawai‘i, and the later arrivals are from a magnitude 7.0 earthquake in the Scotia Sea (ISC 2022). b Screenshot from DigitSeis
v1.5. The image shows the original short-period vertical recording from July 15–16, 1945, that has been digitized (colored lines) and timed (yellow
vertical bars with labels for every hour). This recording is from Tucson, AZ, and the large arrivals in white are those from the Trinity nuclear testIshii and Ishii ﻿Progress in Earth and Planetary Science(2022) 9:50

Efficiency Efficiency of object calculation was improvedby implementing new algorithms for initial cleaning of
the input image, and for evaluating it in portions, rather
than in full. These algorithms were also sped up by parallelizing some parts of the process. Measures have been
added so that classification is more automatic. For example, the user no longer needs to select an image threshold to identify objects, as this is done automatically based
upon regional intensity variations. In addition, this version requires the user to measure the vertical offset oftime marks, which is used in improving classification of
objects. The algorithms have also been examined very
carefully to reduce the number of variables stored andmemory usage, leading to more stable performance (thisversion rarely crashes due to memory shortage with computers with at least 4 GB of RAM).
Accuracy By taking advantage of the fact that timemarks should start where trace objects end (and viceversa) and the user-inputted rough vertical offset value,
the accuracy of identifying time marks associated witha given trace has been significantly improved. This leads
to more automatic and reliable determination of tracezero lines as well as digitized traces. This feature is also
used to distinguish between time mark objects and noise
objects during classification.
Output The user now has an option to output thedigitized trace information in terms of the x-y positions
within the image rather than the time-amplitude information stored in SAC files. This allows more flexibility
if the user wishes to set times or is concerned about thecalculation of trace-zero positions or image rotation.
User interaction Since multiple reports were receivedof issues that turned out to be caused by users clicking
on buttons or icons that should not be used at a particular time, icons and tools have been disabled until
their functionalities are needed. Another problem thatcame up with some complicated traces is that of assigning trace or time mark objects to a specific trace. A new
set of codes has been added to DigitSeis that allows theuser to visualize trace assignment of objects and manually correct them if needed. Finally, the DigitSeis window
has been simplified by removing buttons and options thatare no longer necessary, and for functionalities that are
not regularly needed, optional menus for fine-tuning thesettings of the program have been created. For example,
version 1.3 had a button that was clicked to calculate thetrace-zero lines but version 1.5 does this calculation automatically as part of the digitization process, and hence
the button has been removed.
Notes and feedback Some users suggested that a feature to add notes to the analysis would be desirable. For
example, one may notice an issue with a digitized trace
and want to check the classification of objects. It would

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be straightforward to get into the classification window
and take a look at the problematic region if there is a notethat propagates from the main DigitSeis window to theclassification window. This has now been implemented,
and the user can place a star anywhere within the image
and add comment to it that can be displayed later when
the star is clicked. Furthermore, a hidden functionality
has been implemented where a user can place various
symbols and comments as feedback to work done by aless-experienced user. For example, a teacher can pointout time marks that are not correctly classified or digitized traces that need to be corrected. This feature can be
activated by any user with a password. We have chosen tohide this functionality partly to reduce clutter and mostly
to allow teachers to have control on the feedback items (itmight become confusing if feedback items are placed bydifferent people). However, the feedback tool can easily
be made openly available if necessary.
Modular programming Significant effort has been put
into separating various functions within DigitSeis sothat when issues arise, corresponding codes can be moreeasily found as well as reused if similar calculations areneeded. This was an important step in thinking ahead for
restructuring and reorganization which will be needed
for future improvements.
2.2 Examples of DigitSeis results

Any reliable development of software, especially thosethat are going to be released publicly, requires in-depth
debugging. This is done by digitizing images of analogue
seismograms that are available to us, and we have built
a database of digitized time series, some of which areavailable at Harvard Seismology web pages. In this subsection, we give three examples of digitization resultsfrom three different media, photographic paper, 35-mm
microfilm, and 70-mm microfiche. These examples illustrate how digitized time series provide useful insight into
past events, and highlight the successful usage of DigitSeis software to extract time series.
2.2.1 1938 Off Coast of Northern Ibaraki, Japan Earthquake

A shallow-focus magnitude 7.7 earthquake struck off thecoast of Northern Ibaraki prefecture in Japan on May 23,
1938, at 07:18 UTC, 16:18 local time. This event is suspected to be a precursor to an unusual swarm of magnitude 7 earthquakes, one 7.8 and two 7.7s, that occurred
within a span of about a day in November of the same
year (Ikeda et al. 2008). The event in May was recorded
about 95 degrees away at the Harvard Seismographic Station located at Harvard, Massachusetts. The analogue
seismograms on photographic paper generated by longand short-period Benioff instruments survived years ofstorage, and were subsequently scanned and digitized.

Ishii and Ishii P﻿ rogress in Earth and Planetary Science(2022) 9:50

We show an example of the digitization produced fromthe long-period east–west component recording spanning from May 22 at 13:15 to May 23 at 13:00 (Fig. 4).
The analogue seismogram image has been converted tonegative, i.e., dark background with whiter traces, but
because the digitized lines are so successful at tracing theimage, the bright pixels are difficult to see at this scale.
This example also showcases the ability of DigitSeis toprocess crossing traces where the amplitude is so largethat it goes over traces above and/or below. Finally, using
the time marks, the digitized traces have been timed, i.e.,the x-y positions of the lines have been converted to timeseries.
2.2.2 1945 Trinity nuclear test

The analogue era of seismology covers a period
between 1950s and 1980s when there were frequent
nuclear tests around the world. Some tests are too
small or too far from a seismic station to be visible, butthere are numerous tests that were captured, including

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the first nuclear bomb, Trinity, which was detonated on
July 16, 1945, at 05:30 local time (11:30 UTC) near Los
Alamos, New Mexico as part of the Manhattan Project. The short-period vertical motion of the blast wasrecorded in Tucson, Arizona, on photographic paper,and it was copied onto 35 mm microfilm in an effort
to systematically collect, preserve, and distribute seismograms containing significant events about thirty
years later (Meyers and Lee 1979). Although the original paper seismogram is still available at the National
Earthquake Information Center, United States Geological Survey (USGS), we focus on digitization result fromthe microfilm copy (Fig. 5). Scanning the microfilm
at highest possible resolution forced the seismogram
image to be split in two sides, left and right. Both sidesof the image are digitized using DigitSeis, and the rightside is shown in this example. Even though there are
only 9 time marks on this partial image of the full seismogram, the time can be correctly set with appropriate
time gaps between each trace.

Fig. 4 Digitization Example 1: 1938 Earthquake Off Coast of Northern Ibaraki, Japan. Screenshot from DigitSeis showing the completed analysisof the May 22–23, 1938, long-period east–west seismogram recorded at the Harvard Seismographic Station. The left column contains various
functionalities in the form of push-buttons and editable boxes which the user goes through from top to bottom to process the analogue
seismogram digitization. Most of the screen space is taken up by the image window on the right where the digitized traces in light and dark blue
are overlain on top of the analogue seismogram image. The yellow vertical bars indicate the rightmost location of each time mark, providing theposition at which each minute begins, and the vertical red bars show the beginning and ending of each line. Time stamps (in UTC) are given foreach 15-minute interval. Note that a beta-version of DigitSeis 1.6 is used here; hence, the layout of functionalities is not the same as the released
version 1.5

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Fig. 5 Digitization Example 2: 1945 Trinity Nuclear Test. Digitization of the right side from the 35 mm microfilm copy of the short-period vertical
record from Tucson, Arizona, covering the time between 14:17 on July 15 to 14:35 on July 16, 1945. The features are the same as in Fig. 4 exceptthat only the image part of the DigitSeis screen is shown and the digitized traces are displayed with green and blue colors. There is a trace that isshown with white color to emphasize the blast signals associated with the Trinity nuclear detonation at 11:30 UTC. The time stamps are provided
at 30-minute intervals. Short segments that appear regularly above the digital traces are the time marks in the background image. See Fig. 3 for thedigitization result using the full photographic paper record

2.2.3 1966 Alaskan Twin earthquakes

The expansion of nuclear testing motivated the development of the World-Wide Standardized Seismograph
Network to monitor and quantify nuclear tests. Morethan 120 stations distributed around the globe generated a vast amount of analogue seismograms, whichwere copied onto 70-mm microfiche for preservationand distribution (Peterson and Hutt 2014). They include
nuclear blast signals, but also abundant earthquake signals. As our final example, we take the microfiche imageof the long-period north–south seismogram recorded
at station COL (College, Alaska) from December 19,
1966 (the time window covered by this record included
expected arrivals from the nuclear test codenamed
Greeley, a part of Operation Latchkey). From the image
itself, we can see that there are two earthquake events
that are offset by about half an hour (for this seismogram, each line of trace covers about half an hour), but
the details of the arrivals are difficult to discern due tosignificant overlaps of the traces as well as faded lines

for large-amplitude arrivals (Fig. 6a). Once the digitized traces are superimposed on the image (Fig. 6b),the striking similarities of the arrivals from the two
events become evident. The waveforms can be matchedalmost swing by swing, and their amplitudes are nearly
identical. They correspond to local twin earthquakes in
Alaska that occurred about 200 km from station COL.
The International Seismological Centre earthquake catalogue (International Seismological Centre 2022) lists
them as magnitude 5.0 earthquakes that occurred on
December 20, 1966, at 00:26:26 and 00:57:52 at 66.73◦ N
and 148.52◦ W and 66.74◦ N and 148.34◦ W, respectively.
The availability of digitized time series allows application of modern waveform analysis techniques, such as
calculating correlation of the two waveforms, that canbe used to detect earthquakes through template matching (e.g., Shelly et al. 2007).
2.3 Future directions for DigitSeis

As demonstrated in the previous subsection, themedium from which the seismogram image is taken

Ishii and Ishii P﻿ rogress in Earth and Planetary Science(2022) 9:50

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Fig. 6 Digitization Example 3: 1966 Alaskan Twin Earthquakes. A section of long-period north–south recording from COL (College, Alaska) station
on December 19, 1966. a Scanned image of 70 mm microfiche as loaded into DigitSeis. b Same image as (a) but with digitized traces for lines 12(cyan) and 13 (magenta) superimposed on the image(i.e., photographic paper, microfilm, or microfiche) does
not affect how the digitization process using DigitSeis
is performed. However, how the ground motion wasrecorded on a seismogram does matter. DigitSeis has
been designed with galvanometric seismogram examples (Harvard analogue seismogram collection), and
it is not recommended that the software be used withpen- or Develocorder-type seismograms. Since there is avast amount of the latter two types of recordings, future
efforts to improve DigitSeis will include modifications
to accommodate these data. In this section, we describe
some of the major changes to DigitSeis we will be working on in the next few years.
2.3.1 Pen‑type seismograms

The “pen-type” seismograms are defined as those that
contain waveform distortion due to curvature introduced
when a fixed-length component of the seismograph (e.g.,a pen) attached to a pivot point records large-amplitude
signals (Fig. 7). Some of the commonly seen examplesof these seismograms are needle scratches on smoked
paper, conventional ink on paper, and heated stylus andthermal paper (e.g., World Data Center 1979). The earliest of the seismogram recordings are in pen-type format,
and this system continued to be used until digital recording systems became available. Consequently, a significant
fraction of the analogue seismogram collections aroundthe world are expected to be in this format. For example,
the collection of nearly a million analogue seismogramsfrom the USGS Hawaiian Volcano Observatory between
1912 and 2013 is roughly made up of about 45% on photographic paper (i.e., galvanometer system) and 55% on
either smoked-drum paper (∼30%) or thermal paper (∼20%) (Paul Okubo, personal communications).

The feature that distinguishes the pen-type seismograms from galvanometer seismograms is the additional
waveform curvature introduced by the use of pen or needles (e.g., Inoue and Matsumoto 1988). Because the penor needle is fixed to a point and is allowed to swing tocapture large-amplitude arrivals, it inevitably introduces
arcing motion when it moves off from the zero-amplitude
position (Fig. 7). This implies that for large-amplitude
arrivals, there may be multiple y (amplitude) values fora given x position on the image. The current DigitSeis
algorithm cannot handle such situations. It assumes thatthe amplitude information (y) is a function of horizontal
position (x), i.e., there is only one y value for a given x
position, and thus, it determines the y value based uponpixel positions weighted by their intensity.
Correcting for the pen curvature is more complicatedthan it may appear at first glance. Most analogue seismogram recordings are made using a helicorder, a drum
that rotates with time to allow a long duration of observation. This drum also translates parallel to the rotation
axis, resulting in multiple lines of traces being recorded.
If the helicorder is only rotating, then the trace-zero line
is horizontal and the time (x-axis) and amplitude (y-axis)
are orthogonal on the paper (Fig. 9a). However, whentranslation of the helicorder is added, the time and amplitude directions are no longer orthogonal, with amplitude
instantaneously measured in the vertical direction, buttime axis having both horizontal and vertical components(Fig. 9b). This implies that the waveform is distorted, andassuming the amplitude to be orthogonal to the time axis
gives incorrect amplitude and time information. DigitSeis
takes this non-orthogonal geometry into account andmeasures amplitude in the y direction while determining
x or time information along the trace-zero line. However,Ishii and Ishii ﻿Progress in Earth and Planetary Science(2022) 9:50

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Fig. 7 Curvature introduced by pen-type recording system. Comparison of a raw seismogram showing the beginning of surface-wave arrivals at
station HRV from the November 30, 2018, Anchorage earthquake (cyan line), and a version with curvature as would be generated by a pen-typesystem introduced (red line). The pen (pencil in the figure) swings around a pivot point, making arcs for large-amplitude arrivals, even though forsmall-amplitude arrivals, the cyan and red lines are practically identicalfor images with pen curvature, this geometry significantly
complicates necessary curvature correction (Fig. 9c).
The conventional assumption that curvature can be corrected by modifying the image parallel to the trace-zeroline results in overestimated amplitude. The appropriate
correction must be parallel to the horizontal direction (orparallel to the rotation direction of the helicorder).
A future version of DigitSeis will address these issues
as well as the need to estimate the pen length (an underdetermined problem). Contrary to the conventional
approach, image modification will not be used, and thisis for three reasons. First, it requires knowledge of the
pen length. Not all records have this metadata information, and even though our algorithm for pen-length calculation appears to work well, it would be prudent not torely upon this estimate. Second, the images are pixelated.
Modification of the image, therefore, requires rounding
to the nearest integer, leading to possible step-like output. If the image has been scanned at high resolution,
this may not be a problem, but we have worked with lowresolution images which give jagged output even without image modification. Alternatively, one could use an
average of pixels if the pixel translation is fraction of an
integer. This provides smooth image, but does so at theexpense of blurring some faint traces that often exist fortraditionally interesting targets such as large-amplitude
arrivals associated with significant earthquakes. For
these reasons, the approach DigitSeis will take is to digitize the traces without any corrections and allow user
to correct for curvature once x-y positions of the traces

are extracted (a basic code to do this correction will be
provided).
2.3.2 Develocorder‑Type Seismograms

The “Develocorder-style” seismograms are defined assynchronized seismograms from multiple components
or stations recorded on a single medium (Fig. 8). Anotherfeature of these seismograms is that one or more of the
traces are dedicated to timing, i.e., they do not record
ground motion, but provide time information. Develocorder-type recordings became popular in the 1960s
with development of the ability to accurately telemeterdata from distributed stations back to a central location.
The layout of the seismograms, i.e., multiple traces fromdifferent stations displayed at once, mean that they arequite powerful for quick visual determination of wherean earthquake has occurred. Changes in the ambient“background” level are also easy to detect, for example,
increases in volcanic tremors from a specific location.
These features made Develocorder recordings popular, especially among institutions where monitoring wasamong their main duties (e.g., Okubo et al. 2014).
The most significant difference between galvanometer- and Develocorder-style recordings is that the former has time information embedded within the tracesin the form of time marks while the latter has a separate time channel. This implies that the current time
calculation algorithm in DigitSeis will not work with
Develocorder-style records. Furthermore, in a galvanometer-type helicorder recording, the end of one

Ishii and Ishii P﻿ rogress in Earth and Planetary Science(2022) 9:50

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Fig. 8 An example of a Develocorder-type recording. Three-component strong-motion recordings of the Ferndale, CA earthquake, on December
21, 1954. There are three time channels, at the top, bottom, and between top and middle traces. The middle time channel indicates that a pair ofline and gap covers 0.5 second

trace is connected to the beginning of the next trace.
Consequently, DigitSeis assigns time to digitized traces
so that it is continuous from the end of one trace to thestart of the next, and when data are extracted to SAC
format, filenames are assigned based upon the start andend times of each line. On the other hand, the multiple traces of a Develocorder-type record were set up
so that all traces would have same time at a given horizontal position, i.e., using the current version of DigitSeis would result in incorrect time assignments, andif the user manually sets each trace to have the samestart and end times, the SAC files will overwrite one
another. Modifications to DigitSeis to accommodate
Develocorder-style seismograms are relatively simple compared to challenges associated with pen-styleseismograms, as they will mainly consist of changing
the waveform-output routines and reading in a unified
time (as opposed to any modification of the digitization
itself ).
2.3.3 Crossing Traces and Automation

The ultimate digitization software is one that is all automatic, i.e., the user loads in the seismogram image andIshii and Ishii ﻿Progress in Earth and Planetary Science(2022) 9:50

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Fig. 9 Complexities associated with the helicorder geometry. a Illustration of a helicorder recording with only rotation. The black line is thetrace-zero line that would be produced if the amplitude is always zero. b Same as (a) except that this case includes translation that results in theslope of the trace-zero line. If the amplitude is measured perpendicular to the trace-zero line, it will result in an underestimation of amplitude(dashed blue line) and a time offset (open blue circle). c Effect on curvature introduced by pen or needle recording. The arc is generated byinstantaneous amplitude change from zero to a maximum value with the armature (yellow lines). The solid red arrow shows how this curvature
should be corrected (open red circle) while the blue arrow shows correction if the correction is calculated parallel to the trace-zero line leading tooverestimated amplitude (open blue circle)

corresponding time series are generated without any
user input. Modifications to DigitSeis mostly aim toincrease automation of processing, and we will continue
to automate more components as they become feasible,
although it (nor any other highly accurate digitization
software) is not likely to ever achieve full automation.
The greatest challenge toward automation is crossing or
touching traces which cannot simply be ignored as they
may be the most desirable part of the record with large
swings associated with seismic wave arrivals. The current version of DigitSeis can process crossing or touching traces (e.g., Fig. 6), but this is achieved through userintervention (either tracing the lines or defining objects
manually). For images with no such features, DigitSeis isnear-automatic, requiring only limited user interaction tomove through each step and to set the reference time. It
is not clear if there will ever be a reliable, fully automaticalgorithm for untangling crossing and touching traces
since it is sometimes difficult, if not impossible, evenfor human eyes to decipher how the lines are connected(e.g., Fig. 10).
One suggestion we often receive is to utilize machinelearning in the digitization process. There have indeed
been studies that successfully utilized machine learning
on analogue data to extract information (e.g., Wang et al.
2019). We have investigated the applicability of convolutional neural networks for recognizing and classifying
trace, time mark, and noise objects from an input grayscale image. It worked well for simple images without
trace crossing, but was less than successful with more
complicated images. There are two main reasons for theunsuccessful result. One is that the seismograms aresparse in terms of local features. If one tries to identify
a cat in an image, the object, i.e., a cat, can be definedwithin a small area of the image. Even if the cat is taking

Fig. 10 An example of crossing/touching traces. A portion of thelong-period north–south recording from BKS (Berkeley, CA) on
February 11, 1964. One can take advantage of the time marks that are
horizontally offset for each trace to unravel most parts of this image,but if time marks were occurring at the same horizontal position, one
would have a hard time separating the traces

up the entire image, the relative information (e.g., having a head, tail, body, etc.) can be localized and distinguished. Most importantly, a zoomed-in image of a cat
can be decimated down to a smaller image, i.e., we canstill see the cat. Seismograms, on the other hand, are difficult to define locally. A small portion of the seismogram(basically a line) is so featureless that it is difficult to distinguish a trace or time mark from a non-seismogram
segment in the image, e.g., a handwritten tilde. What
allows us to identify the traces in a seismogram is theircontinuity and relative positions. One can train a neuralIshii and Ishii P﻿ rogress in Earth and Planetary Science(2022) 9:50

network to look for the relative information, but because
we typically down-sample images when looking at a large
area, faint traces tend to disappear. Also, depending upon
the portion of the image being examined, the non-local
aspect of seismograms requires neural networks to befine-tuned to some specific layout of the seismogram
image, losing flexibility of processing in doing so. The second issue is that we do not have a large-enough training
database of digitized analyses from images with severely
crossing traces. These images are difficult to digitize (i.e.,need substantial manual intervention and contemplation); hence, traces such as those shown in Fig. 10 havenot been digitized. This problem could partially be mitigated by creating a training database consisting of digitalseismograms and corresponding synthetic helicorder orDevelocorder images generated using the digital traces.
There are a number of approaches one could take toaddress some of the crossing/touching trace issues without machine learning. One can distinguish certain features associated with crossing traces by identifying pixels
that belong to multiple objects. For example, they tend tooccur around bifurcations or holes and often are thicker
than a typical trace or time mark objects (i.e., cover more
pixels in the y direction). Preliminary attempts showpromising results (Fig. 11a). For relatively simple overlaps, this method successfully isolates overlapping areas(areas 3 through 8 in Fig. 11a). It is, however, not perfect, and for more complicated crossings, the algorithm
returns vertically sliced areas as containing overlapping
areas (areas 1 and 2 in Fig. 11a). This information is still
useful since it ensures that resulting objects (trace andtime mark segments) are isolated, i.e., belongs only to asingle trace.

Page 14 of 16

Accurate definition of overlap pixels is a crucial step in
automating the analysis of crossing traces, but anotherimportant process is determining how these pixels
should be combined with the rest of trace or time markobjects. For example, in Fig. 11b, there are 8 objects, ofwhich 6 are individual trace segments (A through F),
while 2 belong to multiple traces (a and b). Because theseismogram should always move to the right and nevergo back left and vice versa (this is not necessarily the casefor seismograms generated using pen or needles), objects
A and B must belong to separate traces and the same canbe said with pairs C/D and E/F. Assuming that there are
only two traces involved, one could then combine theobjects with the following possibilities (top trace/bottom trace): A-a-C-b-E/B-a-D-b-F, A-a-C-b-F/B-a-D-b-E,
A-a-D-b-E/B-a-C-b-F, and A-a-D-b-F/B-a-C-b-E. In thisparticular example, the correct combination is A-a-D-bE/B-a-C-b-F. In order to automatically deduce the traceassignment, it becomes vital that the number of traces
within the image and their trace-zero positions be known
as the objects are processed. We will be exploring theseapproaches and implement successful algorithms into
future versions of DigitSeis so that it becomes easier toprocess crossing/touching traces.

3 Conclusions
Seismologists have been recording ground motion since
the late 1800s, and a vast amount of data covering nearlya century exists in analogue form. These data capturesome unusual events that are not observed with their digital counterparts (e.g., tsunamigenic earthquakes, submarine or subaerial nuclear tests, rare volcanic eruptions),
and are also essential in understanding time-dependent
processes (e.g., earthquake cycle, subsurface evolution,

Fig. 11 Object crossing. a Screenshot of the classification window where trace objects are shown in white and time marks are in blue. Areas
colored in magenta correspond to overlap pixels that have been automatically determined to belong to multiple objects. b Different objects thatneed to be connected within the area shown by yellow box in (a). The magenta regions a and b separate white regions to define independent
objects A through F; in total, there are 8 objects

Ishii and Ishii ﻿Progress in Earth and Planetary Science(2022) 9:50

climate change). Conversion of these analogue data into
digital time series will facilitate their analysis using modern computational techniques.
This manuscript describes freely available software,
DigitSeis, that has been developed to extract digital timeseries from an input seismogram image, and how thesoftware evolved over different versions. The efficacyof the software is demonstrated with three examples ofimages based upon three types of media, photographic
paper, 35-mm microfilm, and 70-mm microfiche. These
examples show that seismograms can be effectively
traced even when there are crossings, and time can becorrectly set using the time marks. They also indicate thepotential of extracting digital time series for use in a variety of seismological research.
The development of DigitSeis is on-going, and there
are multiple directions in which it will be enhanced for
future releases. The current versions are not designed
to process pen- and Develocorder-type seismograms,
and some of the challenges associated are explained. The
Develocorder issue is relatively easy to address and thenext release of DigitSeis will likely include the capability
to work with Develocorder records. It is more difficult toprocess pen-type seismograms, and it may take a while
before these images can be analyzed using DigitSeis. One
potential approach to digitization that is often brought
up is the applicability of machine learning algorithms.
While image processing using machine learning is usefulfor some very specific projects with well-defined features
such as identifying earthquakes based upon Develocorder records, we find that there are considerable challenges facing the more general goal of tracing any type ofrecording. Therefore, near-term improvements to DigitSeis will be based upon traditional programming, such as
unravelling of simple crossing traces based upon identification of overlapping pixels. DigitSeis will continue to
evolve toward accurate, robust, and automatic processingof analogue seismograms which will be a key to building
digital seismogram databases derived from the analogue
instrumentation era, opening up opportunities for exciting scientific research.
Abbreviations
BKS: Berkeley, California seismic station; COL: College, ALaska seismic station;
dpi: Dots-per inch; ISC: International Seismological Centre; SAC: Seismic analysis code; USGS: United States geological survey; UTC​: Coordinated Universal
time.
Acknowledgements
We thank Thomas Lee for feedback on the manuscript, packaging DigitSeis for
public release and being its ambassador, and Katelyn Lee for preparing material for DigitSeis version 1.5 release. We also thank an anonymous reviewer
and Göran Ekström for comments that improved the manuscript and Toshiro
Tanimoto for handling of the manuscript.

Page 15 of 16

Author contributions
MI began the work with analogue seismograms and developed DigitSeis
versions 1.1 and later. HI used the software to identify bugs and to process
images to obtain digital time series. Both authors have read and approved thefinal manuscript.
Funding
This work has been supported by the Miller Institute for Basic Science at the
University of California, Berkeley, the Earthquake Hazard Program of the UnitedStates Geological Survey (grants G14AP00016, G16AP00021, G17AP00007,
G18AP00018, and G20AS00042), the Department of State (contract number
19AQMM20P1475), and the National Science Foundation (award number
1822136).
Availability of data and materials
The digitization software, DigitSeis, is available at the Harvard Seismology Web
page at http://seismology.harvard.edu/research/DigitSeis.html. The digitized
results in Figures 4 through 6 can be obtained in both SAC and DigitSeis
output file format (.mat) using links from http://​seism​ology.​harva​rd.​edu/​resea​
rch/​analo​gueDa​ta.​html

Declarations
Competing interests
The authors declare that they have no competing interest.
Received: 7 July 2022 Accepted: 21 September 2022

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