Data Cloud

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Earthquakes, phant data  and Arduino madness.

The report I did for this mind bending module INDE402 data cloud Module is given below. Hope its all understandable as it was quite complex with MYSQL, Data cloud , java script based software and visualisations  through Processing  v2.1. I know when I tried explaining it to people they stared blankly at something in the distance.


The aim of my project and this report is to outline the basic nature of seismological readings and processes then later describe process of seismic observations within the Antarctic domain. Also included is a report of my module, where I interpreted a set manufactured seismic like readings through an Arduino and a Triple Axis Accelerometer then through my software made an unique visualization that in some form represented that seismic data. Also parts of the following report on aspect of my project and how the resulting product was achieved and how it could be improved.

Why study Antarctica?

The ice mass across Antarctica is a hugely important part of monitoring any effects of climate change. By measuring using such means and seismology and GPS readings scientists and researchers can gather data on the movement of the ice sheets and any ensuing alteration to the balance of the underlying Lithosphere.


Lithosphere Illustration

The amount of data gathered is huge, amongst many others bodies the two larger organisations involved are Iris (Incorporated Research Institutions for Seismology) and USGS (United States Geological Research. Researchers working on plate tectonic reconstructions have, for a while, had questions regarding the presence of a plate boundary beneath the West Antarctic Rift separating East and West Antarctica.

map of tectonic plates

Although evidence suggests that extensive volcanism (phenomenon of eruption of molten rock) is associated with the Rift beneath the ice and sea, the effect of this rifting on global sea levels and climate remains an issue of scientific interest and public concern.

The Mountains bordering the West Antarctic Rift may have caused the development and subsequent fluctuations of the East Antarctic Ice Sheet, which is the largest ice sheet in the world. The stability of this ice sheet profoundly affects sea level and global climate. Although the East Antarctic Ice Sheet is estimated to be stable within a range of 10,000-100,000 years (a short time frame in geologic terms), its collapse would raise sea level by over 200 feet.

Background data stream and technical aspects within IRIS.

In early days of seismology, many seismic stations and networks used single-component sensors – usually vertical seismometers. Quite a few of them still operate. This was the case because the equipment was analogue and the record was often on paper.

Today however most data recorders and data transmission links are capable of accepting at least three channels of seismic data. Recently developed accelerometers have exceptional dynamic range and fine signal resolution.

monitoring the ice in Antarctica

Greenland Ice Sheet Monitoring Network is an 11-nation collaboration initiated by IRIS

Weak-motion sensors – seismometers – are usually orders of magnitude more sensitive, however, they cannot record as large amount amplitude as an accelerometer. They can record very weak and/or very distant events, which produce ground motion of comparable amplitudes to the background seismic noise.

Lots of stations use a bandwidth filters and statistical outliers to eliminate “noise” that my give false readings. For instance, readings at Ross Ice Shelf, the frequency band from 4 to 18 Hz was dominated by waves that, based on daily temporal variations, researchers concluded were generated by field camp activity.

There are diverse kinds of seismic waves, and they all move in different ways. The two main types of waves are body waves and surface waves. Body waves can travel through the earth’s inner layers, but surface waves can only move along the surface of the planet like ripples on water. Earthquakes radiate seismic energy as both body and surface waves.

Body Waves

The first kind of body wave is the P wave or primary wave. This is the fastest kind of seismic wave, and, consequently, the first to ‘arrive’ at a seismic station the are also known as compressional waves. It can move through solid rock and fluids, like water or the liquid layers of the earth.

The second type of body wave is the S wave or secondary wave  and can only move through solid rock, not through any liquid medium.  S waves move rock particles up and down, or side-to-side, perpendicular to the direction that the wave is traveling in.

Surface Waves

Travelling only through the crust, surface waves are of a lower frequency than body waves, and are easily distinguished on a seismogram as a result they arrive after body waves.

The first kind of surface wave is called a Love wave, named after A.E.H. Love. It’s the fastest surface wave and moves the ground from side-to-side. Confined to the surface of the crust, Love waves produce entirely horizontal motion.

The other kind of surface wave is the Rayleigh wave, named for John William Strutt, Lord Rayleigh. A Rayleigh wave rolls along the ground just like a wave rolls across a lake or an ocean. Because it rolls, it moves the ground up and down, and side-to-side in the same direction that the wave is moving. Most of the shaking felt from an earthquake is due to the Rayleigh wave, which can be much larger than the other waves.


Application Frequency

range (in Hz)

Seismic events associated with mining processes 5 – 2000
Very local and small earthquakes, dam induced seismicity 1 – 100
Local seismology 0.2 – 80
Strong motion applications 0.0 – 100
General regional seismology 0.05 – 20
Frequency dependence of seismic-wave absorption 0.02 – 30
Energy calculations of distant earthquakes 0.01 – 10
Scattering and diffraction of seismic-waves on core boundary 0.02 – 2
Studies of dynamic processes in earthquake foci 0.005 -100
Studies of crustal properties 0.02 – 1
Dispersion of surface waves 0.003 – 0.2
Free oscillations of the Earth, silent earthquakes 0.0005 – 0.01

Tab 1.1. Application description and approximate frequency range of interest.

Types of data recorded

Event Data provides information about earthquakes and other seismic events.

Metadata includes station siting and instrumentation information.

Historical data The IRIS DMC archives historical seismic data including scanned seismograms and other

Time Series Data Channels

The IRIS DMC (data management Centre) archives and distributes data to support the seismological research community.

Most of the data collected and archived at the DMC is time series data. Each channel of time series data is identified by a 3-character channel code , which indicates the type and placement of the sensor. The convention for time series channel codes if a guideline only.

My Project Outline


My interpretation of the brief was that I was to construct, as a minimum, working data uploads, downloads, MySQL and basic processing sketch visualisation. I chose not to include IRIS data as time limits did not allow for me for me to research the nuances of the BHZ and other data formats. The difference between ssv (space separated values) data and my own was an obstacle that rationalised out of my working model early in the project management timetable.

My initial goals were to construct a working Arduino accelerometer device, aggregate the x,y,z, data and upload it to the data cloud and retrieve it. MySQL data format needed to be agreed between at least one other data producer so that my software could retrieve it and then visualise that data.

Step processes

There were many aspects to the project that all had to work in order that I could progress to the visualisation programming.

Firstly an accelerometer module had to have a 5 pin header made. The xyz readings generated from the module were summed into a pseudo-magnitude and the data uploaded to Phant data cloud via a Processing sketch. This first aspect had problems to overcome such as the constant feed from the accelerometer meant an unnecessarily large amount of data was being produced was unmanageable with the software I had written. To overcome this allowance was made of approx. 5 data units either side of a neutral no-movement point so that reading were only present when movement was generated.

stages of data handling

The Phant data was later structured to be read, via another processing sketch, alongside this a MySQL database was made to store other URL from other sources using the same project data structure. The difficulty I faced was that the MySQL data was stored locally on one machine in Plymouth University and working from home was not immediately available for several reasons; my connection at home is intermittent and not of a suitable bandwidth, there are variations on MySQL and it was a confused process which version and installation should be effected as I use both windows and apple OS. Iris data was not used as downloading it into a structure that could be used by my visualisation was beyond my resources and timescale.


Once data retrieval had been achieved and a rudimentary navigation of other data sources completed it was a matter of fail testing that part of the program to ensure its efficaciousness. Difficulties arising from this mechanism were not many however I was constrained to access only 3 data sources including my own. The problems were usual minor such as not entering data correctly such as URLs or that the other student hadn’t used the same magnitude sum, leading to anomalous data, which affected the later process of visualisation.

software design and hierarchy

Project work flow diagram

 Visualisation process

From the out set of the project I already had some idea of how the visualisations in the past have been interpreted. Usually the data is shown as waveforms with peaks and troughs or as mapped data with colour differentials to display magnitude. Therefore some preconceptions were evident in my first theoretical visualisation ideas. Also in the multiple steps of data storage and retrieval meant that there were challenges with acquisition of data.

challenges of the data handling

Flow chart of decision tree for visualisation



Although I would have preferred some audio representation I had know prior understanding of how to carry this through. My initial conceptual processes were to have volume and pitch changes based on magnitude alongside a circular “blipping” based on the same data. Hue would be determined by magnitude where the red spectrum represented larger magnitudes and blues smaller magnitudes. Through experimenting with circles, bar chart and alphanumerical representation of data I was able to complete the visualisation of data and switch data sources. Given below are pictorial representations of the evolution that led to finished visualisation (4).


my visualisations of mock earthquake data

Four main stages of Visualisation Development


The Antarctic is considered a touchstone from climate change monitoring and seismology appears to present itself as a very satisfactory benchmark for determining ice movement and volumes to better understand that domain. The impact of climate change maybe catastrophic and therefor fully justified full scale scientific monitoring.

One of the prime constrictions on the realisation of the project goals, as set out in the module brief, was the small amount of time allocated to develop the theoretical construction into practical realisation.

There were problematic issues in realising a full working model

I would have liked to have made an audio representation of the data reading using tone and volume as variables but this would have required me to acquire new programming skills in a very short time and the resulting outcome may have been too rudimentary to have been successful in completing the brief. Another area I would have preferred to develop further was that the circles were layered to cover whole background in a small range of hues. The bar chart aspect was also only partially successful as it would have been more engaging if the bars shrank and grew in real time with data alongside colour changes based of magnitudes.




The final analysis come down to time versus expectations of results and for this project the two main restriction were my own skills base and the very short timescales allowed for experimentation rather than realisation. I will continue to work towards a better understanding of interpreting raw data into other format than visual, but also look to improving my understanding of Internet Design in the wider developmental concerns.


Adam Clark. (current). Knowledge Base/ What are Channel Codes?.Available: Last accessed 12 April 2016.


Joseph Cheek. (2014). WHY STUDY SEISMIC ACTIVITY IN ANTARCTICA?. Available: Last accessed 11th April 2016.


Amadej Trnkoczy, Jens Havskov and Lars Ottemöller. (n/a). Seismic Networks. Available: Last accessed 12th April 2016.


MICHIGAN TECHNOLOGICAL UNIVERSITY. (2007). What is Seismology and what are seismic waves. Available: Last accessed 12th April 2016

Geophysical Journal International . (2016). Ice shelf structure derived from dispersion curve analysis of ambient seismic noise, Ross Ice Shelf, Antarctica. Available: Last accessed 10th April 2016


Simon Rogers. (2012). Data visualisation DIY: our top tools. Available: Last accessed 12th April 2016.

Below is the outline for this module at Plymouth University for yr 1 BSc Internet Design 2015/16


To expose students to a wide range of data storage technologies.

To develop an understanding of the potential uses and limitations of each.

To gain hands-on experience of using the different technologies through a series of practical lab-based sessions.

To support the sharing of knowledge, experience and understanding by encouraging students to engage in collaboration, cooperation and peer review.

ASSESSED LEARNING OUTCOMES: (additional guidance below)

At the end of the module the learner will be expected to be able to:

1. Demonstrate an ability to respond to a set brief with an appropriate range of production skills.

2. Demonstrate technical, practical and conceptual skills in the use of hardware, software and networked systems.

3. Demonstrate critical creative skills in the application of hardware, software and networked systems to production of complex systems