Evaluation Video Tutorial/Documentation Slides, Script    

This section breaks down the Evaluation Video into separate slides, along with associated scripts.

Slide01

The Kodiak A World Bridge™ Earthquake Signal Precursors team is testing the hypothesis that magnetic field anomalies may precede earthquake events. The left picture is the Kodiak A World Bridge™ Lab at Kodiak High School, Alaska. The right graphic is a thumbnail of the ESP graphic user interface.

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Slide02

We have many project partners from incredible organizations, including NASA Ames Research Center, International Centre for Earth Simulation (Geneva), Copper River School District, Alaska; Ketchikan Gateway Borough School District, Ketchikan, Alaska; European Space Agency, GeoCosmo, Politecnico di Milano, Kings College London, London-United Kingdom; Universidade Estadual de Campinas (UNICAMP) - Campinas - Brazil; Cornell University, Valley Christian Schools, San Jose, California; University of Jordan, Amman, Jordan; Princess Sumaya University of Technology, Amman, Jordan and Trillium Learning.   

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Slide03

This is the front end interface of the ESP Web World Wind application, developed in Javascript.
To view all stations, click on the All Platforms button on the upper left.
Last year (2015), we developed the first ESP prototype which sampled at 1 Hz. These past 12 months, the Kodiak World Bridge team completely redesigned and upgraded all software, firmware and hardware. Researchers at NASA required a minimum sample rate of 20 Hz, along with more sensor stations in order to build ESP models and characterize the signals. At this time, we built and installed four ESP stations, along with one live testing platform, all producing 50 Hz sample rates and 17,280,000 samples per day, or 518,400,000 per month, and have accumulated approximately 2,592,000,000 samples available for all researchers around the world to study and perform analyses. The ESP stations have allowed us to identify an additional 15 new anomalies since last year’s Europa Challenge submission.

There are also 5 older Intelesense platforms that only sample once/minute. Data may be downloaded directly from the Intelesense website.

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Slide04

This is the ESP Data Workflow:

Data is acquired using Python programming, then transmitted to an ESP Server and stored in an Influx database.

The data is analyzed and visualized as live, dynamic vectors written in JavaScript on Web World Wind. The data is also visualized within the interface, as interactive graphic charts, coded in JavaScript for Dygraph.

Data access is open and download is provided 24/7 for research and development.

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ESP Data Acquisition System150Sidebar

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Field Data Collection to Data Presentation:

Slide05


A 3-axis Honeywell HMR 2300 magnetometer acquires magnetic field values at a 50 Hz sample rate.

A Raspberry PI processor receives the data at 50 times greater sample rate than last year's prototype.

This data is transferred to the Influx database for storage and analysis, then visualized on the web front-end. Data visualization is in the form of dual, dynamically updating Vectors, and interactive graphs.

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Slide06

Visualizations are  represented as 4D vectors on Web WorldWind, showing time-series based magnetic field strength, and direction.

The red needles represents small, live variations in the local magnetic field, and the yellow arrows represents the vector animations of live data from all ESP platforms simultaneously.

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GetInfo2

To view station information, click the ‘I’ icon, which opens in a new tab.

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Slide07

Information includes date of installation, Latitude, longitude, and location. Additional site information and/or sensor station background may be included.

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DownloadScreen

To download magnetometer data, click the download icon.

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Screen Shot 2016-08-26 at 4.15.49 PM


Screen Shot 2016-08-26 at 4.15.59 PM
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Choose the file format -JSON or CSV, and the date range. Then, click the download button.

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ChartSelect

To view a magnetic field data graph, click the graph icon, which opens a new tab.

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Slide09


Magnetometer data are represented by 2 graphs for X, Y, and Z axes in Nanotesla units (actually, each graph is subdivided into two related graphs, for a total of 4 interactive graphs per axis).

All graphs are interactive.

The top graph shows the previous 30 minutes of data updated once/second and plots every 1000th point to reduce the data load.

The bottom graph plots data once per hour. The lower portion shows the very first data acquisition date from the left, to the current data point on the far right.

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Slide10

Data is screened to determine any gaps in acquisition which may affect data quality.

When gaps are identified in a data stream, we locate that data on the ESP server, then study the data using the Influx database Chronograph. We then rework the programming and any software/hardware configuration to obtain consistent, quality streaming data. We also perform background research to determine if the data gaps were related to power outages, internet interruptions, or other manmade interference.

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Slide11

We are currently developing algorithms to characterize the pre EQ signals which would enable us to show trigger warnings, based upon the strength of magnetic anomalies, distances, and directions to the hypocenters. In order to bring an “earthquake forecast” system to reality, we must successfully characterize the different magnetic field anomalies, assure consistent alignment with actual seismic activity, and (with sufficient numbers of ESP stations) determine approximate direction and distance to a potential earthquake’s hypocenter.

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Slide12

Our data is accessed and analyzed by, multiple international universities and professional organizations. These include: NASA Ames Research Center, International Centre for Earth Simulation (Geneva), Copper River School District, Alaska; Ketchikan Gateway Borough School District, Ketchikan, Alaska; European Space Agency, GeoCosmo, Politecnico di Milano, Kings College London, London-United Kingdom; Universidade Estadual de Campinas (UNICAMP) - Campinas - Brazil; Cornell University, Valley Christian Schools, San Jose, California; University of Jordan, Amman, Jordan; Princess Sumaya University of Technology, Amman, Jordan, University of Massachusetts at Amherst and Trillium Learning.    

We Have Shared Data With Alaska State Public Schools, New York School Districts , Universities In Turkey , China, Japan, Australia ,Italy, Norway, Brazil, Mexico, GeoCosmo, Mitre Corporation, Florida International University, Kansas University,  CERN, UNITAR (United Nations Institute for Training and Research) – UNOSAT (UNITAR’s Operational Satellite Applications Programme, University of Alaska System. 



   



  



   



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