Organization: NASA Ames Research Center (PX)
Partners: Trillium Learning, Kodiak School District
Managers: Patrick Hogan, Ron Fortunato
Authors: Gabriel Militão, Benjamin Chang, Khaled Sharif, Farah Salah
Backend Team: Enika Biswas, Nidhi Jain
Field Team (Alaska): Seraphim McGann, Kiae Shin, Teyo DeGuzman

Based on: Dr. Friedemann Freund's work

• St-Laurent, F., J. S. Derr, and F. Freund (2006), Earthquake Lights and Stress-Activation of Positive Hole Charge Carriers in Rocks, Phys. Chem. of the Earth, 31, 305-312.

• Freund, F., A. Takeuchi, and B. W. S. Lau (2006), Electric currents streaming out of stressed igneous rocks: A step towards understanding pre-earthquake low frequency EM emissions, Phys. Chem. of the Earth, 31, 389-396.

This project aims to apply Dr. Friedemann Freund’s theory of earthquake precursor science. Dr. Freund's theory states that as stresses in the Earth’s crust increase during the time leading up to a major earthquake, atomic-scale defects in the mineral grains and along the boundaries between mineral grains become activated. These are peroxy defects, typically O3Si-OO-SiO3, and when they break up they generate electron-hole pairs. While the electrons remain trapped in the broken peroxy bonds, the holes have the remarkable ability to flow out of the stressed rock volume and into the adjacent less-stressed or unstressed rocks, probably following the stress gradient. The observations reported in this study indicate that these electronic charge carriers, which are called positive-holes or p-holes for short, can propagate over long distances, potentially hundreds of kilometers. As they propagate through the Earth’s crust, they produce an electric current which, in turn produces a magnetic field. Such p-hole currents are seldom steady. They fluctuate, emitting electromagnetic (EM) waves. EM waves in the frequency range above 20 Hz are quickly attenuated in the rocks, but EM waves below 10 Hz can propagate over long distances, and therefore can be detected at the surface.

The ultimate goal of this project is to observe live anomalous EM field fluctuations and accurately forecast an earthquake within a specified geographical range. To accomplish this, magnetic field data is collected in Alaska, the most seismically active region in the world. First, common magnetic signals are filtered through multiple signal analysis tools to enable observation of the baseline magnetic signal. Second, an anomaly detection algorithm is applied to the data, identifying abnormal points in the data that may be indicative of a pending earthquake. Then, an algorithm clusters the anomalous points, and then generates features statistically. Machine learning algorithms are fed the anomalous features extracted from historical data to build “Earthquake Sensory Precursors” that can be then used to forecast, in real time, future earthquakes.

A noteworthy feature observed so far is that the EM waves recorded on Kodiak Island exhibit a distinct diurnal pattern with intensity variations from day to day. The analysis presented here indicates that, while some of these intensity variations are clearly repetitive and diurnal, there are anomalies in the intensity variations of the EM waves, which seem to correlate with the build-up of tectonic stresses at the origin point of a seismic event that in a few days’ time could initiate an earthquake.

Working with NASA Ames Research Center (PX), Trillium Learning, and the Kodiak Alaska School District, a small team of NASA interns are working to analyze and understand magnetic field data being recorded in Alaska to determine whether or not earthquake forecasting can be accomplished through interpretation of magnetic field fluctuations.

In order for the whole repository to function properly, these packages must be installed on python 3:

Packages:

• time
• datetime
• matplotlib
• pandas
• numpy
• seaborn
• scipy
• sklearn
• statsmodels.api

Because the data being recorded in Alaska is very large (>10G due to the high resolution of 123 samples/sec) and currently being accessed from a database, most of the analyzable data is not publicly available. However we encourage the implementation of this code with independent data sources. Loading the data should be as simple as reading (or parsing) your data into a pandas dataframe, and implementing that dataframe across the available modules for analysis.

Magnetic
Magnetic data (field vectors) needs to be either readable or otherwise easily parsed. Format must be timestamps, X, Y, Z. The current magnetic data is being sampled at 123hz.

Earthquake
Earthquake data can be easily read into the analysis environment using the loadearthquake.() function. This loads from the USGS API database. It is likely that earthquakes of less than magnitude 3 and of greater distance than 300km from the magnetometer station will not have much influence over the magnetic field vectors.

The raw magnetic data (at 1 to 123hz) has a diurnal signal pattern.

In order to eliminate this, we can utilize a bandpass filter bandfilter.butterfilter() to allow for finer analysis of how earthquake signals may affect the magnetic field vectors.

Two methods of anomaly detection are included in the repository. The first one is from Twitter's anomaly detection library ported into python 2. We then ported that library into python 3 (pycularity). The other method involves using numpy convolution to create a moving average to detect anomalies new_anom_det.det_anoms(). They both accomplish similar results.

By analyzing the data in this fashion, we should be able to observe perturbations in the magnetic field vectors, allowing for machine learning to build a confidence on earthquakes occurring in the near future. (The red line is an earthquake event of magnitude >3 within 300km of this magnetic anomaly)

Several plots can be created:

• A subplot of the magnetic vectors plotted against earthquake events and a scatter plot of anomalous points plot.plot_earthquake_anomalies_magnetic()
• Distribution plots (histograms) of the data can be generated plot.plot_histogram()
• or you can compare two magnetic data sets plot.plot_eq_mag_compare()
• etc. explore the plotting functions written!

You can generate features using the learning.preprocess() function. These generated functions can be fed into sklearn functions or any similar machine learning library.

This application is a visualization of earthquake events as accessed through the USGS API. The app can map earthquakes on a 3D globe to display epicenters, hypocenters, and relevant information of recorded earthquakes. Geographically constrained queries can allow for fast visualization of decades worth of earthquake hypocenters. With a high enough density of markers present, the visualization of the subduction zone of the tectonic plates is possible.

1. To run the app, download the repository and run new_eq_app.html (EarthquakeApp/new_eq_app_src/new_eq_app.html) in a web server (WebStorm has a built in web server).
   • Hover over the events to view event specific information
   • The box in the upper right-hand corner provides info on the whole query
2. The sliders on the left-hand side change the opacity, magnitude and date range queried
3. Use mouse or UI controls to change view angle to observe the depth of the events.
   • Currently supports placemarks and polygons, will soon support toggle controls.

• Query functionality for access to the USGS API from front end interface
• Time series of EQ
• EQ Forecasting implementation
• Toggle between magnitude and age color coding
• Recent 'reset' button
• Geographically constrained queries (queries within drawn polygons or radii)
• Greater UI control over layers
  • Toggle between placemarks, polygons, lines, etc.
• Tectonic Plate layer
• Better date query

More research on the spatial distribution of the positive-hole currents would also be worth while. Several ground station networks could be set up to record the EM waves and use the available triaxial information to gain insight into the distributions of the p-hole currents. This would tell us how the currents flow in the Earth’s crust and how their spatial distribution correlates with the "source" locations where the p-hole currents originate. With a sufficiently large number of recording stations, one would be able to map out the current flow patterns in 3-D and compare them to the locations of the earthquake hypocenters that are the “sources” of the outflow currents.

While this study is focusing on the anomalous deviations from the diurnal pattern, it would be interesting to research the diurnal EM patterns themselves. What makes the currents in the Earth’s crust wax and wane regularly over 24 hours and what is the underlying physics? An extended study could involve the recording several parameters influencing the EM field, foremost being solar activity, its UV output, and the arrival bursts of solar wind plasma impacting the Earth known as geomagnetic storms. These high energy bursts cause very short-lived fluctuations to the local magnetic field, as well as a variety of atmospheric and ionosphere phenomena.

Updated as of 30/07/2017 (July 30th, 2017)