Here I posted the InSAR observations from ESA -European Space Agency where they have posted several coseismic interferograms to track the ground deformation on Nepal area.

They compute several interferograms that are available to download. The best interferometric solution, combination of, 17.Apr – 29.Apr (descending), showed around 34 fringes or 1 meter line-of-sight displacement, due the 7.8 earthquake.

http://insarap.org

http://www.bbc.com/news/science-environment-32515059

On the same way, the continuous GPS sites close to the Nepal Earthquake recorded the ground motion.  Then, according UNAVCO report, they have compute some seismogeodetic seismograms using GPS data at 1 Hz and and 30 s. The GPS sites are located 200, 400 and ~ 60 km from the earthquake.

http://www.unavco.org/voce/viewtopic.php?f=59&t=1390&sid=e13c8d62c6054d63cfe308aaf5a7c78c#p2507

Gotta love the title- “Weather Underground.”

This article makes for an interesting read about the recent increase in unusually large earthquakes in Oklahoma caused by fluid injection wells. The article goes on to discuss the transformation of the oil state due to an accidental discovery of oil back in 1859. It also talks about how the lack of transparency of oil companies is irritating some of the citizens of OK.

Here’s a snippet:

“At least eight bills have been proposed that aim to make it difficult for communities to set their own rules for oil drilling.”

“The first oil discovered in Oklahoma was found accidentally, in 1859, in a well drilled to find salt, near present-day Salina; the oil was sold as fuel for lamps. As related in “Oklahoma Oil: Past, Present, and Future,” by Dan Boyd, the next find came in 1889, near Chelsea, where a well produced half a barrel of oil per day; it was used to treat cattle for ticks. Then, in 1897, a well drilled near Bartlesville became a major oil producer, and many others followed. Within ten years, Oklahoma was producing more oil than anywhere else in the world. Not coincidentally, in 1907, Oklahoma went from being a territory to being the forty-sixth state. The state constitution includes a legal definition of kerosene”

http://www.newyorker.com/magazine/2015/04/13/weather-underground

A team of geophysicists have established a proof of concept technique that uses smart phones as an early warning system for earthquakes.  The core of this concept utilizes the fact that smart phones have become prolific and are continuing to increase in number.  The idea is to have a ‘network’ of people’s phones and use the gps and accelerometer of the phone to measure movement.  The proof of concept analysis determined that this network of phones could accurately determine earthquake size and estimate the epicenter.

That then allows the app to send early warning notifications to users around the event.  Warning times could be issued.  The article states, “Assuming only a small number of potentially available smart phones would be active at any moment, the team found that phones representing 0.2 percent of the study area’s population – about 4,700 people – could trigger an alert for the Hayward quake within five seconds of its onset. That would represent a few seconds’ warning time for San Francisco and tens of seconds for San Jose that major shaking was on its way.”  The warning time, although not huge, could allow people to be better prepared for the shaking when it arrives.

You can find the article here.

I found these articles/videos to be helpful in understanding how the different inversion techniques work that Chuck had talked about in class. Hopefully, you will find them useful as well.

Genetic Algorithm

http://www.ai-junkie.com/ga/intro/gat2.html

Simulated Annealing

https://www.youtube.com/watch?v=tdsTfZMqAxw

https://www.youtube.com/watch?v=iaq_Fpr4KZc

http://www.sciencedirect.com/science/article/pii/S0031920199001570

Monte Carlo Inversion

http://onlinelibrary.wiley.com/store/10.1029/2000RG000089/asset/rog1559.pdf;jsessionid=7287C1CE73229D2541651D48A7BE6A84.f01t04v=1&t=i8eihb7a&s=679c56c98ee431899290e0571863ae83469d7cea

http://www.ipgp.fr/~tarantola/Files/Professional/Papers_PDF/MonteCarlo_latex.pdf

A brief summary of each method

Genetic Algorithms (GA’s) mimic mother nature in a sense; GA’s use the same combination of selection, recombination, and mutation to evolve a solution to a problem.  GA’s begin with a randomly generated population. Each member is evaluated and assigned a “fitness” score. The fittest members of the population are kept while the rest are discarded. New members are created by combining aspects of the selected, fittest members. Add a little bit of randomness (mutation) and repeat until the solution converges. One great thing about GA’s – they can avoid being trapped in a local minima unlike other optimization methods. Anyone who can offer an explanation as to why gets two high fives!

“The Simulated Annealing method works by approximating a global optimum solution for a solution space of interest. During each cycle (iteration), the algorithm tries to find a better solution than its current solution. It looks at immediate neighbors and determines what’s better instead of randomly searching through the solution space. One problem with this one- because the technique is greedy, the solution can frequently get trapped in local minima (premature convergence).

“Monte Carlo sampling is useful when the space of feasible solutions is to be explored, and measures of resolution and uncertainty of solution are needed.” With a Monte Carlo inversion, you would first need to identify a mathematical model of the process you want to explore. Next, define the parameters for each factor in your model and create random data according to those parameters. Lastly, simulate and analyze the output of your process. SA’s, GA’s, and Neighborhood Algorithms are Monte Carlo techniques. Monte Carlo methods are favored over linearization techniques for two reasons: “First, they are numerically more stable in the optimization/parameter search stage. Second, they are more reliable in estimating the uncertainty of the solution by means of model covariance and resolution matrices.”

http://www.ipgp.fr/~tarantola/Files/Professional/Papers_PDF/MonteCarlo_latex.pdf