Detecting Hydrologic Responses to Earthquakes using Support Vector Machines Max Rudolph.
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Transcript of Detecting Hydrologic Responses to Earthquakes using Support Vector Machines Max Rudolph.
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Detecting Hydrologic Responses to Earthquakes using Support Vector Machines
Max Rudolph
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Objectives/Questions• Why does stream flow respond or not respond to
earthquakes?– Effects of M, r, E/V, permeability/rock type, consolidated
vs. unconsolidated sediments?• Can we train a computer to automatically detect
these responses?– If so, we can mine a tremendous amount of stream flow
data.
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BackgroundWhat is a Support Vector Machine?
It accepts a vector as input and returns a natural number corresponding to the class that the vector belongs to. The vector is called the ‘Feature Vector’. The SVM is finding a hyperplane that optimally divides parameter space into regions corresponding to each class.
Training:Provide SVM with paired set of feature vectors, labels. In this study, classes are ‘Yes’,
‘No’, ‘Maybe’ – determine 2 free parameters (C, gamma)
‘Linear’ kernel Radial Basis Function
(from
libS
VM w
ebsi
te)
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Data SourcesData Sources:
USGS Instantaneous Data Archive (IDA)-79 stream gages available (with no upstream
regulation). 1-15 minute sample interval. 1980-2008NOAA National Climatic Data Center (NCDC) (230 stations)
NCEDC Earthquake Catalog 1980 to present Unexploited Data Sources (future work?):
NOAA hourly precipitation records 2001-presentUSGS daily stream flow archive - better availability
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Data acquisition (done in Matlab!)myStation = station_numbers{i};
myURL = ['http://wdr.water.usgs.gov/wy2009/pdfs/' myStation '.2009.pdf']; myCMD = ['curl -O ' myURL];
conversionError = unix(['/sw/bin/pdftotext ' myStation '.2009.pdf']);%% check text files for phrase "No regulation or diversion upstream from station"noDiversions(i) = ~unix(['grep "No regulation or diversion upstream from station" ' txtFile]);if(noDiversions(i) == 0)
noDiversions(i) = ~unix(['grep "No regulation upstream from station" ' txtFile]);end
unix(['curl -c cookies.txt http://ida.water.usgs.gov/ida/available_records.cfm?sn=' siteid] unix(['curl -O -S -L -v -b cookies.txt -c cookies.txt -e http://ida.water.usgs.gov/ida/available_records.cfm?sn=' siteid ' -d "fromdate=' sds '&todate=' eds '&mindatetime=1980-01-04%2000%3A00%3A00.0&maxdatetime=2008-09-30%2023%3A45%3A00.0&site_no=' siteid '&rtype=1&submit1=Retrieve Data" http://ida.water.usgs.gov/ida/available_records_process.cfm’] unix(['mv available_records_process.cfm ' siteid '.rdb'])
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Feature Vector1. Earthquake Magnitude/102. Log10(Earthquake Distance)/10
3. Normalized relative daily streamflow for each day in 7 days preceding-following earthquake
4. Histograms of normalized discharge in week after, before earthquake
5. Same as (4), but for first and second derivatives6. Same as (4) + (6), for smoothed stream data with 1 day
centered moving average filter7. Number of days with precipitation (any amount) at
nearest station in week preceding earthquake, week following earthquake
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ResultsAfter training, cross validation indicates that the SVM
classifies the training dataset (500 samples) with 97% accuracy for (c, gamma) ~= (0.0016,0.3969).
When run on the full dataset for earthquakes with M>=3.5, of 113,365 feature vectors, 1121 are classified as ‘Yes’ or ‘Maybe’ (examples follow)
Main problem/shortcoming appears to be poor quality of climatic data.
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Future directions1. Method could also be applied to well level data.
Wells vastly outnumber unregulated gaged streams.
2. Compare with Gleeson 2011 GRL (Permeability Map) – do rivers fed by low-k formations respond more or less readily than high-k formations?