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Caldera Rims of Northeast Honshu Extracted from Gravity Anomalies and Aeromagnetic Data
Oky Dicky Ardiansyah, PRIMA (Prima, O.D.A.)Faculty of Software & Information Science, Iwate Prefectural University152-52 Sugo, Takizawa, Iwate 020-0193, JAPAN
Takeyoshi, YOSHIDA (Yoshida, T.)Institute of Min., Pet. & Eco. Geol., Grad. School of Sciences, Tohoku Univ.6-3 Aoba, Aramaki, Sendai 980-8578, JAPAN
Takeshi, KUDO (Kudo, T.)Department of Natural Science and Mathematics, Chubu University1200, Matsumoto-cho, Kasugai-shi, Aichi 487-8501, JAPAN
2011 IEEE International Geoscience and Remote Sensing Symposium
FR2.T09.5 - GIS Techniques III
Contents
• Background• Research aim• Data• Algorithm• Results• Conclusion
Background
Northeast Honshu, buried calderas, hypocenters
Hypocenters
Onikobe Caldera Akakura Caldera
Sendai
Japa
n Se
a
Background
Buried caldera causes landslide
Sumikawa landslide (1997/5/11) : pre-Yakeyama caldera (a buried caldera) covered by the younger Yakeyama volcano had played an essential role for the landslide (Oyagi, 2000).
★
800 m380 m
The first acknowledged landslide caused by buried calderas in Japan
Yakeyama Mt.
Background
Buried caldera causes landslide
Head cliff : 140m
Length : 1,400m
Width : 810m
The most recent landslide of buried caldera(Iwate-Miyagi Nairiku Earthquake, 2008/6/14)
Background
Hypocenters of Iwate-Miyagi Nairiku Earthquake, calderas, and seismic tomography
Okada et. al(2008)
Age of calderas (Ma)
Background
Caldera’s appearance on gravity anomalies and aeromagnetic data (convex and concave forms)
Gravity anomalies Aeromagnetic data
Concave forms Convex forms Convex forms
Research Aim
To automatically extract the topographic rim of buried caldera (with diameters ranging from 5 to 30 km) from gravity anomalies and aeromagnetic data using “watershed delineation” in GIS.
Ex: Watershed delineated from a DEM Gravity anomalies
Lowest point (sink)
Data
Gravity anomalies(published by the Geological Survey of Japan, 2000, 2004) (published by the Geological Survey of Japan, 2005)
Grid spacing 1 km
Projection UTM
Grid Spacing 200m
Projection UTM
Aeromagnetic data
Algorithm
Gravitational acceleration
Calculating flow direction to the lowest points
Watershed delineation
Candidates for caldera rims
Aeromagneticdata
Calculating flow direction to the lowest points
Watershed delineation
Candidates for caldera rims
Pre-processing
Gridding using tension spline
2-D FFT
2-D iFFT
Data filtering
Gravity anomalies
Calculating Terrain Corrected Bouguer
Gravity
For calderas with concave forms in the data
Inverting the input data will allow the algorithm to delineate calderas with convex forms
Data filtering for gravity anomalies
km
Typical spectral distribution of gravity anomaly (Nozaki, 1997)
Eliminate noises using 2D FFT(Cut-off wavelength is empirically determined)
← long Wavelength short →
Four
ier
ampl
itud
e sp
ectr
um
(lo
g)
Trend
Signal
Noise
Watershed DelineationDelineating boundaries of concave (or convex) in gravity and aeromagnetic data is similar with that of watersheds from a digital elevation model (DEM) using hydrological modeling but without “depression-filling” process.
0
2
4
6
8
10
12
14
16
184 7 7 6 6 8 12
8 5 10 8 2 4 8
10 7 8 1 1 2 6
12 10 6 0 1 2 6
14 8 4 2 2 4 8
18 12 8 6 6 8 12
20 18 14 12 12 14 15
A concave form in the data
Rim extraction with ArcGIS model builder
Input data Reproject data Clip region of interest
Calculate flow directionCalculate watershed
Vectorize the boundary of watershed
Results
Concave and convex forms in gravity anomalies
Concave forms Convex forms
Vol
cani
c fr
ont
Vol
cani
c fr
ont
Results
Caldera rims improved by data filtering
No filteringCut off : 2 kmCut off : 3 kmCut off : 4 kmCut off : 5 kmCut off : 8 kmCut off : 10 km
Blue line: Caldera rims (Yoshida et al., 2005)Yellow line: Automatically delineated caldera rims
MukaimachiOnikobe
NarukoAkakura
Hanayama
Cut off : 4 kmSeamless Geological Map
kmConcave forms
Sendai
Results
Concave and convex forms in aeromagnetic data
Concave forms Convex forms
Vol
cani
c fr
ont
Vol
cani
c fr
ont
Results
Extracted rims from aeromagnetic data
Concave forms
Concave forms
Convex forms
Ushitaki
Tohgatta
Kawafune
Results Observable calderas by each geophysical data
Conc. : Concave Conv.: Convex
Conclusion
In this study, we have extracted caldera rims of Northeast Honshu, Japan depending on their forms: concave and convex, from gravity anomalies and aeromagnetic data.
Gravity anomalies seem to be superior on extracting caldera rims. However, there are some caldera rims that were extracted only from aeromagnetic data.
Thank you for your attention