RUSSIAN JOURNAL OF EARTH SCIENCES VOL. 10, ES2003, doi:10.2205/2007ES000236, 2008
Examples of Tasks Solutions Technology
Types of Analytical Tasks
[8] The complexity of solving tasks of geographic information analysis depends essentially on the
completeness of available data [Gitis and Ermakov, 2004].
[9] Tasks with complete information reveal the qualitative characteristics of GI by visualization,
determine new GI parameters using previously known transformations and evaluate standard
statistical GI parameters.
[10] Tasks with incomplete information emerge at solving problems of prediction which require a
more profound investigation of geographic substances, their parameters and relations between
them. Solution of such tasks is related to a complex GI analysis. Such analysis is necessitated by
interaction of researched processes, impossibility of direct measurements of their key
parameters, lack of the volume of observations and impact of noise on the measurements'
results.
Earthquakes Damage Evaluation (GeoProcessor 2.0)
[11] Let us examine the example of possible damage evaluation of a strong earthquake for the
cities of the North Caucasus with a population of more than 100,000 people (see
http://www.geo.iitp.ru/GeoProcessor-2/new/Caucasus2.htm). The data on peak
acceleration was used [Giardini et al., 2003] (this resource was obtained through the Central portal of the
geographic information environment "Electronic Earth'' http://eearth.viniti.ru).
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Figure 1
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[12] With the help of the transformations Grid layers
Grid layer for a grid layer of peak
accelerations A the field of maximal magnitude of earthquakes
I=( lg A - 0.014)/0.3 was obtained
[Trifunac and Brady, 1975]. For field
I grid layer
V of a proportion of
destruction of buildings 7KP (this type of
buildings was selected only for the illustration of the method):
V=0 at
I<7,
V=3.5 % at
I=7,
V=11.9 % at
I=8,
V=37 % at
I=9. Then the transformation "Grid layers and Vector layers
Attributes of a vector layer'' was applied. With its help the proportions of destructions located at
a distance of 5 kilometers in the vicinity of the cities were determined. Assuming that the area
with buildings of 7KP type is homogenous for the selected size of the zone, the result can be
accepted as an evaluation of damage of a maximal earthquake. Figure 1 shows the grid layer of
destructions of the buildings of 7KP type in percentage terms, the size of circles showing the
damage values for the cities. Below the destruction proportion value for the city of Derbent is
shown, equal to 27%.
Seismic Danger Evaluation (GeoProcessor 2.0)
[13] Let us examine the example of detecting the zones of possible earthquake sources (PES) with
magnitudes
M >6.5 for the Caucasus using the resource http://www.geo.iitp.ru/GeoProcessor-2/new/ARMEAST2-e.htm
developed according to the data of Gitis et al., [1993].
[14] According to [Gitis and Ermakov, 2004; Gitis et al., 1993],
it was assumed that the central zones of strongest earthquakes are timed
to intersection of heterogeneous zones of the Earth crust with the zones of thrust and shear faults,
active in the Cainozoic era.
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Figure 2
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[15] First with the help of analytical transformations and visual investigation an exploratory
analysis of the benchmark and transformed data was made. To illustrate the method the most
simple solution was chosen, using only a field of velocity gradient module of vertical motions in
the post-Sarmatian time (characteristic
X1 ) and thrust faults, active in Cainosoe. By
transformation Vector layer
Grid layer characteristic
X2 was obtained - the field of the
distance to thrust faults. Further the method of inductive logical conclusion was applied [Gitis and Ermakov, 2004]. The
obtained rule appears to be: IF velocity gradient of vertical tectonic motions in the
post-Sarmatian time ( X1 ) exceeds 10 conventional units (cu) OR
(X1)>6.4 cu AND distance to
thrust faults
(X2)<6.75 km, THAN centers with
M >6.0 are possible. The PES zones, obtained
according to this rule and epicenters with magnitudes
M >6.0 are shown in Figure 2. Prediction of
sea zones and the southern and south-eastern zones hasn't been made due to the lack of
geological and geophysical data.
Prediction of Oil and Gas Fields (GeoProcessor 2.0)
[16] Let us examine an example of selective regional prediction of oil and gas field in Western
Siberia using the resource http://www.geo.iitp.ru/GeoProcessor-2/new/WestSiberia2.htm
developed according to the data of Gitis et al., [ 1994a].
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Figure 3
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[17] According to [Gitis and Ermakov, 2004; Gitis et al., 1994a]
it was assumed that a phase state of carbohydrates is determined by the
history of tectonic development. Gas fields are usually characterized by deteriorated quality of
primary organic matter and strong sedimentation, revealed in higher velocity of longitudinal
seismic waves on the foundation surface. Oil deposits are characterized by a high quality of
organic matter, forming a relatively small sedimentary deposit. Provinces with a thin
sedimentary cover have low prospects related to oil and gas deposits. For the problem's solution
the same parameters are chosen as in [Gitis and Ermakov, 2004; Gitis et al., 1994a].
The transformations Grid layers
Grid layer
were used to obtain them. Further a method of recognition was applied, according to the rule of
the closest neighbor, affiliating a point to one or another class by similarity of the point
parameters and reference objects of classes. The results of prediction are shown in Figure 3. Circles
mark the known gas deposits, triangles - oil deposits, squares - unproductive areas. For the
prediction of gas fields the following parameters are used: the depth of occurrence of the
dogger's top and a half-sum of longitudinal seismic waves velocities on the surface of crystalline
and folded foundations. For predicting oil deposits the following parameters are used: the depth
of the top of Middle Jurassic sediments, the depth of the top of Upper Cretaceous sediments, and
the thickness of upper layer of consolidated crust.
Analysis of Precursors According to Earthquakes Catalogue (GeoTime II)
[18] Let us examine the example of detecting precursors of the Susamyrsky earthquake: 19.08.1992,
energy class
K=17, coordinates
l=73.63o longitude east and
f=42.06o latitude
north (see http://www.geo.iitp.ru/geotime/asia.html). The Central
Asian earthquakes catalogue was used, cleared from aftershocks. In the catalogue 16329 events
for 1980-2001 are presented at
K from 7 to 17. The catalogue's preliminary processing was
implemented in IPE RAS by G. Sobolev.
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Figure 4
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[19] At the time of the analysis the method of detection of precursors was applied, elaborated in
[Gitis and Ermakov, 2004; Gitis et al., 1994b].
First by catalogue grid layer 3D of earthquakes centers density in a running cylindrical
window with radius of 100 km and time interval of 10 days was obtained (transformation
Vector layer
Grid layer). Further by transformation Grid layers
Grid layer grid layer
3D of anomalies was obtained. The anomalies detection algorithm is based on the method of
statistical hypotheses verification. For each node of a spatial grid the statistics are prepared,
equal to norm difference of averages
(m2 - m1)
in two running windows:
m1 - the average in the
first window 1440 days long for the estimation of the background value of density of
earthquakes epicenters and
m2 - the average in the second 30-days window for current value
estimation. Figure 4 shows the evolution of density anomaly of epicenters with high negative
statistical values in the interval from -5.5 to -3.5. In the picture one can see 12 cuts of 3D
anomaly from 111 to 1 day before the earthquake. The epicenter of Sysamyrsky earthquake is
marked by a star. Negative values of the anomaly give evidence that average density of
earthquakes is declining. It points at a lull, in many cases preceding a strong earthquake. The
anomaly with a value less than -3.5 appears in the vicinity of the forthcoming earthquake 101
day before the earthquake. 61 day before the earthquake the anomaly transforms into a simply
connected domain. The density of anomaly and numerical value increase monotonously and
reach their maximum 31 day before the earthquake. Then the anomaly subsides.

Citation: Gitis, V. G., A. P. Weinstock, and A. N. Shogin (2008), Distributed network analytical GIS, Russ. J. Earth Sci., 10, ES2003, doi:10.2205/2007ES000236.
Copyright 2008 by the Russian Journal of Earth Sciences
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