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).

2007ES000236-fig01
Figure 1
[12]  With the help of the transformations Grid layers Rightarrow 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.

2007ES000236-fig02
Figure 2
[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 Rightarrow 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].

2007ES000236-fig03
Figure 3
[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 Rightarrow 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.

2007ES000236-fig04
Figure 4
[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 Rightarrow Grid layer). Further by transformation Grid layers Rightarrow 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.


RJES

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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