=Paper=
{{Paper
|id=Vol-1839/MIT2016-p08
|storemode=property
|title= The method of selection of the key geodynamic objects
|pdfUrl=https://ceur-ws.org/Vol-1839/MIT2016-p08.pdf
|volume=Vol-1839
|authors=Anastasia Grecheneva,Vladimir Eremenko,Oleg Kuzichkin,Nicolay Dorofeev
}}
== The method of selection of the key geodynamic objects==
https://ceur-ws.org/Vol-1839/MIT2016-p08.pdf
Mathematical and Information Technologies, MIT-2016 β Information technologies
The Method of Selection of the Key
Geodynamic Objects
Anastasia Grecheneva, Vladimir Eremenko, Oleg Kuzichkin, and
Nicolay Dorofeev
Belgorod National Research University, Belgorod,
308015, 85 Pobedy st., Russia
1155464@bsu.edu.ru
http://www.bsu.edu.ru/bsu
Abstract. In this paper as an indicator manifestations of geodynamic
processes in a large area are invited to select the most sensitive and
informative geological structures to the appearance of endogenous and
exogenous factors that contribute to the development of geodynamic pro-
cesses and negative changes in the geological section. Such places are key
geodynamic objects that can provide early warning of the beginning of
the development of destructive geological processes that have no exter-
nal signs of existence. Watching the local geodynamic key objects and
with the involvement of the hydrology data, geology, meteorology and
geo-information technologies, it is possible to form a forward-looking as-
sessment of destructive geological processes over a large area. The paper
proposes a method for detecting the key geodynamic objects, including
the distributed processing algorithms informative sections of heteroge-
neous data, the temperature and the hydrological correction of the mea-
surement results. The proposed approach is based not only on statistical
methods and morphological analysis of the territory, but also on the use
of mathematical models of the interaction of hydrological, geological and
man-made environments.
Keywords: geoelectrical monitoring of geodynamic object, forecasting,
localization of objects, key objects.
1 Introduction
It is known that the development of suffusion processes intensity of geodynamic
changes of local sites of geological environment characterized by much greater
performance than that of the total of its variations. Consequently, information
about the occurrence of destructive processes through the use of selective geody-
namic control can be obtained much earlier than in the monitoring geodynamic
environment in general. Therefore, the practical use of geomonitoring systems
built on the basis of geoelectric sounding methods is appropriate for monitor-
ing the bearing capacity of overlying and underlying soil during the operation
of industrial facilities, as well as to ensure the protection of natural and man-
made objects from the possible consequences of accidents at suffusion danger [1,
74
Mathematical and Information Technologies, MIT-2016 β Information technologies
2]. Such systems through the application of information processing algorithms
for heterogeneous monitoring data allow to register changes in the geodynamic
control objects and obtain forecasts of the possibility of man-made disasters [3].
In this article, the example of suffusion processes the technique of construct-
ing a regression geoelectric monitoring data processing algorithms with key ge-
ological objects in order to create predictive assessments of geodynamic.
2 The geological features of the site and the selection of
the geodynamic control zones
Geodynamic monitoring carried out at the site of the alleged construction of
Nizhny Novgorod NPP, which is located in the basin of the lower reaches of
the river Oka (Figure 1a). The presence of low-mineralized water in the alluvial
layer, lying in the valley r. Oka close to the surface, as well as the dominant
stratum of carbonate and sulfate rocks, is the cause of the dynamics of the karst
valley. Herewith, man-made increase in groundwater levels is the cause of the
rapid process of karst formation and increase the risk of catastrophic situations
at nuclear power plants.
The organization of the geodynamic control should take into account that
there are two main types of geodynamic movements karst environment. This
cyclic variation with varying intensity and duration of the period, characterized
by cyclical changes in the structure of the medium, as well as the trend of
variation which are of a pronounced character and having a constant direction
for a long time, with the result that they are the main source of mechanisms of
technological disasters [4]. Therefore, based on geological data it was determined
optimum geoelectric zone control which will be geodynamic more pronounced
than in other areas, for the same man-caused load. Monitoring of the local area
will provide more accurate forecasts of geodynamic activity surrounding area
(Figure 1b).
In addition geoelectric monitoring data by supplemented of stationary obser-
vations, including the monitoring of hydrogeological regime fracture-karst aquifer
and overlying and geodetic monitoring of surface subsidence, changes in morpho-
metric characteristics of the relief, the failures and deformations.
3 Key geological objects
For geodynamic control used multipolar equipotential electrical installation, de-
veloped together with IPE RAS. It is designed to monitor of the geodynamics
of surface irregularities in the cases of the need provided increased sensitivity
to the specific changes in the object of investigation. High efficiency is achieved
by increasing the sensitivity of the measuring system,and the initial installa-
tion and operational positioning of the installation by controlling the sources of
probing signals [5]. Operation is based on the fact that the source of the probing
signals in the test environment is created in accordance with the principle of
75
Mathematical and Information Technologies, MIT-2016 β Information technologies
Fig. 1. Geological features of the site and the selection of zones and geodynamic control
a) area of alleged placement of Nizhny Novgorod NPP; b) the geoelectric monitoring
zone
superposition of a spatially-distributed signal forming a total zero signals in the
measurement sensors of geoelectric field.
In this case the control signals of initial setting and positioning of geoelectric
measuring systems, be formed in accordance:
Β―π (π‘0 ) = πΉπ (πππ , π
π Β― * (π‘0 )), (1)
where πΉπ the option forming of primary positioning on the control vector, by
system πΒ― * (π‘) of space-time processing data control at start time π‘ = π‘0 ,πππ a
vector of model parameters.
Later the geoelectric measuring system is functions, directly, in the semi-
automatic mode using the following algorithm:
Β―π (π‘) = π
π Β―π (π‘0 ) + π₯π (πππ , π₯Β― Β― * (π‘)),
ππ ) + πΉπ (π₯πππ , π (2)
where π₯π (πππ , π₯Β― ππ ) the ongoing management of the positioning of the electrical
installation of the vector of geodynamic variationsπ₯Β― ππ ; π₯πππ the correction
model.
Increase of sensitivity leads to an increase in noise level caused by thermal and
tidal deformation effects. In addition, operational management of electrolocation
signals is the presence of the trend component in the recorded signals, which is
determined by the structural changes of the object [6].
Geoelectrical control method is based on the principle of linear and stationary
of the geoelectric section, the transfer function π₯π»ππ (π, πΌ1 , ..., πΌπ ) is determined
by a system of spatial functions of control object πππ (π) with nominal geodynamic
76
Mathematical and Information Technologies, MIT-2016 β Information technologies
parametersπΌ0π :
π
βοΈ
π₯ππ = π₯π»ππ (π)πΌπ (π), (3)
π=1
π [οΈ ]οΈ
πΎ(π) βοΈ ππππ (π, πΌ10 , ..., πΌπ0 )
π₯π»ππ (π, πΌ1 , ..., πΌπ ) = π₯πΌπ , (4)
ππ (π) π=1 ππΌπ
where πΌπ probe signal of π-th source; π₯ππ the response of π-th source; πΎ(π)
Contrast Ratio of environs; ππ (π) the dependence of the measurement channel
gain.
These relations (1-4) makes it possible to solve the inverse problem - selection
of properties of the local geodynamic object by adjusting the parameters of
sensing sources, which is a key aspect of the organization of geodynamic control
[7].
Monitoring of the key geological objects - places with an active geodynamics
and the most sensitive to endogenous and exogenous factors, and further predict-
ing of geodynamics on the entire territory requires a change in the structure of
the geodynamic system of forecasting described in [7]. The main changes relate
to the prediction block, its structure shown in Figure 2.
Data of electric survey Geological map Map of engineering
and monitoring structures
equipment
Geoelectric Geological The ground
section section loading map
Karst map
Data from key Prediction function of Soil The ground
geodynamic object geodynamics assessment compressibility tension map
map
Failures map Hydrological Metrological data
map
Fig. 2. Structural blocks of the prediction unit
Obviously, the key geodynamic objects for example, the suffusion processes ,
be chosen from the condition of the probability of the process itself: the presence
of soluble species, and the solvent approach, removal of soluble species. Identify
key geodynamic objects possible in rose histogram (Figure 2), which characterize
the direction of the formation and propagation of failures, faults, the dominant
77
Mathematical and Information Technologies, MIT-2016 β Information technologies
structure of the network of cracks, etc. The diagram shows not only the direction
of education failures, but also their concentration on the area.
90
180 0
270
Fig. 3. Rose histogram
Expression (4) defines the principle of superposition of the probing signals
by which to judge the possibility of providing separate characteristics of the
environment (the object) by controlling the parameters of the source. This is one
of the most important aspects of the organization of monitoring of geodynamic
objects [10].
Based on the provisions described in this article, it is proposed to carry
out the processing of heterogeneous data through specialized algorithms. Block
diagram of the Information Technology Services of Geodynamic control system
reflects the principle of joint processing of hydro-geological data (Figure 4).
The physical layer describes the physical methods of obtaining information
that may be required to detect errors ππ and measurement errors in data analysis.
On the same level a scheme of placing primary transducers (sensors, measuring
tools and devices) are described. The main objective of this level is acquisition
(measurement) of raw data Di . This level is the hardware and hardware-software
(in the case of digital sensors). Such devices as sensing devices, sensors, blocks of
a positioning in space that define the coordinates Xi , Yi , Zi measuring devices
function in this level.
The link layer is represented by all kinds of measuring complexes, systems
and instrumentation, and is a hardware-software. A modules and services related
to prior and primary data processing, presentation and storage of primary Di
and processed Diβ² data, supporting information: methods of measurement and
processing, a model of locative level, required Xiβ² , Yiβ² , Ziβ² and fixed Xi , Yi , Zi
positions of the primary converters in space are working in this level.
The link layer describes the working of the geographic information-analytical
systems regulation and control (GIASC) of natural-technical systems (NTS) at
the locative level, so there are also function modules and forecasting services,
and the development of administrative decisions at the locative level. A con-
trol solutions are formed on the basis of received predictive estimates of f and
functioning models of natural, technical, natural-technical and social systems.
78
Mathematical and Information Technologies, MIT-2016 β Information technologies
A errors e forecasting and regulation at the NTS of the locative level are trans-
ferred network layer and serve as the basis for the correction components of
operating at the link layer.
Physical layer Channel level Network lavel
Algorithms for Algorithms
Xβi,Yβi,Zβi Management processing
The layout of Measurement techniques for distributed
decisions at the heterogeneous data
transducers local level correction
Heterogeneous
Primary converters
data
Predictive Algorithms and Forecasting
Di, Ξ΅i The modules of the Cβt Key algorithms
functions methods of
correction and detection
at the local level objects detecting
of errors
hidden processes Forecast
Dβi, Ξ΅βi evaluation
Xi, Yi, Zi Algorithms and methods
Positioning of detection of mistakes Algorithms of A model for the
Dβi, Ξ΅βi
and errors at the local level primary processing local and D, Ξ΅, Crd, = {Dβi, Ξ΅βi, Di, Ξ΅i, Xi, Yi, Zi}
regional levels
Fig. 4. Block diagram of information and technical support geodynamic monitoring
system. ππ the errors; πβ²π the compensation factor; π·πβ² the raw data; π·π the processed
data; ππ , ππ , ππ the recorded position in space of the primary converters; ππβ² , ππβ² , ππβ²
the desired position in space of the primary converters; πΆπ‘β² the synchronization signals
and control.
One of the key factors determining the performance indicators of hemody-
namic assessment at the geoelectric monitoring are used the earth models and
models of geodynamic objects themselves. For a qualitative prediction of suffu-
sion processes necessary to carry out an assessment of the expected location of
displays and take into account their size, it is also necessary to take account of
spatial-temporal geodynamic parameters. Therefore, the forecast is built on the
basis of geomechanical models of different orders that can take into account the
mechanism of interaction with the technosphere suffusion processes and geolog-
ical conditions of its development.
For reasons of forecasting by the geoelectric monitoring necessary step is
to establish the conformity of spatial functions in equation (4) for the transfer
function of the geoelectric section geomechanical conditions of formation of local
failures as described in [11,12].
This ratio can be set by considering the problem of the distribution of the
geoelectric field of a point source field in the presence of a spherical in homo-
geneity, in which you can take as suffusion processes. The solution described
in [13, 14, 15] to determine the characteristics of the occurrence of the ball on
the observed distortions introduced them to the spatial distribution of potential
geoelectric field.
The transfer function of the geoelectric section, which defines the displace-
ment of equipotent lines i-source in space, taking into account the double anoma-
lous component of the field is of the form:
π3 πππ
π₯π»ππ (π, π, β) = πΎ(π)πππ (π, β) = 2πΎ(π) 2 + β2 )3/2
, (5)
(πππ
79
Mathematical and Information Technologies, MIT-2016 β Information technologies
where β = π§ + π the depth of the sphere below the surface, π the radius of the
sphere, πππ the distance between the electrodes π and π.
Depth assessment of the changes occurrence spherical near-surface hetero-
geneity and its size can be made on the basis of (5), using it to forecast future
geodynamic suffusion processes as an assessment:
βοΈ β
3 3
Λ 2
3π₯π»β
π
π§ = , (6)
2πΎ(π)
where π₯π» Λ the maximum estimated offset value of the equipotent line. The re-
sults of the regression of processing time series are geodynamic background infor-
mation for predictive modeling underlying the decision of geodynamic processes
forecasting problem [16, 17].
Geoelectric model of geodynamics suffusion processes can be represented by
a discrete linear system [11] defined by the difference equation:
π βοΈ
βοΈ π
ππ [π] + πππ ππ [π β π] = ππ [π], (7)
π=1 π=1
where ππ [π] the counts recorded geodynamic process on π-th registration point;
πππ the model coefficients; ππ [π] samples generated by a random process with
geodynamic parameters π {ππ [π]} = 0, π {ππ [π]ππ [π]} = ππ2 πΏππ (πΏππ the weights
of the model).
System regression of the original equations is formed on the basis of the
expression (7):
π π [π] = πΉπ [π] + ππ [π] + π π [π], (8)
where π π [π] = [π [π + 1], ..., π [π]π ;
β‘ β€
βπ [π] βπ [π β 1] Β· Β· Β· βπ [1]
β’ β₯
β’ βπ [π + 1] βπ [π] Β· Β· Β· βπ [2] β₯
πΉπ [π] = β’
β’ .. .. .. .. β₯;
β₯
β£ . . . . β¦
βπ [π β 1] βπ [π β 2] Β· Β· Β· βπ [π β π]
ππ [π] = [π[1], ..., π[π]]π ; π π [π] = [π [π + 1], ..., π [π + π]]π ; π the depth of predic-
tive estimates.
Application in the analysis of suffusion processes predictive estimate of a
regression model (8) allows you to make predictions that take into account not
only the impact of cyclical planetary factors, but also man-made impacts. Figure
5 shows the preliminary interpretation of geological and geoelectric section in
the area of geodynamic control.
On the basis of regime observations were interpreted registered signals geo-
dynamic variations. Figure 6 shows the variations registered geodynamic gain
bipolar equipotential geoelectric installation during the annual observations from
May 2013 to April 2014.
80
Mathematical and Information Technologies, MIT-2016 β Information technologies
Fig. 5. The preliminary interpretation of geological and geoelectric section in the area of geodynamic control
81
Mathematical and Information Technologies, MIT-2016 β Information technologies
Fig. 6. Time series of geodynamic variations of the transmission coefficient of a two-
pole equipotential geoelectric installation
4 Conclusions
The data are in good agreement with the hydrological observations of the water
level in the river Oka and calculated as the ratio of mineralized areas at the top
and bottom of the river.
Based on these algorithms in this article was obtained prognosis estimation
of dip by models karst suffusion processes. As a result, it was found that the use
of these algorithms, the formation of forward-looking assessments in geoelectric
monitoring promotes the release of a high degree of reliability and the conditions
of dip karsting the development of suffusion processes. Increasing the depth of
predictive assessments and improving the efficiency of the proposed method is
achieved by increasing the number of sensing points of the geoelectric field and
the number of sounding sources.
Acknowledgments. This work was supported by grants of the President of
the Russian Federation Β
MK-7406.2015.8.
References
1. Tsaplev, A.V., Dorofeev, N.V., Kuzichkin, O.R.: Registration of polarization signals
of the electrical field in geodinamic objects monitoring systems with the use of local
primary converters. In: Proceedings of the 2015 IEEE 8th International Conference
on Intelligent Data Acquisition and Advanced Computing Systems: Technology and
Applications, IDAACS 2015, pp 38-41.
2. Dorofeev, N.V.: Geoecological safety of industrial facilities in the geodynamic active
zones. In: Scientific notes of Russian State Hydrometeorological University. 2013. N
28. C. 32-37.
82
Mathematical and Information Technologies, MIT-2016 β Information technologies
3. Dorofeev, N.V., Kuzichkin, O.R.: Processing of heterogeneous data in GIAS of geo-
dynamic monitoring. In: Proceedings of the 2015 IEEE 8th International Conference
on Intelligent Data Acquisition and Advanced Computing Systems: Technology and
Applications, IDAACS 2015, pp 33-37.
4. Grecheneva, A.V., Dorofeev, N.V.: The method of obtaining predictive estimates of
deformation processes of the geological structure, taking into account the impact of
multi-factor. In: Algorithms, methods and data processing systems. 2015. number
3 (32). Pp 3-8.
5. Orekhov, A.A., Dorofeev, N.V.: Evaluation of Geodynamics of surface irregularities
on the basis of identification of parameters of the controlled object. In: Techno-
spheric Security Technologies. Number 2014. 4 (56). P. 18.
6. Orekhov, A.A., Dorofeev, N.V.: Geoelectric geodynamic modeling of surface facil-
ities for the effects of endogenous factors. In: Algorithms, methods and data pro-
cessing systems. Number 1, 2014. (26). Pp 32-38.
7. Kuzichkin, O.R.: The algorithm of the optimal probing signals at elektrolokatsion-
nom monitoring. In: Radio engineering. 2006. N 6.
8. Penzel M. Bemerkungen zur Erdfallgenese in Auslaugungsgebeitenaus geomechanis-
cher Sicht // N. Bergbautechn. 1980. 10Jg, N 1.
9. Bykov A.A., Kuzichkin O.R.: Regression prediction algorithm of suffusion processes
development during geoelectric monitoring In: Advances in Environmental Biologi.
2014. N 8. P. 1404.
10. Dorofeev, N.V., Kuzichkin, O.R., Eremenko, V.T.: Processing of geodynamic mon-
itoring data based on the data of geographic information and analytical systems.
In: Herald of computer and information technologies. 2015. N 3 (129). P. 9-15.
11. Granovsky,V.A., Siraya, T.N.: Methods of processing of experimental data in the
measurements - AL: Energoatomisdat, 1990. - 288 p. - ISBN 5-283-04480-7.
12. Korolev, V.A.: The monitoring of the geological environment. - M.: MSU, 1995. -
272 p.
13. Sharapov,R.V., Kuzichkin,O.R.: Monitoring of karst-suffusion formation in area of
nuclear power plant In: Proceedings of the 2013 IEEE 7th International Conference
on Intelligent Data Acquisition and Advanced Computing Systems, IDAACS 2013.
14. Israel, Y.A.: Ecology and control of the natural environment. M. Gidrometeoizdat.
1985, 560 p.
15. Kuzichkin, O.R.: The algorithm for generating the forecast geodynamic evaluations
in geoelectric monitoring suffusion processes. In: Devices and systems. Management,
monitoring and diagnostics, 2008. - N 5. - P. 50-54.
16. O. Kuzichkin, N. Dorofeev.: SPATIO-temporal processing of electromagnetic sig-
nals in the systems of the geodynamic forecasting. In: International Multidisci-
plinary Scientific GeoConference Surveying Geology and Mining Ecology Manage-
ment, SGEM 2015.
17. A. Bykov, O. Kuzichkin.: Approximation of equivalent transfer function of the geo-
electric section in geodynamic inspection. In: International Multidisciplinary Scien-
tific GeoConference Surveying Geology and Mining Ecology Management, SGEM
2014.
83