=Paper=
{{Paper
|id=Vol-2608/paper66
|storemode=property
|title=Neural network forecasting of earth globe seismic activity level
|pdfUrl=https://ceur-ws.org/Vol-2608/paper66.pdf
|volume=Vol-2608
|authors=Vadym Tiutiunyk,Leonid Chernogor,Olha Tiutiunyk,Tural Agazade
|dblpUrl=https://dblp.org/rec/conf/cmis/TiutiunykCTA20
}}
==Neural network forecasting of earth globe seismic activity level==
https://ceur-ws.org/Vol-2608/paper66.pdf
Neural Network Forecasting
of Earth Globe Seismic Activity Level
Vadym Tiutiunyk1[0000-0001-5394-6367], Leonid Chernogor2[0000-0001-5777-2392],
Olha Tiutiunyk3[0000-0002-3330-8920], Tural Agazade1[0000-0002-0165-1118]
1
National University of Civil Defence of Ukraine,
Chernyshevska Str., 94, Kharkiv, 61023, Ukraine
tutunik_v@ukr.net, agazade.tural.2019@gmail.com
2
V.N. Karazin Kharkiv National University,
Svobody Sq., 4, Kharkiv, 61022, Ukraine
leonid.f.chernogor@univer.kharkov.ua
3
Simon Kuznets Kharkiv National University of Economics,
Science Ave., 9-А, Kharkiv, 61166, Ukraine
tutunik_o@ukr.net
Abstract. In order to develop the scientific and technical foundations of creat-
ing an artificial intelligence system for monitoring tectonic origin emergencies,
the paper in the results of modeling and forecasting conducted on the basis of
neural network technologies, total number of occurrences, with discretion in
one month, earthquakes on Earth as one of the main characteristic indicators of
the seismic activity of the Earth’s globe are presented.
Keywords. emergency, earthquake, tectonic source emergencies monitoring,
artificial intelligence system, artificial neural network, neural network simula-
tion, seismic activity level prediction
1 Introduction
The tendency to an abrupt increase in the number and destructive power of natural
disasters over the past few decades of the life of society leads to a deterioration of
socio-economic and environmental consequences. It indicates the need to develop
effective measures to prevent and eliminate emergencies (ES) of various nature on the
Globe [1–3].
A promising direction for solving this problem is the development of an effective
hazard detection system at the stage of their inception. Also, the causes will be estab-
lishing of the occurrence these factors manifestations and effects on them in order to
prevent the occurrence of emergencies. This has been implemented on the basis of the
classical control loop presented in Fig. 1 [4–7].
This article is part of a planned set of scientific studies aimed at developing a
safety system. This eliminates or minimizes losses as much as possible under condi-
tions of manifestation of an emergency. The work is focused on studying the proc-
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
esses of emergence and spreading of emergencies of lithospheric origin, which repre-
sent or may pose a serious danger to the life of society [8–12].
DECISION SUPPORT
SYSTEM The person making
the decision
SYSTEM
Determination of PERFORMANCE
CHOICE
parameter SOLUTIONS
Management decisions
GEOGRAPHIC INFORMATION Modeling and Formalization of
SYSTEM
for the prevention and
prediction of management
elimination of
development of decisions
emergency emergencies
Analysis and
DataBase of Bringing a decision
knowledge
systematization to the performers
of information
CLASSICAL MONITORING Data processing
EMERGENCY SYSTEM
ES
Hazard
Influence on
registration
the hazard
LOCAL TERRITORY
Fig. 1. Diagram of the emergencies monitoring structure as a means of control
(Source: author's own elaboration)
When solving the problem of creating an artificial intelligence system for monitor-
ing emergencies of the tectonic nature, there is a need to studying and simulation the
processes of occurrence and propagation of the seismic activity level of the local terri-
tory under the conditions of seismic activity of the Earth’s globe, as an element of the
system of nonlinear energy interactions Sun – Earth – Moon.
2 Formal problem statement
Development of scientific and technical bases for creation of artificial intelligence
system for monitoring of tectonic origin of the emergency, which is realized by con-
structing neural network models of forecasting the level of seismic danger of the
Earth’s territory by the amount and destructive energy of the emergency of tectonic
origin.
The construction of neural network models for predicting the level of seismic dan-
ger in the Earth’s globe was performed using the STATISTICA statistical package
[13, 14].
3 Literature review
Today, the basis of predictive observations of Earth’s seismic activity is knowledge
of the physical laws of the earthquake mechanism and control of the physical fields in
the seismic zone [15–19]. The basis of these studies is the idea of the absence of ex-
ternal factors affecting the study area that arise in the system of nonlinear energy
interactions Sun – Earth – Moon.
The mechanistic approach, which has been developing in various countries for
quite some time, has provided answers to many questions about the preparation of the
earthquake. Currently, more than a dozen earthquake models have been developed,
the most famous of which is following papers [20–22]:
a model of avalanche-unstable crack formation (AUC), which consists in the rapid
growth of the number of cracks, their interaction with each other and eventually the
occurrence of a main or main rupture, the shift of which instantly drops the accumu-
lated elastic with the formation elastic waves;
dilatant diffusion model (DD), in which crack formation also plays a decisive role,
but unlike the AUC model, fractures occur and the presence of water in the rocks of
the epicentral region is essential;
consolidation model of earthquake preparation (Dobrovolskiy’s model) describes
earthquake preparation as a process of emergence and evolution of rigid inhomoge-
neities in a continuous environment, as well as some other models that emerged as a
generalization of the study of stress and fracture processes of solids samples in the
laboratory and their subsequent transfer to natural seismic events.
At the same time, the following methods are now used to obtain a comparative as-
sessment of the level of life-threat in the conditions of manifestation of a emergency
[23–30]:
statistical, based on the analysis of emergency statistics on local territories over
several years to determine the risk indicators;
probable, based on the application of mathematical models, which connects the
prerequisites for the appearance of emergency with the possibility of their manifesta-
tion;
expert based on expert judgment in combination with fuzzy set theory.
The advantage of the statistical method is objectivity. Probable and expert methods
allow to take into account the sources of potential danger, which are rare in the form
of emergencies, but the consequences of which are catastrophic. However, the prob-
able method is extremely cumbersome and time-consuming, requiring a large number
of outputs, which results in low accuracy of the results obtained. In the absence of
tried and tested mathematical models and reliable enough initial data for them, it is
advisable to carry out an expert evaluation of the impact on the possibility of imple-
menting large-scale emergencies of a large number of difficultly formalized initial
data.
The prospects of using a statistically probabilistic approach to predict earthquakes
in a separate Earth globe, without taking into account the external factors (as an ele-
ment of the Sun – Earth – Moon nonlinear energy interaction system), are given
in [31]. This paper presents the results of a study of earthquake prediction in the
northwestern area of Vietnam using neural network technologies.
Thus, the use of neural network technologies is one of the promising directions for the
development of approaches to predicting earthquakes across the globe into systems of
nonlinear Sun – Earth – Moon interactions. This determines the direction of our scientific
research in the field of monitoring of the tectonic origin emergencies [8–10, 32–34].
4 Particularities of the processes occurring in the Sun – Earth –
Moon system and affecting the level of seismic hazard
of the Earth’s globe functioning
The dynamics of the physical processes of the Sun – Earth – Moon system affect-
ing the seismic hazard level of the local territory functioning can be schematically
represented according to Fig. 2 – 4. This can be characterized by the following spatial
constructions within the solar galaxy.
1. The axis of rotation of the Earth in the celestial sphere describes a complex
wave-like trajectory. The points of the axis of rotation are at an angular distance of
about from the pole ecliptic (Fig. 2). The vertex of the cone coincides with the Earth
center. The points of equinoxes and solstices move along the ecliptic towards the sun.
Moments of gravitational forces influence on the equatorial bulges and vary depend-
ing on the positions of the Moon and the Sun relative to the Earth. When the Moon
and the Sun are in the plane of the Earth’s equator the moments of forces disappear. If
tilts of Moon and Sun are the maximum, then the magnitude of the torque will be
greatest. The nutations, owing to fluctuations in the moments of the forces of the axis
of rotation of the Earth have been observed by consist of a series of small periodic
oscillations. The main nutations have a period of 18.6 years - the time of the orbital
nodes of the Moon. Movement with this period occurs on an ellipse. The major axis
of the ellipse is perpendicular to the direction of the precessional motion and is equal
to; small - parallel to it and equal. Next in magnitude of the amplitude are the compo-
nents with a period of 0.5 year, 13.7 days., 9.3 years, 1 year, 27.6 days. etc., therefore
the trajectory has the form of “thin laces” (shown on the enlarged fragment in the left
part of Fig. 2) [35–38].
2. The pressure from the solid inner core and the surrounding melt (outer core)
onto the mantle arises as a result of the eccentric revolution of the Earth’s shell
around the displaced inner core, which squeezes the shell from the inside. The forces
compressing the shell of the sphere (planet) and drawing it inward to the core arise in
other parts of the planet. This process has two components: impact at the expense of
the annual displacement of the center nucleus relative to the center of the globe
(Fig. 2 – 4); impact at the expense of eccentric circulation of the core relative to the
lower mantle, when due to the difference in angular rotation velocity of the core and
lower mantle ( 1 – angular velocity of rotation of the mantle; 2 – angular velocity
of rotation of the outer core; 3 – angular velocity of rotation of the inner core;
2 1 – angular velocity of rotation of the outer core relative to mantle
(“western drift”)), therefore, there are zones of high pressure and vacuum
( P1 P2 where P1 and P2 are indicators of pressure of the inner core of the globe on
its surface), affecting the level of seismic activity of the surface of the Earth (Fig. 3).
As long as there is a difference in the angular velocity of rotation and displacement of
the nucleus, the appearance of such zones will be maintained [39, 40].
18,4 PRECESSION
n
NUTATION
P
PRECESSION AND NUTATION Axis of rotation
23,50
Axis tilt of orbital curve
1 ; 2 ; 3
Perpendicular to the orbit
Axis rotation
SUN
EARTH N
Orbit direction
Planetary
equator
MOON Moon center
Ecliptic The motion of the center of the
Earth's outer Earth (inner core) around the center
core of mass
S The center of mass in the Earth-Moon
Earth's inner
core Center of the Earth
Fig. 2. Motion diagram of the inner core of the Earth in the Sun–Earth–Moon sys-
tem (Source: author's own elaboration)
P1
Inner core
Outer core
3
ES
2 1
P2
Fig. 3. Influence diagram of internal core oscillations on seismic activity (Source:
author's own elaboration)
3. Internal elastic stresses arise in the process of moving lithospheric plates
(Fig. 4), which are energy sources of earthquakes. The occurrence depth of elastic
stresses depends on the nature of a movement plates. The relative motion of litho-
spheric plates leads to the emergence of shallow (not deeper than 20–25 km) earth-
quake sources and dipping of lithospheric plates into the mantle initiates the appear-
ance of sources of deep (exceeding 70 km) earthquakes. The probability of elastic
stresses - sources of earthquakes decreases with increasing distance from the interface
of the lithospheric separation plates.
4. Surface and bulk seismic waves are the propagation factors of earthquake haz-
ards Z0 , that can cause secondary earthquakes [41, 42].
5. The probability of mutual amplification or weakening of bulk seismic waves in-
creases in the process of spatial-vibrational movement of the Earth’s internal core and
its effect on the external core. Consequently, the possibility of secondary earthquakes
Z increases also [43].
7. The territorial-temporal changes in the intensity of the natural electromagnetic
field pulses of the Earth initiating anomalous processes in the atmosphere occur due
to the movement of the Earth’s inner core has been established [44–46].
Directions of Earthquake
movement probability
lithospheric
plates (arrows)
Earthquake
Elastic stresses Surface seismic
waves
x
Z0
Z’ Z’’
Plate І Plate ІІ
1
Lithospheric
plate section
Bulk seismic Mantle 2
waves 3
Outer core Inner core
Fig. 4. Process diagram of earthquake and the spread of seismic activity (Source:
author's own elaboration)
Thus, combining the analysis results of the impact dynamics and energy of the in-
ternal physicochemical processes of the Earth on the origin generating tectonic proc-
esses allowed to formulate an approach to studying the nature of seismic phenomen. It
is an important tool for analyzing the results of civil defense research on the devel-
opment of models for the development of ES tectonic nature.
5 Experiments and results of neural network prediction of the
level of seismic activity of the Earth
According to the purpose of the research, the solution of the scientific problem in
the work is ensured by constructing an artificial neural network (ANN) model – a
model for predicting the level of seismic activity of the planet from the conditions of
the system functioning of nonlinear energy interactions Sun – Earth – Moon. This is a
time series model, where the initial indicator, according to Fig. 5, is the total number
of earthquakes in the world N t (provided that M 5 the magnitude of the earth-
quake), depending on the current time of analysis ( t ), as well as the distance of the
Earth’s inner core from the center of the planet t and the change in the length of
r0
day LODt St 86400s (where St 86400s – the length of day,
r t
r0 7,292115 10 5 rad s – constant (average) angular velocity of the Earth’s own
rotation).
For the development presented in Fig. 5 models have a multilayer perception
(MLP) selected. The input parameters of this model are the results of the analysis of
the monthly dynamics of indicators, N t , t and LODt for 2009 – 2018,
which are presented in [8–10, 32–34] and Fig. 6.
Year
January
February
March
April
Artificial neural network
May
t June
July N t
August
September
October
November
December
* t
LOD* t
Fig. 5. Scheme of the ANN time series model for the monthly forecasting of the
Earth’s number of earthquakes, depending on the indices of the remoteness of the
Earth’s inner core from the center of the planet and changes in the length of day
(Source: author's own elaboration)
Fig. 6. Monthly dynamics of the variations in the rate of variation of the Earth’s axial
rotation ( LODt ), the distance of the inner core from the center of the Earth ( t )
and the total seismic activity ( N t ) with magnitude during 2009 – 2018 are sum-
marized on the Globe (Source: author's own elaboration)
When developing a mathematical model for predicting the level of seismic activity
of the Earth ball, based on the decision parameter N t on the complexity of the
MLP architecture, it was based on five analyzes of the learning results of networks,
which accidentally included five hundred neural networks. The criterion for choosing
the optimal network was the relationship between the forecast error and the complex-
ity of the architecture. The results of the analysis are presented in Table 1.
Table 1. Statistical characteristics of the three-layer perceptions suggested by the
“decision-maker” as the best for neural network time series analysis of the forecast of
the monthly dynamics of the Earth’s seismic activity ( N t )
Study Control Test Activation
Activation
№ Architecture produ- produ- produ- hidden
of yield
ctivity ctivity ctivity layer
1. MLP 15-199-1 0,944 0,859 0,976 Exp. Logistic
2. MLP 15-233-1 0,935 0,634 0,964 Identity Exp.
3. MLP 15-243-1 0,939 0,859 0,986 Exp. Logistic
4. MLP 15-187-1 0,938 0,884 0,985 Exp. Logistic
5. MLP 15-12-1 0,967 0,727 0,668 Tanh Identity
For neural network training, all observations were divided into three samples. By
default, random sampling was performed between samples to avoid retraining the
network and to ensure quality generalization (forecasting). The first sample (Educa-
tional – 50% of observations) was used to train the network; the second (Control –
25% of observations) – for cross-validation of the learning algorithm during its opera-
tion; third (Test – 25% of observations) – for the final independent testing of a trained
neural network. The training was done with speed 0,01 .
Presented in Table. 1 networks are characterized by a more effective balance be-
tween modeling error and architecture complexity for time series analysis of the fore-
cast of the monthly dynamics of Earth’s seismic activity ( N t ). This was the basis
for further construction of the three-layer MLP 15-12-1 network, which has fifteen
inputs (Fig. 5), 12 elements in the hidden layer and one logical output function of
thirteen inputs, presented in Fig. 7.
Fig. 7. MLP 15-12-1 Three-Layer Perceptron Architecture with Logical Signal
Transmission for Time Series Prediction of Globe Seismic Monthly Dynamics
( N t ) (Source: author's own elaboration)
In this case, the use of logical activation functions, with scaling parameters, was
based on a given fraction of span of a logical function equal 0,9 (respectively range
0,05; 0,95 ) to the range of training of the neural network. The function of activating
the hidden layer of the perceptron MLP 13-10-1 is hyperbolic tangent. This allows for
a slight extrapolation of the data. In addition, the use of logical functions stabilizes
learning.
The results of checking the adequate prognostic performance of MLP 15-12-1 net-
work are presented in Fig. 8, where the dependence observed ( N* t ) statistic the
values of the Earth’s seismic activity indicator via predicted ( N t ) values. The
coefficient of correlation between these indicators on the results of the training "Edu-
cational Choice" is equal rN2 * t N t 0,967 .
Fig. 8. Dependence of observed ( N* t ) statistic values of the Earth’s seismic activ-
ity indicator by via predicted values ( N t ) by MLP Network 15-12-1 (Source:
author's own elaboration)
Fig. 9. Graph of the time dependence (of observation number) where the depend-
ence observed ( N* t ) statistic the values of the Earth’s seismic activity indicator via
predicted ( N t ) values by MLP network 15-12-1 (Source: author's own elabora-
tion)
The graph of time dependence (from observation number), with the appropriate
level of prognostic adequacy, where the dependence observed ( N* t ) statistic the
values of the Earth’s seismic activity indicator via predicted ( N t ) values by the
MLP 15-12-1 network is presented in Fig. 9.
According to the data analysis Table 2 it is necessary to state that obtained within
the limits of the ideas about the dynamics of physical processes that occur in the sys-
tem Sun – Earth – Moon and affect the level of seismic danger of functioning of the
local territory of the planet (see Fig. 2 – 4), the MLP neural network 15-12-1 and the
results of its forecast allow us to ascertain the adequacy, in accordance with the data
of Figs. 8 and the rN2 * t N t 0,967 parameter figure presented in Fig. 5 models for
predicting the level of seismic activity of the globe.
Table 2. The result of the prediction by the MLP network 15-12-1 level of seismic
activity-those of the Globe
2019
t
01 02 03 04 05 06 07 08 09 10 11 12
t
*
3,00 4,002,50 -1,00 -2,00 -3,00 -4,50 -5,00 -3,00 -0,50 0,50 2,00
LOD t
*
0,81 1,00 0,96 0,98 0,70 0,19 0,01 0,05 0,17 0,27 0,37 0,24
N* t 120 104 118 113 93 103 117 127 131 137 138 123
N t 116 113 132 120 83 97 125 141 144 124 147 138
N t ,% 3 9 11 6 10 6 7 11 9 9 7 12
6 Conclusion
The paper presents a neural network model of the time series for the monthly fore-
casting of earthquakes with magnitude М 5 . The obtained neural network allows us
to predict the level of seismic activity of the globe with adequacy at the
level rN2 * t N t 0,967 .
The results obtained are the basis for the development of scientific and technical
bases for the creation of a system of artificial intelligence for the performance of tasks
of monitoring of tectonic origin.
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