**Short-period variations in the specific electrical resistance of the Earth's crust**

It is established that remote earthquakes of M>5.0 magnitude are the important factor that has a short-term impact on the stress-strain state of the Earth's crust on Bishkek Geodynamic Proving Ground territory, which manifests itself in synchronous or pre-directional changes in the increments of the electrical resistivity *Dr _{t}*

*(t)*

*by a value of the order of 5-12 Ohms*m (more than ±3σ) (Fig. 1.1). Variations in the increment of electrical resistivity*

*Dr*

_{t}*(t)*characterize the rate of change in the deformation of the geological medium as a result of compression / stretching with uniform sampling over time and are associated with the seismic process. The dynamics of the stress-strain state of the Earth's crust on Bishkek Geodynamic Proving Ground territory showed a clear response to the most severe remote earthquakes (M≥5.0) in the form of an abnormal increase in absolute values of increments of average daily values of electrical resistivity. The deformation process at different depths is not consistent and abnormal changes of

*r*

_{t}*(t)*at one of the levels are not always accompanied by changes at other levels. This effect is more pronounced in the upper part of the section (~7.0-10.0 km).

*Fig. 1.1 Comparison of the average daily values of the increment of electrical resistivity **(**Dr _{t}*

*)*

*corresponding to the different times of field formation (0.14, 0.56 and 3.14 s) at the "Ak-Suu" regime electromagnetic observations point (*

*a-c*

*), with the magnitudes distribution time of local (*

*1.8*

*≤*

*M<4.5*

*), regional (*

*M³4.0*

*) and very far distant (*

*M³5.0*

*) earthquakes*

*(d)*

**Fractal analysis of geoacoustic signals registered on Bishkek Geodynamic Proving Ground territory**

During the electric sounding sessions using the ERGU-600-2 installation, a change in the fractal characteristics of the geoacoustic signal registered on the RS RAS territory is noted. The geoacoustic signal registered at the stationary point on the Research Station RAS territory is a set of non-stationary components of various amplitudes and frequencies and demonstrates multifractal behavior during electric sounding using the ERGU-600-2. By analyzing fluctuations after excluding scale-dependent trends (DFA), it was found that periods of sharp drop in the values of the Hurst exponent relative to the background level are observed in the geoacoustic signal during electric sounding (Fig. 2.1). Outside of periods of electric sounding, the values of the Hurst exponent were at the background level of ~0.5, indicating that the signal is not correlated during this period, as in the case of classical Brownian motion.

*Fig. 2.1 The Hurst exponent variations of geoacoustic signal (Y component, start of recording, 2019-01-08 07:08:21), thickened blue curve – moving average smoothing; yellow lines – ERGU-600-2 sessions*

**Fractal analysis of seismic-acoustic signals of near-surface sedimentary rocks**

Fractal properties of seismic-acoustic signals of various amplitudes were analyzed using mono - and multifractal analysis based on the DFA method. It was found that the background noise before and after the entry of the seismic wave is characterized by a narrow width of the multifractality spectrum (Δα≈0.1) and an almost constant value of the generalized Hurst exponent (*H** _{q}*≈0.5), demonstrating a monofractal behavior similar to white noise. The sections of the signal containing a seismic wave have wider multifractality spectrums and ranges of changes in the Hurst parameter corresponding to different phases of the seismic wave (Fig. 3.1). These differences can be used to detect the moment when a seismic wave is registered (Fig. 3.2), and to identify P-, S- and Coda-waves sections in signals with a low amplitude that is commensurate with the noise level.

*Fig. 3.1 Acoustic responses to various phases of a seismic wave (a) and their multifractal characteristics: (b) – generalized Hurst exponent H*_{q}*, (c) – multifractality spectrum f(α)*

*Fig. 3.2 Seismic-acoustic responses of different amplitudes and dynamics of Hurst exponent variations. Red triangles - seismic event according to the catalog*

**Algorithm for data processing from satellite temperature measurements and pre-seismic anomalies in the atmospheric temperaturetime series**

The algorithm is based on the general principles of the universal RST method (Robust Satellite Techniques), which is used in combination with spectral and correlation analysis. In contrast to traditional approaches, it is supplemented with a special module for diagnosing short-period temperature anomalies (*d**T*) in the upper troposphere / lower stratosphere separated by the tropopause (UTLS) (Fig. 4.1), followed by the calculation of integral indicators of anomalous variations (*D* and *D _{CORR}*), the spatial and temporal distribution of which was compared with seismic activity. The problem of identifying pre-seismic atmospheric disturbances was solved taking the peculiarities of changes in the amplitude and phase of temperature variations in the upper troposphere and the tropopause layer, and the choice of altitude levels was made on the basis of the construction of correlation matrices. The algorithm is constructed in such a way that values of the integral parameter D>1.0 exceeding one indicates the presence of abnormal antiphase temperature perturbations. Suppression of "false" (cophased) anomalies was performed by calculating the

*D*parameter taking the correlation coefficient

_{CORR}*(R)*between temperature changes at the considered UTLS levels:

*D*if

_{CORR}=0,*R*

*≥*

*0*and

*D*×ï

_{CORR}=D*R*ïif R<0. The structure chart of the modified version of the algorithm for seismic and temperature data processing is shown at Fig. 4.2.

*Fig. 4.1 Vertical temperature profiles transformation (a) and temperature anomalies (**б**) in UTLS before the earthquake, M = 5.8 (November 17, 2015)*

*Fig. 4.2 **The structure chart of the algorithm for seismic and temperature data processing*

A typical example of time series processing of temperature during the preparation and passage of an earthquake (*M = 5.8)* registered on November 17, 2015 is shown at Fig. 4.3.

*Fig. 4.3 The time series of T _{1} and T_{2 }temperatures at the levels of 350 and 100 hPa, respectively (a), wavelet transform coefficients (*

*б*

*,*

*в*

*), correlation coefficient (*

*г*

*), temperature anomalies (*

*д*

*) and their moving variance (*

*е*

*), parameters D and D*

_{CORR}(*ж*

*), M magnitude and number of earthquakes per day N*

_{events}(*з*

*) in the period from October 15 to December 15, 2015*

The graphs illustrate all stages of applying the algorithm to the time series of atmospheric temperature in the period from October 15 to December 15, 2015. There are the initial series of temperature in the upper troposphere (*Т _{UT} = T_{350}*) and the tropopause region (

*Т*) at levels of 350 and 100 hPa, respectively (Fig. 4.3а), wavelet spectrograms (Fig. 4.3б and в), the correlation coefficient R between

_{TP}= T_{100}*T*and

_{UT }*Т*(Fig. 4.3г), temperature anomalies (

_{TP}*dТ*and

_{UT }*dТ*) (Fig. 4.3д), sliding dispersion of temperature anomalies and in the time "window"

_{TP}*m = 4*days (Fig. 4.3е) and their math product (

*D*and

*D*) (Fig. 4.3ж), which correlates with seismic data (Fig. 4.3з), represented by a sequence of magnitudes

_{CORR}*(M)*and the number of events per day (

*N*). The main condition leading to an increase in the

_{events}*D*parameter is the simultaneous presence of two factors - an increase in the amplitude of short-period variations above and below the tropopause and the antiphase nature of these changes. As the time of the earthquake was approached, there was a decrease in the intensity of oscillations with a period of ~10-11 days, with a noticeable increase in variations in the band of periods of 4-6 days (Fig. 4.3б, в), which led to an increase in the value of sliding dispersions (Fig. 4.3е), and, accordingly, indicators of anomalous variations. It can be seen that high

_{CORR}*D*values were observed approximately 2 days before the

_{CORR}*M =*5.8 earthquake. The second

*D*peak of higher intensity coincided in time with the

_{CORR}*M*= 4.3 earthquake (19.11.2015; 00:00:41 UTC).

**About aftershock processes accompanying moderate and weak earthquakes on the territory of Bishkek Geodynamic Proving Ground and in its vicinity**

Based on the analysis of 21 aftershock sequences events in the range of 10<K<15 classes that occurred on the territory of Bishkek Geodynamic Proving Ground and in its immediate vicinity (Fig. 5.1), estimates of the duration of aftershock processes released in aftershocks of seismic energy were obtained, and the dependences of various characteristics of aftershocks on the class of the main event were considered.

*Fig. 5.1 Epicentral location of events for 1994-2017 period (more than 9000 events). Triangles are the KNET network stations. Rectangle - Bishkek Geodynamic Proving Ground. Grey circles are the main events of the considered aftershock sequences*

An extremely uneven distribution of events in time within aftershock sequences has been established. In 50 % of cases, 30% to 77% of aftershock events are registered in the first day after the main shock, and 10 % of earthquakes are characterized by their complete absence.

The duration of aftershock processes in most cases of earthquakes is determined by the class of the main event.

Weak links between the main event class and the number of aftershocks and the class of the strongest aftershock are established. However, in general, it can be stated that the well-known empirical Bot law, which determines the approximate ratio between the magnitudes (classes) of the strongest aftershock and the main event, is fulfilled.

There is a dependence of the ratio of the total energy of *E** _{aft}* aftershocks to the energy released during the main

*E*

*shock (with rare exceptions that require verification) on the class of the main event.*

_{ME}Spatially, the epicenters of most of the main events are confined to tectonic disturbances of different order and type. The sizes and configurations of aftershock areas are different and are determined by the deformation processes occurring in the geological environment within a specific aftershock area (Fig. 5.2).

*Fig. 5.2 Examples of configurations of aftershock areas against the background of relief and tectonic disturbances (faults). The main events (red stars), the strongest and ordinary aftershocks (dark circles) are displayed on the map at a scale proportional to the event classes*

The assumption that relaxation processes are longer and more intense the lower the average seismic activity in the region where they occur is fully valid only for one moderate event – the Lugovsky earthquake (22.05.2003, K = 14.3).

Detailed analysis of data and graphs of dependencies of various characteristics of aftershocks on the class of main events indicates that the algorithm implemented in the Smirnov program is not perfect enough, and in some cases, when studying individual aftershock series, mandatory visual control (manual verification) of the results of machine processing is required.