Spatio-temporal Inconsistency Research Articles

Improvement of Spatio-Temporal Inconsistency of Time Series Land Cover Products

In recent years, time series land cover products have been developed rapidly. However, the traditional classification strategy rarely considers time continuity and spatial consistency, which leads to the existence of unreasonable changes among the multi-period products. In order to solve the existing problems, this paper proposes a matrix decomposition model and an optimized hidden Markov model (HMM) to improve the consistency of the time series land cover maps. It also compares the results with the spatio-temporal window filtering model. The spatial weight information is introduced into the singular value decomposition (SVD) model, and the regression model is constructed by combining the eigenvalues and eigenvectors of the image to predict the unreasonable variable pixels and complete the construction of the matrix decomposition model. To solve the two problems of reliance on expert experience and lack of spatial relationships, this paper optimizes the model and proposes the HMM Land Cover Transition (HMM_LCT) model. The overall accuracy of the matrix decomposition model and the HMM_LCT model is 90.74% and 89.87%, respectively. It is found that the matrix decomposition model has a better effect on consistency adjustment than the HMM_LCT model. The matrix decomposition model can also adjust the land cover trajectory to better express the changing trend of surface objects. After consistent adjustment by the matrix decomposition model, the cumulative proportion of the first 15 types of land cover trajectories reached 99.47%, of which 83.01% were stable land classes that had not changed for three years.

Exploring urban compactness impact on carbon emissions from energy consumption: A township-level case study of Hangzhou, China

Given that cities are the major contributors to carbon emissions, studying urban compactness (UC) and its impact on carbon emissions from energy consumption (CEECs) is crucial. This study calculated Hangzhou's township-level urban UC and CEECs using a hybrid subjective-objective weighted regression model on integrated panel datasets. By employing a geographically weighted regression (GWR) model, the spatio-temporal heterogeneity of the UC-CEEC relationship from 2006 to 2019 was uncovered. The results indicated an overall increase in UC, with significant variations across different counties. CEECs were higher in the central region, shifting eastward due to distinct urban development levels and policies. Moreover, the effects of various UC factors exhibited significant spatiotemporal inconsistency, with the impact intensity gradually diminishing. Additionally, the explanatory power of these factors declined and diversified over time. These findings emphasize the need for a comprehensive understanding of the relationship between UC and CEECs within the complex metropolitan environment and the importance of regulating their coordinated development. The research not only offers a more scientific approach to managing the growth of county-level cities and supporting balanced urbanization but also presents policy recommendations.

Spatiotemporal Inconsistency Learning and Interactive Fusion for Deepfake Video Detection

With the rise of the metaverse, the rapid advancement of Deepfakes technology has become closely intertwined. Within the metaverse, individuals exist in digital form and engage in interactions, transactions, and communications through virtual avatars. However, the development of Deepfakes technology has led to the proliferation of forged information disseminated under the guise of users’ virtual identities, posing significant security risks to the metaverse. Hence, there is an urgent need to research and develop more robust methods for detecting deep forgeries to address these challenges. This paper explores deepfake video detection by leveraging the spatiotemporal inconsistencies generated by deepfake generation techniques, and thereby proposing the interactive spatioTemporal inconsistency learning and interactive fusion (ST-ILIF) detection method, which consists of phase-aware and sequence streams. The spatial inconsistencies exhibited in frames of deepfake videos are primarily attributed to variations in the structural information contained within the phase component of the Fourier domain. To mitigate the issue of overfitting the content information, a phase-aware stream is introduced to learn the spatial inconsistencies from the phase-based reconstructed frames. Additionally, considering that deepfake videos are generated frame-by-frame and lack temporal consistency between frames, a sequence stream is proposed to extract temporal inconsistency features from the spatiotemporal difference information between consecutive frames. Finally, through feature interaction and fusion of the two streams, the representation ability of intermediate and classification features is further enhanced. The proposed method, which was evaluated on four mainstream datasets, outperformed most existing methods, and extensive experimental results demonstrated its effectiveness in identifying deepfake videos. Our source code is available at https://github.com/qff98/Deepfake-Video-Detection

Integrating bi-temporal VHR optical and long-term SAR images for built-up area change detection

ABSTRACT With the rapid expansion of urbanization, it is imperative to monitor built-up areas changes to promote the sustainable development of cities, aligning with the goals of Sustainable Development Goal 11(SDG 11). Remote sensing big data is valuable for automatically mapping these changes. Bi-temporal very high-resolution (Bi-VHR) optical images have been widely utilized for fine-grained change detection (CD). However, the significant spatiotemporal inconsistency due to imaging conditions and seasonal variations poses challenges for VHR optical CD. Unlike optical images, synthetic aperture radar (SAR) images are unaffected by atmospheric interference and provide robust spatiotemporal features as a supplement. Previous CD algorithms with SAR overlooked the exploration of long-term features of time series. In this study, we propose a novel CD framework combining long-term SAR with Bi-VHR images. It incorporates a spatial-frequency learning module to enhance SAR temporal features and a multisource feature fusion module to adaptively fuse the heterogeneous features. The experiments are conducted on OS-BCD dataset, which is the first dataset specifically designed for this task. The results demonstrate that our proposal outperforms advanced CD methods with F1 score, IoU and OA of 64.99%, 48.13%, and 95.11%, validating the efficacy of our proposal in accurately detecting changes in built-up areas.

High-compressed deepfake video detection with contrastive spatiotemporal distillation

Deepfake detection in high-resolution videos has made significant progress in recent years, but detecting high-compressed deepfake videos remains challenging due to the low quality of synthesized videos. Existing video-level approaches fail to fully exploit the spatiotemporal inconsistencies in low-quality high-compressed deepfake videos, leading to poor generalization and robustness. In this work, we propose a Contrastive Spatio-Temporal Distilling (CSTD) approach that leverages spatial-frequency cues and temporal-contrastive alignment to improve high-compressed deepfake video detection. Our approach employs a two-stage spatiotemporal video encoder to fully exploit spatiotemporal inconsistency information. A fine-grained spatial-frequency distillation module is used to retrieve invariant forgery cues in spatial and frequency domains in high-compressed deepfake videos. Additionally, a mutual-information temporal-contrastive distillation module is introduced to enhance the temporal correlated information and transfer the temporal structural knowledge from the teacher model to the student model. We demonstrate the effectiveness and robustness of our method on low-quality high-compressed deepfake videos on public benchmarks against state-of-the-art competitors using extensive experiments and visualizations.

Differential effects of climatic and non-climatic factors on the distribution of vegetation phenology trends on the Tibetan plateau

The study of vegetation phenology changes is important because it is a sensitive indicator of climate change, affecting the exchange of carbon, energy and water fluxes between the land and the atmosphere. Previous studies have focused on the effects of climatic factors among environmental factors on vegetation phenology, thus the effects of non-climatic factors among environmental factors have not been well quantified. This study endeavors to scrutinize the spatiotemporal inconsistency in the start-of-season (SOS) and the end-of-season (EOS) on the Tibetan Plateau (TP) and to quantify the effects of environmental factors on phenology. To this end, the Moderate-resolution Imaging Spectroradiomater (MODIS) Normalized Difference Vegetation Index (NDVI) data from 2001 to 2018 and four common used methods were employed to extract SOS and EOS, and the site data was used to select the most appropriate phenology results. The Geodetector model was used to assess and measure the explanatory power of different environmental factors. The research results indicate that temperature exerts a more substantial impact on phenology than precipitation on TP. non-climatic factors such as longitude, latitude, and elevation are more influential in determining the distribution of phenological trends than climatic factors. Among these non-climatic factors, latitude has the most prominent effect on the trends of SOS. Furthermore, non-climatic factors exhibit a stronger effect on SOS, whereas EOS is more susceptible to climatic factors and less influenced by non-climatic factors. These discoveries bear great significance in comprehending the intricate outcomes of regional changes on vegetation phenology and enhancing phenology models.

Temperature outweighs light and flow as the predominant driver of dissolved oxygen in US rivers

The concentration of dissolved oxygen (DO), an important measure of water quality and river metabolism, varies tremendously in time and space. Riverine DO is commonly perceived as regulated by interacting and competing drivers (light, temperature and flow) that define rivers’ climate. Its continental-scale drivers, however, have remained elusive, partly due to the scarcity and spatio-temporal inconsistency of water quality data. Here we show, via a deep learning model (long short-term memory) trained using data from 580 rivers, that temperature predominantly drives daily DO dynamics in the contiguous United States. Light comes a close second, whereas flow imparts minimal influence. This work showcases the promise of using deep learning models for data filling that enables large-scale systematic analysis of patterns and drivers. Results show fairly accurate prediction of DO by temperature alone, and declining DO in warming rivers, which has important implications for water security and ecosystem health in the future climate. Concentrations of dissolved oxygen are considered as comparably driven by light, temperature and flow regimes in individual rivers, although their continental-scale drivers remain elusive due to data scarcity. Results from data and a long short-term memory deep learning model suggests that temperature is the most predominant driver of daily DO in US rivers.

Augmented Multi-Scale Spatiotemporal Inconsistency Magnifier for Generalized DeepFake Detection

Recently, realistic DeepFake videos have raised severe security concerns in society. Existing video-based detection methods observe local spatial regions with the coarse temporal view, thus it is difficult to obtain subtle spatiotemporal information, resulting in limited generalization ability. In this paper, we propose a novel Augmented Multi-scale Spatiotemporal Inconsistency Magnifier (AMSIM) with a Global Inconsistency View (GIV) and a more meticulous Multi-timescale Local Inconsistency View (MLIV), focusing on mining comprehensive and more subtle spatiotemporal cues. Firstly, the GIV that includs the global spatial and long-term temporal views is established to ensure comprehensive spatiotemporal clues are captured. Then, the MLIV with the critical local spatial and multi-timescale local temporal views is designed for magnifying the indetectable spatiotemporal abnormality. Subsequently, GIV is utilized to guide MLIV to dynamically find local spatiotemporal anomalies that are highly relevant to the overall video. Finally, to further obtain a generalized framework, the adversarial data augmentation is specially designed to expand source domains and simulate unseen forgery domains. Extensive experiments on six large-scale datasets show that our AMSIM outperforms state-of-the-art detection methods and remains effective when applied to unseen forgery techniques and datasets.

Observing Spatiotemporal Inconsistency of Erosion and Accretion in the Barak River Using Remote Sensing and GIS Techniques

Alluvial rivers all over the world have one common problem, which is their meandering pattern. This meander formation is because of natural and anthropogenic processes. Barak River is dynamic, and due to this, it is exposed to regular shifting and creates many problems for the people who reside near the river. The livelihood of many people depends on agriculture, which they conduct on the nearby sides of the river. However, the regular shifting of riverbanks makes their life miserable and leads to severe economic losses. Further, roadways and railways run along the banks of the Barak River, and during monsoon, Assam (Silchar), along with three states, Mizoram, Manipur, and Tripura, become disconnected from the rest of India because the road and rail connections fail due to riverbank erosion. Therefore, considering the catchment area and the importance of this river, we have tried to understand the spatiotemporal changes (erosion, deposition, and unchanged area) in the Barak River. From our analysis, we found that the maximum and minimum amount of erosion occurred from 2012–2017 and 2002–2012 and were 727.56 ha and 332.69 ha, respectively. While the highest amount of deposition that occurred during 1984–2017 was 1054.21 ha, the minimum amount of deposition that occurred during 2012–2017 was 351.32. Overall, it was identified that the area under the deposition was more dynamic than the erosion from 1984–2017. Moreover, from the temporal analysis of land use/land cover from 1984–2017, it was found that the area that comes under the settlement and arable land has increased by 10.47% and 5.05%, respectively. The dynamic factors, such as the nature of channel gradient, land use/land cover, and riparian vegetative cover, could be the probable driving forces that cause changes in the erosional and depositional areas. This study will help us understand the dynamics of the Barak River and other rivers of this type worldwide. This study shall help implement strategies that will help manage bank erosion by adapting scientific bank protection measures.

Comprehensive study of aerosols properties over various terrain types.

Aerosols are a crucial part of the climate system. Numerous factors, including aerosols, govern Earth's radiation balance. Different aerosols have distinct radiational effects on the earth system, and thus the slight change in their composition may lead to a drastic change in their radiative effects. Aerosols' chemical and physical properties also depend on generation processes, generation source, and geographical location. Significant spatio-temporal inconsistency is noticed in the distribution of aerosols. It makes it much difficult task to assess their radiative properties. We attempted to explore aerosol's optical properties and wavelength dependence over different locations. We have used AERONET (Aerosol Robotic Network) data over various stations (Kanpur, Jaipur, Gandhi College, Pune) with varying terrain properties in the Indian continent. We have studied the variation of different optical parameters: aerosol optical depth (AOD), single scattering albedo (SSA), and Angstrom exponent (α), and their wavelength dependence. This study indicated that Jaipur is the cleanest site, with dust aerosols as a primary aerosol. Though over Pune also aerosol concentration was relatively low but the anthropogenic aerosols contributed primarily over this site. Over the Indo-Gangetic Plain (IGP) sites, dust aerosols dominated the pre-monsoon season, while anthropogenic aerosols dominated the post-monsoon and winter seasons. The scatter plot of AOD with α gives the details of different aerosols (desert dust, continental aerosols, mixed aerosol, biomass burning aerosols, and sulfate aerosols) in the different seasons and places. This study provides an overview of aerosol properties, dominant aerosols in the aerosol system, and their seasonal and spectral variation.

Measuring hub locations in time-evolving spatial interaction networks based on explicit spatiotemporal coupling and group centrality

ABSTRACT As the essence of urban spatiotemporal interaction systems, hubs and centers empower cities to enhance socioeconomic prosperity and sustainability. However, a city manifests a time-evolving spatial interaction network with latent temporal interactions and irregular spatial partitions. This phenomenon is termed the spatiotemporal inconsistency problem. The aggregate, single-layer network model is defective for capturing the importance of locations in such time-evolving spatial interaction systems. This article therefore proposes a novel multilayer network model based on the nature of inherent spatial and temporal dependencies of urban interactions. First, the spatial agglomeration and the temporal correlation are explicitly modeled in multilayer networks for alleviating the spatiotemporal inconsistency problem. Secondly, generalized centrality metrics from a single-layered static network to the multi-layered dynamic network are acquired in order to discover grouped hub locations over time. Lastly, the capability of the proposed method is evaluated by an empirical analysis of the taxi mobility networks of Beijing, China, from 2012 to 2017. The empirical analysis indicates that the proposed method enables the identification of typical hub locations clustered in space and stable over time. This ability is essential to understand the centrality of locations informed by noisy and inconsistent data in their spatial and temporal dimensions.

DIBR‐synthesised video quality assessment by measuring geometric distortion and spatiotemporal inconsistency

Depth-image-based rendering (DIBR), as the most popular view synthesis method, is commonly used in the application of multi-view and free-viewpoint videos. However, the quality evaluation of DIBR-synthesised videos remains largely unexplored, which may hinder the development of more advanced view synthesis technology. With this motivation, this Letter presents a new quality metric for DIBR-synthesised videos. Specifically, the disoccluded regions are first detected based on an adaptive threshold to quantify geometric distortions. An energy-based sequence mapping strategy is proposed to portray spatiotemporal inconsistency by calculating first-order and second-order similarities in the gradient magnitude domain and the Laplace-of-Gaussian domain, respectively. Finally, the overall quality score is generated by pooling the scores of geometric distortion and spatiotemporal inconsistency. Experimental results demonstrate that the proposed metric outperforms the state-of-the-art metrics dedicated to DIBR-synthesised images and videos.

Spatiotemporal reconstruction of MODIS land surface temperature with the help of GLDAS product using kernel-based nonparametric data assimilation

Spatiotemporal inconsistency in MODIS land surface temperature (LST), one of the most widely used geospatial data parameters, is a concern for its application in various studies. Moreover, there are limited methods that can address spatiotemporal reconstruction of LST in diverse physiography. The use of kernel-based spatiotemporal assimilation in a multitemporal approach to reconstruct LST in a complex physiographic region, northeast India, is addressed. Global land skin temperature (Ts) from Global Land Data Assimilation System (GLDAS) is chosen as a temporal covariate for MODIS LST due to its consistent temporal resolution. Considering the high temporal correlation between GLDAS Ts and MODIS LST, temporal assimilation is done in the first stage followed by spatial assimilation in the second stage. Due to data gaps, a kernel-based nonparametric estimator is adopted to map the spatiotemporal distribution and reconstruct LST with spatiotemporal consistency. This approach shows satisfactory performance in restoration of spatial variation in the study area with a coefficient of determination (R2 = 0.98), root mean square error (RMSE = 0.61 K), and absolute value of bias (B = 0.22 K). Also, the credibility of reconstructed LST from this method is found to be as good as the baseline data (MODIS LST) given that there is no severe data deficiency with uneven spatial availability in the baseline data itself. Comparison of gap-filled LST showed positive correlation with ground-based measurements (average R = 0.71), reflecting decent agreement with seasonality. Hence, the kernel-based nonparametric assimilation could be used to reconstruct LST using a multitemporal approach in complex physiographic regions, which could be used to fill the spatiotemporal data gaps and increase data consistency in MODIS LST.

Integrating Latent Heat Flux Products from MODIS and Landsat Data Using Multi-Resolution Kalman Filter Method in the Midstream of Heihe River Basin of Northwest China

An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is crucial to the management and planning of water resources for arid and semi-arid areas, for which LE estimations from different satellite sensors unfortunately often contain data gaps and are inconsistent. Many integration approaches have been implemented to overcome these limitations; however, most suffer from either the persistent bias of relying on datasets at only one resolution or the spatiotemporal inconsistency of LE products. In this study, we exhibit an integration case in the midstream of the Heihe River Basin of northwest China by using a multi-resolution Kalman filter (MKF) method to develop continuous and consistent LE maps from satellite LE datasets across different resolutions. The Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16), the Landsat-based LE product derived from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor, and ground observations of eddy covariance flux tower from June to September 2012 are used. The integrated results illustrate that data gaps of MOD16 dropped to less than 0.4% from the original 27–52%, and the root-mean-square error (RMSE) between the LE products decreased by 50.7% on average. Our findings indicate that the MKF method has excellent capacity to fill data gaps, reduce uncertainty, and improve the consistency of multiple LE datasets at different resolutions.

An insight into the Palmer drought mechanism based indices: comprehensive comparison of their strengths and limitations

Considering the drawbacks of the original Palmer drought severity index (PDSI) in terms of its simplified hydrologic algorithm and spatio-temporal inconsistency, we compare six variants of PDSI derived from different combinations of two hydrologic algorithms and three standard processes so as to provide deep insights into the individual impacts of hydrological processing and standardization on final PDSI values as well as their combined effects. Investigations are conducted in whole Yellow River basin. On basis of 52 years’ (1961–2012) hydro-meteorological data, comprehensive analysis on multiple drought characteristics are carried out for each PDSI variant, combined with comparison of three crucial intermediate variables of PDSI. Results show that variable infiltration capacity (VIC) model based modification in the hydrologic accounting section significantly improve drought trends with more reasonable spatial distributions presented. For the statistical characteristics of drought areas and frequency, comparable performance is found between VIC-based modification and self-calibrating standard procedure-based modification, though they are derived from different mechanisms. However, in case of the coupling of these two modifications, indices derived from combined modifications perform poorly than single modification-based indices with unexpected high frequency of extreme events detected in certain regions. This reflects the complicated mechanism of PDSI and it is essential to propose an appropriate standardization to match the hydrological algorithm and further improve the performance of relevant drought index. With the crucial findings mentioned above, this study is promising to provide some theoretical supports and serve as a competent reference for future PDSI based researches.

Model Estimation of Water Use Efficiency for Soil Conservation in the Lower Heihe River Basin, Northwest China during 2000–2008

There has been very limited research on water use efficiency for soil conservation (WUE-SC) in typical water scarce regions such as the lower Heihe River Basin, where there is serious wind erosion and the soil conservation service plays a key role in guaranteeing the ecological safety of Northern China. The soil conservation service, which was represented by the soil conservation amount (SC), was first estimated with an experiment-based model in this study. Then, the WUE-SC (i.e., SC/ET) was calculated on the basis of evapotranspiration (ET) data, and management implications were finally discussed. The results indicated the WUE-SC ranged between 0–98.69 t mm−1, and it first decreased and then increased on the whole during 2000–2008. Besides, the inter-annual variation of WUE-SC was mainly due to change in the potential soil loss. In addition, the WUE-SC showed significant spatial heterogeneity, and the average WUE-SC of the whole study area was very low due to spatiotemporal inconsistency between the potential soil loss and the vegetation coverage rate. Although there are some uncertainties, these results still can provide local managers with valuable information for water resource utilization and ecosystem management to improve water use efficiency.

Annual spatio-temporal variation of the euphotic depth in the SW-Finnish archipelago, Baltic Sea

We measured depth profiles of underwater PAR (photosynthetically active radiation) together with optically derived turbidity and chlorophyll fluorescence values at 11 sampling stations in the South-West Finnish archipelago of the Baltic Sea. The data were collected eight times during the spring, summer and early autumn of 2010. The results illustrate complex and multidimensional variations in the euphotic depth, which was subject to fourfold and twofold differences in the geographical and seasonal dimensions respectively. The spatio-temporal inconsistency and non-linearity of the seasonal euphotic depth variation calls for further studies at different spatial and temporal scales.

Prediction of Bluetongue virus seropositivity on pastoral properties in northern Australia using remotely sensed bioclimatic variables

To monitor Bluetongue virus (BTV) activity in northern and eastern Australia the National Arbovirus Monitoring Program (NAMP) collects data from a network of sentinel herds. Groups of young cattle, previously unexposed to infection, are regularly tested to detect evidence of seroconversion. While this approach has been successful in fulfilling international surveillance requirements, it is labour and cost intensive and operationally challenging in the remote area of the northern Australian rangelands.The aim of this study was to assess the suitability of remotely sensed data as a means for predicting the distribution of BTV seroprevalence. For the period 2000–2009, bioclimatic variables were derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) data products for the entire Northern Territory. A generalised linear model, based on the seasonal Normalised Difference Vegetation Index (NDVI) and minimum land surface temperature, was developed to predict BTV seropositivity. The odds of seropositivity in locations with NDVI estimates >0.45 was 3.90 (95% CI 1.11 to 13.7) times that of locations where NDVI estimates were between 0 and 0.45. Unit increases in minimum night land surface temperature in the previous winter increased the odds of seropositivity by a factor of 1.40 (95% CI 1.02 to 1.91). The area under a Receiver Operator Characteristic curve generated on the basis of the model predictions was 0.8.Uncertainty in the model's predictions was attributed to the spatio-temporal inconsistency in the precision of the available serosurveillance data. The discriminatory ability of models of this type could be improved by ensuring that exact location details and date of NAMP BTV test events are consistently recorded.

Dynamics of stresses in the lithosphere of the Baikal Rift: Episodes of synchronization

Application of correlation analysis methods to theinvestigation of the lithosphere evolution dynamics inthe Baikal Rift Zone (BRZ) and in three regions withinthis zone at the present-day (instrumental) stageallowed us to reveal statistically significant episodes ofhigh synchronization between the geophysical pro-cesses at the end of the 1960s to the beginning of the1970s, the end of the 1970s and the beginning of the1980s, and the end of the 1980s. The episode at the endof the 1970s and beginning of the 1980s is distinguishedfor its long duration and maximal level of correlation,which gives us the possibility to consider it as the domi-nating event in the lithosphere of the Baikal Rift. Theobserved synchronization evidences that rearrange-ments of stresses in the BRZ lithosphere are takingplace in the form of virtually contemporaneous pulsesover the vast territory of the Baikal region and, there-fore, can be classified as “catastrophes of stresses.”Field measurements and laboratory experimentsevidence that spatiotemporal variations in diverse fieldspreceding and accompanying geodynamic reconstruc-tions and strong earthquakes are one of the most char-acteristic geophysical phenomena [1, 2]. At the phe-nomenological level, such processes are usually associ-ated with the approach to the critical value of a“controlling parameter,” which characterizes the evolu-tion of the geological medium. Approach to the criticalvalue brings the stressed object to an unstable state [3],which can terminate as a catastrophe of a specific scalelevel and transition of the medium to the state of relax-ation. In this presentation, the term “catastrophe ofstresses” has a real sense as a set of prominent varia-tions in the dynamics of lithospheric stresses during arelatively short time compared with the duration of agroup of shocks [4] or a seismic cycle [5]. As the timeapproaches the critical value, the correlation distanceincreases, over which one can observe accompanyingcoherent events of precursors. Investigation of variable-scale processes of synchronized cooperative phenom-ena in inhomogeneous media is carried out in terms ofphysical mesoscale mechanics, synergetics, and non-linear system dynamics [6–8].The stressed–strained state of the BRZ lithosphereis not uniform. Spatiotemporal inconsistency of the for-mation of displacement types is observed in the sourcesof weak (energetic class K