The purpose of the article is to create an effective method to monitor the state of the drill string and the bit without interfering with the drilling process itself in low-time delay mode. For continuous monitoring of the well drilling process, an experimental setup was developed that operates on the basis of the use of the phase-metric method of control. Any movement of the bit causes a change in the electrical characteristics of the probing signal. To obtain a stable signal from a bit immersion depth of up to 250 m, a frequency of probing electrical signals of 166 Hz and an amplitude of up to 500 V were used; sampling rate (analog-to-digital converter) ADC - 10101 Hz. To identify the state of the drill string and the bit according to the graphs of dependences of changes in the electrical characteristics of the probing signal on time, the authors of the article investigated a number of deep learning methods, based on the results of the research, a line of capsule neural network (CapsNet) methods was selected. The authors have developed two modifications of 1D-CapsNet and Windowed Fourier Transform (WFT) - 2D-CapsNet. To identify the transition between two rock layers with different properties, WFT-2D-CapsNet showed an accuracy of 99%, which is 2-3% higher than the results of modern rock studies based on measurement-while-drilling (MWD) and logging-while-drilling (LWD) methods. The WFT-2D-CapsNet method unambiguously detects self-oscillations in the drill string and detects the good condition of the bit with an accuracy of 99%.

Currently, the load on railway tracks is increasing due to the increase in freight traffic. Accordingly, more and more serious requirements are being imposed on the reliability of the roadbed, which means that studies of methods for monitoring the integrity of the railway roadbed are relevant. The article provides a mathematical substantiation of the possibility of using seismoelectric and phasemetric methods of geoelectric control of the roadbed of railway tracks in order to identify defects and deformations at an early stage of their occurrence. The methods of laboratory modeling of the natural–technical system “railway track” are considered in order to assess the prospects of using the presented methods. The results of laboratory studies are presented, which have shown their high efficiency in registering a weak useful electrical signal caused by seismoacoustic effects against the background of high-level external industrial and natural interference. In the course of laboratory modeling, it was found that on the amplitude spectra of the output electrical signals of the investigated geological medium in the presence of an elastic harmonic action with a frequency of 70 Hz, the frequency of a harmonic electrical signal with a frequency of 40 Hz is observed. In laboratory modeling, phase images were obtained for the receiving line when simulating the process of sinking the soil base of the railway bed, confirming the presence of a transient process that causes a shift in the initial phase of the signal Δφ = 40° by ~45° (Δφ’ = 85°), which allows detection of the initial stage of failure formation.

This study analyzes the spatial aggregation and activity of the urban population in Almaty using anonymized population density data provided by a telecommunications operator and geographic data from OpenStreetMap. The study focuses on identifying stable zones of high population activity, which facilitates the optimization of transport routes, urban infrastructure planning, and the efficient allocation of city resources. The novelty of this work lies in the integration of aggregated spatiotemporal data with advanced clustering methods, including DBSCAN, KMeans++, and agglomerative clustering. The research methodology involves dividing the city into 500 × 500 m quadrants, calculating normalized population density metrics, and identifying high-activity clusters. Based on a comparative analysis of clustering algorithms, DBSCAN exhibited the highest clustering quality according to the silhouette coefficient and the Davies–Bouldin index, allowing for the identification of key zones of urban activity. The identified clusters were utilized to assess transport load, analyze disparities in the distribution of public transport stops, and develop recommendations to improve public transport accessibility in the most congested areas. The study’s findings are applicable not only to optimizing the transport network but also to addressing a broader range of urban planning challenges, including the strategic placement of infrastructure facilities and the management of population flows. The proposed methodology is scalable and can be adapted to other cities requiring effective tools for analyzing the spatiotemporal activity of urban populations.

The paper deals with the issues of combined using the two-mode seismic method and resistance technique in geodynamic control of geoecological monitoring. The paper considers the principles of creating information processing algorithms for control. The paper presents the results of experimental studies.

In article the structure of an informational-hardware support of the automated system of data processing at electromagnetic monitoring of geodynamic objects is considered. The structure of interrelations of the object-oriented and serving subsystems realizing methodological, program and technical and information support of processes of filing and data processing of geodynamic monitoring is defined. It is noted that increase of geodynamic sensitivity due to selection of the abnormal components of signals and obligation of monitoring of variations of separate geodynamic objects in the studied environment results in need of expansion of informational saturation and a variety of geoelectric models. In this case the applied algorithms of informational processing can be focused only on the analysis of the abnormal signals, and the geodynamic assessment will be defined by structure of the models of geodynamic objects used at interpretation. It is defined that the problem of prediction of geodynamic processes can be solved on the basis of expected model operation.