Fizika zemli

Based on the data of several spaced magnetic stations, the spectrum of geomagnetic variations is studied in the range of periods from two to 40 years. Special attention is paid to spectral features in the supposed range of action of intraterrestrial processes that cause geomagnetic jerks. It is shown that the detected spectral peak in the vicinity of the period of 6.5 years aligns with the previously revealed recurrence pattern of jerks with a period of three to four years; however, this peak is absent in the spectrum of solar activity. The possible wave mechanisms of the occurrence of jerks and their 6-year quasi-periodicity caused by known types of magnetohydrodynamic waves in the liquid core of the Earth, are considered, and it is shown that theyare not sufficiently convincing in reproducing observations of jerks.

Linear sequences of earthquake epicenters (“chains”) related in space and time are studied. A new approach to understanding earthquake chains as a special kind of group (clustered) events is proposed. It is believed that clusters of group earthquakes with pronounced spatial anisotropy potentially represent the desired chains. Such chains are given the physical meaning of markers of activated tectonic faults. A formalized algorithm for the allocation of linear sequences of earthquake epicenters based on the proposed approach has been developed. The search for chains is conducted in the catalog of group earthquakes. Previously, single earthquakes (i.e., not included in clusters) are removed from seismicity. For this, a previously developed algorithm was used, focused on the selection of any interrelated events, and not only (mainly) aftershock and/or foreshock series (Descherevsky et al., 2016a). The proposed method of isolating earthquake chains has been successfully tested on earthquake catalogs of Garm, Iran and central Turkey. Chain maps are provided, and summary statistics of the chain field are discussed. As a rule, these chains can be compared with various tectonic disturbances, but a significant part of them are not tied to known structures. For the Garm district, the continuity of the results obtained with previously performed studies is shown. Like almost any method of analyzing seismic data, the earthquake chain algorithm has a significant number of configurable parameters. Within certain limits, you can vary the criteria for allocating group events, the minimum number of events in the chain and its minimum length, as well as the required level of straightness of the chain. However, all these settings primarily affect the total number of chains found in the catalog, and their location and orientation (azimuths) they almost do not depend on the algorithm settings. This allows us to consider the proposed analysis method as a fundamentally new way of extracting and visualizing information about the spatial and temporal organization of seismicity. A more detailed study of both the structure of earthquake chains and its changes over time in various seismically active regions of the world can contribute to a better understanding of the dynamics of the seismotectonic process.

Neural networks (NNs) are successfully used to solve inverse and other problems in geophysics. The aim of this work, which is a continuation of a series of works by a group of authors, is to improve the efficiency of the NN method for solving nonlinear inverse 3D problems of geoelectrics, based on the construction of the author’s convolutional neural network. The network includes a number of additional special transformations (data compression, suppression of the influence of an unknown background environment, etc.) preceding the training of a classical MLP neural network and adapted to the inverse problem that is being solved. This allows us to formally, excluding the human factor, solve inverse problems of geoelectrics of large dimensions without specifying a first approximation based on data measured in areas whose dimensions exceed the dimensions of the network training area. The inversion speed is a few tens of seconds and does not depend on the physical dimensionality (2D or 3D) of the data. The solution to the inverse problem found using a trained neural network can, if necessary, be refined using a random search method. Numerical results of solving 3D geoelectric problems on model and field data are presented, confirming the stated development parameters.

Numerical reconstructions of thermal regime of sedimentary strata of the Mannar basin (Sri Lanka) in the area of the Dorado North well and the West Siberia basin (Tomsk region) in the area of the Ostaninskaya well presented in [Premarathne et al., 2016; Isaev et al., 2021] are compared with the corresponding reconstructions obtained in the GALO system for basin modeling. These examples show that the use of modeling systems with the specification of the heat flow at the base of sedimentary strata can give false picture of the thermal history of the basin, despite the coincidence of the calculated values of vitrinite reflectance with the values measured in the modern sedimentary section of the basin. The application of the analysis of variations in tectonic subsidence of the basin in the GALO modeling system makes it possible to estimate the amplitude and duration of the thermal activation events and extension (thinning of the crust) of the lithosphere and thereby overcome the problem with specification of the heat flow at relatively shallow basin depths. The alternative model of the basin thermal evolution constructed in this way relies on the same modeling input database as the heat flow fitting systems at the base of sedimentary strata, but only with the addition of the modern depth of the Mohorovicic discontinuity.

An EIGEN-6C4 model for the Altai-Sayan region and northwestern Mongolia constructed using data from satellite gravimetric missions and the results of ground-based measurements with absolute gravimeters and space geodesy receivers is considered. Using the EIGEN-6C4 geopotential (ETOPO1 relief), within the framework of a homogeneous crust model with the involvement of seismic exploration data on the platform part of the study area, an idea was obtained about the changes in the thickness of the earth’s crust in central Asia for the territory extending from 56° to 46° north latitude and from 80° to 100° east longitude, covering Gorny Altai, Kuznetsk Alatau, Western Sayan and Eastern Sayan, Tuva Basin, Tarbagatai Ridge (Kazakhstan), Mongolian Altai (PRC, Mongolia), Great Lakes Basin and Khangai Ridge (Mongolia). Research has shown that the depth of the Mohorovičić boundary increases from the northwest to the southeast of the territory from 40 to 55 km. For the mountainous regions in the south (Mongolian Altai, Khangai Range), the maximum crustal thickness was 55 km. For intermountain valleys and depressions (Tuva Basin, Big Lakes Basin) the depth of the Moho surface is within 45–47 km. In the north, in the flat part of the territory, the thickness of the crust is from 40 to 43 km. The differences between models constructed using gravimetric and seismic data are considered.

To extend the capabilities of using records of local earthquakes (for constructing regional ground motion prediction equations, assessing seismic hazard, etc.), the classification of seismic stations in the North Caucasus by the ground conditions was performed. A technique has been developed that allows assessment of ground conditions by comparing spectra of weak earthquakes selected in narrow ranges of magnitudes and hypocentral distances, at different stations. The use of machine-learning methods showed the complexity of the problem, but at the same time, the application of logical operations and techniques allowed us to find the most effective approaches to solve it. As a result, 70 seismic stations of the North Caucasus were classified according to the ground conditions; the conditions were characterized by one dimensionless parameter based on the calculation of spectral characteristics. We are planning to refine the estimates in the future.