Geophysical research: article

Application of machine learning methods in tomography of receiving functions
E.G. Kozlovskaya3
I.V. Malygin1
1 Schmidt Institute of Physics of the Earth, Russian Academy of Sciences, Moscow, Russia 2 Geophysical Center, Russian Academy of Sciences, Moscow, Russia 3 Oulu Mining School Faculty of Technology University of Oulu, Finland
Journal: Geophysical research
Tome: 23
Number: 1
Year: 2022
Pages: 49-61
UDK: 550.8.05+004.85
DOI: 10.21455/gr2022.1-4
Full text
Keywords: 3D image, receiving functions, machine learning method, nearest neighbor method, Fennoscandia, postglacial uplift, near-surface layer of low S-wave velocities
Аnnotation: A method for constructing a three-dimensional digital seismic model from a set of one-dimensional dependences of the elastic properties of the medium on depth is described. Such problem arises, for example, when interpreting interborehole measurements. A similar problem is also relevant when processing data from passive seismic experiments, when a significant number of seismic stations are installed close to each other with-in the studied region. If we use the method of receiving functions to process remote earthquakes recorded at sta-tions, then from the record at each of the stations we can obtain a velocity section – the dependence of the elastic properties of the medium under the station on depth. It is usually assumed that the medium directly under the station is laterally homogeneous. In this case, the dependence of the elastic parameters on the vertical coordinate can be parametrized by a set of flat layers. The presented method allows to build a three-dimensional seismic image of the region under study using a set of such models. The main challenge is the strong anisotropy of the spatial distribution of the input data. The distance between stations in experiments of this kind is determined by the first Fresnel zone of teleseismic phases and averages 50–100 km. At the same time, the distance between the values specified by the layered model is an order of magnitude less. It is shown that the problem can be solved by a scaling transformation of the horizontal coordinates. To determine the scale factor, methods developed in the theory of machine learning were used. This method is applicable when the elastic parameters of the medium change smoothly between stations. In other words, there are no sub-vertical faults between the stations. As an illustration, a three-dimensional digital model of the southern part of Fennoscandia was built according to the data of the European passive seismic experiment SVEKALAPKO. The obtained result made it possible to reveal some structural features of the upper crust in the area of contact between Archean and Proterozoic rocks caused by postglacial relaxation.
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