## 1 Introduction
Field data on rock masses may be generally divided into three categories with respect to their source. Data on rock mass physical properties may be obtained from seismic surveys, well logs, and laboratory experiments on core samples. These three sources represent three distinct spatial scales: seismic surveys make it possible to deal with rocks physical properties averaged for volumes compared to seismic wavelength (from 10 to 100 m); well logs characterize the lower scales of the first meters; finally, special experiments on rock samples can be carried out at the lowest scale--from microns to centimeters, depending on the kind of experiment. As far as rock masses are heterogeneous, the same properties are different at each of these scales [13]. There are special techniques, covered by the general approach known as 'rock physics modeling' aimed at construction of mathematical models describing the scaling of effective properties of heterogeneous rock masses with respect to this effect [14]. Once again, these methods are developed to deal with widely used mechanical properties, such as elastic moduli, but not with brittleness. Heterogeneity of rocks results in the inability of the best techniques to estimate brittleness from deformation curves to characterize brittleness at any scale aside from the lowest one. The main idea of alternative methods of brittleness evaluation from geophysical data was to avoid laboratory tests and estimate brittleness from well logs. [PERSON] et al., 2019 [4] proposed a general classification of developed methods of estimating brittleness with respect to the data used for calculation. Three groups of brittleness indices (BI) were introduced: mineral-based brittleness indices (MBI), log-based brittleness indices (LBI), and elastic-based brittleness indices (EBI). The general idea of mineral-based brittleness indices suggests that mineral composition (weight fractions of different minerals contained in rock mass) governs the brittleness of the whole rock. Log-based brittleness indices imply that certain correlations exist between well logs interpretation data and brittleness. The correlations are purely empirical and are to be set based on laboratory experiments. Finally, elastic-based brittleness indices can be generally subdivided into two groups--indices can be calculated from either deformation curves from laboratory experiments on core material or data on dynamic elastic moduli. A brief overview of various brittleness indices will be provided below, but the main problem is related to the existence of a vast number of ways to introduce brittleness, some of which may contradict each other. The problem worsens as there is no way to check whether any brittleness index is ultimately correct--while there is no universal, generally accepted brittleness index, it cannot be used to check the validity of new introduced indices. The following general workflow is used in practice nowadays. First of all, any form of brittleness calculation is chosen to evaluate brittleness index from deformation curves obtained after carrying out laboratory tests on core samples. Hence, brittleness of rock masses is evaluated at certain points, corresponding to depths of core extractions. As the ultimate goal of brittleness estimation is the construction of brittleness index profile along the well trajectory, a certain correlation is chosen among the existing LBIs or MBIs. The parameters of the chosen correlation are obtained from the solution of optimization problem of minimizing the difference between brittleness indices obtained from laboratory tests and calculated via correlation function. The drawback of this workflow is clearly related to the ill-posed nature of the inverse problem being solved. Existence of a number of various correlations provides a possibility to obtain completely different solutions using differing correlations. These solutions can contradict each other, which leads to clear problems with using the results of brittleness estimations.