## 3 Methods and Materials  
### Velocity Model Validation Method  
For many years studies on various aspects of multivariant integrated inversion of independent datasets, based upon the same or physically related petrophysical parameters, have been carried out [1; 2; 3]. However, an important issue lies in integrating data that represent diverse properties of rock formations, even though they lack direct physical connections, making it impossible to establish precise analytical links for such datasets [4; 5]. The solution to this problem was proposed by [PERSON] [3], who developed a methodology for effective linking of non-correlated physical properties of the rock formations in an analytical manner, based upon the so-called structural resemblance of parameters distribution [30]. Their concept was built upon the Electric Resistivity Tomography (ERT) methodology combined with the Refraction Seismic Tomography (SRT) [3; 31]. The analysis included 2D models of seismic wave velocity and resistivity. The idea of a 2D inversion solution proposed by [PERSON] et al. [3] took advantage of the fact that even in the absence of analytical links between various physical properties of the rock formation; the possibility was expected of their structural resemblance within the fragments or the entire of the analyzed geological formation. This resemblance resulted from the statistical correlation of the changes in their distribution trends in the zones relatively uniform structurally and genetically [30; 31]. The usefulness of this correlation methodology of geophysical models was confirmed by [PERSON] et al. [21] and [PERSON] et al. [9; 30], who developed the code for 3D inversion of magnetotelluric, gravity and seismic data [9; 30]. The physical properties of geological formations vary with their spatial position and changes may occur in any direction due to the phenomenon of anisotropy. Hence, at each point of the analyzed space, changes may be characterized by their intensity and direction [3]. The similarity of these changes determines the structural convergence of the distribution of the two parameters  
Figure 2: Visualization of the Raczyna and Jódiówka gas fields ([27] modified). **(A)**—Interpretation along the seismic section 16A-2-91K (Drohbyczka—Skopów fragment, POGC seismic works). **(B)**—Interpretation along seismic section XL-100 (Jódówka 3D, POGC seismic works). More explanation in the text. **(C)**—Stratigraphy profile for R-4 well (Source: [[https://otworywiertnicez.pgi.gov.pl](https://otworywiertnicez.pgi.gov.pl)]([https://otworywiertnicez.pgi.gov.pl](https://otworywiertnicez.pgi.gov.pl)); downloading date: 1 August 2023). in the rock formation. These parameters are mathematically expressed as a gradient field vector of the so-called Cross Gradient (CG) [1; 2; 3; 9; 30]. In order to test the SJI as a tool for validation of the interval velocity model applied to run the PreSDM, the results of the long-offset refraction survey and seismic survey were used, provided within the INGA 2 project. The workflow for this procedure is presented in Figure 3. The implementation of the test was enabled by the application of a schema generally known and described by other authors, allowing the utilization of archival seismic and gravity data. It should be emphasized that, until now, these data have not been used in the seismic data processing within this part of the Carpathians. The available schemas were supplemented with a procedure for creating independent velocity models, which serve to address ambiguities resulting from the exclusive use of velocity fields that are intermediate results of the seismic data processing itself. As is known, the acquisition process and initial processing procedures are subject to certain statistical errors and necessitate simplifications in algorithms for the applied procedures. Refraction datasets were determined for 0-6 km offsets using the reflection seismic records.