## 4 Discussion
This means they seemingly act in one direction, but the essential capability to influence the built environment and cause damage is its vibratory (alternating) behavior when two maximum opposite SFs, as a part of the most prominent EF, run over the structures. It is interesting to note that the head of the R\({}_{\rm Q}\)-envelope corresponds with the main part. **Does the transform \(\mathcal{T}\) produce a simple or weighted bigraph? **--This paper is written under the assumption that all forms of bigraphs that result from transform \(\mathcal{T}\) are simple, which means that they are not directed nor weighted (the nodes and edges are simple). Let us remember that the ordering operation rearranges SFs; therefore, the significant ones are separated from less significant or insignificant ones. In other words, the significance of SFs rises along the ordered sequence. Since the SFs are transposed into nodes of a bigraph, it is quite natural to consider input nodes as weighted, different from each other. Similarly, an edge that connects significant (input and output) nodes (read: SF with bigger maximum amplitude) cannot be considered equal to the edge connecting insignificant edges. Therefore, the answer to the title question is obvious. Analysis of weighted bigraphs is more complex than that of simple ones. We followed an implicit assumption in this paper, which was to treat all forms of bigraphs introduced in the paper as simple, as our intention was to present these fundamental concepts in a clear context. In further research, it will be natural to consider weighted bigraphs. **Bipartite graph randomness**--In the Introduction, we mentioned deterministic and random bipartite graphs. The first show all existing relations (edges) between input and output nodes. It is seemingly the same in the case of a random graph--one sees the firm structure of edges between nodes in such a graph. Actually, a random graph shows possible (potential) edges between nodes. This means, in the case of a (deterministic) conventional graph, the actual graph is the only one, while in the second case, the considered graph is just one of many possible realizations, creating a whole statistical ensemble in which any of its elements is a graph with a firm structure, but slightly different from the others. Each 'tossing' (trial) of the potential random graph produces a different realization--a graph with a firm structure. **EFs/SFs and wavelets**--There is an apparent similarity between the concept of EF/SF and wavelets. How, otherwise, to call these 'wavelets' when they are small chunks of a complex waveform, a seismogram in this case? The authors deliberately decided not to use the term wavelet in this case. The term wavelet can have two associations: as a small wave (impulse) and as a mathematical object. What is more, these associations can be mixed, and one can complain about borrowing that name for a new concept. The concept of EF/SF truly reminds us of wavelets, which begs for a comparison of these two approaches to seismogram description. Wavelets are particularly suitable for localized signals and random transients; a typical example is the seismic signal represented by a seismogram. Regarding the comparison between the concept of EF/SF and wavelets, there are essential differences. The wavelets (of a certain type) represent a system of similar elementary waveforms and impulses, a kind of offspring originating from a generative function, the so-called mother function. They form the basis within which a signal is to be decomposed. It is quite similar to the FT, where a sinusoid is a generative function, the function of a parameter, which can create a set of harmonics, a basis for signal decomposition. As for the concept of EF/SF introduced in this paper, it is impossible to create a basis because they are not only parametrically interrelated but are of random shape, i.e., the SFs do not have shape similarity.