## 1 Introduction  
In this paper, we propose the use of a _Discrete Wavelet transform_ as the convolution operation in a convolutional neural network [1, 2] revised in [3]. In recent years, convolutional neural networks (CNNs) have had multiple applications in different areas, mainly in image recognition or feature separation in an _information cube_ due to their excellent classification capacity, the flexibility of its algorithm [2], and the ability to pair to solve different problems in various areas [4] such as feature analysis [5]. The design of different CNN architectures [6, 7] has progressed to improve the results of _certainty_, as well as save the computing resources used. To enhance these architectures, different hybrid CNN algorithms have been proposed [8, 9] that apply to various problems and present advantages over traditional CNN [10] by being able to process information on multiple scales and various measurements [4] such as presented in [11]. Hybrid CNNs are a combinatorial strategy that uses other algorithms to improve several results, such as accuracy. In particular, using the multi-resolution strategy allows one to separate the multifrequency response, a typical situation within seismic signatures such as those presented by [12, 13]. In that respect, several procedures may be followed from geophysics studies. Nevertheless, this particular approach of highlighting several features independently from frequency responses, as well as reclassifying this behavior at the rest of those geophysical characteristics, is quite valuable from these authors as well. Information flows in a traditional CNN and is redefined based on interaction in the design of its architecture with the _Discrete Wavelet_ transform [14, 15] to provide an alternative to the convolution of a CNN and analyze the advantages and disadvantagesthat this proposal contains. In different works, the contribution of the Wavelet transform in the use of a CNN is known [6; 16; 17; 18]. However, it is commonly used to process the data before it enters the CNN [17; 19]. Unlike these approaches, a time-frequency decomposition method is proposed [9; 20; 21], seeking to recover as much information as possible and then organize it sensitively into patterns that allow categorizing specific characteristics with better sensitivity compared to a traditional CNN [22; 23]. It should be noted that the approach followed in this paper incorporates the use of the _Pooling_ algorithm as a matrix-based information compaction strategy, achieving the representation of relevant information between neighboring vectors. This method aims to extract features of specific interest in a data interval scaled in power, time, and frequencies containing multidimensional data using a proximity map between them. The assertiveness rate designed by the user is of particular interest. A Discrete Wavelet transform explores focused frequency response in terms of several characteristics that may be highlighted later. In the context of this work, it is interesting to point out the number of variables (Table 1), both global and local, which play an essential factor in the decision-making for the construction of the method, making this composition of local algorithms a global strategy feasible to be optimized under multiple metrics such as processing speed or assertiveness in the extraction of features, as well as other Merit features that are analyzed at the end of this work.