(untitled) (Research, Society and Development, 2024)
The application of machine learning in geophysics has steeply increased in the last decade, with the quality of its results varying according to the type of seismic problem in focus and the employed computational method. Deep Learning methods are achieving impressive results in this area, but we note that there is still a lack of certainty on whether classical machine learning methods can provide similar results. In the present paper, the objective was to attempt to fill part of that gap, by comparing a well-known non-DL machine-learning method with a DL method for the direct wave propagation and the seismic inversion problems for 2D horizontally-layered models. Both methods are evaluated in different scenarios, but under similar conditions, so that it is possible to understand the effect of parameter configuration on their final results. The dataset has 20,000 samples, each consisting of three vectors: a velocity vector with 236 values (representing a vertical profile of a randomly generated 2D layered model), a reflective vector with 600 values obtained directly from the velocity vector, and the associated seismogram vector with 11 traces, each containing 600 values. The overall results show that the WaveNet produces a lower Mean Squared Error between predicted and correct outputs than that of XGBoost. One challenge yet not dealt with is that the WaveNet can train well in GPU, but we did not succeed in doing the same with the XGBoost, due to the amount of data to be processed.