Establishment of Seismic Convolution Model in Seismic Data

In self-developed seismic data processing, the principles and procedures for establishing seismic convolution models can be explained in greater detail. For instance, we can demonstrate how seismic wave data collection determines model parameters through signal processing algorithms, and illustrate computational methods for calculating model depth and shape using wave velocity and amplitude data with mathematical formulations like: depth = (wave_velocity * time) / 2. Additionally, the application of seismic convolution models in geological research can be explored, showing how convolutional neural network architectures or traditional convolution operations (implemented via functions like scipy.signal.convolve) help study Earth's internal structure and tectonic evolution. These detailed explanations, complemented by code snippets showcasing matrix operations and Fourier transform implementations, will help beginners better understand the model establishment process and master seismology knowledge through practical programming examples.