Generating Synthetic Seismograms with Specific Reflection Coefficients Under Defined Conditions

Synthetic seismograms are technical tools that utilize mathematical models to simulate the propagation of seismic waves through subsurface media. They generate artificial seismic signals based on specific reflection coefficients and geological conditions, holding significant value in geophysical exploration, seismological research, and educational demonstrations.

To create synthetic seismograms under specific reflection coefficient conditions, several key elements must be defined: Reflection coefficient sequence - Describes acoustic impedance contrasts at subsurface lithological interfaces, directly affecting reflection wave amplitude and polarity. Seismic wavelet - Typically implemented using Ricker wavelets or other model wavelets, where frequency parameters determine the resolution of seismic records. Propagation model - Incorporates wave attenuation characteristics, multiple reflections, and other propagation effects through media.

The implementation process involves convolving the reflection coefficient sequence with the seismic wavelet, then superimposing necessary noise components or propagation effect corrections. This synthetic approach enables researchers to test interpretation algorithms or train machine learning models without conducting actual field surveys. Advanced implementations incorporate physical characteristics of wavefield propagation, such as spherical divergence compensation and absorption attenuation factors. Code implementations typically utilize convolution operations (e.g., numpy.convolve in Python or conv in MATLAB) combined with noise generation functions to simulate realistic conditions.

Extended applications of this technology include: validating inversion algorithms, designing acquisition parameters, and serving as standard test data for seismic interpretation training. By adjusting reflection coefficients and wavelet parameters, practitioners can simulate typical geological scenarios like thin-bed tuning effects and complex structural configurations through parameterized modeling approaches.