BibTex Citation Data :
@article{JPA25867, author = {Wiji Raharjo and Indriati Retno Palupi and Oktavia Dewi Alfiani}, title = {Comparison Between Seismic Inversion and Seismic Inversion with Bayesian Inference in Acoustic Impedance}, journal = {Journal of Physics and Its Applications}, volume = {7}, number = {3}, year = {2025}, keywords = {Acoustic Impedance; Reflection Coefficient; Model Based Inversion; Bayesian MCMC;}, abstract = { Finding reflection coefficient of seismic trace data is very important to be analyzed in some geological features. Reflection coefficient describes the medium of the subsurface based on Acoustic Impedance (AI) data. Model based seismic inversion is one way that can be used to find reflection coefficient of trace seismic. It needs several steps, like generating calculated trace seismic due to the original one before inversion. Unfortunately, the process is very complicated to reach a best result indicated by error value tends to be zero. While Bayesian MCMC offers the easier way, by setting mean and standard deviation values, it will generate calculated seismic trace data automatically with high similarity to the original one. In other words, Bayesian MCMC helping the inversion process to be shorter. Finally, we have proven that Bayesian MCMC gives the better result of reflection coefficient of model based seismic inversion method. }, issn = {2622-5956}, pages = {78--81} doi = {10.14710/jpa.v7i3.25867}, url = {https://ejournal2.undip.ac.id/index.php/jpa/article/view/25867} }
Refworks Citation Data :
Finding reflection coefficient of seismic trace data is very important to be analyzed in some geological features. Reflection coefficient describes the medium of the subsurface based on Acoustic Impedance (AI) data. Model based seismic inversion is one way that can be used to find reflection coefficient of trace seismic. It needs several steps, like generating calculated trace seismic due to the original one before inversion. Unfortunately, the process is very complicated to reach a best result indicated by error value tends to be zero. While Bayesian MCMC offers the easier way, by setting mean and standard deviation values, it will generate calculated seismic trace data automatically with high similarity to the original one. In other words, Bayesian MCMC helping the inversion process to be shorter. Finally, we have proven that Bayesian MCMC gives the better result of reflection coefficient of model based seismic inversion method.
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