WebSpecifically, seismic traces and elastic impedance (EI) traces are modeled as a time series. Then, a neural-network-based inversion model comprising convolutional and recurrent neural layers is used to invert seismic data for EI. The proposed workflow uses well-log data to guide the inversion. WebABSTRACT During linearized waveform inversion (LWI), the presence of small inaccuracies in the background subsurface model can lead to unfocused seismic events in the final image. The effect on the amplitude can mislead the interpretation. We have developed a joint inversion scheme in the model domain of the reflectivity and the background …

Debiasing of seismic reflectivity inversion using basis …

WebApr 13, 2024 · ABSTRACT P/S-wave separation is a key step for data processing in multicomponent seismic exploration. The conventional methods rely on either the prior information of near-surface elastic properties or the carefully selected parameters to estimate the polarization directions of the P- and S-modes when arriving at the … WebSeismic inversion is a technique that has been in use by geophysicists for over forty years. Early inversion techniques transformed the seismic data into Pimpedance (the product of density and P-wave velocity), from which … heroes eclairs

Optimal Seismic Reflectivity Inversion: Data-Driven --Loss ...

WebNov 4, 2024 · Reflectivity inversion methods based on a stationary convolution model are essential for seismic data processing. They compress the seismic wavelet, and by … WebFeb 5, 2012 · A complete seismic inversion would solve for the physical parameters in the scattering potential. Most imaging algorithms stop short of that, solving instead for a reflectivity function. To produce the physical model … WebMay 29, 2024 · Seismic inversion is a procedure that helps extract underlying models of the physical characteristics of rocks and fluids from seismic and well-log data. In the absence of well data, the properties can also be inferred from the … heroes edytor map