Abstract: Provided is a method of processing elastic wave data, the method including selecting some elastic wave traces as a first label from among a plurality of elastic wave traces received without at least some elastic wave traces missing from whole elastic wave data, training an interpolation model on a machine learning basis by using at least two or more of remaining elastic wave traces except for the first label and the first label, restoring the at least some elastic wave traces missing from the whole elastic wave data by using the trained interpolation model, training an extrapolation model on a machine learning basis by using an elastic wave trace selected as a second label from among a plurality of elastic wave traces included in whole restored elastic wave data and at least two or more of remaining elastic wave traces except for the second label, and generating an additional elastic wave trace, which have not been included in the whole elastic wave data, by using the trained extrapolation model.

Abstract: Techniques for a machine learning-based two-step impedance inversion method using seismic data are disclosed. In some embodiments of the disclosed technology, an impedance inversion method includes generating a domain adaptation model configured to predict, based on source data associated with a source area that includes a well, a P-impedance value of a target area that does not include a well, and generating, using the P-impedance value generated by the domain adaptation model, a P-impedance low frequency model configured to predict a final P-impedance value of the target area by performing an inversion. In this way, it is possible to accurately predict P-impedance value of an area where a well does not exist.

Abstract: Provided is a method of processing elastic wave data, the method including selecting some elastic wave traces as a first label from among a plurality of elastic wave traces received without at least some elastic wave traces missing from whole elastic wave data, training an interpolation model on a machine learning basis by using at least two or more of remaining elastic wave traces except for the first label and the first label, restoring the at least some elastic wave traces missing from the whole elastic wave data by using the trained interpolation model, training an extrapolation model on a machine learning basis by using an elastic wave trace selected as a second label from among a plurality of elastic wave traces included in whole restored elastic wave data and at least two or more of remaining elastic wave traces except for the second label, and generating an additional elastic wave trace, which have not been included in the whole elastic wave data, by using the trained extrapolation model.

Abstract: The present invention relates to a one-way wave equation prestack depth migration method using an elastic generalized-screen (EGS) wave propagator capable of efficiently expressing the movement of an elastic wave passing through a mutual mode conversion between a P-wave and an S-wave while propagating boundary surfaces of an underground medium, by expanding, to an elastic wave equation, a conventional scalar generalized-screen (SGS) technique capable of quickly calculating the propagation of a wave in a medium in which there is a horizontal speed change, and according to the present invention, provided is a prestack EGS migration method for seismic wave multi-component data, which: can calculate a wave field with higher accuracy in a medium having a complex structure by expanding up to a second term of a Taylor series expansion of a vertical slowness term of a propagator; includes a mode separation operator in the propagator so as to directly use a shot gather as a migration input, without the need to separat