Abstract: A computer-implemented method for augmented inversion and uncertainty quantification for characterizing geophysical bodies is disclosed. The method includes machine-learning-augmented inversion that also facilitates the characterization of uncertainties in geophysical bodies. The method may further estimate wavelets without a well-log calibration, thereby enabling a pre-discovery exploration phase when well log data is unavailable. The machine learning component incorporates a priori knowledge about the subsurface and physics, such as distributions of expected rock types and rock properties, geological structures, and wavelets, through learning from examples. The methodology also allows for conditioning the characterization with the information extracted a priori about the geobodies, such as probabilities of rock types, using other analysis tools. Thus, the conditioning strategy may make the inversion more robust even when a priori distributions are not well balanced.

Abstract: A method, including: determining, with a computer, a reflectivity spectrum; generating, with a computer, a target spectrum that is a convolution of a roll-off function and the reflectivity spectrum; and filtering, with a computer, seismic data to cause the seismic data to have the target spectrum.

Abstract: A method, including: calibrating a linear rock physics model to well log properties; generating a plurality of pseudo-well models for a subsurface region using a Monte Carlo approach; generating synthetic seismic traces from each of the plurality of pseudo-well models; computing top and base isochron from the synthetic seismic traces; computing seismic attributes in an interval specified by the top and base isochron on the synthetic seismic traces; correlating the seismic attributes to rock properties; and transforming seismic data into low-side, most-likely, and high-side estimates of rock properties.

Abstract: A method, including: determining, with a computer, a reflectivity spectrum; generating, with a computer, a target spectrum that is a convolution of a roll-off function and the reflectivity spectrum; and filtering, with a computer, seismic data to cause the seismic data to have the target spectrum.

Abstract: A method, including: calibrating a linear rock physics model to well log properties; generating a plurality of pseudo-well models for a subsurface region using a Monte Carlo approach; generating synthetic seismic traces from each of the plurality of pseudo-well models; computing top and base isochron from the synthetic seismic traces; computing seismic attributes in an interval specified by the top and base isochron on the synthetic seismic traces; correlating the seismic attributes to rock properties; and transforming seismic data into low-side, most-likely, and high-side estimates of rock properties.