Combined quasi-newton and adaptive gradient optimization scheme used in seismic data processing

A computer program stored in a non-transitory computer readable medium, the program having logic operable to cause a programmable computer to perform actions for determining spatial distribution of properties of formations in a subsurface volume using geophysical sensor signals detected proximate the volume, the actions comprising:

• accepting as input to the computer the geophysical sensor signals;
• inversion processing an initial model of the spatial distribution, the inversion processing comprising calculating expected geophysical sensor signals using the initial model and comparing the expected geophysical sensor signals to the detected geophysical sensor signals, the inversion processing comprising at least second order optimizing, the at least second order optimizing comprising, calculating a scalar and a sparsity-modified matrix using an adaptive gradient type scheme to estimate an inverse Hessian matrix in limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization, using the scaled sparsity-modified matrix to improve the estimated inverse Hessian matrix in order to optimize the inversion processing;
• finalizing the model of the spatial distribution when a value of an objective function in the inversion processing is minimized;
• and
• generating an output representing the spatial distribution in the finalized model, wherein the output comprises the spatial distribution for which the calculated seismic signals most closely match the detected geophysical sensor signals.

A computer program stored in a non-transitory computer readable medium, the program having logic operable to cause a programmable computer to perform actions for determining spatial distribution of properties of formations in a subsurface volume using geophysical sensor signals detected proximate the volume, the actions comprising:

• accepting as input to the computer the geophysical sensor signals;
• inversion processing an initial model of the spatial distribution, the inversion processing comprising calculating expected geophysical sensor signals using the initial model and comparing the calculated geophysical sensor signals to the detected geophysical sensor signals, the inversion processing comprising at least second order optimizing, the at least second order optimizing comprising, calculating an estimate of the inverse Hessian matrix as a convolutional operator and using the estimated inverse Hessian in a modified limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimization, using the modified L-BFGS optimization to optimize the inversion processing;
• and
• finalizing the model of the spatial distribution when a value of an objective function in the inversion processing is minimized, the finalized model representing the spatial distribution for which the expected geophysical sensor signals most closely match the detected geophysical sensor signals.