HARMONIC ENCODING FOR FWI

摘要

A deterministic method for selecting a set of encoding weights for simultaneous encoded-source inversion of seismic data that will cause the iterative inversion to converge faster than randomly chosen weights. The encoded individual source gathers are summed (83), forming a composite gather, and simulated in a single simulation operation. The invention creates multiple realizations of the simulation (84), each with its own encoding vector (82) whose components are the weights for the shots in the composite gather. The encoding vectors of the invention are required to be orthogonal (82), which condition cannot be satisfied by random weights, and in various embodiments of the invention are related to eigenvectors of a Laplacian matrix, sine or cosine functions, or Chebyshev nodes as given by the roots of Chebyshev polynomials. For non-fixed receiver geometry, an encoded mask (61) may be used to approximately account for non-listening receivers.

主权项

1. An iterative method for inversion of seismic data to update a model of subsurface velocity or other physical property, wherein a plurality of encoded source gathers of data are inverted simultaneously, said method comprising:
(a) selecting a plurality of individual source gathers of the seismic data; (b) in a first iteration, encoding the selected gathers with weights, said weights forming components of a weight vector, and summing the encoded gathers to form a composite gather; (c) generating at least one realization of predicted data for the entire composite gather, wherein the predicted data are computer-simulated, using a current model, in a single forward-modeling operation, a different realization being characterized by a different weight vector; (d) updating the current model using the composite gather and the simulated composite gather from each of the at least one realization; (e) in a second iteration, repeating (b)-(d), using the updated model from the first iteration as the current model for the second iteration, resulting in a further updated model; wherein, (i) each iteration has a plurality of realizations, and the weight vectors for each realization are orthogonal to one another; or (ii) the weight vector or weight vectors for the first iteration are orthogonal to the weight vector or weight vectors for the second iteration; or both (i) and (ii); and wherein the orthogonal weight vectors are generated using a smoothly varying periodic function.