Reconstructed low-frequency data multi-scale full-waveform inversion method and device based on down-sampling

Abstract

The invention discloses a reconstructed low-frequency data multi-scale full-waveform inversion method based on down-sampling. The method comprises the following steps: acquiring earth surface observation data; reconstructing missing low-frequency part data of the earth surface observation data through a sliding down-sampling method; performing data processing on the obtained reconstructed data; and constructing a background speed model by using the processed reconstruction data through a multi-scale method, taking the background speed model as an initial speed model of traditional full-waveform inversion, and performing iteration to obtain an inversion result. The method comprises the following steps: selecting a sampling point with the maximum cross-correlation value with real observationdata by using a sliding downsampling method to interpolate and reconstruct low-frequency data; according to the invention, the reconstructed low-frequency data is divided into two scales of 0-8Hz andfull-band time domain full waveform inversion on the full band to obtain a high-precision speed model. The modle is used as an initial model of traditional full-waveform inversion, and the cycle skipping phenomenon of full-waveform inversion is relieved.

Description

technical field [0001] The invention relates to the technical field of restoration of low-frequency seismic signals, in particular to a method for reconstructing low-frequency signals with missing seismic data based on the concept of downsampling, and realizing full waveform inversion in combination with the concept of multi-scale. Background technique [0002] The full waveform inversion (FWI, Full waveform inversion) method relies on all the collected waveform information to construct the subsurface model by matching the simulated data with the observed data. The generation of simulated data requires the use of elastic wave equations to describe the propagation process of seismic waves in the subsurface medium. Due to the complexity of fitting data, there are also a large number of local minima in the local optimization problem based on minimizing the objective functional. These local minima will cause huge deviations in the inversion results, and the wrong inversion velo...

AI Technical Summary

Problems solved by technology

However, in actual situations, low-frequency sources and low-frequency data are often difficult to obtain

When the low-frequency part that needs to be extracted in the data is weak or missing, various multi-scale full-waveform inversion methods are usually difficult to achieve the expected results

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