Deep Learning Seismic Inversion Based on Prestack Waveform Datasets

doi:10.1109/TGRS.2022.3195858

2022-01-01

PRE

DOI：10.1109/TGRS.2022.3195858

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摘要

En 中文

Prediction of elastic parameters (e.g., P-, S-wave velocity, and density) from observed seismic data is one of the most common means of reservoir characterization. Recently, deep learning (DL), as a data-driven approach, has been attracting increasing interest in seismic inversion. DL is proven to have the potential to learn complex systems and solve inverse problems efficiently. One of the most key components of DL is the training dataset, and an effective training dataset is a prerequisite for the success of DL-based methods. In seismic inversion, the training dataset needs to be artificially expanded due to the limited number of actual training data pairs. Traditional approaches of using the exact Zoeppritz equation (EZE) or its approximations for training dataset construction have limitations, principally, the single interface assumption and the neglect of wave propagation effects. Alternatively, the analytical solution of the 1-D wave equation (i.e., reflectivity method [RM]) can simulate the full wave, including transmission losses and internal multiples, and can be executed in a target-oriented manner. Inspired by this, we develop a data-driven elastic parameter prediction method based on waveform formulation. The method uses RM to construct training dataset, which both compensates for the inadequate training dataset in data-driven seismic inversion and improves the accuracy of the inversion results. We implement the method in a synthetic model as well as field data. The results are compared with model-driven methods (EZE and RM) and data-driven method based on EZE, and it is shown that the proposed method outperforms these three methods.

Keyword:

Training

Mathematical models

Reservoirs

Propagation losses

Training data

Reflection coefficient

Impedance

Deep learning (DL)

reflectivity method (RM)

reservoir characterization

waveform inversion