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Model-data-driven P-wave impedance inversion using ResNets and the normalized zero-lag cross-correlation objective function
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DOI：10.1016/j.petsci.2022.09.008.png)
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Abstract
En 中文
Model-driven and data-driven inversions are two prominent methods for obtaining P-wave impedance, which is significant in reservoir description and identification. Based on proper initial models, most model-driven methods primarily use the limited frequency bandwidth information of seismic data and can invert P-wave impedance with high accuracy, but not high resolution. Conventional data-driven methods mainly employ the information from well-log data and can provide high-accuracy and high-resolution P-wave impedance owing to the superior nonlinear curve fitting capacity of neural networks. However, these methods require a significant number of training samples, which are frequently insufficient. To obtain P-wave impedance with both high accuracy and high resolution, we propose a model-data-driven inversion method using ResNets and the normalized zero-lag cross-correlation objective function which is effective for avoiding local minima and suppressing random noise. By using initial models and training samples, the proposed model-data-driven method can invert P-wave impedance with satisfactory accuracy and resolution. Tests on synthetic and field data demonstrate the proposed method's efficacy and practicability. (c) 2022 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
Keywords:
Model-data-driven
P-wave impedance inversion
ResNets
Zero-lag cross-correlation
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