库车坳陷山地复杂构造速度场研究及其应用效果
China Petroleum Exploration(2011)
Abstract
在库车坳陷盐下构造油气勘探中,因受上覆地层构造形态和层速度结构的影响,在时间剖面里发生畸变而形成假构造,与地下实际构造形态存在很大差异.变速成图过程包括构造建模和速度场建立这两部分,而速度场精度成为影响圈闭落实精度“瓶颈”之一.采用多种有关的速度信息(测井、地震、沉积相、构造、压实及应力)建立的速度场,具有高的精度,更能反映地层速度的纵横向变化.为变速成图提供准确的速度模型,有效地校正时间剖面上的构造畸变现象,从而提高构造圈闭落实精度.
MoreTranslated text
Key words
trap identification,complex structure,velocity information,velocity field,high accuracy
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined