Laplace-Domain Crosstalk-Free Source-Encoded Elastic Full Waveform Inversion Using Time-Domain Solvers
Geophysics(2024)SCI 2区
Formerly Princeton Univ
Abstract
Crosstalk-free source-encoded elastic full-waveform inversion (FWI) using time-domain solvers demonstrates skill and efficiency at conducting seismic inversions involving multiple sources and receivers with limited computational resources. A drawback of common formulations of the procedure is that, by sweeping through the frequency domain randomly at a rate of one or a few sparsely sampled frequencies per shot, it is difficult to simultaneously incorporate time-selective data windows, as necessary for the targeting of arrivals or wave packets during the various stages of the inversion. Here, we solve this problem by using the Laplace transform of the data. Using complex-valued frequencies allows for damping the records with flexible decay rates and temporal offsets that target specific traveltimes. We present the theory of crosstalk-free source-encoded FWI in the Laplace domain, develop the details of its implementation, and illustrate the procedure with numerical examples relevant to exploration-scale scenarios.
MoreTranslated text
Key words
Seismic Waveform Inversion,Full-Waveform Tomography,Wave Equation Migration,Elastic Properties,Inverse Problems
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
Related Papers
Waveform Modeling of Hydroacoustic Teleseismic Earthquake Records from Autonomous Mermaid Floats
GEOPHYSICAL JOURNAL INTERNATIONAL 2024
被引用0
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