High-performance CPU-GPU heterogeneous computing method for 9-component ambient noise cross-correlation Article Swipe
Jingxi Wang
,
Wang Weitao
,
Chao Wu
,
Lei Jiang
,
Zou Hanwen
,
Yao Huajian
,
Ling Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.eqrea.2024.100357
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1016/j.eqrea.2024.100357
Ambient noise tomography is an established technique in seismology, where calculating single- or nine-component noise cross-correlation functions (NCFs) is a fundamental first step. In this study, we introduced a novel CPU-GPU heterogeneous computing framework designed to significantly enhance the efficiency of computing 9-component NCFs from seismic ambient noise data. This framework not only accelerated the computational process by leveraging the Compute Unified Device Architecture (CUDA) but also improved the signal-to-noise ratio (SNR) through innovative stacking techniques, such as time-frequency domain phase-weighted stacking (tf-PWS). We validated the program using multiple datasets, confirming its superior computation speed, improved reliability, and higher signal-to-noise ratios for NCFs. Our comprehensive study provides detailed insights into optimizing the computational processes for noise cross-correlation functions, thereby enhancing the precision and efficiency of ambient noise imaging.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.eqrea.2024.100357
- OA Status
- diamond
- Cited By
- 1
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405970316
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4405970316Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.eqrea.2024.100357Digital Object Identifier
- Title
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High-performance CPU-GPU heterogeneous computing method for 9-component ambient noise cross-correlationWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
-
Jingxi Wang, Wang Weitao, Chao Wu, Lei Jiang, Zou Hanwen, Yao Huajian, Ling ChenList of authors in order
- Landing page
-
https://doi.org/10.1016/j.eqrea.2024.100357Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://doi.org/10.1016/j.eqrea.2024.100357Direct OA link when available
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Component (thermodynamics), Computer science, Noise (video), Parallel computing, Supercomputer, CUDA, Correlation, Computational science, Artificial intelligence, Mathematics, Physics, Image (mathematics), Thermodynamics, GeometryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.innovative | 73 |
| abstract_inverted_index.introduced | 27 |
| abstract_inverted_index.leveraging | 58 |
| abstract_inverted_index.optimizing | 110 |
| abstract_inverted_index.tomography | 2 |
| abstract_inverted_index.9-component | 42 |
| abstract_inverted_index.accelerated | 53 |
| abstract_inverted_index.calculating | 10 |
| abstract_inverted_index.computation | 93 |
| abstract_inverted_index.established | 5 |
| abstract_inverted_index.fundamental | 20 |
| abstract_inverted_index.seismology, | 8 |
| abstract_inverted_index.techniques, | 75 |
| abstract_inverted_index.Architecture | 63 |
| abstract_inverted_index.reliability, | 96 |
| abstract_inverted_index.comprehensive | 104 |
| abstract_inverted_index.computational | 55, 112 |
| abstract_inverted_index.heterogeneous | 31 |
| abstract_inverted_index.significantly | 36 |
| abstract_inverted_index.nine-component | 13 |
| abstract_inverted_index.phase-weighted | 80 |
| abstract_inverted_index.time-frequency | 78 |
| abstract_inverted_index.signal-to-noise | 69, 99 |
| abstract_inverted_index.cross-correlation | 15, 116 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.4099999964237213 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.82746184 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |