Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data Article Swipe
Shotaro Nakayama
,
Gerrit Blacquière
,
Tomohide Ishiyama
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1111/1365-2478.12841
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1111/1365-2478.12841
Blended acquisition along with efficient spatial sampling is capable of providing high‐quality seismic data in a cost‐effective and productive manner. While deblending and data reconstruction conventionally accompany this way of data acquisition, the recorded data can be processed directly to estimate subsurface properties. We establish a workflow to design survey parameters that account for the source blending as well as the spatial sampling of sources and detectors. The proposed method involves an iterative scheme to derive the survey design leading to optimum reflectivity and velocity estimation via joint migration inversion. In the workflow, we extend the standard implementation of joint migration inversion to cope with the data acquired in a blended fashion along with irregular detector and source geometries. This makes a direct estimation of reflectivity and velocity models feasible without the need of deblending or data reconstruction. During the iterations, the errors in reflectivity and velocity estimates are used to update the survey parameters by integrating a genetic algorithm and a convolutional neural network. Bio‐inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it carries optimal ones to the subsequent step, which improves the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimation attributable to the choice of survey parameters.
Related Topics
Concepts
Computer science
Workflow
Inversion (geology)
Sampling (signal processing)
Data acquisition
Detector
Algorithm
Convolutional neural network
Regional geology
Data mining
Remote sensing
Artificial intelligence
Hydrogeology
Geology
Telecommunications
Database
Geotechnical engineering
Metamorphic petrology
Paleontology
Structural basin
Operating system
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/1365-2478.12841
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1365-2478.12841
- OA Status
- hybrid
- Cited By
- 2
- References
- 70
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2955643805
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2955643805Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/1365-2478.12841Digital Object Identifier
- Title
-
Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-07-06Full publication date if available
- Authors
-
Shotaro Nakayama, Gerrit Blacquière, Tomohide IshiyamaList of authors in order
- Landing page
-
https://doi.org/10.1111/1365-2478.12841Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1365-2478.12841Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/1365-2478.12841Direct OA link when available
- Concepts
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Computer science, Workflow, Inversion (geology), Sampling (signal processing), Data acquisition, Detector, Algorithm, Convolutional neural network, Regional geology, Data mining, Remote sensing, Artificial intelligence, Hydrogeology, Geology, Telecommunications, Database, Geotechnical engineering, Metamorphic petrology, Paleontology, Structural basin, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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70Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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