Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-10T14:15:16.562441Z
Paper Citation Record · LEDGER
Physics-Driven Self-Supervised Deep Learning for Free-Surface Multiple Elimination
As of 18 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 0 inbound Pith citation observations for arXiv:2502.05189.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
3 of 3 outbound references displayed
- verified exact0
- verified fuzzy3
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation eea2087e-bc95-442f-82b3-4b027356de1c · outbound
This paper cites The governing physical equation,which links the full wavefield, primaries and multiples, serves as the foundation of the geophysical primary estimation method SRME.
Physics-Driven Self-Supervised Deep Learning for Free-Surface Multiple Elimination The governing physical equation,which links the full wavefield, primaries and multiples, serves as the foundation of the geophysical primary estimation method SRME
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:15:16.595443Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation bb296338-fc52-44b8-90fc-846b65c17983 · outbound
This paper cites In geophysics, DL methods are commonly based on supervised learning from large amounts of high-quality labelled data.
Physics-Driven Self-Supervised Deep Learning for Free-Surface Multiple Elimination In geophysics, DL methods are commonly based on supervised learning from large amounts of high-quality labelled data
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:15:16.622540Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation b5d4a482-d987-4cc1-9d3a-ed97cedf6a09 · outbound
This paper cites The established practice of SRME methods consists of two parts: first, predicting the multiples, and second, subtracting the predicted multiples from the full wavefield.
Physics-Driven Self-Supervised Deep Learning for Free-Surface Multiple Elimination The established practice of SRME methods consists of two parts: first, predicting the multiples, and second, subtracting the predicted multiples from the full wavefield
Reference 1992
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-10T14:15:16.607976Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.