First-arrival picking through pattern matching and threshold adjustment

Gao, Lei; Liang, Dongsheng; Min, Fan

doi:10.1007/s11600-024-01385-5

Artykuł - szczegóły

Tytuł artykułu

First-arrival picking through pattern matching and threshold adjustment

Autorzy

Gao Lei , Liang Dongsheng , Min Fan

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-024-01385-5

Warianty tytułu

Języki publikacji

Abstrakty

First-arrival picking is a crucial and fundamental task in seismic data processing. Existing direct picking methods are often sensitive to background noise and complex near-surface conditions. In this paper, we propose a first-arrival picking through pattern matching and threshold adjustment (FPMA) method, which comprises two subroutines. The range detection subroutine obtains a first-arrival range with adaptive pattern selection and pattern matching techniques. The former selects an appropriate pattern, while the latter obtains the first-arrival range. The first-arrival detection subroutine determines first arrivals in the range with the threshold adjustment technique, which automatically selects an appropriate threshold for picking. Experiments on five datasets demonstrated that FPMA is more accurate and efficient than four popular methods.

Słowa kluczowe

adaptive pattern-selection pattern matching seismic data processing threshold adjustment first arrival picking

adaptacyjny wybór wzorców dopasowanie wzorców przetwarzanie danych sejsmicznych regulacja progów

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2025

Tom

Vol. 73, no. 1

Strony

321--345

Opis fizyczny

Bibliogr. 53 poz.

Twórcy

autor

Gao Lei

gaolei@swpu.edu.cn

School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China
Lab of Machine Learning, Southwest Petroleum University, Chengdu 610500, China

https://orcid.org/0000-0001-9168-816X

autor

Liang Dongsheng

202122000523@stu.swpu.edu.cn

School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China
Lab of Machine Learning, Southwest Petroleum University, Chengdu 610500, China

autor

Min Fan

minfan@swpu.edu.cn

School of Computer Science and Software Engineering, Southwest Petroleum University, Chengdu 610500, China
Lab of Machine Learning, Southwest Petroleum University, Chengdu 610500, China
Institute for Artificial Intelligence, Southwest Petroleum University, Chengdu 610500, China

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).

Typ dokumentu

Bibliografia

Identyfikator YADDA

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