The first-break detection for real seismic data with use of convolutional neural network

G. Loginov, D. Anton, D. Litvichenko, S. Alyamkin

Результат исследования: Публикации в книгах, отчётах, сборниках, трудах конференцийстатья в сборнике материалов конференциинаучнаярецензирование

3 Цитирования (Scopus)


In this study, we appraise a convolutional neural network for the detection of the first breaks on the real 3D seismic data set. The use of convolution as a learning kernel is followed by an assumption that the seismic trace can be considered as a convolution of source signal with the reflectivity function. The investigation area includes mixed elevations, floodplains of the rivers and the regions of strong permafrost, where the shingling effect is observed. We consider the first-break detection for each trace independently to preserve the complicated structure of the arrival times. The proposed approach was apprised on real exploration 3D seismic data set with size over 4.5 million traces. This test showed that the error between the original and predicted first breaks is not more than 3 samples for 95 percents of data set. The final quality control of picking results was established by the calculation of static corrections and computing seismic stacks, which showed that the proposed approach provides better results.

Язык оригиналаанглийский
Название основной публикации81st EAGE Conference and Exhibition 2019
ИздательEAGE Publishing BV
Число страниц5
ISBN (электронное издание)9789462822894
СостояниеОпубликовано - 3 июн. 2019
Событие81st EAGE Conference and Exhibition 2019 - London, Великобритания
Продолжительность: 3 июн. 20196 июн. 2019

Серия публикаций

Название81st EAGE Conference and Exhibition 2019


Конференция81st EAGE Conference and Exhibition 2019


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