Evaluation of Neural Network Architectures for First Break Picking

Zwartjes, P. M.; Fernhout, M.; Yoo, Jae Hung

doi:10.3997/2214-4609.202010331

Cited by 3 publications

(1 citation statement)

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“…Zwartjes et al . (2020) considered comparison of different convolution‐based models on real data and concluded that the best results are achieved with UNet architecture consisting of seven convolution blocks. In Xie et al .…”

Section: Introductionmentioning

confidence: 99%

Convolution neural network application for first‐break picking for land seismic data

Loginov

Duchkov

Litvichenko

et al. 2022

Geophysical Prospecting

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An automatic and robust algorithm for the first-break picking is necessary to build the near-surface velocity model. We propose the algorithm based on a convolution neural network. The introduced first-break picking strategy and neural network architecture are suited for processing large volumes of seismic exploration data with reasonable computational resources. To develop an optimal neural network topology and architecture, extensive testing was performed. We compared several architectures of neural networks, including one-and two-dimensional approaches. Our tests justify that the one-dimensional approach (trace-by-trace processing) provides the most reliable results in the case of first-break travel-time variations typical of complicated near-surface structures. This study demonstrates that the four-layered neural network trained on 5,000 traces is enough for robust first-break picking. The algorithm is evaluated on two land-acquisition field datasets from West Siberia with a total used size of about 7 million traces. The first dataset is used for training, and the second one is used only for testing. For both datasets, the error between the original and the predicted first breaks is not more than three samples for 95% of traces. The final evaluation is done by a comparison of seismic stacks to prove the benefits of the approach and its robustness for offsets over 600 m. Finally, the influence of choosing the locations for the training dataset is discussed, and a strategy for using the proposed approach in production work is introduced.

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Section: Introductionmentioning

confidence: 99%

Convolution neural network application for first‐break picking for land seismic data

Loginov

Duchkov

Litvichenko

et al. 2022

Geophysical Prospecting

View full text Add to dashboard Cite

show abstract

References

2021

Land Seismic Case Studies for Near-Surface Modeling and Subsurface Imaging

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A deep learning benchmark for first break detection from hardrock seismic reflection data

St-Charles,

Rousseau,

Ghosn

et al. 2023

GEOPHYSICS

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Deep learning techniques are used to tackle a variety of tasks related to seismic data processing and interpretation. While many works have shown the benefits of deep learning, assessing the generalization capabilities of proposed methods to data acquired in different conditions and geological environments remains challenging. This is especially true for applications in hardrock environments where seismic surveys are still relatively rare. The primary factors that impede the adoption of machine learning in geosciences include the lack of publicly available and labeled datasets, and the use of inadequate evaluation methodologies. Since machine learning models are prone to overfit and underperform when the data used to train them is site-specific, the applicability of these models on new survey data that could be considered “out-of-distribution” is rarely addressed. This is unfortunate, as evaluating predictive models in out-of-distribution settings can provide a good insight into their usefulness in real-world use cases. To tackle these issues, we propose a simple benchmarking methodology for first break picking to evaluate the transferability of deep learning models that are trained across different environments and acquisition conditions. For this, we consider a reflection seismic survey dataset acquired at five distinct hardrock mining sites combined with annotations for first break picking. We train and evaluate a baseline deep learning solution based on a U-Net for future comparisons, and discuss potential improvements to this approach.

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Evaluation of Neural Network Architectures for First Break Picking

Cited by 3 publications

References 0 publications

Convolution neural network application for first‐break picking for land seismic data

Convolution neural network application for first‐break picking for land seismic data

References

A deep learning benchmark for first break detection from hardrock seismic reflection data

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