StorSeismic: A New Paradigm in Deep Learning for Seismic Processing

Machine learned tasks on seismic data are often trained sequentially and separately, even though they utilize the same features (i.e., geometrical) of the data. We present StorSeismic as a dataset-centric framework for seismic data processing, which consists of neural network (NN) pretraining and fi...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-15
Main Authors: Harsuko, Randy, Alkhalifah, Tariq A.
Format: Article
Language:English
Subjects:
Bit error rate
Coders
Computer architecture
Data analysis
Data processing
Datasets
Deep learning
Feature extraction
Information processing
Inversion
machine learning (ML)
Machine learning algorithms
Natural language processing
Neural networks
Procedures
Seismic data
Seismic data processing
seismic processing
Seismic response
self-supervised learning
Task analysis
Training
transformer
Transformers
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Summary:Machine learned tasks on seismic data are often trained sequentially and separately, even though they utilize the same features (i.e., geometrical) of the data. We present StorSeismic as a dataset-centric framework for seismic data processing, which consists of neural network (NN) pretraining and fine-tuning procedures. We, specifically, utilize a NN as a preprocessing tool to extract and store seismic data features of a particular dataset for any downstream tasks. After pretraining, the resulting model can be utilized later, through a fine-tuning procedure, to perform different tasks using limited additional training. Used often in natural language processing (NLP) and lately in vision tasks, bidirectional encoder representations from transformer (BERT), a form of a transformer model, provides an optimal platform for this framework. The attention mechanism of BERT, applied here on a sequence of traces within the shot gather, is able to capture and store key geometrical features of the seismic data. We pretrain StorSeismic on field data, along with synthetically generated ones, in the self-supervised step. Then, we use the labeled synthetic data to fine-tune the pretrained network in a supervised fashion to perform various seismic processing tasks, such as denoising, velocity estimation, first arrival picking, and normal moveout (NMO). Finally, the fine-tuned model is used to obtain satisfactory inference results on the field data.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3216660