Pretraining system and method for seismic data processing using machine learning
AssigneeKing Abdullah University of Science and Technology
InventorsBingbing Sun, Tariq Ali Alkhalifah
Life of this patent
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Abstract
A method for processing waveform data for determining a characteristic inside an object includes receiving raw waveform data collected with waveform sensors over a given object; pretraining a first neural network with the raw waveform data so that structural information within the raw waveform data is extracted; receiving a first task to be performed with regard to the object, based on the raw waveform data; fine-tuning a second neural network, based on (1) pretrained parameters from the first neural network, and (2) the first task; and generating an image of the object using the second neural network with refined parameters.
Claims20
A method for processing waveform data for determining a characteristic inside an underground geophysical formation, the method comprising:
- receiving raw waveform data collected with waveform sensors over the underground geophysical formation;
- pretraining a first neural network with the raw waveform data so that structural information within the raw waveform data is extracted and pretrained parameters of the first neural network are generated;
- receiving a first seismic data processing task to be performed with regard to the underground geophysical formation, based on the raw waveform data;
- fine-tuning a second neural network by using the pretrained parameters from the first neural network, and generating refined parameters in response to the first task;
- and
- generating an image of the underground geophysical formation using the second neural network with the refined parameters to map the raw waveform data for oil and gas exploration.
A computing device for processing waveform data for determining a characteristic of an underground geophysical formation, the computing device comprising:
- an input/output interface configured to receive raw waveform data collected with waveform sensors over the underground geophysical formation;
- and
- a processor connected to the input/output interface and configured to,
- pretrain a first neural network with the raw waveform data so that sequential structural information within the raw waveform data is extracted and pretrained parameters of the first neural network are generated;
- receive a first seismic data processing task to be performed with regard to the underground geophysical formation, based on the raw waveform data;
- fine-tune a second neural network by using the pretrained parameters from the first neural network, and generating refined parameters in response to the first task;
- and
- generate an image of the underground geophysical formation using the second neural network with the refined parameters to map the raw waveform data for oil and gas exploration.
A non-transitory computer readable medium including computer executable instructions, wherein the instructions, when executed by a processor, implement a method for processing waveform data for determining a characteristic of an underground geophysical formation, the medium comprising instructions for:
- receiving raw waveform data collected with waveform sensors over the underground geophysical formation;
- pretraining a first neural network with the raw waveform data so that structural information within the raw waveform data is extracted and pretrained parameters of the first neural network are generated;
- receiving a first seismic data processing task to be performed with regard to the underground geophysical formation, based on the raw waveform data;
- fine-tuning a second neural network by using the pretrained parameters from the first neural network, and generating refined parameters in response to the first task;
- and
- generating an image of the underground geophysical formation using the second neural network with the refined parameters to map the raw waveform data for oil and gas exploration.
Dependencies are inferred from the claim text.