[HTML][HTML] MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning
Among the biggest challenges we face in utilizing neural networks trained on waveform (ie,
seismic, electromagnetic, or ultrasound) data is its application to real data. The requirement …
seismic, electromagnetic, or ultrasound) data is its application to real data. The requirement …
MLReal: Bridging the gap between training on synthetic data and real data applications in machine learning
The requirement for accurate labels in supervised learning often forces us to train our
networks using synthetic data. However, synthetic experiments do not reflect the realities of …
networks using synthetic data. However, synthetic experiments do not reflect the realities of …
A compact program for 3D passive seismic source‐location imaging
Microseismic source‐location imaging is important for inferring the dynamic status of
reservoirs during hydraulic fracturing. The accuracy and resolution of the located …
reservoirs during hydraulic fracturing. The accuracy and resolution of the located …
Deep Earth: Leveraging neural networks for seismic exploration objectives
Machine learning has already made many inroads in developments related to acquisition,
processing, imaging, inverting, and interpreting seismic data. In spite of the many success …
processing, imaging, inverting, and interpreting seismic data. In spite of the many success …
Passive seismic event locating with full waveform inversion and machine learning methods
H Wang - 2021 - repository.kaust.edu.sa
One of the key goals of microseismic monitoring is the accurate estimation of the source
location. The accuracy of both P-and S-wave velocities strongly influences the estimation of …
location. The accuracy of both P-and S-wave velocities strongly influences the estimation of …