- Using Neural Networks in Seismic Processing to Cut Turnaround🔍
- Towards Using Neural Networks to Complement Conventional ...🔍
- AI|ML Techniques help Reduce Seismic Interpretation Cycle Time🔍
- Seismic drifts of buildings through deep neural networks ...🔍
- Machine learning for seismic processing🔍
- 1 A Neural Network Approach for Improved Seismic Event Detection ...🔍
- Advancing the First Break Picking with Neural Networks🔍
- Universal neural networks for real|time earthquake early warning ...🔍
Using Neural Networks in Seismic Processing to Cut Turnaround
Using Neural Networks in Seismic Processing to Cut Turnaround
A well-trained neural network may significantly reduce the turnaround of a seismic processing task, which is commercially very attractive.
Towards Using Neural Networks to Complement Conventional ...
Towards using neural networks to complement conventional seismic processing algorithms ... workflows or decreasing turnaround time. However, using the ...
AI: a Game Changer in Seismic Acquisition and Processing
Convolutional neural networks (CNNs) are one example of AI that can be trained to perform similar analysis but in an accelerated timeframe.
reduce the time to “first oil” through faster seismic ... deep neural networks can solve complex seismic to well ties, structural interpretation, and.
Seismic drifts of buildings through deep neural networks ...
To this end, this study investigates artificial neural networks (ANN) as prediction models to bypass IDA and quickly and reliably determine the ...
Machine learning for seismic processing - Viridien
Reduce human efforts with machine learning ... Hoeber, 2020, Seismic processing with deep convolutional neural networks: Opportunities and challenges: 2020 Annual ...
1 A Neural Network Approach for Improved Seismic Event Detection ...
Over the past decades, the Groningen Gas Field (GGF) has been increasingly faced by induced earthquakes resulting from gas production. The ...
Advancing the First Break Picking with Neural Networks - Medium
In terms of Machine Learning, it is intuitive to treat this task as Regression, when given a seismic trace, the model predicts a single value — ...
Universal neural networks for real-time earthquake early warning ...
Deep learning enhances earthquake monitoring capabilities by mining seismic waveforms directly. However, current neural networks, ...
Analysis of Deep Learning Neural Networks for Seismic Impedance ...
Neural networks have been applied to seismic inversion problems since the 1990s. More recently, many publications have reported the use of ...
Machine learning to reduce cycle time for time-lapse seismic data ...
The idea is to use ML methods, for example, neural networks, to learn from the data and create simple proxy models to replace the nonlinear physics models ...
Applications of Deep Neural Networks in Exploration Seismology
They have outperformed many traditional algorithms for the automation of seismic data acquisition, data preprocessing, data processing, ...
Full seismic waveform analysis combined with transformer neural ...
Overall, data-driven models perform analogous operations, where a set of explanatory variables is used to optimize the numerical distinction ...
Shortening turnaround time for high-resolution velocity model ... - PGS
In contrast, our approach takes a different path by migrating the data before feeding it into the neural network. ... with neural operators: The Seismic Record,1, ...
Post-stack seismic inversion through probabilistic neural networks ...
So, inversion with AI becomes appropriate to extract information from post-stack data. A probabilistic neural network (PNN) and deep feedforward ...
Using a Deep Neural Network and Transfer Learning to Bridge ...
Approximate earthquake locations can be estimated promptly and automatically; however, accurate locations depend on precise seismic phase ...
Benchmark on the accuracy and efficiency of several neural network ...
Seismic records and dense array observations provide abundant and continuous data; as a result, efficient automatic phase pickers have been extensively used to ...
Deep learning for seismic data processing and interpretation
As examples, Krasnopolsky &. Fox-Rabinovitz (2006) proposed to use a neural network to forecast the weather and Röth. & Tarantola (1994) to ...
Predict passive seismic events with a convolutional neural network
To reduce the size of the input-to-the-network seismic data, we correlate the traces with ... have it cut into several data segments once an event ...
Predicting Unconventional Properties from Seismic and Well Data ...
See how Convolutional neural networks (CNNs) are used to predict unconventional properties from seismic and well data in this Geoconvention ...