.png?width=150&height=38&name=ESA_Logo2022_CW%20(3).png){kind=logo}
- MODEL TRAINING
- SCALING
- SEISMIC WELL-TIE
- MODEL QC
- UNCERTAINTY CUBE
- ACTIVE LEARNING
Model training
With EarthNET Seismic properties, you can train models to predict many different properties from seismic, including vp, vs, vp/vs, density, acoustic impedance, lithology, porosity, vclay, permeability, water saturation, and more. The property you want to predict needs to be represented in the form of a well log, and there should be a discernable relationship between the property and the seismic data that can be learned during the model training process.
You get access to a large number of deep learning model architectures in EarthNET including neural networks such as Unet’s and transformers. We ensure that we keep up with the latest advancements in ML architecture and provide you with the most advanced and accurate models.
Scaling methods
Since seismic data has a coarse sampling interval, whereas well data has a fine sampling interval, you need to scale the well data to the seismic data.
The scaling functionality in EarthNET ensures that the detail level between the 3D volume features and the well log responses are comparable.
Seismic well-tie
Seismic data is recorded in time (TWT) whereas well data is recorded in depth (MD). In order to use well data in combination with seismic data, we need to ensure that both datasets are aligned. EarthNETs 'seismic well tie tool lets you quantify this time-depth relationship.
Model QC
Review and compare model results and performance metrics for multiple models to identify the models that perform best for your tasks.
All your trained ML models will be stored in your model library, along with all the metadata that describes the training data used, the model architecture, parameters, and results. With full information about each model, you can quickly identify potential causes for suboptimal performance, mitigate them and re-train the model.
Uncertainty cube
The prediction of properties from seismic is a process that provides non-unique solutions, meaning that the results are always uncertain. It is therefore crucial to quantify this uncertainty, and EarthNET provides the tools to do so.
By using our uncertainty cube functionality, you can obtain a standard deviation volume that describes the uncertainty in your predicted property. This enables you to assess where the uncertainty is high and where it is low, which is critical when providing the basis for decision-making.
Active learning
The infrastructure in EarthNET facilitates active learning to ensure that you have a set of models that improve over time with minimal effort.
EarthNET supports active learning workflows through quantifying model performance, improving input data through data cleaning and labeling, retraining models using the improved input data, and tracking model performance over time.
Get in touch
We are dedicated to helping our clients unleash the full potential of their data.
Let's talk about the opportunities, and explore how we can assist you.