Scotty Salamoff, Global Geoscience Product Manager and Geophysical Advisor at Bluware
Introduction
Bias in deep learning is a critical factor to consider, especially as it pertains to seismic interpretation. It is typically seen as something to avoid, but when applying interactive deep learning methods to seismic interpretation tasks, it embodies the real-world expertise and decision-making of geoscientists, helping models learn more effectively from human experience.
In traditional deep learning, “bias” often has a negative connotation. It is associated with skewed data or algorithmic assumptions that distort a model’s accuracy and fairness. However, in interactive deep learning, bias takes on a different meaning—it represents the input of human experts, directly enhancing the model’s ability to perform tasks based on real-world expertise.
This article will explain how bias functions differently in traditional versus interactive deep learning, highlighting its crucial role in the latter.
The Nature of Bias in Traditional Deep Learning
In conventional deep learning, bias is a potential problem. It can arise from unbalanced training data or flawed algorithms, leading to skewed or suboptimal outcomes. For instance, a model trained on a data set dominated by a particular class (e.g., images of dogs) might struggle to correctly identify another class (e.g., cats). This kind of bias hampers the model’s generalization ability and can cause it to reinforce existing prejudices within the data.
Traditional deep learning models are often plagued by “confirmation bias,” where they reinforce patterns learned from biased data sets. In the case of seismic interpretation, if a model is trained on a limited range of geological formations, it might fail to accurately predict formations outside of its learned scope, resulting in less reliable interpretations.
Bias in Interactive Deep Learning: A Positive Force
Interactive deep learning flips the traditional view of bias. Here, bias is seen as an extension of the domain expert’s experience and judgment. Instead of distorting results, it enhances the model’s learning process. Geoscientists provide real-time feedback during training, encoding their expertise into the model.
In this process, a seismic interpreter’s bias—their knowledge about specific geological formations or interpretation strategies—becomes a guiding force for the model, enabling it to perform more like a human expert. This isn’t a harmful, algorithmic bias but a practical reflection of field experience.
For example, while interpreting seismic data, a geoscientist might know from experience that certain patterns are indicative of fault lines. As they interact with the deep learning system, this knowledge shapes how the model interprets similar data in the future, thereby building a model that mirrors expert-level interpretation.
Traditional Bias vs. Interactive Bias: A Key Distinction
It’s crucial to differentiate between bias in traditional deep learning and bias in interactive deep learning:
- Traditional Deep Learning Bias: Typically, a negative influence, causing the model to generalize poorly due to imbalanced or incomplete training data.
- Interactive Deep Learning Bias: A positive influence, where expert knowledge guides the model, helping it learn more effectively and adapt to specialized tasks like seismic interpretation.
User bias as experience is not just a philosophy; a major oil and gas operator recently leveraged InteractivAI interactive deep learning seismic analysis tool to come up with a team-driven fault network interpretation. A group of geoscientists with varying degrees of experience all trained a fault network in the same area. The results from all interpretations were summed to produce a combined interpretation result. This exercise demonstrated a clear relationship between interpreter experience and network bias by showing where the overlap between different interpretations occurred. More experienced interpreters were able to add detailed label information to the training set, sharpening the overall result.
Building High-Quality Bias Through Interactivity
Once bias in interactive deep learning is redefined as a beneficial aspect, the challenge becomes ensuring that the bias being introduced is high-quality. This is where interactivity becomes crucial. Through active participation, geoscientists continuously shape and refine the model’s learning process. As they label seismic data, their knowledge and decision-making process are embedded into the model, allowing it to evolve and become more precise with each iteration.
InteractivAI seismic analysis tool, for example, utilizes an interactive deep learning methodology by allowing geoscientists to interact directly through a live feedback loop, teaching it to interpret seismic data based on their expertise. Over time, the model doesn’t just generate standard predictions—it starts to mirror the geoscientist’s interpretation style, improving accuracy and efficiency in seismic interpretation tasks.
In the realm of interactive deep learning, bias isn’t something to be avoided. While traditional deep learning strives to minimize the impact of bias with the eventual goal of enhancing network results, interactive deep learning harnesses the interpreter’s experience to create models that perform better in specific, real-world tasks. By embedding human expertise into the model, interactive deep learning ensures the model reflects both the data and knowledge of the experts guiding it.