In this article, we will discuss recent advances in machine learning techniques for geoscience data, including supervised estimation. We also discuss some unique aspects of literature review for geological modelling with machine learning with machine learning. In particular, we will discuss challenges associated with estimating geoscience variables in thin rock sections. In addition, we will discuss a few recent advances in multi-task learning frameworks. This review will provide valuable information to the geology community.
Recent Advances in Machine learning
The use of machine learning methods in geological modelling has a number of advantages. This type of modelling does not require complex mathematical models. It can be trained with data from a variety of sources, including geological maps. It is particularly useful in situations where a single dataset is not sufficient to capture the entire geological history. ML can be trained using a variety of data sources and can be used to improve the accuracy of geological models.
A number of applications of machine learning in geological modelling have been demonstrated. In reservoir modelling, surface-based models are a computationally efficient tool for generating realistic representations of heterogeneity. However, these models can be complex to condition to well data due to the fact that they are ill-posed inverse problems. However, machine learning techniques can improve these models by using stochastic pix2pix, a stochastic neural network approach that can learn from input data and maintain a consistent match between the geological concepts and the resulting facies.
Challenges in Supervised Estimation of Geoscience Variables
The use of machine learning (ML) has become increasingly common among geoscientists, who use it to analyze large sensor data sets to build complex, predictive models. These models often lack human-assisted inspection, which is difficult to do with such large datasets. However, these algorithms can produce useful scientific insights from data, characterize hazards, and provide decision-support information.
The application of genetic programming is also a form of ML, which uses Darwinian natural selection to generate computer programs. This type of programming is a particularly effective solution to the black-box problem of geological modelling. GP also helps predict elasticity modulus and strength of granitic rocks. This approach is an important component of ML. However, its use is currently restricted to a small number of fields.
Recent Advances in Multi-Task learning Frameworks
In geological modelling, recent advances in machine learning and data assimilation methods are proving very helpful. Geosciences typically require multiple spatial grid points, ranging from 10 m to 10,000 m. In addition to surface processes, these data span multiple layers of the mantle and atmosphere. To scale existing machine learning methods, we need to scale our data to thousands of dimensions.
The underlying problem with geoscience data and the difficulty of training machine learning models is that the variable properties are heterogeneous. In this case, multi-task learning frameworks address this problem by allowing learning models at homogeneous partitions of the data and then sharing them across similar tasks. This ensures regularization and avoids overfitting problems. This article discusses five broad categories of geoscience problems and their respective machine learning solutions.
Unique Aspects of Geological modelling with Machine learning
The challenges of geological modelling are many. This field involves large datasets, many variables, and a limited number of samples. This requires novel machine learning advances. Several areas of geoscience can benefit from machine learning. Here are some examples of these applications. In geophysics, Bayesian statistics have been used extensively for modelling rock physics problems. Machine learning can be used in a variety of geoscience applications, including soil classification.
Geoscience research is complex and involves diverse questions. Geoscience data analysis is unique, and differs from typical problems in commercial domains. The nature of geoscience phenomena necessitates novel problem formulations and variables, as well as complicated latent variables. In addition, geoscience data is large and may not be easily labeled. Thus, geoscientists’ involvement in data analysis is crucial.