Machine learning-based estimation and clustering of statistics within stratigraphic models as exemplified in Denmark

Estimating a covariance model for kriging purposes is traditionally done using semivariogram analyses, where an empirical semivariogram is calculated, and a chosen semivariogram model, usually defined by a sill and a range, is fitted. We demonstrate that a convolutional neural network can estimate such a semivariogram model with comparable accuracy and precision by training it to recognise the relationship between realisations of Gaussian random fields and the sill and range values that define it, for a Gaussian type semivariance model. We do this by training the network with synthetic data consisting of many such realisations with the sill and range as the target variables. Because training takes time, the method is best suited for cases where many models need to be estimated since the actual estimation itself is about 70 times faster with the neural network than with the traditional approach. We demonstrate the viability of the method in three ways: (1) we test the model’s performance on the validation data, (2) we do a test where we compare the model to the traditional approach and (3) we show an example of an actual application of the method using the Danish national hydrostratigraphic model

Introducing INPOX: a method for informed point extraction from geological 2D surfaces exemplified on the Danish national hydrostratigraphic model

This study presents a probabilistic method for extracting informed points from geological surfaces, named INPOX. The method generates a probability map from the existing surface by calculating the Laplacian at each location and combining it with a user-defined transfer function. A set of points from the surface is then extracted with a density proportional to the probability map. The method allows a de-coupling of the most informative points in the surface from points carrying less or even biased information. INPOX can be applied on any geological surface where the user needs to retrieve the structurally relevant parts and remove the information created by the initial interpolation. Here, we test INPOX on synthetic data, with and without supressing interpolation artifacts. In both cases, the informed points extracted with INPOX outperforms a uniform probability map in recreating the original features. We show that the method requires a minimum of points to be extracted for INPOX to be more informative than a uniform point retrieval. Finally, to showcase the strength of the method in both retrieving the relevant geological features and suppressing the existing interpolation artifacts, we apply INPOX to a real case surface from the Danish national hydrostratigraphic model

Development of a Precision Medicine Workflow in Hematological Cancers, Aalborg University Hospital, Denmark

Within recent years, many precision cancer medicine initiatives have been developed. Most of these have focused on solid cancers, while the potential of precision medicine for patients with hematological malignancies, especially in the relapse situation, are less elucidated. Here, we present a demographic unbiased and observational prospective study at Aalborg University Hospital Denmark, referral site for 10% of the Danish population. We developed a hematological precision medicine workflow based on sequencing analysis of whole exome tumor DNA and RNA. All steps involved are outlined in detail, illustrating how the developed workflow can provide relevant molecular information to multidisciplinary teams. A group of 174 hematological patients with progressive disease or relapse was included in a non-interventional and population-based study, of which 92 patient samples were sequenced. Based on analysis of small nucleotide variants, copy number variants, and fusion transcripts, we found variants with potential and strong clinical relevance in 62% and 9.5% of the patients, respectively. The most frequently mutated genes in individual disease entities were in concordance with previous studies. We did not find tumor mutational burden or micro satellite instability to be informative in our hematologic patient cohort