# Deep Learning Applications in Geosciences: Insights into Technological Analysis
###### Abstract  
Ichnological analysis, particularly assessing bioturbation index, provides critical parameters for characterizing many oil and gas reservoirs. It provides information on reservoir quality, paleodepositional conditions, redox conditions, and more. However, accurately characterizing ichnological characteristics requires long hours of training and practice, and many marine or marginal marine reservoirs require these specialized expertise. This adds more load to geoscientists and may cause distraction, errors, and bias, particularly when continuously logging long sedimentary successions. In order to alleviate this issue, we propose an automated technique to determine the bioturbation index in cores and outcrops by harnessing the capabilities of deep convolutional neural networks (DCNNs) as image classifiers. In order to find a fast and robust solution, we utilize ideas from deep learning. We compiled and labeled a large data set (1303 images) composed of images spanning the full range (BI 0-6) of bioturbation indices. We divided these images into groups based on their bioturbation indices in order to prepare training data for the DCNN. Finally, we analyzed the trained DCNN model on images and obtained high classification accuracies. This is a pioneering work in the field of ichnological analysis, as the current practice is to perform classification tasks manually by experts in the field. i 2021 11 7736 10390 /app11167736  
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Although facies analysis is crucial, ichnological analysis is equally important to geoscientists in determining paleodepositional environments. Unfortunately, it also requires long hours of training, practice, and visual observation. Therefore, ichnological analysis is predisposed to benefiting from deep learning algorithms. There are various ways of applying ichnological analysis in core-based datasets, including identification of individual trace fossils, assessing trace fossil assemblages, calculating bioturbation index, recording the diversity of trace fossil suites, and evaluating burrow size distributions [10; 11; 12; 13; 14; 15; 16; 17]. Among these, bioturbation index, expressed as Bioturbation Index (BI), is perhaps one of the most fundamental criteria for understanding paleodepositional conditions, particularly with respect to recognizing the presence of physico-chemical stress factors that operate in the paleoenvironments deposited in various hydrocarbon reservoir units. There are six grades of bioturbation intensities encompassed by the original BI definition: BI 0-6. BI 0 characterizes facies with no visible bioturbation, whereas BI 6 indicates complete biogenic homogenization of the media, wherein no preserved primary physical sedimentary structures survive, and all sedimentary fabric is biologically induced. Although BI is technically a quantitative index [18], determining precise values is challenging in practice. Correspondingly, many studies tend to employ alternative techniques, such as using rough percentage values and visually defining low/moderate/intense bioturbation indices, instead of the six grades of bioturbation in the BI classification. In order to find a more robust, fast, and consistent solution to the problem of bioturbation index classification, we employed the capabilities of DCNNs. We compiled a large data set (\(n=1303\)) composed of images with different bioturbation indices. We first divided these images into two groups to test whether the proposed image classification technique can aid in differentiating unbioturbated facies from bioturbated facies. This approach can be critical in unconventional reservoirs, where unbioturbated units, possibly suggesting anoxic conditions, tend to have higher total organic carbon preservation compared to bioturbated units; however, not all unbioturbated muddy units are indicative of oxygen-depleted conditions [19; 20].