# Deep Learning Applications in Geosciences: Insights into Technological Analysis
## 5 Conclusions and Implications  
Applications of deep learning to ichnological studies are documented here for the first time. Our results clearly show high accuracies in predicting bioturbation indices in various siliciclastic sedimentary rock formations. These applications can be vital in oil and gas exploration through reducing uncertainty, lowering the cost and labor time of experts, maximizing efficiency by directing the experts' attention to more problematic intervals (i.e., those yielding low accuracy results), and in academia by facilitating accurate, reliable, comparable, and consistent paleoenvironmental interpretations.  
Our algorithm can be applied to industry and academia as long as image dimensions are roughly comparable to those observed in subsurface cores. Therefore, it can be used by any geologist with limited or no ichnological training. One needs to ensure that the image dimension is consistent with the input size of the network (224 \(\times\) 224 pixels) and the resolution of the image is consistent with that used in training the model. We used images with approximately 6-8 cm width and height, resulting in a resolution of 28-37 pixels/cm.  
The algorithm is also suitable for both core- and outcrop-based studies. With such an application, ichnology can be utilized widely worldwide, and geoscientists can achieve a better understanding of the ichnological characteristics of siliciclastic reservoir units. Applications of automated ichnology in various formations can also provide more accurate and correlatable results between different formations.  
Perhaps one of the most interesting findings in our algorithm is that, even should some features resemble trace fossils, such as rip-up clasts and concretions, they were mostly correctly classified as non-trace fossils in our tests. This may be due to their shape, internal structure, and the marked contrast between them and the surrounding media. For non-ichnologists in particular, these features can be misleading; therefore, our algorithm can yield superior results in these cases.  
From a deep learning perspective, our experiments reveal that the number of expert-labeled images in the training dataset plays an important role in achieving high accuracies. In an earlier attempt, we used 1141 images and achieved 94.7% and 85.0% accuracies in EXP#1 and EXP#2, respectively. Given that the EXP#2 accuracy was initially relatively low, we introduced 162 more images, which led to a 3.0% and 3.9% increase in EXP#1 and EXP#2, respectively. With further additions to the training set, higher accuracies can be expected.  
Geosciences require significant numbers of visual observations in many different sub-disciplines, such as mineralogy, volcanology, sedimentology, and petroleum geology. Therefore, deep learning offers a wide range of geological applications. In the future, individual trace fossil identifications and detailed facies identifications will be implemented in our dataset, providing not only accurate assessments of bioturbation index but a comprehensive core-based analysis.  
**Author Contributions:** Conceptualization, K.A. and U.b.W.; methodology, U.b.W. and I.E.Y.; resources, J.A.M.; data curation, K.A. and J.A.M.; writing--original draft preparation, K.A., I.E.Y. U.b.W. and J.A.M.; writing--review and editing, K.A., I.E.Y., U.b.W. and J.A.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** Funding to J.A.M. is through a Natural Sciences and Engineering Research Council (NSERC) Discovery Grant, and is gratefully acknowledged. The study was supported by start-up grants from the College of Petroleum and Geosciences, KFUPM.  
**Conflicts of Interest:** The authors declare no conflict of interest.