Capturing interpretational uncertainty of depositional environments with Artificial Intelligence

Geological interpretations are always linked with interpretational and conceptual uncertainty, which is difficult to elicit and quantify, often creating unquantified risks for understanding the subsurface. The complexity and variability of geological systems may lead geologists to analyse the same data and arrive at different conclusions based on their subjective interpretations, personal expertise, or biases. In order to address the associated uncertainty, it is valuable to consider multiple plausible interpretations of outcrop data and acknowledge the degree of ambiguity associated with each interpretation. By examining a diverse range of outcrop analogues, it becomes possible to derive multiple potential geological interpretations and identify variations within and across depositional systems. This thesis proposes a new AI system that learns valuable geological information from surface data (outcrop images), transfers this knowledge to the fragmented data of the subsurface (core data), and finally, links all the extracted information with the geological literature to produce plausible interpretations of the depositional environment based on a single outcrop image. To identify patterns and geological features within image data, three Supervised Learning Computer Vision techniques were employed: Image Classification, Object Detection, and Instance Segmentation. Natural Language Processing was utilised to extract geological features from textual information from heritage geological texts, thus complementing the analysis. Lastly, a custom Neural Network was deployed to assimilate the gathered information into meaningful sequences, apply geological constraints to these sequences, and generate multiple plausible interpretational scenarios, ranked in descending order of probability. The results of this study demonstrate that combining approaches from different areas of Artificial Intelligence within cross-disciplinary workflows under the umbrella of a broader AI system holds significant potential for subsurface characterization, better risk analysis, and potentially enhancing decision-making under uncertain conditions during subsurface exploration stages.Heriot-Watt University fundin

The importance of applying computational creativity to scientific and mathematical domains

Science and mathematics are currently underrepresented in the computational creativity (CC) community. We discuss why the CC community should apply their work to mathematical and scientific domains, and argue that this would be mutually beneficial for the domains in question. We identify a key challenge in Automated Reasoning – that it has not achieved widespread adoption by mathematicians; and one in Automated Scientific Discovery – the need for communicability of automatically generated scientific knowledge. We recommend that CC researchers help to address these two challenges by: (i) applying systems based on cognitive mechanisms to scientific and mathematical domains; (ii) employing experience in building and evaluating interactive systems to this context; and (iii) using expertise in automatically producing framing functionality to enhance the communicability of automatically generated scientific knowledge.EPSRC funded project EP/P017320/1 "Example-driven machine-human collaboration in mathematics

Geo Fossils-I: A synthetic dataset of 2D fossil images for computer vision applications on geology

Geo Fossils-I is a synthetic image dataset used as a solution for resolving the limited availability of geological datasets intended for image classification and object detection on 2D images of geological outcrops. The Geo Fossils-I dataset was created to train a custom image classification model for geological fossil identification and inspire additional work in generating synthetic geological data with Stable Diffusion models. The Geo Fossils-I dataset was generated through a custom training process and the fine-tuning of a pre-trained Stable Diffusion model. Stable Diffusion is an advanced text-to-image model that can create highly realistic images based on textual input. An effective technique for instructing Stable Diffusion on novel concepts is the application of Dreambooth, a specialized form of fine-tuning. Dreambooth was used to generate new images of fossils or to modify existing ones per the provided textual description.The Geo Fossils-I dataset contains six different fossil types present in geological outcrops, each one being characteristic of a particular depositional environment. The dataset contains a total of 1200 fossil images equally spread among different fossil types such as ammonites, belemnites, corals, crinoids, leaf fossils, and trilobites.This dataset is the first set within a series to be compiled aiming to enrich the available resources with respect to 2D outcrop images allowing geoscientists to progress in the field of automated interpretation of depositional environments

Coherent XUV Multispectral Diffraction Imaging in the Microscale

The rapid growth of nanotechnology has increased the need for fast nanoscale imaging. X-ray free electron laser (XFEL) facilities currently provide such coherent sources of directional and high-brilliance X-ray radiation. These facilities require large financial investments for development, maintenance, and manpower, and thus, only a few exist worldwide. In this article, we present an automated table-top system for XUV coherent diffraction imaging supporting the capabilities for multispectral microscopy at high repetition rates, based on laser high harmonic generation from gases. This prototype system aims towards the development of an industrial table-top system of ultrafast soft X-ray multi-spectral microscopy imaging for nanostructured materials with enormous potential and a broad range of applications in current nanotechnologies. The coherent XUV radiation is generated in a semi-infinite gas cell via the high harmonic generation of the near-infrared femtosecond laser pulses. The XUV spectral selection is performed by specially designed multilayer XUV mirrors that do not affect the XUV phase front and pulse duration

Coherent XUV Multispectral Diffraction Imaging in the Microscale

The rapid growth of nanotechnology has increased the need for fast nanoscale imaging. X-ray free electron laser (XFEL) facilities currently provide such coherent sources of directional and high-brilliance X-ray radiation. These facilities require large financial investments for development, maintenance, and manpower, and thus, only a few exist worldwide. In this article, we present an automated table-top system for XUV coherent diffraction imaging supporting the capabilities for multispectral microscopy at high repetition rates, based on laser high harmonic generation from gases. This prototype system aims towards the development of an industrial table-top system of ultrafast soft X-ray multi-spectral microscopy imaging for nanostructured materials with enormous potential and a broad range of applications in current nanotechnologies. The coherent XUV radiation is generated in a semi-infinite gas cell via the high harmonic generation of the near-infrared femtosecond laser pulses. The XUV spectral selection is performed by specially designed multilayer XUV mirrors that do not affect the XUV phase front and pulse duration

Coherent XUV multispectral diffraction imaging in the microscale

Summarization: The rapid growth of nanotechnology has increased the need for fast nanoscale imaging. X-ray free electron laser (XFEL) facilities currently provide such coherent sources of directional and high-brilliance X-ray radiation. These facilities require large financial investments for development, maintenance, and manpower, and thus, only a few exist worldwide. In this article, we present an automated table-top system for XUV coherent diffraction imaging supporting the capabilities for multispectral microscopy at high repetition rates, based on laser high harmonic generation from gases. This prototype system aims towards the development of an industrial table-top system of ultrafast soft X-ray multi-spectral microscopy imaging for nanostructured materials with enormous potential and a broad range of applications in current nanotechnologies. The coherent XUV radiation is generated in a semi-infinite gas cell via the high harmonic generation of the near-infrared femtosecond laser pulses. The XUV spectral selection is performed by specially designed multilayer XUV mirrors that do not affect the XUV phase front and pulse duration.Παρουσιάστηκε στο: Applied Science