Using human-like creative computing (AI) to assist in geological interpretation

这位博士的目标是创建一个基于人工智能的新系统来解释岩石记录，该系统基于类人类学习的概念，可以快速创建一系列符合观察并解释其逻辑基础的可信的地质解释。博士学位将利用基于概念发明的新技术，这些技术已被应用于广泛的问题，如数学证明、音乐创作和绘画(http://www.thepaintingfool.com).这些技术是新颖的，因为它们建立在预先存在的知识基础上，这些知识被封装为一组规则或启发式规则，系统可以从这些规则或启发式知识中创建适合任何观察的广泛的新概念集。简而言之，他们发明的想法并不违反我们可能拥有的任何预先存在的知识/规则。这种解释的广度对地质学家特别感兴趣，因为关于地下的可用信息往往有限，因此必须探索广泛的地质学概念，以充分涵盖不确定性。邦德等人(2008)通过展示不同的地球科学家可以对同一条地震线做出多少不同的解释(由于他们的背景/经验的不同)证明了这一需求，这表明理想(但不切实际的)解决方案是让许多解释人员处理相同的问题。本博士论文中使用的方法的主要好处是它们能够自动生成可信的地质学解释，并解释其逻辑基础(是什么以及为什么)，而不需要编码一套完整的地质规则/关系。这些方法的作用就像人类地质学家，因为它可以告诉你什么是什么，为什么会得出这样的答案。为了使任何基于计算机的解释结果对地质界有用，而不是充当“黑匣子”，这种能力是必不可少的。在这项博士学位中开发的技术将能够调查大量可能的解释，并可以向用户(地质学家)返回每种可能性背后的逻辑，例如，以特定顺序创建符合观察结果的一组地质规则/事件。候选人将与Heriot-Watt的地质学家和Dundee大学的人工智能专家合作，开发这些新型人工智能工具的独特知识，同时将它们应用于地质背景。对于任何应用程序来说，这都是一个学习创新人工智能技术的独特机会。最初的任务将是研究从露头提取的简单地质问题，理想情况下是建立一个能够解决真正油田的全部复杂性的系统。其目的是建立一个足够强大的代码框架，以适应于处理一系列地下问题，包括石油和天然气储藏、地热系统和能源储存地点，并可以根据现有数据和观察结果解释一系列地质情景。

This PhD aims to create a new AI-based system for interpretation of the rock record, based on the concept of human-like learning, which can rapidly create a wide range of plausible geological interpretations that fit observations and explain their logical basis. The PhD will make use of novel technology based on concept invention that has been applied to a wide range of problems such as mathematical proofs, music composition and painting (http://www.thepaintingfool.com). These techniques are novel in that they build upon pre-existing knowledge, encapsulated as a set of rules or heuristics, from which the system can create a wide ranging set of new concepts that fit any observations. In short they invent ideas that don't violate any pre-existing knowledge/rules we might have.This breadth of interpretation is of particular interest to geologists as often there is limited information available about the subsurface, thus a wide range of concepts of the geology must be explored to adequately cover uncertainty. Bond et al (2008) demonstrated this need by showing how many different interpretations of the same seismic line can be produced by different geoscientists, due differences in their background/experience, suggesting the ideal (but impractical) solution is for many interpreters to work on the same problem.The key benefit of the approaches used in this PhD are their ability to automatically generate plausible interpretations of the geology and explain their logical basis (what it is and why) without the need to encode a complete set of geological rules/relationships. These approaches act like a human geologist in that it can tell you what something is and why it came to that answer. This ability is essential in order for the results of any computer based interpretation to be useful to the geological community and not act as a "black box". Technology developed in this PhD will be able to investigate a large number of possible interpretations and could present back to the user (geologist) the logic behind each possibility as, for instance, a set of geological rules/events in a specific order that create an outcome that fits the observations.The candidate will work with geologists at Heriot-Watt and AI experts at Dundee university to develop unique knowledge of these novel AI tools, while applying them to the geological context. This represents a unique opportunity to learn about innovative AI techniques for any applicant.Initially the task will be to work on simple geological problems taken from outcrops, ideally building up to a system that can tackle the full complexity of a real oil field. The aim is to build a code framework that is robust enough to be adapted to work on a range of subsurface problems including oil and gas reservoirs, geothermal systems and energy storage sites and can interpret a range of geological scenarios based on available data and observations.