Reducing uncertainty in the subsurface interpretation of fold-thrust structures - machine learning from outcrop

复杂的构造很难在地震中成像，这使得它们的地下解释不确定。因此，在建立地下解释、量化圈闭碳氢化合物体积和评估断层行为时，通常使用理想化的结构模型。但这些模型有多好呢？使用特定理想化模型的含义是什么，它们在地下解释中降低风险的效果如何？该项目通过研究褶皱-冲断杂岩来解决这些问题，褶皱-逆冲杂岩是在许多前沿油气省形成重要圈闭的构造。不是‘挑选’单一结构的经典例子，这可能会偏离我们对褶皱-逆冲几何学的理解，而是将对露头的褶皱-逆冲系统的暴露良好的横断面进行数字测绘，以收集完全具有代表性的构造套件，以及相关的数字数据。数字测绘将使用无人机和地面摄影测量来获得准确的结构几何图形。田野调查将考虑在德文郡彭布罗克郡的例子，亚高山链条。这个地质结构数据库：肢体间角度、推力位移、曲率等，将与褶皱-逆冲构造的理想化模型和一系列地震成像的地下褶皱-逆冲构造的解释进行比较。模型、地震解释和数字模拟之间的不匹配将量化和确定构造解释的哪些部分风险最大，并将告知使用模拟数据的大型数字数据集的决策如何为地下解释和风险分析提供信息。这项研究将更好地了解褶皱-冲断杂岩的结构演化，并通过使用可以统计分析结构形式的大型数字数据集，改进对地下勘探的风险评估。研究结果对机器学习口译的发展具有一定的指导意义。在完成博士学位期间，学生将在3D结构解释、模型构建、假设检验和可视化方面获得出色的培训。在建立和解释3D虚拟露头模型的同时，通过对地震数据的解释，将在实地发展和增强3D思维。我们将培训学生分析和操作大数据集，并使用行业标准和学术软件。将通过阿伯丁大学SeisLab套件中的培训模块、自学和同行支持，进行软件使用方面的培训(如Petrel、Move、Agisft)。学生将成为横截面构造、结构建模和分析方面的专家，包括恢复和正演建模工具的使用，以及数字数据处理和分析。

Complex structures are difficult to image seismically making their subsurface interpretation uncertain. Consequently, idealised models of structures are commonly used when building subsurface interpretations, to quantify trapped hydrocarbon volumes and to assess fault behaviour. But how good are these models? What are the implications of using particular idealised models, and how well do they reduce risk in subsurface interpretation? This project addresses these issues by examining fold-thrust complexes, structures that form important traps in many frontier hydrocarbon provinces. Rather than 'picking' classic examples of single structures, which likely bias our understanding of fold-thrust geometries, well-exposed transects of outcropping fold-thrust systems will be mapped digitally to collect fully-representative suites of structures, plus associated digital data. Digital mapping will employ UAV and ground-based photogrammetry to obtain accurate structural geometries. Fieldwork will consider examples in Pembrokeshire, Devon, the SubAlpine chains. This database of geological structures: inter-limb angles, thrust displacements, curvature etc. will be compared with idealised models of fold-thrust structures and interpretations of a range of seismically imaged subsurface fold-thrusts. Mismatch between models, seismic interpretations and digital analogues will quantify and identify which parts of the structural interpretation carry the greatest risk, and will inform how decisions using large digital datasets of analogue data can inform sub-surface interpretation and risk analysis. The research will yield much better understanding of the structural evolution of fold-thrust complexes and improve risk assessment for sub-surface exploration through the use of large digital datasets that can statistically analyse structural form. The outcomes can inform the development of machine learnt interpretation. The student will gain excellent training in 3D structural interpretation, model building, hypothesis testing and visualization during the completion of the PhD. 3D thinking will be developed in the field and augmented whilst building and interpreting 3D virtual outcrop models, and through the interpretation of seismic data. We will train the student in the analysis and manipulation of big datasets and the use of industry standard and academic software. Training in software use (e.g. Petrel, Move, Agisoft) will be through training modules, self-teaching and peer-support in the University of Aberdeen's SeisLab suite. The student will become an expert in cross-section construction, structural modelling and analysis including the use of restoration and forward modelling tools, and digital data handling and analysis.