Bayesian geological and geophysical data fusion for the construction and uncertainty quantification of 3D geological models

Traditional approaches to develop 3D geological models employ a mix of quantitative and qualitative scientific techniques,which do not fully provide quantification of uncertainty in the constructed models and fail to optimally weight geological field observations against constraints from geophysical data.Here,using the Bayesian Obsidian software package,we develop a methodology to fuse lithostratigraphic field observations with aeromagnetic and gravity data to build a 3D model in a small(13.5 km×13.5 km)region of the Gascoyne Province,Western Australia.Our approach is validated by comparing 3D model results to independently-constrained geological maps and cross-sections produced by the Geological Survey of Western Australia.By fusing geological field data with aeromagnetic and gravity surveys,we show that 89%of the modelled region has>95%certainty for a particular geological unit for the given model and data.The boundaries between geological units are characterized by narrow regions with<95%certainty,which are typically 400-1000 m wide at the Earth's surface and 500-2000 m wide at depth.Beyond~4 km depth,the model requires geophysical survey data with longer wavelengths(e.g.,active seismic)to constrain the deeper subsurface.Although Obsidian was originally built for sedimentary basin problems,there is reasonable applicability to deformed terranes such as the Gascoyne Province.Ultimately,modification of the Bayesian engine to incorporate structural data will aid in developing more robust 3D models.Nevertheless,our results show that surface geological observations fused with geophysical survey data can yield reasonable 3D geological models with narrow uncertainty regions at the surface and shallow subsurface,which will be especially valuable for mineral exploration and the development of 3D geological models under cover.     

不同赋存状态油裂解条件及油裂解型气源灶的正演和反演研究

油裂解型气源灶是一种特殊的气源灶,是优质生烃母质在成气过程中派生出的。烃源岩生成的油主要有3种赋存形式:源内分散状液态烃、源外分散状液态烃和源外富集型液态烃。3种赋存状态液态烃的数量及分配比例受内因和外因多种因素控制,就排油率而言,有机碳含量分别为0.67%、0.62%和10.6%的泥岩、灰岩和油页岩,最大排油率分别为45%、55%和80%。原油与不同介质配样的生气动力学实验表明,不同介质条件下甲烷的生成活化能分布有差异,碳酸盐岩对油裂解条件影响最大,可大大降低其活化能,导致原油裂解热学条件降低,体现为油裂解温度的降低;泥岩次之,砂岩影响最小。碳酸盐岩、泥岩和砂岩对油的催化裂解作用依次减弱,不同介质条件下主生气期对应的Ro值:纯原油1.5%～3.8%;碳酸盐岩中的分散原油1.2%～3.2%;泥岩中的分散原油1.3%～3.4%;砂岩中的分散原油1.4%～3.6%。油裂解型气源灶是一种中间体,可以直观看到的是原生气源灶和由此形成的气藏,而对油裂解型气源灶的赋存形式、分布范围、成气数量和储量规模等问题,只能通过正演和反演的研究去确定且相互映证。正演研究以塔里木盆地中下寒武统为例,原始生油量2232.24×108t,剩余油量806.21×108t,油裂解气量106.95×1012m3。反演研究以川东北飞仙关组白云岩中油裂解气为例,圈定的古油藏面积约735km2,古油藏原油数量45×108t,油裂解气量及油裂解气资源量分别为2.72×1012m3和1.36×1012m3。     

Setting minimum head capsule abundance and taxa deletion criteria in chironomid-based inference models

Criteria for removing training set lakes and taxa in chironomidbased inference models, due to low abundances, have largely been ad hoc. We used an anoxia inference model and a hypolimnetic oxygen model from southcentral Ontario to determine what effect subfossil head capsule abundance and taxa deletion criteria have on fossil inference statistics. Results from six training set lakes suggest that a minimum abundance of 40–50 head capsules is sufficient for use in inference models, however more diverse samples likely require more than 50 head capsules. Taxa deletion criteria substantially improved the predictive ability of inference models (lowered the root mean squared error of prediction (RMSEP)). The common practice of including taxa with only 2% abundance in at least two lakes was one of the deletion criteria that much improved inference models. Similar deletion criteria, such as 2% in at least 3 lakes and 3% in at least 1 lake, produced comparable improvements (up to 18% reduction in RMSEP).     

专家系统及其在矿产预测中的应用

专家系统是一门新兴的计算机人工智能实用学科，它能够应用专家的知识和思维方式去解决实际问题。它主要由知识库和推理机组成。ＨＮＤＰＥＳ系统是在总结河南主要矿产成矿规律的基础上建造的矿床预测专家系统。在河南省富铝土矿成矿预测中取得了良好的效果。     

A practical solution for linear inference computations

Summary. In this paper, we present a matrix form of Backus' theory of linear inference with multiple predictions. The Bayesian approach used by Backus allows the treatment of erroneous data and the imposition of the essential a priori bound on the model norm. We introduce a modification which involves translating the problem in accordance with an estimated model. Such a model may be known a priori or it may be constructed from the data. We are effectively able to bound the norm of all acceptable models from above and below and this results in more confining estimates of the predictions than provided by just an upper bound. In addition, the model construction approach allows us to make maximum use of the data in the inference computation. Our algorithm is robust and efficient, and estimates comparable to to those obtained from linear programming techniques have been achieved.     

Artificial intelligence methods in geological forecasting

The objective of applications of expert systems to geological forecasting is to use heuristic knowledge of experienced geologists, as well as their intuition and facility for drawing the right forecast conclusions based on incomplete data. At the same time, considerable experience is accumulated in geological forecasting with formal mathematical models that generalize the setting of some forecast problem classes. Thus, the bottom-up heuristic approach and the more formal top-down methods are aspiring to the same aim, so they supplement and strengthen each other. A forecast expert system called GENESIS that allows both approaches to be used has been produced. The heuristic part of GENESIS is the empty expert system, which consists of the knowledge definition language, destined for an expert geologist, the knowledge base used for forecast models storing, then forming knowledge base facilities, relational database for storing user's data and forecast specimen, components that organize the certain inference and its explanation.     

POPULATIONS,SAMPLES AND STATISTICAL INFERENCE IN GEOGRAPHY

This paper questions the validity and relevance of the application of procedures of classical statistical inference to population data in geography. Arguments for such procedures include the notions of temporal and spatial samples, measurement error, modifiable areal units, data vetting and stochastic processes. It is concluded that such arguments cannot be justified in terms of statistical theory.     

鄂尔多斯盆地东部石盒子组盒8沉积环境的地球化学表征

采用Sr、Sr／Ba、Rb、Rb／K、K＋Na质量分数、K／Na、B／Ga、B元素法、V／Cr、Ni／Co、U／Th、V／（V＋Ni）、铈异常（Ce_anom）值、Fe^2＋／Fe^3＋、Cu／Zn、Sr／Cu、Fe／Mn、Mg／Ca、 Mg／Sr和Al₂O₃／MgO等微量元素和常量元素等地球化学方法对鄂尔多斯盆地东部盒8期沉积环境的古盐度、氧化还原态和古气候进行综合判断。结果表明：古盐度介于2.76‰～14.32‰之间,平均值为8.62‰,显示沉积水介质以淡水—微咸水为主;铈异常（Ce_anom）值主要集中在-0.05～0.1之间,整体表现为Ce富集,指示缺氧弱还原的古水体环境;Sr/Cu比值为1.66～9.41,平均值为4.60,环境为温湿气候。纵向上盒8_下古盐度高于盒8_上,盒8_下比盒8_上气候略微干旱。平面上研究区南部米脂榆79井—榆28井一带和研究区北部通岗浪沟—什汗水利一带古盐度较大,大于10‰,属半咸水—咸水沉积,相对应地这两个区块古气候比其它区块较为干旱,这一规律和沉积体系在研究区的分布高度吻合。分析认为：盒8期研究区湖岸线分布于南部米脂榆79井—榆28井一带,受湖水和干旱气候的影响,造成古盐度较高;北部通岗浪沟—什汗水利一带发育辫状河沉积,受干燥气候、蒸发环境的影响,古盐度增大。研究区微咸水—半咸水—咸水的沉积环境有利于绿泥石膜胶结发育,也有利于储层的发育。该认识对鄂尔多斯盆地盒8沉积环境研究起到一定的补充作用,同时也对该区盒8段天然气勘探具有重要意义。