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| 1 | Three-dimensional geological modeling of potential-field data显示文摘 | Mark Jessell | 2001 | Computer & Geosciences2001，27，4： | 2 |
| 2 | Three- dimensional geoglogical modeling of potential-- field data 显示文摘 | Mark Jessell | 2007 | Computer & Geosciences2007，，： | 1 |
| 3 | Three-dimensional geological modeling of potentialfield data显示文摘 | Mark Jessell | 2001 | Computer & Geosciences2001，27，3： | 1 |
| 4 | Assessing the impact of conceptual mineral systems uncertainty on prospectivity predictions显示文摘The past two decades have seen a rapid adoption of artificial intelligence methods applied to mineral exploration. More recently, the easier acquisition of some types of data has inspired a broad literature that has examined many machine learning and modelling techniques that combine exploration criteria,or ’features’, to generate predictions for mineral prospectivity. Central to the design of prospectivity models is a ’mineral system’, a conceptual model describing the key geological elements that control the timing and location of economic mineralisation. The mineral systems model defines what constitutes a training set, which features represent geological evidence of mineralisation, how features are engineered and what modelling methods are used. Mineral systems are knowledge-driven conceptual models, thus all parameter choices are subject to human biases and opinion so alternative models are possible.However, the effect of alternative mineral systems models on prospectivity is rarely compared despite the potential to heavily influence final predictions. In this study, we focus on the effect of conceptual uncertainty on Fe ore prospectivity models in the Hamersley region, Western Australia. Four important considerations are tested.(1) Five different supergene and hypogene conceptual mineral systems models guide the inputs for five forest-based classification prospectivity models model.(2) To represent conceptual uncertainty, the predictions are then combined for prospectivity model comparison.(3)Representation of three-dimensional objects as two-dimensional features are tested to address commonly ignored thickness of geological units.(4) The training dataset is composed of known economic mineralisation sites(deposits) as ’positive’ examples, and exploration drilling data providing ’negative’sampling locations. Each of the spatial predictions are assessed using independent performance metrics common to AI-based classification methods and subjected to geological plausibility testing. We find that different conceptual mineral systems produce significantly different spatial predictions, thus conceptual uncertainty must be recognised. A benefit to recognising and modelling different conceptual models is that robust and geologically plausible predictions can be made that may guide mineral discovery. | Mark D Lindsay Agnieszka M.Piechocka Mark W Jessell Richard Scalzo Jeremie Giraud Guillaume Pirot Edward Cripps | 2022 | Geoscience Frontiers2022，13，6： | 1 |
| 5 | Three-dimensional geological modeling of potentialfield data显示文摘 | Mark Jessel | 2001 | Computer & Geosciences2001，27，4： | 1 |
| 6 | Three-dimensional geological modeling of potential-field data 显示文摘 | Mark Jessell | 2001 | Computer & Geosciences2001，27，3： | 1 |
| 7 | Three-dimensional geological modeling of potential-field data显示文摘 | Marks Jessell | 2001 | Computers & Geosciences2001，27，40： | 1 |
| 8 | Three-dimensional geological modelling of potential-field data显示文摘 | Mark Jessell | 2001 | Computers and Geosciences2001，，4： | 1 |
| 9 | Mapping the Dyke Swarms of the Eglab-Yetti Region,Southwestern Algeria显示文摘The Eglab-Yetti region of southwestern Algeria is a main part of the Reguibat shield of the West African craton(WAC)which consists of Archean to Paleoprotzrozoic basement.The region hosts | Massinissa AMARA Anissa BENMAMMAR Sonia OUADAHI Richard E.ERNST Abderahmane BENDAOUD Mark JESSELL Safouane DJEMAI Mohamed HAMOUDI | 2016 | Acta Geologica Sinica(English Edition)2016，90，S1： | 0 |
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