在勘探地球科学中降低风险的综合地球物理和机器学习

P. Dell’Aversana, B. Ciurlo, S. Colombo
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引用次数: 8

摘要

我们讨论了机器学习(ML)如何在勘探风险评估和/或现场评估过程中支持异构地球物理数据集的集成工作流程。数据集包括地震、电磁、重力和井眼测量。我们将顺序地球物理建模和反演与机器学习领域常用的统计和自动分类方法相结合。我们将这种“混合方法”应用于在不同地质环境下记录的两个多学科地球物理数据集,在两种情况下都获得了令人鼓舞的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrated Geophysics and Machine Learning for Risk Mitigation in Exploration Geosciences
Summary We discuss how Machine Learning (ML) can support the integration workflow of heterogeneous geophysical data sets in the process of exploration risk evaluation and/or in the process of field appraisal. Data set includes seismic, electromagnetic, gravity and borehole measurements. We combine sequential geophysical modelling and inversion with statistical and automatic classification approaches commonly used in the field of Machine Learning. We applied this “hybrid approach” to two multidisciplinary geophysical data sets recorded in different geological settings, obtaining encouraging results in both cases.
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