增强的随机森林与地质信息的特征优化,用于复杂的火山岩石石质识别:在王富断层沟,Songliao盆地的一个案例研究
Xiu Jin1, Taiji Yu2,3, Pujun Wang3
1School of Business Administration, Liaoning Technical University, Huludao, Liaoning, China.
PloS one
|November 6, 2025
概括
一个增强的机器学习模型使用井日志准确地对Huoshiling形成中的火山石质学进行了分类. 这种方法改善了罕见岩石类型的识别,有助于在复杂的地质环境中进行碳化合物勘探.
科学领域:
- 地质科学是地球科学.
- 机器学习 机器学习
- 石油地质学 石油地质学
背景情况:
- 由于异质性和有限的核心数据,Huoshiling形成中的火山水库存在重大石质识别挑战.
- 传统的井日志反应往往是模两可的,用于区分复杂的火山序列.
研究的目的:
- 开发一个高精度的机器学习框架,用于对异质火山水库中的石质物种进行分类.
- 为了提高石质识别的准确性,在Huoshiling形成的未经测试的间隔.
主要方法:
- 一个增强的随机森林 (eRF) 模型是使用马射线 (GR),补偿中子 (CNL),散密度 (DEN),声学旅行时间 (AC) 和深层阵列后传电阻 (RLA5) 记录开发的.
- eRF集成了Borderline-SMOTE用于阶级不平衡,C4.5决策树与增益比率分割,以及肯德尔的W用于特征重要性稳定.
- 将eRF的性能与标准的随机森林,反向传播神经网络 (BPNN),k-最近邻居 (kNN) 和支持矢量机器 (SVM) 进行了比较.
主要成果:
- 该eRF的整体精度为96.34%,明显超过了其他测试的机器学习模型.
- 对于所有18种石质学,每种类别的准确度都超过了0.88,其中可见的增长为43个百分点.
- 灵敏度分析显示,马射线 (GR) 和声学旅行时间 (AC) 记录是主要的预测因素,对模型的决策贡献超过60%.
结论:
- 在地质上调整的eRF框架为复杂的火山环境中的高分辨率石质记录提供了强大的和可转移的方法.
- 这种方法通过在缺少核心数据的间隔提供准确的石质学表征来增强碳化合物甜点的预测.
- 该研究表明,先进的机器学习技术在克服表征异质火山水库的挑战方面具有有效性.
更多相关视频
相关概念视频
Methods of Obtaining Topography
270
Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
270
Fault Types
391
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
For line-to-line faults occurring between phases B and C, the...
391
Survival Tree
375
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
375


