DEEPEN Global Standardized Categorical Exploration Datasets for Magmatic Plays

DEEPEN stands for DE-risking Exploration of geothermal Plays in magmatic ENvironments. As part of the development of the DEEPEN 3D play fairway analysis (PFA) methodology for magmatic plays (conventional hydrothermal, superhot EGS, and supercritical), weights needed to be developed for use in the weighted sum of the different favorability index models produced from geoscientific exploration datasets. This was done using two different approaches: one based on expert opinions, and one based on statistical learning. This GDR submission includes the datasets used to produce the statistical learning-based weights. While expert opinions allow us to include more nuanced information in the weights, expert opinions are subject to human bias. Data-centric or statistical approaches help to overcome these potential human biases by focusing on and drawing conclusions from the data alone. The drawback is that, to apply these types of approaches, a dataset is needed. Therefore, we attempted to build comprehensive standardized datasets mapping anomalies in each exploration dataset to each component of each play. This data was gathered through a literature review focused on magmatic hydrothermal plays along with well-characterized areas where superhot or supercritical conditions are thought to exist. Datasets were assembled for all three play types, but the hydrothermal dataset is the least complete due to its relatively low priority. For each known or assumed resource, the dataset states what anomaly in each exploration dataset is associated with each component of the system. The data is only a semi-quantitative, where values are either high, medium, or low, relative to background levels. In addition, the dataset has significant gaps, as not every possible exploration dataset has been collected and analyzed at every known or suspected geothermal resource area, in the context of all possible play types. The following training sites were used to assemble this dataset: - Conventional magmatic hydrothermal: Akutan (from AK PFA), Oregon Cascades PFA, Glass Buttes OR, Mauna Kea (from HI PFA), Lanai (from HI PFA), Mt St Helens Shear Zone (from WA PFA), Wind River Valley (From WA PFA), Mount Baker (from WA PFA). - Superhot EGS: Newberry (EGS demonstration project), Coso (EGS demonstration project), Geysers (EGS demonstration project), Eastern Snake River Plain (EGS demonstration project), Utah FORGE, Larderello, Kakkonda, Taupo Volcanic Zone, Acoculco, Krafla. - Supercritical: Coso, Geysers, Salton Sea, Larderello, Los Humeros, Taupo Volcanic Zone, Krafla, Reyjanes, Hengill. **Disclaimer: Treat the supercritical fluid anomalies with skepticism. They are based on assumptions due to the general lack of confirmed supercritical fluid encounters and samples at the sites included in this dataset, at the time of assembling the dataset. The main assumption was that the supercritical fluid in a given geothermal system has shared properties with the hydrothermal fluid, which may not be the case in reality. Once the datasets were assembled, principal component analysis (PCA) was applied to each. PCA is an unsupervised statistical learning technique, meaning that labels are not required on the data, that summarized the directions of variance in the data. This approach was chosen because our labels are not certain, i.e., we do not know with 100% confidence that superhot resources exist at all the assumed positive areas. We also do not have data for any known non-geothermal areas, meaning that it would be challenging to apply a supervised learning technique. In order to generate weights from the PCA, an analysis of the PCA loading values was conducted. PCA loading values represent how much a feature is contributing to each principal component, and therefore the overall variance in the data.

DEEPEN即岩浆环境地热选区勘探降险（DE-risking Exploration of geothermal Plays in magmatic ENvironments, DEEPEN）。作为面向岩浆型地热系统的DEEPEN三维选区有利带分析（Play Fairway Analysis, PFA）方法开发工作的一部分，该方法涵盖常规热液型、超热增强型地热系统（Enhanced Geothermal System, EGS）以及超临界型三类地热系统，需为基于地球科学勘探数据集构建的各类有利性指数模型的加权求和制定权重。本研究采用两种路径制定权重：其一基于专家意见，其二基于统计学习法。本次GDR提交包含用于生成基于统计学习法权重的数据集。

专家意见法虽可在权重中纳入更细致的信息，但易受人为偏差影响；以数据为中心的统计学习方法仅聚焦数据并从中推导结论，有助于克服此类潜在人为偏差。不过应用此类方法需要数据集支撑，因此本研究尝试构建标准化的综合数据集，将各类勘探数据集的异常映射至对应地热系统的各组成部分。

本数据集依托针对岩浆热液型地热系统的文献调研，以及已被充分表征的、被认为存在超热或超临界条件的区域完成数据收集。研究为三类地热系统均构建了数据集，但常规热液型数据集因优先级相对较低，完整性最差。

对于每个已知或假定的地热资源，数据集明确了各勘探数据集的异常与该系统各组成部分的对应关系。数据采用半定量形式，数值按相对于背景水平的高低分为高、中、低三类。此外，数据集存在显著缺口：并非所有已知或疑似地热资源区域都已收集并分析了全部潜在勘探数据集，且未覆盖所有可能的地热系统类型。

本次研究选用的训练站点如下：
- 常规岩浆热液型：阿库坦（源自AK PFA）、俄勒冈喀斯喀特PFA、俄勒冈玻璃山、冒纳凯阿（源自HI PFA）、拉奈岛（源自HI PFA）、圣海伦斯山剪切带（源自WA PFA）、风河谷谷（源自WA PFA）、贝克山（源自WA PFA）。
- 超热EGS：纽贝里（EGS示范项目）、科索（EGS示范项目）、盖瑟斯（EGS示范项目）、东蛇河平原（EGS示范项目）、犹他FORGE、拉德雷洛、花卷、陶波火山带、阿科库科、克拉夫拉。
- 超临界型：科索、盖瑟斯、索尔顿海、拉德雷洛、洛斯乌梅罗斯、陶波火山带、克拉夫拉、雷克雅内斯、亨吉尔。

**免责声明：对超临界流体异常应持审慎态度。由于在数据集编制时，相关点位尚未确认存在超临界流体并获取对应样本，此类异常基于相关假设。核心假设为：特定地热系统中的超临界流体与热液流体具有共通属性，但实际情况未必如此。**

数据集构建完成后，对每个数据集均应用了主成分分析（Principal Component Analysis, PCA）。PCA是一种无监督统计学习技术，无需为数据标注标签，可总结数据的方差方向。选择该方法的原因在于：我们的标注并不确定——即无法100%确信所有假定的有利区域均存在超热资源；同时我们也未获取任何已知非地热区域的数据，因此难以应用监督学习技术。为从PCA结果中生成权重，研究对PCA载荷值开展了分析。PCA载荷值表征了某一特征对各主成分及数据整体方差的贡献程度。