In geothermal exploration, magnetotelluric (MT) data and inversion models are commonly used to image shallow conductors typically associated with the presence of an electrically conductive clay cap that overlies the main reservoir. However, these inversion models suffer from nonuniqueness and uncertainty, and the inclusion of useful geologic information is still limited. We have developed a Bayesian inversion method that integrates the electrical resistivity distribution from MT surveys with borehole methylene blue (MeB) data, an indicator of conductive clay content. The MeB data were used to inform structural priors for the MT Bayesian inversion that focus on inferring with uncertainty the shallow conductor boundary in geothermal fields. By incorporating borehole information, our inversion reduced nonuniqueness and then explicitly represented the irreducible uncertainty as estimated depth intervals for the conductor boundary. We used the Markov chain Monte Carlo and a 1D three-layer resistivity model to accelerate the Bayesian inversion of the MT signal beneath each station. Then, inferred conductor boundary distributions were interpolated to construct pseudo-2D/3D models of the uncertain conductor geometry. We compare our approach against deterministic MT inversion software on synthetic and field examples, and our approach has good performance in estimating the depth to the bottom of the conductor, a valuable target in geothermal reservoir exploration.

中文翻译：

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利用亚甲基蓝结构先验对地热场中的浅层导体成像的贝叶斯大地电磁反演

在地热勘探中，大地电磁（MT）数据和反演模型通常用于对浅层导体成像，这些浅层导体通常与覆盖主要储层的导电粘土盖有关。但是，这些反演模型存在非唯一性和不确定性，有用地质信息的包含仍然受到限制。我们已经开发出一种贝叶斯反演方法，该方法将MT测量中的电阻率分布与井内亚甲基蓝（MeB）数据（导电粘土含量的指标）相结合。MeB数据用于告知MT贝叶斯反演的结构先验，其重点在于不确定地推断地热田中的浅层导体边界。通过合并钻孔信息，我们的反演减少了非唯一性，然后将不可减少的不确定性明确表示为导体边界的估计深度区间。我们使用马尔可夫链蒙特卡罗（Monte Carlo）和一维三层电阻率模型来加速每个站下MT信号的贝叶斯反演。然后，对推断出的导体边界分布进行插值，以构造不确定导体几何形状的伪2D / 3D模型。我们在合成和现场实例中将我们的方法与确定性MT反演软件进行了比较，并且该方法在估算导体底部的深度方面具有良好的性能，这是地热储层勘探的重要目标。我们使用马尔可夫链蒙特卡罗（Monte Carlo）和一维三层电阻率模型来加速每个站下MT信号的贝叶斯反演。然后，对推断出的导体边界分布进行插值，以构造不确定导体几何形状的伪2D / 3D模型。我们在合成和现场实例中将我们的方法与确定性MT反演软件进行了比较，并且该方法在估算导体底部的深度方面具有良好的性能，这是地热储层勘探的重要目标。我们使用马尔可夫链蒙特卡罗和一维三层电阻率模型来加速每个站下MT信号的贝叶斯反演。然后，对推断出的导体边界分布进行插值，以构造不确定导体几何形状的伪2D / 3D模型。我们在合成和现场实例中将我们的方法与确定性MT反演软件进行了比较，并且该方法在估算导体底部的深度方面具有良好的性能，这是地热储层勘探的重要目标。