Abstract
An optimal strategy for building realistic geological models must combine different geophysical techniques, each with its advantages and limitations. However, dealing with multiple geophysical datasets to derive a geological interpretation is not straightforward since geophysical parameters are not always functionally related. In this work, we propose an innovative approach consisting of using machine learning techniques to jointly interpret three geophysical datasets (a pseudo-3D resistivity model, a 3D velocity model, and 4 well-logs). These datasets, among others, were acquired to characterize the suitability of an evaporitic sequence for hosting a temporary storage facility of hazardous radioactive waste, which was planned in Villar de Cañas (Spain). Our strategy consisted of integrating both models in a single 3D bi-parametric grid that nested the velocity and resistivity values in each node. We then used a supervised learning algorithm to lithologically classify each node according to a training set based on the borehole data, which acts as ground truth. The training set is composed of classifiers that lithologically label resistivity-velocity pairs. However, the very shallow nodes lack classifiers due to the poor well-log coverage at the top part of the evaporitic sequence. To fill this gap, we computed an unsupervised cluster analysis that provided new classes to complete the training set. Finally, the supervised classification was applied, providing a new 3D lithology model that is far more consistent with the geology than the models derived from each parameter independently. The 3D model also revealed geological features previously unknown, notably the existence of an inactive fault. The proposed method can be applied to integrate and jointly interpret any kind of multidisciplinary datasets in a wide range of geoscientific problems, including natural resource exploration, geological storage, environmental monitoring, civil engineering practice, and hazard assessment. • The joint interpretation of geophysical data improves subsurface characterization. • Machine learning makes the interpreter's job easier while reducing bias. • Combined supervised and unsupervised learning makes the most of data. • Machine learning to enhance geological interpretation of historic datasets. • Geophysical surveys should be planned anticipating their subsequent integration.
Highlights
The Earth‟s subsurface plays an increasingly important role in our modern society to address some of its most demanding challenges
The joint interpretation scheme resulted in a 3D lithology model, combining the information provided by the resistivity and velocity models, the well-logs, and the geology
The combined model allows a joint interpretation of Res and Vp benefiting from both methods‟ prediction capabilities
Summary
The uppermost layer of the Earth is the most heterogeneous since it hosts the transition from unconsolidated to consolidated lithologies. It is affected by weathering, water recharges-discharges, and notably by the interaction with living organisms. Some authors define this portion of the Earth, where rock meets life, as the Critical Zone (Richter and Mobley, 2009). The complexity of this zone translates into a high variability of physical properties. To properly characterize this layer, different research methods must be applied, each one with its own lateral and depth resolution, geological targets, and limitations
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