This repository contains code and data for an ERT (Electrical Resistivity Tomography) modelling and inversion project. The goal is to create a subsurface image that shows the aquifer and the hot water infiltration into that aquifer at Aguas de Vichy, a hot spring located in San Andrés, Santander, Colombia.
- Abstract
- Forward Modelling
- Data Collection and Processing
- Inversion
- Results and Discussion
- Conclusion and Future Work
- References
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The Aguas de Vichy thermal spring (SAN-001), located near San Andres, Santander, has geothermal potential with known temperature, stored heat, and geochemistry. The spring's thermal waters are sodium-chloride type with notable salt concentrations. The region has significant faulting and folding trends.
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A fault near the spring site marks the contact between Paleozoic and Cretaceous sedimentary units. The Colombian Geological Survey suggests that the spring's water, salinized and heated by deep infiltration, rises due to temperature differences. Using techniques like ERT and VES, the geothermal fluid's distribution and mechanisms can be studied.
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These findings will enhance understanding of the geothermal system and encourage geothermal energy exploration in Santander.
The forward modelling process involves simulating electrical resistivity tomography (ERT) data based on the given subsurface geometry and resistivity distribution. This process enables the generation of synthetic data that can be compared with actual measurements.
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Geometry Definition: The subsurface geometry was defined using a combination of layers representing different rock formations, faults, and plume structures. The geometry was constructed to mimic the geological features present near the hot spring location.
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Measuring Scheme: A Schlumberger measuring scheme with 96 electrodes was created along a 700-meter line. Electrode positions were carefully distributed to ensure sufficient mesh refinement and accuracy. The measuring scheme defined how measurements would be taken across the subsurface.
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Mesh Generation: A mesh was generated based on the defined geometry and electrode positions. The mesh quality was controlled to ensure accurate numerical results. Nodes were added to enforce mesh refinement and improve accuracy.
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Fluid simulation:: An inyected diffusive fluid was simulated for the complex model, it has an injection point (fault) and also values of concentration. There are 2 possible behaviors to model the diffusing fluid: Isotropic and anisotropic.
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Resistivity Distribution: Different resistivity values were assigned to various regions within the mesh. These values represented different rock formations and plume structures present in the subsurface. The resistivity values were used to simulate the conductivity variations in the forward modelling process.
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Fluid Resistivity Definition: The fluid resistivity was defined as a linear function of the concentration, see the notebook for more details.
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Simulation: Using the defined geometry, measuring scheme, and resistivity distribution, synthetic ERT data was generated. The forward simulation process considered factors such as geometric factors and noise levels. The resulting data container contained apparent resistivity values, geometric factors, and estimated data errors.
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Data Filtering: To ensure the quality of the synthetic data, negative data values resulting from noise were removed from the data container. Filtering ensured that only physically meaningful data points were used for further analysis.
The forward modelling process allowed the generation of synthetic ERT data that closely resembled real-world measurements. This synthetic data serves as a foundation for subsequent inversion processes, where the goal is to reconstruct the subsurface resistivity distribution based on the measured data.
NOTE: You can find the forward modelling base code in "Modelling_complex.ipynb" notebook, we are using Modelling_scenarios.py to try different posible geological scenarios and define what acquisition will we do. You can run the script on a PyGimli environment (see https://www.pygimli.org for more information).
Forward modeling was employed to establish potential distribution of the resistivity and chargeability anomalies based on the initial hypothesis, subsequently, three Electrical Resistivity Tomography (ERT) transects around the fault map trace were conducted. Two of them were complemented by induced polarization (IP) method.
The inversion process has been done using PyGimli for Schlumberger array and Res2Dinv for mixed (robust) array. PyGimli robust inversion is yet to be available after data conversion. Data analysis has been conducted to identify outliers and remove disperse values.
Two of the ERT transects traverse the fault and revealed resistivity values ranging from near zero to over 1500 ohm.m, with a similar distribution pattern. Low resistivity areas, possibly indicating accumulation of groundwater and geothermal saline fluids, were more pronounced adjacent to the inferred fault trace and at the profile's boundaries. High resistivity anomalies appear at 5 meters depth, defining a possible lower boundary of the quaternary aquifer, and are likely indicative of consolidated or impermeable materials. The third ERT, which was located within the aquifer but doesn’t intersect the fault, showed higher baseline resistivities suggesting a reduced presence of geothermal fluids. IP findings are in alignment with ERT results, where low chargeability suggests the presence of groundwater and geothermal saline fluids.
NOTE: The results are not in this repository, they will be published once the citable paper is available.
The inverted sections support the presence of a geothermal system dominated by fluid circulation, which may correlate to faults and fractures ; however, the studied fault trace did not show the expected anomaly for a main geothermal fluid path. We suggest that the following studies improve the geological and structural uncertaintity and contemplate other alternatives such as lateral (advective) fluid flow as the main geothermal water source for the thermal springs. The results obtained reveal a potential energy resource which requires further understanding and encourages continued research into geothermal energy within the Santander department.
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