Focused Geophysical Imaging of the Chiweta Geothermal Field (Malawi)

Geophysical surveys may image buried tectonic structures and variations of lithology, hydrothermal alteration and porosity/fluid content of geothermal fields and inverse models can be directly used for the assessment of the conceptual models. However, conventional interpretation is commonly based on minimum-structure inverse modelling that produces inherently smooth and blurred images of the investigated geological structures (e.g Zhdanov, 2002). Therefore, while smooth modelling helps inversion convergence and prevent artefacts in the solutions, some important features with sharp transitions can be missed or smoothed out. Typical examples are the lithological variations between the sedimentary infill and the bedrock in a basin or the bedrock steps due to faulting in graben/horst structures. Focused geophysical imaging (Portniaguine and Zhdanov, 1999) with different regularization methods can be more effective to detect sharp boundaries because they did not excessively penalize sharp physical property variations. The total modified variation (MTV, Acar and Vogel, 1994) and the minimum gradient support (MGS, Portniaguine and Zhdanov, 1999) stabilizers were applied to the inversion of gravimetric and magnetotelluric data collected at the Chiweta geothermal prospect in Malawi. The gravimetric data was used to map the interface between the Karoo formation and the underlying Precambrian gneiss basement complex, assuming a density contrast of 200 kg/m3. The resulting horst-graben structure imaged by the 2D and 3D MTV and MGS inversion allowed to identify major faults affecting the basement and possibly driving the up-flow of hydrothermal fluids. The 1D magnetotelluric MTV and MGS inversions with lateral constrain provided a focused pseudo-2D image of the resistivity distribution. The models showed the lithological contact between the Karoo formation and the basement complex in the central portion of the survey area. Geothermal alteration in the Karoo formation has been revealed by different conductive anomalies. Some of them are associated with low temperature clay alteration in the Chiweta hot springs area, while others may represent fossil geothermal zones. Good correspondence between higher density and resistivity values has been observed where geothermal alteration is plausibly weaker or absent. It was found that focused inversion is strongly dependent upon the initial model and the chosen inversion parameters, but if a proper choice is done, it can be an effective tool to get detailed geological images. It was concluded that it can be considered as a refinement of the classical maximum smoothness approach, to be used when abrupt physical property changes are expected. REFERENCES Acar, R., and Vogel, C. R. \u201cAnalysis of total variation penalty methods\u201d Inverse Problems, 10, 1217-1 229 (1994). Portniaguine, O., and Zhdanov, M. S. \u201cFocusing geophysical inversion images\u201d. Geophysics, 64, 3, 874-887 (1999). Zhdanov, M. S. \u201cGeophysical inverse theory and regularization problems\u201d, Methods in Geochemistry and Geophysics, 36, Elsevier (2002)

Smooth MT Transfer Function Estimation by an Inverse Scheme

The geophysical exploration of geothermal areas strongly relies upon Magnetotellurics (MT) method, that exploits the measurement of the low-power natural EM field at the Earth's surface. Through the estimation of the MT transfer functions (TF), relating the electric to the magnetic field, the subsurface resistivity distribution can be inferred. Due to the diffusive nature of the low-frequency EM field, the MT TF's are inherently smooth, and smoothness is the main criterion adopted by EM community to assess the estimation quality. In the presence of Gaussian noise, the frequency-domain the least squares (LSQ) method provides the best possible estimate; natural MT data, however, contains a significant amount of non-stationary data that constitute outliers for the LSQ procedure. These outliers make the TF\u2019s sharp at several frequencies, according to the nature of the noise. In order to circumvent this problem, robust methods were introduced, and provide a smooth TF, if non-stationarity is a minor fraction of the record. However, as geothermal development proceeds on a global scale, investigations can involve densely populated and industrialised areas. In these zones, high-power artificial is more likely present. Since these disturbance signals can by persistent, robust methods can fail; moreover, preliminary filtering can be ineffective. In these conditions, the applicability of MT is severely hampered. In some cases, smoothness is a-posteriori introduced by splines or smoothing procedures, but this approach lacks physical consistency. It can also be part of estimation methods but implying the adoption of some arbitrary assumptions. Here we propose a new heuristic algorithm to reach the maximum TF's smoothness through an inverse scheme applied to event rejection in frequencydomain. The algorithm searches for frequency-dependent power thresholds to be applied to the events, in order to achieve the maximum smoothness in the TF's. The smoothness is the objective function to be minimized, and the model space is constituted by the infinite set of threshold vectors. We found that after the completion of the process, the distribution of the event powers is more Gaussian, and then, more suitable for LSQ estimation; the corresponding residuals are consequently closer to a Rayleigh distribution. Physical consistency of the resulting TF has been tested by 1D inversion. The algorithm can, therefore, be combined with the MT remote-reference technique, and we found that it is effective to reduce the effects of artificial strong-power signal that can deteriorate the acquisitions. We present the successful application of this new technique to MT data collected over an East African Rift System geothermal area