Geophysical Inversion

Geophysical inversion is the mathematical process of estimating the values of geological model parameters (such as size and depth) from a set of observed geophysical data. Simply put, an inversion can reconstruct geological structures from geophysical data. Independent single-property inversion can often encounter difficulties when the geology is complicated. Inverting a geophysical dataset jointly with another complementary geophysical dataset can solve and improve the construction of a single Earth model. This process is called joint inversion. Physical property data and drill-hole data can be used as constraints in both independent and joint inversions. Our new and modern codes and software enable us to invert magnetic, gravity, gravity gradiometry, IP and DC resistivity, magnetotelluric and first arrival travel-time seismic data. For 3D cases, we use unstructured tetrahedral meshes, the advantage of which is that it can honour the topography (as well as constraints) to as fine a resolution as the topography is known. Here are some real examples of our geophysical inversion services:
 
P.S. Because of the policy of real data, information on figures has been removed. 
Gravity and Magnetic Joint and Constrained Inversions

Gravity and Magnetic Inversions

Independent inversions of gravity and magnetic data are becoming more common at interpretation. However, we believe that joint inversions can significantly improve the results, especially after applying constraints.

Constrained Inversions and Overburden Stripping

Constrained Inversions and Overburden Stripping

The geophysical responses (e.g., the gravity response) of exploration targets (e.g., deposits and mineralizations) can be masked by the variation of the overburden thicknesses. To solve this problem, we use new ways to separate the overburden contribution from geophysical data so that deeper targets can be detected and delineated by means of an innovative application of new, modern, state-of-the-art modelling and (constrained and joint) inversion of geophysical methods (such as seismic refraction, magnetic and gravity). 

Tomography and Seismic Refraction Inversion

Tomography and Seismic Refraction Inversion

Tomography and the inversion of seismic refraction data give us a seismic velocity model of the subsurface. To obtain sharp boundaries between geological structures in the reconstructed model, we use advanced methods such as clustering in the inversion. 

Magnetic Vector Inversion and Magnetic Remanence

Inversion and Magnetic Remanence

The interpretation of magnetic data can be complicated because of the presence of magnetic remanence.  However, inverting magnetic data for subsurface magnetization (magnetic vector inversion) as opposed to magnetic susceptibility (magnetic susceptibility inversion) can be a potential solution. 

3D Inversion of Magnetotelluric Data

3D Inversion of Magnetotelluric Data

Natural source methods such as magnetotelluric (MT) are suitable for deeper exploration. In the shallow exploration, a transmitter can be used, which is referred to as control source MT (CSAMT). MT methods use electromagnetic signals to investigate the electrical resistivity of geological structures. MT exploration has been used in oil & gas, mineral, environmental, tectonic, geothermal and geotechnical explorations. Our 3D inversion code can reconstruct geological structures from MT data.

Topography Effect in Geophysical Data

Topography Effect in Geophysical Data

The difference between 2D (or 1D) and 3D inversion results can be significant, especially when we have topography effects in geophysical data. This figure (from one of our projects) shows that our advanced 3D inversions (using unstructured meshes) perfectly dealt with the topography effect in electromagnetic data (the 3D inversion result is consistent with geology).

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3D Inversion of IP and DC Resistivity

3D inversion of IP and DC resistivity data using unstructured meshes will improve the accuracy of results by incorporating high-resolution topography (and/or geological surfaces) into the models. Models (in the figure) show a correlation between the zones of high chargeability and the known gold mineralization.