Gravity and Magnetic Processing and Inversion Over the Mahallat Geothermal System Using Open Source Resources in Python']Python

The gravity and magnetic data sets over a geothermal source are investigated to explore the geological features and geothermal system manifestations. The raw gravity data sets are firstly corrected for drift, latitude and free-air effects to obtain freeair anomalies. A new approach is utilized for Bouguer and terrain corrections. The model space, which is the mass from the reference surface to the ground surface, is parameterized by a very accurate and advanced meshing algorithm (octree and quadtree). The gravity effect of the model space is computed via numerical forward modelling and is considered as the Bouguer and terrain corrections. These corrections are subtracted from the free-air anomalies, which yields the complete Bouguer anomaly. The computed Bouguer anomalies are de-trended and transferred to residual anomalies by methods including polynomial fitting and two-stage methods. The residual gravity anomalies are inverted to obtain the subsurface density distribution. The density contrasts are estimated by minimizing the data and model objective functions through an efficient algorithm. The probable source of the geothermal system and other new gravity anomalies are successfully detected and inverted contrary to the results in previously published papers. The residual magnetic anomaly is also inverted and the obtained geometrical and physical parameters of the source body including minimum depth, shape and susceptibility are very close to the previous results. The new detected gravity anomaly and the main magnetic anomaly in the overlapped area of gravity and magnetic grids are located at almost the same place with almost the same depths, which confirms their probable common source. We developed the open source package (http://docs.simpeg.xyz/content/tutorials/03-gravity), which is accessible through SimPEG (Simulation and Parameter Estimation in Geophysics), for inverting the gravity and magnetic data sets.