The data assimilation process for geothermal reservoirs often relies on well data which primarily offers insights into the immediate vicinity of the borehole. However, integrating geophysical methods can provide valuable information beyond well proximity, possibly enhancing reservoir predictions. Electromagnetic methods can be sensitive to the decreasing conductivity from heat extraction in geothermal reservoirs. A scheme to incorporate electromagnetic data into a data assimilation process for geothermal reservoirs is presented and implemented in this study. First, an ensemble of prior models representing the reservoir uncertainty is used to determine the moments of the resulting temperature field using a forward geothermal simulation. Source and receiver locations are determined by maximizing the distance of the path through the expected temperature changes while ensuring that the source and receiver are not excessively distant. Subsequently, a conductivity model is implemented using an empirical relationship. The expected electric field response can then be simulated using an electromagnetic forward model. To assimilate the data, the Ensemble Smoother with the Multiple Data Assimilation (ES-MDA) method is employed. The findings demonstrate that the incorporation of electromagnetic data provides more information regarding the temperature field, which when combined with the localized data from the production well improves the temperature forecast accuracy of both the production well and the entire reservoir model.

In the last decade, technological advances in the field of multicopters enabled a widespread use of drones that include professional research applications covering a wide field from civil engineering to geosciences and agriculture. In recreational spheres drones became popular for sport and leisure activities like private photography. In geophysics, multicopters opened new doors for easier and cheaper airborne surveying especially with electromagnetic sensors such as geomagnetometers and georadar.
In this study we developed an airborne geomagnetic mapping system by combining commercially available components. Our setup consists of a DJI M600 drone with a SENSYS Magdrone R3 sensor mounted on the drone’s landing gear. First measurements showed that data processing requires correcting for the multicopter’s varying orientation during the flight, which impacts the magnetic recordings. The correction of this so-called heading error can be addressed using a scalar calibration scheme, which was originally developed for satellite missions. The calibration is performed by a specific maneuver in flight and compensates the vehicles magnetic influence on the magnetic recordings. We demonstrate how a high data quality can be achieved using a newly developed calibration algorithm for our drone-sensor-setup and how geomagnetic data can be processed in such a way that a reliable qualitative interpretation is achievable.
In a case study we conducted an airborne geomagnetic mission in Forel (FR, Switzerland) at a military bombing range near Payerne, which is used as a training facility for target practices. The study area consists of an inaccessible swamp and shallow water zone, where ammunition was documented to be shot and dumped throughout the last century. The results show that our setup allows to reliably locate magnetic anomalies as they are produced by dropped ammunition. Therefore, fast and safe geomagnetic surveying is possible, and it can aid in future not only for UXO detection but also for identifying abandoned landfills and geological structures.

All over the world a so called oceanic 'hum' can be recorded, dominating the ambient noise field. The hum is generated by nonlinear interaction of ocean surface waves, radiating acoustic signals in the atmosphere and water (named microbaroms), and coupling to the solid earth as microseisms. The hum can be generated by local near-coastal sources and distant deep ocean sources. Deep oceanic generated 'hum' occurs within the range 0.1-0.3 Hz, dominantly around 0.2 Hz. The near-coastal generated 'hum' occurs at higher frequencies. Microseisms are mainly recorded as surface waves corresponding to near coastal activities. These surface waves overwhelm the weaker deep ocean signals, body and surface waves, because they attenuate less due to the shorter travel path. Previous studies are done with individual 3-component sensors, thus spectral data of all kind of microseisms, or for teleseismic distances only. This study of deep ocean microseisms is done by seismic arrays to analyze regional microseisms. Arrays provide more information about the microseisms by applying beamforming. This enables an improved localization of the source area, in specific near coastal activity or deep ocean generated. To validate the observed results a comparison with the numerical simulated source area is be made. Denote that the use of seismic arrays requires verification on the performance of arrays due to local conditions (sensitivity variances) to find possible deviations regarding the back azimuth. It is shown that, using classical beamforming, the recordings are dominated by near coastal surface waves. Applying adaptive beamforming using multiple signal classification, also other events become visible in the f-k spectrum. Due to this method an improved deep ocean source area is obtained. This observed source area agrees with the simulated source area. Improving the localization requires to define array correction factors.