Allan Haas

Electrical burst signature of pore‐scale displacements

[1] Electric field bursts were passively observed when a nonwetting fluid displaced a wetting fluid in a porous material (drainage) as well as during imbibition experiments. A sandbox experiment was conducted to study these electrical disturbances using a network of very sensitive nonpolarizing electrodes located at the top surface of the tank. Drainage exhibited many more electrical bursts, with a higher magnitude, than imbibition. These events were only observed during drainage or imbibition, not prior to or after the water flowed inside the porous sandbox. We point out the possible relationship between the formation of Haines jumps and the occurrence of these electrical bursts. These bursts show a power law distribution during drainage with a power law exponent of about -1.7, in agreement with a previous published study using acoustic and hydroacoustic events. Imbibition does not display such a power law relationship.

Self-potential monitoring of a salt plume

Nonintrusively monitoring the spread of contaminants in real time with a geophysical method is an important task in hydrogeophysics. We have developed a sandbox experiment showing that the self-potential method can locate both the source of leakage and the front of a contaminant plume. We monitored the leakage of a plume of salty water from a hole at the bottom of a small tank located at the top of a main sandbox. Initially, the sand was saturated by tap water. At a given time, a hole was opened at the bottom of the tank, allowing the salty water to migrate by diffusion and buoyancy-driven flow in the main sandbox. The bottom of the sandbox contained a network of 32 nonpolarizing silver-silver chloride electrodes with amplifiers, connected to a multichannel voltmeter. The self-potential response associated withthe migration of the salt plume in the sandbox was recorded over time. A self-potential anomaly was observed with amplitude varying from a few millivolts at the start of the leak to a few tens of mi...

Reduced Data Communication for Parallel CMA-ES for REACTS

Covariance Matrix Adaptation - Evolutionary Strategy (CMA-ES) is a black-box optimization method useful for applications where no direct inversion is possible. We present the development of a parallel CMA-ES algorithm that reduces the runtime for a specific geophysical data analysis, dipole localization. We compare our parallel algorithm against several other parallel CMA-ES variants on a sample dataset for dipole localization. We improve the performance of CMA-ES for the problem of finding dipoles in a subsurface environment as part of a closed-loop near-real-time wireless bioremediation system, REACTS (near-REal-time Autonomous bioremediation of ConTamination in the Subsurface). The goal of the performance improvement is to enable near-real-time analysis of geophysical data. For this application, our algorithm shows significant performance improvement over the other variants.