Joint Microearthquake Source Location and Velocity Updates for Carbon Sequestration Monitoring

ABSTRACT sequestration has shown an important potential to reduce greenhouse gases in the atmosphere. One of the key aspects of sequestration is geological storage, which aims to store safely over a long period with low risks. Geophysical monitoring of the reservoir is required for this objective, with microearthquakes from injection serving as indicators for assessing changes in seismic velocity over time. We develop a geometry optimization algorithm that can significantly reduce the number of required microearthquake events for velocity estimation while balancing the subsurface illumination. A modified source‐independent waveform inversion algorithm is proposed to eliminate the negative effects of the usually unknown origin time and source wavelets. However, the non‐linearity of the objective function also increases, requiring at least 94% of the starting model's accuracy to avoid convergence to local minima in a synthetic experiment. Furthermore, we design a neural network that takes the traveltime difference of P‐ and S‐wave arrivals to relocate the microearthquakes. We test various scenarios, including noisy data, parameter crosstalk, inaccurate source location and unknown source wavelet, based on the Illinois Basin Decatur storage project. Finally, we apply the developed algorithms to the field data and discuss their advantages and weaknesses. In practice, the poor azimuthal coverage of microearthquakes and borehole sensors may limit the quality of final images. The developed algorithms are also applicable to seismological imaging of the Earth using ambient or active seismic sources.