One of the key goals of microseismic monitoring is the accurate estimation of the
source location. The accuracy of both P- and S-wave velocities strongly influences
the estimation of source locations and, hence, the fracture detection’s reliability. I use
advanced methodologies based on full waveform inversion methods to obtain accurate
P- and S- wave velocities and locate the source and its characteristics. I first use an
elastic FWI for passive source and velocity inversion, in which an equivalent source
represents the conventional source term of the elastic wave equation. Thus, I update
the source locations, source functions, and velocities simultaneously using a waveform
inversion scheme. Waveform inversion of passive events has severe nonlinearity due
to the unknown source locations in space and their functions in time. I, thus, use a
source-independent objective function based on convolving reference traces with both
modeled and observed data to avoid cycle skipping caused by the unknown sources.
I test the method on real microseismic monitoring data. Then, I extend the method
to a 3D acoustic orthorhombic case. I also analyze the relationship of the proposed
equivalent source term and the conventional elastic wave equation’s seismic moment
tensor source term. Besides, locating numerous microseismic events by solving wave
equations is computationally expensive, and manually picking all the event arrivals
is challenging. To address the issues without event picking or detection, I use a novel
artificial neural network framework to directly map seismic data to their potential
locations. I train two convolutional neural networks (CNN) on labeled synthetic
5
acoustic data containing simulated micro-seismic events to fulfill such requirements.
At last, I use the developed convolutional neural network to predict the source location
for field micro-seismic monitoring data. I, especially, train the CNN with a large
amount of synthetically generated data and the extracted coherent noise from the
field data. The synthetic training data allow us to control the corresponding labels,
and the extracted noise from the field data and the pre-processing steps vastly reduce
the di↵erence between the field and the synthetic data.
| Date of Award | Aug 2021 |
|---|
| Original language | English (US) |
|---|
| Awarding Institution | - Physical Sciences and Engineering
|
|---|
| Supervisor | Shuyu Sun (Supervisor) |
|---|