The confluence of our ability to handle big data, significant increases in instrumentation
density and quality, and rapid advances in machine learning (ML) algorithms have placed …

In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal
component in existing models. The advancement of deep learning (DL) enables solving …

The microseismic monitoring technique is widely applied to petroleum reservoirs to
understand the process of hydraulic fracturing. Geophones continuously record the …

Microseismic source localization methods with deep learning can directly predict the source
location from recorded microseismic data, showing remarkably high accuracy and efficiency …

Locating microseismic sources is critical to monitoring the hydraulic fractures that occur
during fluid extraction/injection in unconventional oil/gas exploration, geothermal …

The ample size of time-lapse data often requires significant event detection and source
location efforts, especially in areas such as shale gas exploration regions where a large …

To improve the accuracy of microseismic inversion, seismic anisotropy and moment tensor
source should be carefully considered in the forward modelling stage. In this study, 3D …

Traditional full waveform inversion (FWI) highly depends on sufficient low-frequency data or
a good initial model. Passive seismic data contain rich low-frequency components, and …

In microseismic monitoring, the reconstruction of a reliable velocity model is essential for
precise seismic source localization and subsurface imaging. However, traditional methods …

Geophysical subsurface characterization plays a key role in the success of geologic carbon
sequestration (GCS). While deterministic inversion methods are commonly used due to their …