[HTML][HTML] Machine learning in microseismic monitoring
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 …
density and quality, and rapid advances in machine learning (ML) algorithms have placed …
Sensing prior constraints in deep neural networks for solving exploration geophysical problems
One of the key objectives in geophysics is to characterize the subsurface through the
process of analyzing and interpreting geophysical field data that are typically acquired at the …
process of analyzing and interpreting geophysical field data that are typically acquired at the …
Microseismic location in hardrock metal mines by machine learning models based on hyperparameter optimization using bayesian optimizer
In recent years, with the gradual depletion of shallow mineral resources, the exploitation of
deep mineral resources has become an inevitable trend. Microseismic monitoring is one of …
deep mineral resources has become an inevitable trend. Microseismic monitoring is one of …
[HTML][HTML] Microseismic event waveform classification using CNN-based transfer learning models
The efficient processing of large amounts of data collected by the microseismic monitoring
system (MMS), especially the rapid identification of microseismic events in explosions and …
system (MMS), especially the rapid identification of microseismic events in explosions and …
Microseismic Monitoring Signal Waveform Recognition and Classification: Review of Contemporary Techniques
Microseismic event identification is of great significance for enhancing our understanding of
underground phenomena and ensuring geological safety. This paper employs a literature …
underground phenomena and ensuring geological safety. This paper employs a literature …
Unsupervised deep clustering of seismic data: Monitoring the Ross Ice Shelf, Antarctica
Advances in machine learning (ML) techniques and computational capacity have yielded
state‐of‐the‐art methodologies for processing, sorting, and analyzing large seismic data …
state‐of‐the‐art methodologies for processing, sorting, and analyzing large seismic data …
Hierarchical exploration of continuous seismograms with unsupervised learning
Continuous seismograms contain a wealth of information with a large variety of signals with
different origin. Identifying these signals is a crucial step in understanding physical …
different origin. Identifying these signals is a crucial step in understanding physical …
Deep clustering to identify sources of urban seismic noise in Long Beach, California
D Snover, CW Johnson… - … Society of America, 2021 - pubs.geoscienceworld.org
Ambient seismic noise consists of emergent and impulsive signals generated by natural and
anthropogenic sources. Developing techniques to identify specific cultural noise signals will …
anthropogenic sources. Developing techniques to identify specific cultural noise signals will …
Hybrid deep learning-based identification of microseismic events in TBM tunnelling
X Yin, Q Liu, J Lei, Y Pan, X Huang, Y Lei - Measurement, 2024 - Elsevier
For TBM's safe and efficient tunnelling, the microseismic monitoring technique has been
widely applied in rockburst warning. However, useful microseismic events are often mixed …
widely applied in rockburst warning. However, useful microseismic events are often mixed …
Unsupervised learning from three-component accelerometer data to monitor the spatiotemporal evolution of meso-scale hydraulic fractures
A Chakravarty, S Misra - International Journal of Rock Mechanics and …, 2022 - Elsevier
Enhanced geothermal systems can provide a substantial share of the global energy
demand. There exist several hurdles in the engineering implementations of such geothermal …
demand. There exist several hurdles in the engineering implementations of such geothermal …