Application of Microseismic Monitoring Technology in Forecasting of Roof Weighting
The paper introduced application of microseismic monitoring system in forecasting roof weighting of Qianqiu Coal Mine. The practical result showed that the microseismic monitoring system can forecast and judge the first weighting of main roof of fully ...
LI Bao-fu +3 more
doaj
Research on STA/LTA Microseismic Arrival Time-Picking Method Based on Variational Mode Decomposition
The complex environment of metal mines causes significant noise interference in microseismic signals. This leads to low accuracy and high false alarm rates when using the conventional Short-Term Average/Long-Term Average (STA/LTA) method for first ...
Zhiyong Fang +3 more
doaj +1 more source
Efforts were made to determine the seismicity of Mars as well as define its internal structure by detecting vibrations generated by marsquakes and meteoroid impacts.
Anderson, D. L. +9 more
core +1 more source
Optimization of Hydraulic Fracture Stimulation in Field Development
Imperial Users ...
Schofield, Jordan, Schofield, Jordan
core
Robust Phase Association and Simultaneous Arrival Picking for Downhole Microseismic Data Using Constrained Dynamic Time Warping. [PDF]
Wang T, Li L, Wen S, Lv Y, Yu Z, He C.
europepmc +1 more source
Automatic detection of arrival time for noisy microseismic data using a transformed difference between multiwindow energy ratios method. [PDF]
Zhang Z +7 more
europepmc +1 more source
Unveiling the signals from extremely noisy microseismic data for high-resolution hydraulic fracturing monitoring. [PDF]
Huang W, Wang R, Li H, Chen Y.
europepmc +1 more source
A prediction model for microseismic signals based on kernel extreme learning machine optimized by Harris Hawks algorithm. [PDF]
Zhu W +5 more
europepmc +1 more source
Early-warning method for rock bursts based on the fractal characteristics of microseismic source. [PDF]
Wu Y, Zhu Z, Chen K, Lv F.
europepmc +1 more source
High-Precision Coal Mine Microseismic P-Wave Arrival Picking via Physics-Constrained Deep Learning. [PDF]
Qin K, Deng Z, Li X, Lian Z, Ye J.
europepmc +1 more source

