Results 81 to 90 of about 1,182 (196)
Research on Precise Identification of Rock Strength Based on Bolt Drilling Parameters
Energy Science &Engineering, Volume 14, Issue 3, Page 1353-1371, March 2026.Drilling detection test platform. ABSTRACT During roadway excavation, the presence of weak interlayers and fractured rock masses significantly affects roof stability. To achieve timely and effective roadway support, it is crucial to identify and predict different rock types based on drilling signals from roof bolters.
Qiang Zhu +4 more
wiley +1 more source
Seismic Signal Denoising Based on Surelet Transform for Energy Exploration [PDF]
Seismic signals are critical for subsurface energy exploration like oil, coal, and natural gas. Processing these signals while minimizing environmental impacts is crucial but lacking in several appropriate multi-scale geometric analysis (MGA) techniques.
Ding, Mu
core +2 more sources
Seismic Random Noise Attenuation Based on PCC Classification in Transform Domain
IEEE Access, 2020 Random noise attenuation of seismic data is an essential step in the processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals is weaker and the signal to noise (SNR) is much
Yu Sang +5 more
doaj +1 more source
Seismic data denoising and deblending using deep learning
CoRR, 2019 An important step of seismic data processing is removing noise, including interference due to simultaneous and blended sources, from the recorded data. Traditional methods are time-consuming to apply as they often require manual choosing of parameters to obtain good results.
Alan Richardson, Caelen Feller
openaire +2 more sources
Deglitching Martian Seismic Data: Application to Marsquake Detection
Earth and Space Science, Volume 13, Issue 3, March 2026.Abstract NASA's InSight mission investigates the interior structure of Mars. The data is characterized by multiple non‐seismic signals with varying attributes, including high‐energy instrumental noise, known as glitches, which frequently exhibit large linear polarization.
Jair Zampieri +2 more
wiley +1 more source
EURASIP Journal on Advances in Signal ProcessingSuppressing random noise in seismic signals is an important issue in research on processing seismic data. Such data are difficult to interpret because seismic signals usually contain a large amount of random noise.
Feng Yang, Jun Liu, Qingming Hou, Lu Wu
doaj +1 more source
Distributed Acoustic Sensing Denoising Using a Self‐supervised Conditional Diffusion Model
Geophysical Prospecting, Volume 74, Issue 3, March 2026.ABSTRACT Distributed acoustic sensing (DAS) data are characterized by a low signal‐to‐noise ratio due to the complex noise present in its challenging operational environment. To enhance the quality of the DAS data, we propose a self‐supervised diffusion model to attenuate the DAS noise.
Omar M. Saad, Tariq Alkhalifah
wiley +1 more source
Simultaneous denoising and interpolation of 2D seismic data using data-driven non-negative dictionary learning [PDF]
, 2017 As a major concern, the existence of unwanted energy and missing traces in seismic data acquisition can degrade interpretation of such data after processing.
Yangkang Chen +7 more
core +1 more source
Geophysical Research Letters, Volume 53, Issue 3, 16 February 2026.Abstract Slow, aseismic fault slip has emerged as a significant contributor to the seismic cycle. However, whether slow and fast slip arise from similar physical processes remains unresolved, due to detection biases affecting noisy surface measurements and the analysis of the source properties of slow slip.
Giuseppe Costantino +3 more
wiley +1 more source
Bayesian feature learning for seismic compressive sensing and denoising [PDF]
, 2017 Extracting the maximum possible information from the available measurements is a challenging task but is required when sensing seismic signals in inaccessible locations.
Georgios Pilikos, A. C. Faul
core +1 more source