This repository contains MATLAB scripts and sample seismic data for appying seismid denoising proposed in:

Mousavi, S. M., and C. A. Langston (2017). Automatic Noise-Removal/Signal-Removal Based on the General-Cross-Validation Thresholding in Synchrosqueezed domains, and its application on earthquake data, Geophysics.82(4), V211-V227 doi: 10.1190/geo2016-0433.1

@article{mousavi2017automatic,
  title={Automatic noise-removal/signal-removal based on general cross-validation thresholding in synchrosqueezed domain and its application on earthquake data},
  author={Mousavi, S Mostafa and Langston, Charles A},
  journal={Geophysics},
  volume={82},
  number={4},
  pages={V211--V227},
  year={2017},
  publisher={Society of Exploration Geophysicists}
}

and

Mousavi, S. M., and C. A. Langston, (2016a). Fast and novel microseismic detection using time-frequency analysis. SEG Technical Program Expanded Abstracts 2016: pp. 2632-2636. doi: 10.1190/segam2016- 13262278.1

@incollection{mousavi2016fast,
  title={Fast and novel microseismic detection using time-frequency analysis},
  author={Mousavi, Seyed Mostafa and Langston, Charles},
  booktitle={SEG Technical Program Expanded Abstracts 2016},
  pages={2632--2636},
  year={2016},
  publisher={Society of Exploration Geophysicists}
}

demo.m includes all info you need to know for running the code.

you need MATLAB statistics and signal processing toolbox to run this code.

(https://www.researchgate.net/publication/315917772_Automatic_noise-removalsignal-removal_based_on_general_cross-validation_thresholding_in_synchrosqueezed_domain_and_its_application_on_earthquake_data)

(https://library.seg.org/doi/pdfplus/10.1190/segam2016-13262278.1)

(https://earthquake.usgs.gov/contactus/menlo/seminars/1093)

Recorded seismic signals are often corrupted by noise. An automatic noise attenuation method for single-channel seismic data is presented, based upon high-resolution time-frequency analysis. Synchrosqueezing is a time-frequency reassignment method aimed at sharpening a time–frequency picture. Noise can be distinguished from the signal and attenuated more easily in this reassigned domain. The threshold level is estimated using a general cross validation approach that does not rely on any prior knowledge about the noise level. Efficiency of thresholding has been improved by adding a pre-processing step based on Kurtosis measurement and a post-processing step based on adaptive hard-thresholding. The proposed algorithm can either attenuate the noise (either white or colored) keeping the signal or remove the signal and keep the noise. Hence, it can be used in either normal denoising applications or pre-processing in ambient noise studies. We test the performance of the proposed method on synthetic, microseismic, and earthquake seismograms.

Seismic data recorded by surface arrays are often contaminated by unwanted noise. In many conventional seismic methods, the reliability of the seismic data and accuracy of parameter extraction, such as onset time, polarity, and amplitude, are directly affected by the background noise level. As a result, the accuracy of event location and other attributes derived from seismic traces are also influenced by the noise content. Therefore, there is a great need for developing suitable procedures that improve signal-to-noise ratios allowing for robust seismic processing. In this presentation, I introduce four different methods for automatic denoising of seismic data. These methods are based on the time-frequency thresholding approach. The efficiency and performance of the thresholding-based method for seismic data have been improved significantly. Proposed methods are automatic and data driven in the sense that all the filter parameters for denoising are dynamically adjusted to the characteristics of the signal and noise. These algorithms are applied to single channel data analysis and do not require large arrays of seismometers or coherency of arrivals across an array. Hence, they can be applied to every type of seismic data and can be combined with other array based methods. Results show these methods can improve detection of small magnitude events and accuracy of arrival time picking.

(a) The OBS and (b) microearthquake data as presented in Figures 9 and 10. The left column shows raw data, and the right column shows data after removing the signal’s energy (designaling) from the traces. As you can see, the algorithm is successful in removing the signal from the waveform event in the case that the signal is completely buried under the background noise (a-3) and it is hard to identify the presence of the signal and removing it using the commonly used time normalizations in ambient noise studies.