A Matlab code package for 2D/3D local slope estimation and structural filtering

Geophysics ◽  
2022 ◽  
pp. 1-102
Author(s):  
Hang Wang ◽  
Yunfeng Chen ◽  
Omar M. Saad ◽  
Wei Chen ◽  
Yapo Abolé Serge Innocent Oboué ◽  
...  
Keyword(s):  
Open Source ◽  
Seismic Data ◽  
Signal Analysis ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Local Slope ◽  
Matlab Code ◽  
Slope Estimation ◽  
And Mathematics ◽  
2D And 3D

Local slope is an important attribute that can help distinguish seismic signals from noise. Based on optimal slope estimation, many filtering methods can be designed to enhance the signal-to-noise ratio (S/N) of noisy seismic data. We present an open-source Matlab code package for local slope estimation and corresponding structural filtering. This package includes 2D and 3D examples with two main executable scripts and related sub-functions. All code files are in the Matlab format. In each main script, local slope is estimated based on the well-known plane wave destruction algorithm. Then, the seismic data are transformed to the flattened domain by utilizing this slope information. Further, the smoothing operator can be effectively applied in the flattened domain. We introduce the theory and mathematics related to these programs, and present the synthetic and field data examples to show the usefulness of this open-source package. The results of both local slope estimation and structural filtering demonstrate that this package can be conveniently and effectively applied to the seismic signal analysis and denoising.

scholarly journals Dynamics of the Askja caldera July 2014 landslide, Iceland, from seismic signal analysis: precursor, motion and aftermath

2017 ◽  
Author(s):  
Anne Schöpa ◽  
Wei-An Chao ◽  
Bradley Lipovsky ◽  
Niels Hovius ◽  
Robert S. White ◽  
...  
Keyword(s):  
Seismic Data ◽  
Signal Analysis ◽  
Slope Failure ◽  
Stable State ◽  
Seismic Signal ◽  
Early Warning Systems ◽  
High Energy ◽  
Stick Slip ◽  
Short Period ◽  
Large Landslide

Abstract. Using data from a network of 58 seismic stations, we characterise a large landslide that occurred at the southeastern corner of the Askja caldera, Iceland, on 21 July 2014, including its precursory tremor and mass wasting aftermath. Our study is motivated by the need for deeper generic understanding of the processes operating not only at the time of catastrophic slope failure, but also in the preparatory phase and during the transient into the subsequent stable state. In addition, it is prompted by the high hazard potential of the steep caldera lake walls at Askja as tsunami waves created by the landslide reached famous tourist spots 60 m above the lake level. Since direct observations of the event are lacking, the seismic data give valuable details on the dynamics of this landslide episode. The excellent seismic data quality and coverage of the stations of the Askja network made it possible to jointly analyse the long- and short-period signals of the landslide to obtain information about the triggering, initiation, timing, and propagation of the slide. The seismic signal analysis and a landslide force history inversion of the long-period seismic signals showed that the Askja landslide was a single, large event starting at the SE corner of the caldera lake at 23:24:05 UTC and propagating to the NW in the following 2 min. The bulk sliding mass was 7–16 × 1010 kg, equivalent to a collapsed volume of 35–80 × 106 m3, and the centre of mass was displaced horizontally downslope by 1260 ± 250 m during landsliding. The seismic records of stations up to 30 km away from the landslide source area show a tremor signal that started 30 min before the main landslide failure. It is harmonic, with a fundamental frequency of 2.5 Hz and shows time-dependent changes of its frequency content. We attribute the complex tremor signal to accelerating and decelerating stick-slip motion on failure planes at the base and the sides of the landslide body. The accelerating motion culminated in aseismic slip of the landslide visible as a drop in the seismic amplitudes down to the background noise level 2 min before the landslide high-energy signal begins. We propose that the seismic signal of the precursory tremor may be developed as an indicator for landslide early-warning systems. The 8 hours after the main landslide failure are characterised by smaller slope failures originating from the destabilised caldera wall decaying in frequency and magnitude. We introduce the term afterslides for this subsequent, declining slope activity after a large landslide.

Simulation Research on Synthesis Algorithm of Directional Illumination Data

2011 ◽  
Vol 403-408 ◽  
pp. 2337-2340
Author(s):  
Shu Cong Liu ◽  
Yan Xing Song ◽  
Jing Song Yang
Keyword(s):  
Seismic Data ◽  
Seismic Waves ◽  
Seismic Analysis ◽  
Signal To Noise Ratio ◽  
A Priori ◽  
Effective Means ◽  
Seismic Signal ◽  
Geological Structure ◽  
Seismic Exploration ◽  
The Impact

Seismic illumination analysis was an effective means of recognizing and studying the energy distributions in the underground geological structure in seismic data acquisition. Effective seismic illumination analysis to a priori targeted-geological model to identify the energy distribution of seismic waves, can apply to seismic analysis and amplitude compensation analysis. To increase the signal to noise ratio and resolution of seismic data when vibrator seismic exploration, it was necessary to strengthen the energy of a certain direction to get the High-Precision imaging and the best illumination of the target areas.Simulation research were done on single source directional illumination seismic technology, with seismic illumination analysis, and the impact of source number, spacing change on directional illumination seismic technology were also analyzed. Simulation results showed that the directional seismic technology could improved SNR of seismic data, and could be used for seismic signal processing.

Deep denoising autoencoder for seismic random noise attenuation

Geophysics ◽  
2020 ◽  
Vol 85 (4) ◽  
pp. V367-V376 ◽  
Author(s):  
Omar M. Saad ◽  
Yangkang Chen
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Singular Spectrum Analysis ◽  
Synthetic Data ◽  
Seismic Signal ◽  
Denoising Autoencoder ◽  
Levels Of Abstraction ◽  
Data Set ◽  
Effective Manner

Attenuation of seismic random noise is considered an important processing step to enhance the signal-to-noise ratio of seismic data. A new approach is proposed to attenuate random noise based on a deep-denoising autoencoder (DDAE). In this approach, the time-series seismic data are used as an input for the DDAE. The DDAE encodes the input seismic data to multiple levels of abstraction, and then it decodes those levels to reconstruct the seismic signal without noise. The DDAE is pretrained in a supervised way using synthetic data; following this, the pretrained model is used to denoise the field data set in an unsupervised scheme using a new customized loss function. We have assessed the proposed algorithm based on four synthetic data sets and two field examples, and we compare the results with several benchmark algorithms, such as f- x deconvolution ( f- x deconv) and the f- x singular spectrum analysis ( f- x SSA). As a result, our algorithm succeeds in attenuating the random noise in an effective manner.

Broadband marine controlled-source electromagnetic for subsalt and around salt exploration

2016 ◽  
Vol 4 (4) ◽  
pp. T521-T531 ◽  
Author(s):  
Andrea Zerilli ◽  
Marco P. Buonora ◽  
Paulo T. L. Menezes ◽  
Tiziano Labruzzo ◽  
Adriano J. A. Marçal ◽  
...  
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Signal To Noise ◽  
Data Set ◽  
Depth Imaging ◽  
Controlled Source ◽  
Research Survey ◽  
Starting Model ◽  
Seismic Images

Salt basins, mainly Tertiary basins with mobilized salt, are notoriously difficult places to explore because of the traditionally poor seismic images typically obtained around and below salt bodies. In areas where the salt structures are extremely complex, the seismic signal-to-noise ratio may still be limited and, therefore, complicate the estimation of the velocity field variations that could be used to migrate the seismic data correctly and recover a good image suitable for prospect generation. We have evaluated the results of an integrated seismic-electromagnetic (EM) two-step interpretation workflow that we applied to a broadband marine controlled-source EM (mCSEM) research survey acquired over a selected ultra-deepwater area of Espirito Santo Basin, Brazil. The presence of shallow allochthonous salt structures makes around salt and subsalt seismic depth imaging remarkably challenging. To illustrate the proposed workflow, we have concentrated on a subdomain of the mCSEM data set, in which a shallow allochthonous salt body has been interpreted before. In the first step, we applied a 3D pixel-based inversion to the mCSEM data intending to recover the first guess of the geometry and resistivity of the salt body, but also the background resistivity. As a starting model, we used a resistivity mesh given by seismic interpretation and resistivity information provided by available nearby wells. Then, we applied a structure-based inversion to the mCSEM data, in which the retrieved model in step one was used as an input. The goal of that second inversion was to recover the base of the salt interface. The top of the salt and the background resistivities remained fixed throughout the process. As a result, we were able to define better the base of the allochthonous salt body. That was reinterpreted approximately 300–700 m shallower than interpreted from narrow azimuth seismic.

Deblending of simultaneous source data using a structure-oriented space-varying median filter

2020 ◽  
Vol 222 (3) ◽  
pp. 1805-1823 ◽  
Author(s):  
Yangkang Chen ◽  
Shaohuan Zu ◽  
Yufeng Wang ◽  
Xiaohong Chen
Keyword(s):  
Seismic Data ◽  
Median Filter ◽  
Noisy Data ◽  
Real Data ◽  
Filter Method ◽  
Local Slope ◽  
Median Filters ◽  
Call Structure ◽  
Slope Estimation ◽  
Simultaneous Source

SUMMARY In seismic data processing, the median filter is usually applied along the structural direction of seismic data in order to attenuate erratic or spike-like noise. The performance of a structure-oriented median filter highly depends on the accuracy of the estimated local slope from the noisy data. When local slope contains significant error, which is usually the case for noisy data, the structure-oriented median filter will still cause severe damages to useful energy. We propose a type of structure-oriented median filter that can effectively attenuate spike-like noise even when the local slope is not accurately estimated, which we call structure-oriented space-varying median filter. A structure-oriented space-varying median filter can adaptively squeeze and stretch the window length of the median filter when applied in the locally flattened dimension of an input seismic data in order to deal with the dipping events caused by inaccurate slope estimation. We show the key difference among different types of median filters in detail and demonstrate the principle of the structure-oriented space-varying median filter method. We apply the structure-oriented space-varying median filter method to remove the spike-like blending noise arising from the simultaneous source acquisition. Synthetic and real data examples show that structure-oriented space-varying median filter can significantly improve the signal preserving performance for curving events in the seismic data. The structure-oriented space-varying median filter can also be easily embedded into an iterative deblending procedure based on the shaping regularization framework and can help obtain much improved deblending performance.

scholarly journals ADAPTIVE SIGNAL DECOMPOSITION AND FILTERING USING BINOMIAL FILTERS APPLIED TO THE GROUND-ROLL ATTENUATION

2016 ◽  
Vol 34 (3) ◽  
Author(s):  
Cristian D. Ariza A. ◽  
Milton J. Porsani
Keyword(s):  
Seismic Data ◽  
Adaptive Filters ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Noise Signal ◽  
Signal Decomposition ◽  
Signal To Noise ◽  
Ground Roll ◽  
Noise Ratio ◽  
Adaptive Signal

ABSTRACT. The ground-roll is a type of noise normally present in land seismic data. It strongly harms the signal-to-noise ratio, and interferes in several stages of the seismic data processing, strongly affecting the final quality of the obtained seismic images...Keywords: seismic noise, signal-to-noise ratio, adaptive filters, Burg algorithm, seismic signal decomposition.  RESUMO. O ground-roll é um tipo de ruído normalmente presente nos dados sísmicos terrestres. Ele prejudica muito a razão sinal-ruído e interfere em vários est´ágios do processamento de dados sísmicos, afetando fortemente a qualidade final das imagens sísmicas obtidas...Palavras-chave: ruídos sísmicos, relação sinal-ruído, filtragem adaptativo, algoritmo de Burg, decomposição do sinal sísmico.

A geophone subarray beam‐steering process

Geophysics ◽  
1984 ◽  
Vol 49 (11) ◽  
pp. 1838-1849 ◽  
Author(s):  
Dale E. Biswell ◽  
Larry F. Konty ◽  
Alfred L. Liaw
Keyword(s):  
Plane Wave ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Incident Angle ◽  
Beam Steering ◽  
Cost Effective ◽  
Seismic Signal ◽  
Obliquely Incident ◽  
Single Output ◽  
Cost Effective Technique

The outputs of geophone array elements are conventionally summed into a single output trace. This summation attenuates incoherent noise, horizontally propagating surface waves, and obliquely incident events. The geophone subarray beam‐steering process is a plane‐wave stacking technique which removes the differential moveout and improves the resolution of seismic data by directing the subarray peak gain to the incident angle of the seismic wavefront. The plane‐wave stacking process transforms the data from the offset domain to the ray parameter (p) domain, and restricts the range of p as a function of time. Studies of synthetic and marine field data show that the beam‐steering process improves the signal‐to‐noise ratio of obliquely incident events as compared to conventional subarray summing operations. The beam‐steering process, compressing high‐density data while preserving the high‐frequency content of the seismic signal, is a cost‐effective technique to process large quantities of closely spaced seismic data for stratigraphic exploration.

scholarly journals DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation

2021 ◽  
Vol 17 (3) ◽  
pp. e1008374
Author(s):  
Ilya Belevich ◽  
Eija Jokitalo
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Open Source ◽  
Open Source Software ◽  
Learning Network ◽  
Matlab Code ◽  
Deep Learning Network ◽  
User Friendly ◽  
2D And 3D ◽  
Biological Image

We present DeepMIB, a new software package that is capable of training convolutional neural networks for segmentation of multidimensional microscopy datasets on any workstation. We demonstrate its successful application for segmentation of 2D and 3D electron and multicolor light microscopy datasets with isotropic and anisotropic voxels. We distribute DeepMIB as both an open-source multi-platform Matlab code and as compiled standalone application for Windows, MacOS and Linux. It comes in a single package that is simple to install and use as it does not require knowledge of programming. DeepMIB is suitable for everyone interested of bringing a power of deep learning into own image segmentation workflows.

NOISE AND TARGET SIGNALS SIMULATION IN PASSIVE SEISMIC LOCATION SYSTEMS

2018 ◽  
pp. 73-78
Author(s):  
Yu. V. Morozov ◽  
M. A. Rajfeld ◽  
A. A. Spektor
Keyword(s):  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Seismic Signals ◽  
Simulation Procedure ◽  
Proposed Model ◽  
Seismic Location ◽  
Passive Seismic ◽  
System Characteristics ◽  
Primary Pulse

The paper proposes the model of a person seismic signal with noise for the investigation of passive seismic location system characteristics. The known models based on Gabor and Berlage pulses have been analyzed. These models are not able wholly to consider statistical properties of seismic signals. The proposed model is based on almost cyclic character of seismic signals, Gauss character of fluctuations inside a pulse, random amplitude change from pulse to pulse and relatively small fluctuation of separate pulses positions. The simulation procedure consists of passing the white noise through a linear generating filter with characteristics formed by real steps of a person, and the primary pulse sequence modulation by Gauss functions. The model permits to control the signal-to-noise ratio after its reduction to unity and to vary pulse shifts with respect to person steps irregularity. It has been shown that the model of a person seismic signal with noise agrees with experimental data.

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