Seismic Signal Processing Research Articles

Optimizing Energy-Efficient in VLSI Architecture for FIR Filter in Seismic Signal Processing

This project aims to developing an optimized and energy-efficient VLSI design for FIR filters specifically designed for seismic signal processing. The process begins by converting seismic signal dataset values into binary format, and then, simulating them in MATLAB, and generating a coefficient file. This coefficient file is then analyzed using Verilog HDL code and simulated in the Xilinx Vivado 2022.2 tool to assess key parameters such as area, delay, and power. The suggested architecture lessens the complexity of computing, hardware efficiency compared to traditional FIR filters that utilize the CSE technique. This design employs a critical method known as the CSD-based Matrix-Grouped Common Subexpression Elimination (MCSE) algorithm, which significantly reduces the quantity of logic operators (LOs) and logic depths (LDs). Additionally, the design incorporates a Half-Unit Biased (HUB) rounding technique to minimize the truncation errors and cut-set retiming method to reduce the critical-path delay (CPD). This hardware-efficient FIR filter constructs integrates the CSD-based MCSE algorithm, HUB rounding, and cut-set retiming techniques to offer a reconfigurable FIR filter configuration optimized for hardware efficiency, thereby enhancing the real-time alert seismic signal processing. The implementation of an Artix-7 FPGA board including clock gating techniques to further reduces the power consumption.

A Spectral Precursor Indicative of Artificial Water Reservoir-Induced Seismicity: Observations from the Xiangjiaba Reservoir, Southwestern China

Spectral analysis is an effective tool for processing seismic signals, particularly when time-domain characteristics are challenging to capture. In this study, we developed a method using P-wave signals to calculate the power spectrum, enabling the estimation of two spectral parameters—peak frequency and shape factor—for earthquakes recorded by regional seismic networks in the Xiangjiaba (XJB) reservoir area from 2010 to 2015. The temporal evolution of the two spectral parameters was analyzed, revealing that the mean values of individual spectral parameters remain relatively stable despite variations in reservoir water levels. However, a notable increase in the ratio of the shape factor to the peak frequency is observed when the XJB reservoir reaches its maximum water level, suggesting its potential as a precursor indicator for reservoir-induced seismicity (RIS). Furthermore, we performed spatial interpolation on the spectral parameters, and the results show that reservoir impoundment significantly influences the spatial distribution of these parameters. In addition, several regions between the two faults in the tail section of the XJB reservoir exhibit an elevation in the proposed precursor indicator. This study presents a new approach for monitoring and early warning of RIS.

Signal to Noise Ratio Improvement Using Stacking Method on Shallow Refraction Seismic Data

Abstract The improvement of signal-to-noise ratio in refraction seismic survey data is intriguing, especially when a long source-sensor distance and low energy vibration source is in use. In this work, we analyzed the effectiveness of the stacking procedure to extract geophone response signals buried under the noise level. The straight forward strategy to improve the signal quality is by doing multiple acquisition on every offset location and performing stacking procedure on the recorded data. For such activities, the sensor offset location is classified as near-offset (4-26m), middle-offset (28-50m) and far-offset (52-72m). To increase the usefulness of the proposed method, we developed the algorithm using an opensource programming language, and presented it in a step-by-step manner. We presented the procedure accompanied by the pre- and post- processing algorithm, consisting of seismic signal filtering and cross-correlation procedure in order to allow a precise determination of the first break time of the propagated seismic wave. The results give an overview on how to develop a seismic signal processing routine, as well as the possibility to improve the range of the refraction seismic instrumentation systems. The stacking procedure has a high level of significance for the middle offset. For the near offset the stacking procedure is not necessary, whereas for the far offset the large number of data is more important.

PROBLEMS OF CHOICE EFFECTIVE METHODS OF TREATMENT OF SEISMO SIGNALS FOR INTELLECTUAL OF SYSTEMS

The paper analyzes the problem of processing seismic signals an intellectual autonomous seismic system for reconnaissance, signaling and security purposes and identifies the main directions for creating a new energy-efficient dual-use seismic system with an increased identification range for detecting moving objects. Various methods of processing seismic signals are considered, their comparison in terms of efficiency based on the characteristics of their characteristics and energy consumption.

Seismic resolution improving by a sequential convolutional neural network.

Thin-bed soft rock is one of the main factors causing large deformations of tunnels. In addition to relying on some innovative construction techniques, detecting thin beds early during surface geological exploration and advanced geological prediction can provide a basis for planning and implementing effective coping measures. The commonly used seismic methods cannot meet the requirement for thin beds detection accuracy. A high-resolution (HR) seismic signal processing method is proposed by introducing a sequential convolutional neural network (SCNN). The deep learning dataset including low-resolution (LR) and HR seismic is firstly prepared through forward modeling. Then, a one-dimension (1D) SCNN architecture is proposed to establish the mapping relationship between LR and HR sequences. Training on the prepared dataset, the HR seismic processing model with high accuracy is achieved and applied to some practical seismic data. The applications on both poststack and prestack seismic data demonstrate that the trained HR processing model can effectively improve the seismic resolution and restore the high-frequency seismic energy so that to recognize the thin-bed rocks.

Seismic signal denoising using Swin-Conv-UNet

In recent years, a notable surge has occurred in the adoption of deep neural networks for addressing the challenges of seismic signal denoising. However, many existing methodologies are constrained by simplistic noise assumptions and exhibit limited generalisation capabilities, particularly when confronted with field seismic signals. Consequently, the need for a robust denoising solution tailored to the complexities of field seismic signals remains unresolved. To address this gap and effectively enhance the signal-to-noise ratio (SNR), we proposed a novel denoising approach. Central to our methodology is the incorporation of a custom-designed network structure that leverages the unique characteristics of seismic signals. Specifically, we introduced the swin-link-conv block, which amalgamates the modelling capabilities of the swin transformer block with the adaptive properties of the residual connection layer. This innovative building block was seamlessly integrated into the UNet architecture, which is a widely acclaimed framework for seismic signal processing. The resulting network architecture was a christened seismic swin-conv UNet (SSC-UNet). Extensive experiments conducted on both synthetic and field seismic signals underscore the efficacy of the proposed network structure. The SSC-UNet methodology achieved state-of-the-art denoising performance, demonstrating superior accuracy in removing additive white Gaussian noise (AWGN). This success not only validates the generalisation capabilities of SSC-UNet but also underscores its viability as a robust solution for field seismic signal denoising applications.

Picking of weak signal in seismic exploration of non-coal solid mines based on phase-locked technology

The exploration and development of mineral resources are of crucial significance to national economy, people's livelihood and national security. Therefore, in order to extract the weak signal in seismic exploration of non-coal solid mines in shallow surface layer to achieve high-resolution exploration, the mathematical model and physical model of multiplier are established by referring to the frequency point acquisition technology in wireless radar communication, that is, phase-locked technology. The weak effective seismic signals of different frequency points are picked out from the collected 120 dB dynamic range spectrum, and the information of stratigraphic structure under high noise background is obtained more effectively by using mixing detection and two-phase demodulation technology in the harmonic component spectrum, thereby improving the high resolution and high precision of mineral exploration. The lock-in amplifier is added to the front stage of the seismograph to obtain the effective seismic wave reflected by the wave impedance interface required in the exploration task. Experimental results show that this method significantly improves the SNR and protects the weak effective signal from loss. It adds new technology and method to seismic signal acquisition and processing, and provides a new way to obtain high-quality seismic data in the field of mineral geophysical exploration, and will be widely used after being promoted in the fields of mineral exploration, geological disaster prediction, military geophysics, and archaeology.

Crustal Imaging with Noisy Teleseismic Receiver Functions Using Sparse Radon Transforms

ABSTRACT The receiver function (RF) is a widely used crustal imaging technique. In principle, it assumes relatively noise-free traces that can be used to target receiver-side structures following source deconvolution. In practice, however, mode conversions and reflections may be severely degraded by noisy conditions, hampering robust estimation of crustal parameters. In this study, we use a sparsity-promoting Radon transform to decompose the observed RF traces into their wavefield contributions, that is, direct conversions, multiples, and incoherent noise. By applying a crustal mask on the Radon-transformed RF, we obtain noise-free RF traces with only Moho conversions and reflections. We demonstrate, using a synthetic experiment and a real-data example from the Sierra Nevada, that our approach can effectively denoise the RFs and extract the underlying Moho signals. This greatly improves the robustness of crustal structure recovery as exemplified by subsequent H−κ stacking. We further demonstrate, using a station sitting on loose sediments in the Upper Mississippi embayment, that a combination of our approach and frequency-domain filtering can significantly improve crustal imaging in reverberant settings. In the presence of complex crustal structures, for example, dipping Moho, intracrustal layers, and crustal anisotropy, we recommend caution when applying our proposed approach due to the difficulty of interpreting a possibly more complicated Radon image. We expect that our technique will enable high-resolution crustal imaging and inspire more applications of Radon transforms in seismic signal processing.

A novel approach for seismic signal denoising using optimized discrete wavelet transform via honey badger optimization algorithm

Seismograms are a vital source of information in seismic signal processing. These records are contaminated by noise, which should be reduced before processing in seismic applications. Wavelet-based methods have shown great success in denoising seismic signals, and their performance is determined based on the wavelet transform (WT) parameters. Therefore, in this paper, four swarm optimization algorithms are suggested to estimate the WT parameters optimally to mitigate the noise in the seismic signal. First, this article examines the efficacy of the optimization algorithms for achieving the minimum fitness value and better convergence curve. Then, based on the fitness value, optimal wavelet transform parameters are obtained to perform discrete wavelet transform on the seismic signal. The analysis is conducted on seismic trace and seismic section to disclose the dominance of the proposed algorithm over the traditional approaches. The results demonstrate the effectiveness of our proposed strategy for seismic signal denoising over other traditional approaches.

Time-synchroextracting of generalized S-transform and its application in fault identification

Seismic signals are typical non-stationary signals, and time-frequency analysis method is a powerful tool for processing non-stationary signals, which is also widely used in seismic signal processing. S-transform is a frequently used time-frequency analysis method. However, the time and frequency resolution of S-transform is limited due to Heisenberg uncertainty principle. The post-processing method of time-frequency analysis can solve this problem by further calculation on the basis of the original time-frequency spectrum of the signal. In order to improve the time and frequency resolution, we propose a new time-frequency analysis post-processing method - time synchroextracting of generalized S-transform algorithm. We first deduce the group delay operator expression of the generalized S-transform, and then remove a large amount of fuzzy energy by extracting only the time-frequency coefficients at the group delay operator in the time-frequency spectrum to improve the time-frequency resolution. The comparative analysis of synthetic signals shows that the proposed algorithm has better time-frequency aggregation and can more accurately describe the time-frequency characteristics of seismic signals. We apply the proposed method to the fault identification of the field seismic data, and the results show that the coherent attribute slice extracted by the time-synchroextracting of the generalized S-transform can well describe the fault.

DeepSeg: Deep Segmental Denoising Neural Network for Seismic Data

Noise attenuation is a crucial phase in seismic signal processing. Enhancing the signal-to-noise ratio (SNR) of registered seismic signals improves subsequent processing and, eventually, data analysis and interpretation. In this work, a novel noise reduction framework based on an intelligent deep convolutional neural network is proposed that works on segments of the time-frequency domain and, hence named as DeepSeg. The proposed network is efficient in learning sparse representation of the data simultaneously in the time-frequency domain and adaptively capturing seismic signals corrupted with noise. DeepSeg is able to achieve impressive denoising performance even when seismic signal shares common frequency band with noise. The proposed approach properly tackles a variety of correlated (color) and uncorrelated noise, and other nonseismic signals. DeepSeg can boost the SNR considerably even in extremely noisy environments with minimal changes to the signal of interest. The effectiveness of the proposed methodology is demonstrated in enhancing passive seismic event detection/denoising. However, there are other obvious applications of the DeepSeg in active and passive seismic fields, e.g., seismic imaging, preprocessing of ambient noise data, and microseismic event monitoring. It is worth pointing out here that the deep neural network is trained exclusively using synthetic seismic data, negating the need for real data during the training phase. Furthermore, the proposed setup is general and its potential applications are not confined to passive event denoising or even seismic. The method proposed is also adaptable to other diverse signals in different settings, like medical images/signals magnetic resonance imaging (MRI), electroencephalogram (EEG) signals, electrocardiograms (ECG) signals, and retinal images, to name a few, radar signals, speech signals, fault detection in electrical/mechanical systems, daily life images, etc. Experiments on synthetic and real seismic data reveal the efficacy and supremacy of the proposed method in terms of SNR improvement and required training data when compared to the state-of-the-art deep neural network-based denoising technique.

SEISMIC WAVE RECORDER DESIGN FOR DEVELOPMENT FOOTSTEP HUMAN DETECTION ALGORITHMS

The article shoving the results of development seismic signal recorder for purpose of registration human footstep seismic signals. Design of recorder was adopted for collection seismic signals and store it on PC, for next seismic signal processing and creation signal detection and classification algorithms.
 The article provides an overview of the types of seismic waves, their classification is given. The conditions for the propagation of seismic waves are determined, and the features of Rayleigh waves are clarified. The design structure of the seismic sensor has been developed. The specifics of the functioning of the seismic sensor are described, the place and conditions of power supply and placement of the external interface are recognized. The specifics of using the sensor of an electromechanical transducer - geophone and the features of measurements using a stationary earth are described. It is shown that the development of a seismic signal sensor circuit implies the placement of input amplifiers in its structure. The main amplitude-frequency and phase-frequency characteristics of the geophone are presented. Geophone equivalent circuit, amplifier circuit, low-pass filter circuit are presented. Illustrations of types of seismograms are given, recommendations on the features of digital signal processing are given. The order of seismic signal processing is connected with its transmission from the sensor through the amplifying stage, passing through a low-pass filter in order to minimize noise and interference. The next step is to send a signal to the ADC. From the presented graphs of sensitivity and phase shift of the geophone, it follows that special attention should be paid to the curve corresponding to the sensor with an open output. This configuration of the sensor is optimal in the context of the formation of the maximum equal frequency response.
 As papers result was proposed structural and schematic design for all main recorders blocks and software features that required for properly using recorder. Tests in real conditions on test sites have shown the correctness of the choice of schemes of technical solutions for the task of registering seismic waves.

Introducing Nonuniform Sparse Proximal Averaging Network for Seismic Reflectivity Inversion

We consider the problem of seismic reflectivity inversion, which pertains to the high-resolution recovery of interface locations and reflection coefficients from seismic measurements, which are vital for estimating the subsurface structure. We develop two model-based neural networks within the framework of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deep-unfolding</i> . First, we propose a nonuniform minimax concave penalty regularized formulation for reflectivity inversion and unfold the resulting iterative algorithm into a network. Second, we propose a nonuniform sparse model that relies on a combination of regularizers (composite regularization) and develop the nonuniform sparse proximal averaging network (NuSPAN). We demonstrate the efficacy of the proposed approaches over the benchmark techniques through numerical experiments on synthetic 1-D seismic traces and 2-D wedge models. We also report validations on the 2-D Marmousi2 simulated model and 3-D real field measurements from the Penobscot 3D survey off the coast of Nova Scotia, Canada. The accuracy of the proposed approaches is higher than the state-of-the-art techniques in terms of amplitude and support recovery. Further, for Marmousi2, the proposed deep-unfolding networks result in 600× faster inference than the fast iterative shrinkage-thresholding algorithm (FISTA). In terms of combined training and inference times, the learned iterative shrinkage-thresholding algorithm (LISTA) is the fastest. The inference speed-up is significant given that the volume of data is typically large in seismic signal processing.

Seismic Data Enhancement Based on Common-Reflection-Surface-Based Local Slope and Trimmed Mean Filter

Seismic data often contain noise that can disturb or mask effective information. Noise elimination is an important and challenging task in seismic signal processing. Considering the high amplitude continuity of seismic events in the shot domain, this article proposes a structure-oriented denoising method that can enhance the effective events and suppress disturbing noise, including both incoherent and coherent noise. Based on the common-reflection-surface (CRS) travel time, the local slope of seismic events in the shot domain is deduced and estimated to provide structural information for plane-wave prediction. The proposed CRS-based slope depends on fewer parameters (two in 2D) than the conventional full CRS travel time (three in 2D), making it computationally efficient. Using the local slope, the third dimension is created using the plane-wave differential equation to predict the current trace from its neighbor traces and trimmed mean filtering (TMF) is applied in this dimension. The added dimension can be regarded as flattening the seismic events within a neighboring window and collapsing after application of the TMF. Synthetic and field datasets are employed to demonstrate the effectiveness of the proposed structure-oriented TMF. Compared with wavelet and plane-wave destruction methods, the proposed method can preserve more useful information with greater continuity in amplitude.

Computer Vision Algorithms of DigitSeis for Building a Vectorised Dataset of Historical Seismograms from the Archive of Royal Observatory of Belgium

Archived seismograms recorded in the 20th century present a valuable source of information for monitoring earthquake activity. However, old data, which are only available as scanned paper-based images should be digitised and converted from raster to vector format prior to reuse for geophysical modelling. Seismograms have special characteristics and specific featuresrecorded by a seismometer and encrypted in the images: signal trace lines, minute time gaps, timing and wave amplitudes. This information should be recognised and interpreted automatically when processing archives of seismograms containing large collections of data. The objective was to automatically digitise historical seismograms obtained from the archives of the Royal Observatory of Belgium (ROB). The images were originallyrecorded by the Galitzine seismometer in 1954 in Uccle seismic station, Belgium. A dataset included 145 TIFF images which required automatic approach of data processing. Software for digitising seismograms are limited and many have disadvantages. We applied the DigitSeis for machine-based vectorisation and reported here a full workflowof data processing. This included pattern recognition, classification, digitising, corrections and converting TIFFs to the digital vector format. The generated contours of signals were presented as time series and converted into digital format (mat files) which indicated information on ground motion signals contained in analog seismograms. We performed the quality control of the digitised traces in Python to evaluate the discriminating functionality of seismic signals by DigitSeis. We shown a robust approach of DigitSeis as a powerful toolset for processing analog seismic signals. The graphical visualisation of signal traces and analysis of the performed vectorisation results shown that the algorithms of data processing performed accurately and can be recommended in similar applications of seismic signal processing in future related works in geophysical research.

Filter Banks and Hybrid Deep Learning Architectures for Performance-Based Seismic Assessments of Bridges

The existing machine learning (ML) models for vibration-based damage assessments often use highly compressed features or are restricted to preprocessed signals with fixed durations and sampling rates. Additionally, the learning capacities of ML models and computational resources are limited, which restricts using raw signals as direct input. This paper studied Mel filter banks (MFBs) for seismic signal processing, inspired by speech recognition technology. It is argued that the same filter designs in audio engineering may not be appropriate for seismic records, and therefore, a customized filter bank formulation was developed. Hybrid deep learning models for rapid assessments (HyDRA) were introduced as multibranch neural network architectures that enable end-to-end training for different types of processed vibration data structures. Moreover, the performance-based earthquake engineering (PBEE) equation was adjusted to integrate ML model uncertainties for probabilistic assessments. The proposed concepts were validated in a case study based on a data set of 32,400 nonlinear time-history analyses of a highway bridge in California. Insights and guidelines are provided for optimum filter design based on 5,184 experiments. Several HyDRA architectures were compared with benchmark models. The optimized MFB feature type outperformed features obtained from continuous wavelet transform and a stacked vector of conventional earthquake engineering indexes. A Bayesian variant of HyDRA was investigated to showcase its integration in the modified PBEE equation. Adopting custom filter banks with the HyDRA architecture enables effective feature extraction from raw vibration records by diversifying feature space and preserving information in the time and frequency domains.

Ground roll intelligent suppression based on spatial domain synchrosqueezing wavelet transform convolutional neural network

AbstractGround roll is usually considered as a common linear noise in land seismic data. The existence of the ground roll often masks the effective reflection information of underground media, resulting in the deterioration of seismic data quality. Therefore, ground roll suppression is one of the main tasks in seismic data processing. A large number of previous studies have proved that the time‐frequency signal processing method based on mathematical transformation has shown excellent performance in ground roll attenuation and still has development potential. Meanwhile, a convolutional neural network, as one of the popular deep learning technologies, has also been widely used in the field of seismic signal processing. In this paper, we combine the convolutional neural network with the time‐frequency signal processing method based on mathematical transformation, that is, spatial domain synchrosqueezing wavelet transform, and propose a complete ground roll suppression workflow of shot gathers in spatial wavenumber domain, realizing high‐precision and automatic ground roll removal. Field data examples show that compared with bandpass filtering, FK filtering, time domain synchrosqueezing wavelet transform, spatial domain synchrosqueezing wavelet transform and the convolutional neural network, the spatial domain synchrosqueezing wavelet transform convolutional neural network has achieved satisfactory results in effectively attenuating ground roll and retaining valid information.

The 3D conical Radon transform for seismic signal processing

Recently, the attenuation of highly aliased broadband surface waves has received significant attention as their high-frequency and low-speed components render f- k x- k y filters ineffective. As a popular tool widely used in seismic signal processing, the 3D linear Radon ([Formula: see text]) transform does not match the surface wave events in the time domain. This leads to the dispersal of energy over ellipses on the zero-intercept slice, whereas the reflected events over the ellipses are reflected on each intercept slice of the 3D linear Radon transform (LRT) domain, decreasing the resolution in slowness. We have introduced a new type of Radon transform defined on circular cones called 3D conical Radon transform (CRT) for seismic signal processing. Unlike LRT, the CRT maps seismic data to surface integrals on circular cones in the 3D seismic records. Consequently, surface wave events are focused as points on the zero-intercept slice, whereas the reflected events are points on each intercept slice of the CRT domain, which significantly improves the resolution in slowness compared with the LRT. We determine the performance of the CRT for seismic signal processing for random noise attenuation, primary and multiple separations, and surface wave attenuation. Based on a comparison with the LRT, synthetic data and field data sets validate the effectiveness of the CRT method at improving the focusing properties.

A Recognition Algorithm of Seismic Signals Based on Wavelet Analysis

In order to meet the requirements of mobile marine seismometers to observe and record seismic signals, a study of fast and accurate seismic signal recognition was carried out. This paper introduces the use of the wavelet analysis method for seismic signal processing and recognition, and compares and analyzes the abilities of different wavelet basis functions to detect the seismic signal. By denoising and reconstructing the signal, the distribution law of the wavelet coefficients of seismic signal at different scales was obtained. On this basis, this paper proposes an identification model of seismic signals based on wavelet analysis and thereby solves the conflict between high speed and high accuracy of seismic signal recognition methods. In this study, the simulation was carried out in the Matlab2020b environment, and the feasibility of wavelet recognition algorithm was proven by applying this algorithm to the seismic signal database for experimental verification.

An improved variational mode decomposition for seismic random noise attenuation using grasshopper optimization via shape dynamic time warping

In seismic signal processing, attenuation of noise is one of the most difficult challenges. Variational mode decomposition (VMD) is a recently developed adaptable signal processing technique that subdivides a signal into bandlimited intrinsic mode functions (BIMFs). In VMD, the key elements are the number of intrinsic modes and the quadratic penalty factor for better denoising. In this paper, we have applied the Grasshopper optimization algorithm (GOA) to determine the key elements of VMD and decomposes the signal into optimized BIMFs. Then, the Shape dynamic time warping (shapeDTW) is applied for determining the relevant modes. Finally, the relevant modes are deal with Wavelet thresholding (WT) to obtain a better result of signal to noise ratio (SNR). Initially, the analysis is made on the synthetic signals to represent the dominance of the new approaching method with Empirical mode decomposition (EMD) based and VMD based methods. We also applied new approaching method to a 1D real seismic signal from the Pacific Earthquake Engineering Research Center (PEER) ground motion database to suppress the random noise interferences. The proposed approach is also tested in a 2D synthetic seismic section corrupted by random noise, and also with a 2D real Forrest seismic survey in South Australia's resources information gateway. Application of the proposed GOA-VMD-shapeDTW-WT method on both the synthetic and the real seismic signal shows supremacy with other denoising techniques.