... to acquire accurate information about the electrical properties and distribution characteristics of the subsurface rock layers. We develop a new MT denoising method for attenuating the noise in MT signals using data augmentation and CS-ResNet. First, we extract some noisy data samples from the measured MT...

...Yang Cui; Juan Wu; Min Bai; Yangkang Chen ABSTRACT Seismic denoising methods using supervised training normally rely on a large number of high-quality paired training data sets to obtain satisfactory results. There are two ways to generate labels for network training: one is to create synthetic...

...Ji Li; Daniel Trad; Dawei Liu ABSTRACT Seismic data denoising is a critical component of seismic data processing, yet effectively removing erratic noise, characterized by its non-Gaussian distribution and high amplitude, remains a substantial challenge for conventional methods and deep-learning (DL...

... is often challenging and in some cases is even impossible with traditional denoising methods such as filtering. To address this challenge, we develop a new convolutional neural network model, NodalWaden, using decades of high‐quality global broadband teleseismic body waves for training. The broadband data...

...Claire Birnie; Sixiu Liu; Ali AlDawood; Andrey Bakulin; Ilya Silvestrov; Tariq Alkhalifah Abstract Self-supervised blind-mask denoising networks overcome the challenge of requiring clean training targets by employing a mask on raw noisy data to form the input for training while using the unmasked...

...Jia Zhang; Charles A. Langston; Hongfeng Yang ABSTRACT To remove background noise from seismic data recorded by spatially dense arrays, we have developed a space‐based denoising procedure using the discrete curvelet transform. Based on a detailed statistical characterization of noise coefficients...

... to transform the 1D flattened vector into the feature latent space. Afterward, an attention network is used to highlight the important information within these features, which improves the denoising performance of the deep-learning model. Furthermore, we use several skip connections between the fully connected...

... network (DR-Unet) is trained with synthetic samples, which are generated by adding field noise to synthesized noise-free migration images. Then, the trained DR-Unet is applied on the gradient of LSRTM to remove high-wavenumber artifacts in each iteration. Compared to directly applying DR-Unet denoising...

...Louis Quinones; Rigobert Tibi ABSTRACT Seismic waveform data recorded at stations can be thought of as a superposition of the signal from a source of interest and noise from other sources. Frequency‐based filtering methods for waveform denoising do not result in desired outcomes when the targeted...

... geoelectrical structures decreases when the data collected in mining areas are of poor quality and contain complex anthropogenic noise, leading to distorted apparent resistivity-phase curves and posing significant challenges for mineral exploration. To effectively denoise AMT data, we develop a new denoising...

...Lihua Liu; Tianyao Hao; Yonggang Guo; Chuanchuan Lü; Sujing Wang ABSTRACT Denoising is a critical step in signal processing. We develop a method for random noise reduction in active source seismic data using spectrum reconstruction. Two methods are developed for modifying the observed data’s...

... networks have been used for automatically picking dispersions. Dispersion curves are picked based on deep learning mainly for denoising these spectrograms. In several studies, the neural network was solely trained, and its performance was verified for the denoising. However, they all learn single‐source...

... applied to seismic data denoising as well as reconstruction and achieved good performance compared with traditional methods. However, relatively few CNN-based studies have been done on the simultaneous denoising and reconstruction of seismic data, especially for the complex DAS seismic data with a low...

... denoising network into the plug-and-play (PnP) framework. A novel network is introduced whose design extends an existing blind-spot network architecture for partially coherent noise (i.e., correlated in time). The network is then trained directly on the noisy input data at each step of the PnP algorithm...

...Yangkang Chen; Alexandros Savvaidis; Sergey Fomel Abstract Passive seismic denoising is mostly performed using a simple band‐pass filter, which can be problematic when signal and noise share the same frequency band. More advanced passive seismic denoising methods take advantage of fixed‐basis...

... severe and diverse types of noise with varying amplitudes, resulting in a low signal-to-noise ratio (S/N) and making the extraction of hidden signals a challenging task. Therefore, exploring a high-efficiency and high-generalization denoising method is crucial for improving the S/N of DAS data...

... in a low-S/N scenario, we have developed an end-to-end network that jointly performs denoising and classification tasks (JointNet) and applied it to fiber-optic distributed acoustic sensing (DAS) microseismic data. JointNet consists of 2D convolution layers that are suitable for extracting features...

...-optic vibration and horizontal noise from optical defects. Therefore, reducing the various complex noise in DAS data is beneficial to subsequent processing. Currently, there are two challenges in DAS data denoising. First, the various complex noise in DAS data seriously masks the useful signal...

... filtering functionalities are slope estimation, structural denoising (mean or median filtering), and structural interpolation, which are all included in the current version of Pyseistr. Figure  1a demonstrates what the structural filtering refers to. Regarding 2D or 3D (or even higher dimensions...

...-consistent loss to replace the commonly used L1 norm or L2 norm to train the network. The obtained hybrid training set containing unpaired synthetic and unpaired field data sets for model pretraining and fine tuning effectively improves the denoising performance of the seismic field data. In summary...