Fundamental Noise — the Key to Recovering Your Signal: an Integrated Workflow for Seismic Survey Design

Legacy seismic reflection data constitute infrastructure of tremendous value for basic research. This is especially relevant in seismically hazardous areas, as such datasets can significantly contribute to the seismotectonic characterization of the region. The quality of the data and the resulting image can be effectively improved by using modern tools, such as pre-conditioning techniques and seismic attributes. The latter are extensively used by the hydrocarbon exploration industry, but are still only poorly applied to the study of active faults. Pre-conditioning filters are effective in removing random noise, which hampers the detection of subtle geologic structures (i.e., normal faults). In this study, a workflow including pre-conditioning and extraction of seismic attributes is used to improve the quality of the CROP-04 deep seismic reflection profile. CROP-04 was acquired in the 1980s across the Southern Apennines mountain range, one of the most hazardous seismically active regions in Italy. The results show the capacity of this method to extract, from low-resolution legacy data, subtle seismic fabrics that correspond to a dense network of fault sets. These seismic signatures and the enhanced discontinuities disrupting the reflections, which were invisible in the original data, correlate well with the main regional normal faults outcropping at the surface. Moreover, the data reveal higher structural complexity, due to many secondary synthetic and antithetic structures, knowledge of which is useful in modeling of the local and regional distribution of the deformation and potentially in guiding future field mapping of active faults. This proposed approach and workflow can be extended to seismotectonic studies of other high-hazard regions worldwide, where seismic reflection data are available.

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“…CROP-04 Frontiers in Earth Science frontiersin.org (Manning et al, 2019;Dieulangard et al, 2021;Strobbia et al, 2022).…”

Section: Parameterunclassified

Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profile

et al. 2023

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“…One type of noise we address in this study is coherent along the recorded traces (vertical direction) and incoherent in the offset (horizontal) direction. This trace‐wise noise includes cross‐feed noise (after stacking), swell noise, streamers bird noise, velocity sensor noise and spike‐like noise in marine data (Hlebnikov et al., 2021), receivers malfunctioning noise and noises resulting from external sources of energy in common receiver gathers (Strobbia et al., 2022). Also, in pseudo‐deblending, the blending noises become trace‐wise incoherent after specific rearrangements of data (S. Wang et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

Abedi,

Pardo,

Alkhalifah

2023

Geophysical Prospecting

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Trace‐wise noise is a type of noise often seen in seismic data, which is characterized by vertical coherency and horizontal incoherency. Using self‐supervised deep learning to attenuate this type of noise, the conventional blind‐trace deep learning trains a network to blindly reconstruct each trace in the data from its surrounding traces; it attenuates isolated trace‐wise noise but causes signal leakage in clean and noisy traces and reconstruction errors next to each noisy trace. To reduce signal leakage and improve denoising, we propose a new loss function and masking procedure in a semi‐blind‐trace deep learning framework. Our hybrid loss function has weighted active zones that cover masked and non‐masked traces. Therefore, the network is not blinded to clean traces during their reconstruction. During training, we dynamically change the masks' characteristics. The goal is to train the network to learn the characteristics of the signal instead of noise. The proposed algorithm enables the designed U‐net to detect and attenuate trace‐wise noise without having prior information about the noise. A new hyperparameter of our method is the relative weight between the masked and non‐masked traces' contribution to the loss function. Numerical experiments show that selecting a small value for this parameter is enough to significantly decrease signal leakage. The proposed algorithm is tested on synthetic and real off‐shore and land datasets with different noises. The results show the superb ability of the method to attenuate trace‐wise noise while preserving other events. An implementation of the proposed algorithm as a Python code is also made available.This article is protected by copyright. All rights reserved

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“…For example, recorded noise from an external source that appears as a coherent event in a CSG can become random in a CRG shows the location of the section presented in (a). [33]. Therefore, for reconstructing the missing or corrupted parts of data, which rely on the predictability of events, it is beneficial to use all these arrangements of the data for learning the continuity of different events.…”

Section: Introductionmentioning

confidence: 99%

A Multidirectional Deep Neural Network for Self-Supervised Reconstruction of Seismic Data

Abedi

Pardo

2022

IEEE Trans. Geosci. Remote Sensing

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Fundamental Noise — the Key to Recovering Your Signal: an Integrated Workflow for Seismic Survey Design

Cited by 3 publications

References 0 publications

Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profile

Evidencing subtle faults in deep seismic reflection profiles: Data pre-conditioning and seismic attribute analysis of the legacy CROP-04 profile

Semi‐blind‐trace algorithm for self‐supervised attenuation of trace‐wise coherent noise

A Multidirectional Deep Neural Network for Self-Supervised Reconstruction of Seismic Data

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