Imaging the Chicxulub Central Crater Zone from Large-Scale Seismic Acoustic Wave Propagation and Gravity Modeling

Ortíz-Alemán, C.; Martin, Ronald L.; Urrutia‐Fucugauchi, J.; Castillo, Mauricio Orozco del; Nava-Flores, Mauricio

doi:10.1007/s00024-020-02638-2

Cited by 4 publications

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“…Acoustic and seismic measurements related to the formation are essential for the geophysical exploration field, where high‐quality imaging results can provide more intuitive evaluation references than waveform records [1]. In remote acoustic imaging measurement, reflection imaging with information properties on acoustic waves has been developed to survey geological structures [2].…”

Section: Introductionmentioning

confidence: 99%

“…In many applications, the target domain is challenging to obtain or even unknown before the model is deployed [33]. Moreover, there are three specific problems to be addressed for the neural network application in acoustic parametric imaging systems: (1) align the feature distribution between the public dataset and the acoustic waveforms, (2) the backbone network can analyse basic waveform time-domain expressions, and (3) the overall framework maintains the small-scale advantage.…”

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confidence: 99%

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Unsupervised deep domain adaptation framework in remote acoustic time parametric imaging

Jun

et al. 2021

IET Signal Processing

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Intelligent waveform analysis surveying geological structures is a challenging task in remote acoustic measurement for formation detection, which has two problems: (1) time parametric imaging is disturbed by noisy environments and (2) manually annotated data for machine learning are unattainable. These restrict the deployment of advanced parameter extraction methods in imaging instruments. As a potential theory, domain adaptation makes the intelligent prediction implementable in the above situations. Hence, to counter high precision travel time extraction for acoustic imaging, a deep adaptation-picking network (DAPN) framework is proposed, which consists of three modules: signal-to-signal generator, domain adaptation encoder, and feature recognition decoder. The translation module preprocesses the different datasets to improve the training accuracy and confuses the distribution between source and target domains based on the adversarial network. The core convolution neural network achieves travel time measurement, where the backbone extracts content features with modified maximum mean discrepancy embedding. Meanwhile, the decoding structure preserves the signal features in the training process. The effectiveness and feasibility of DAPN are verified by experimental results, suggesting that DAPN accurately extracts time parameters. Compared with traditional parametric imaging schemes, the proposed method has the advantages of high accuracy and anti-noise ability.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%