Inverting Rayleigh surface  wave  velocities  for eastern Tibet and   western
Yangtze   craton crustal   thickness based on deep learning neural networks

Cheng, Xianqiong; Liu, Qihe; Li, Ping Ping

doi:10.5194/npg-2016-39

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…The crustal thickness in eastern Tibet and the western Yangtze craton are estimated by Rayleigh surface wave velocities based on DNN (Cheng et al 2019). The mantle thermal state of simplified model planets was predicted based on DL with an accuracy of 99% for both the mean…”

Section: The Earth's Structurementioning

confidence: 99%

“…Volcanic deformation was detected by using a CNN to classify interferometric fringes in wrapped interferograms (Anantrasirichai et al, 2018). The crustal thickness in eastern Tibet and the western Yangtze craton are estimated by Rayleigh surface wave velocities based on DNN (Cheng et al, 2019). The mantle thermal state of simplified model planets was predicted based on DL with an accuracy of 99% for both the mean mantle temperature and the mean surface heat flux compared to the calculated values (Shahnas & Pysklywec, 2020).…”

Section: The Earth's Structurementioning

confidence: 99%

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Deep Learning for Geophysics: Current and Future Trends

2021

Reviews of Geophysics

284

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Section: The Earth's Structurementioning

confidence: 99%

Section: The Earth's Structurementioning

confidence: 99%

Deep Learning for Geophysics: Current and Future Trends

2021

Reviews of Geophysics

284

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“…In several studies, seismological applications of neural networks were considered, in which surface velocities were inverted for the layer thickness (Moho depth) (Devilee et al ., 1999; Meier et al ., 2007; Cheng et al ., 2019) or smooth velocity models were used (Hu et al ., 2020). In recent research (Hu et al ., 2020), a new algorithm for deriving 1D shear‐wave velocity models from surface‐wave dispersion data with convolutional neural networks (CNNs) is proposed.…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network approach for the inversion of surface wave dispersion curves

Yablokov

Serdyukov

Loginov

et al. 2021

Geophysical Prospecting

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We describe a new algorithm for the inversion of one‐dimensional shear‐wave velocity profiles from dispersion curves of the fundamental mode of Rayleigh surface waves. The novelties of our approach are that the layer velocities and thicknesses are set as unknowns, and an artificial neural network is proposed to solve the inverse problem. We suggest that training data should be calculated for a set of random synthetic velocity layered models, while layer thicknesses and velocities should be set to fixed intervals, with ranges estimated based on the systematic application of empirical relations between Rayleigh and S‐wave velocities to the dispersion data. Our main challenge is a total overhaul of the artificial neural network, which includes selecting the optimal artificial neural network architecture and parameters by performing a large number of numerical experiments. Our synthetic results show that the accuracy of the proposed approach outperforms that of the Monte Carlo approach. We illustrate our proposed method with West Siberia data processing obtained from an area of approximately 800 km2. From a user perspective, the main strength of our method is the computationally efficient processing of large amounts of dispersion data, which make it well suited for four‐dimensional near‐surface monitoring.

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“…Then, the method is applied to field data. Some studies proposed machine learning approaches for extracting dispersion curves (Dai et al ., 2020) or for surface waves inversion (Meier et al ., 2007; Cheng et al ., 2019; Hu et al ., 2020), especially at a seismological scale. Other recent studies used RNN to solve geophysical problems (Wang et al ., 2020; Yang et al ., 2020; Othman et al ., 2021).…”

Section: Introductionmentioning

confidence: 99%

A hybrid residual neural network–Monte Carlo approach to invert surface wave dispersion data

Aleardi

Stucchi

2021

Near Surface Geophysics

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Surface-wave inversion is a non-linear and ill-conditioned problem usually solved through deterministic or global optimization approaches. Here, we present an alternative method based on machine learning. Under the assumption of a local onedimensional model, we train a residual neural network to predict the non-linear mapping between the full dispersion image and the model space, parameterized in terms of shear wave velocity and layer thicknesses. On the one hand, compared to standard convolutional neural networks, the residual network prevents the vanishing gradient problem when training a deep network. On the other hand, the use of the full dispersion image avoids the time-consuming and often ambiguous picking procedure and allows considering higher modes in the inversion framework. One key aspect of any machine learning inversion strategy is the definition of an appropriate training set. In this case, the models forming the training and validation examples are uniformly drawn from previously defined ranges that cover a wide range of possible near-surface layered Vs models. The reflectivity method constitutes the forward modelling operator that converts the model parameters into the observed shot gathers. The inversion also includes a Monte Carlo simulation strategy that propagates onto the model space the uncertainties related to noise in the data and the modelling error introduced by the network approximation. We first discuss synthetic inversions to assess the applicability of the proposed method and to analyse the effect of erroneous model parameterizations. The inversion results are also benchmarked with those provided by a more standard approach in which the particle swarm optimization algorithm inverts the fundamental mode only. Then, we discuss a field data application. Our tests confirm that the residual neural network inversion provides accurate model estimations and reliable uncertainty appraisals. One of the main benefits of the proposed approach is that once the network is trained it provides the near-surface shear wave velocity profile in near real-time.

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Inverting Rayleigh surface wave velocities for eastern Tibet and western Yangtze craton crustal thickness based on deep learning neural networks

Cited by 3 publications

References 16 publications

Deep Learning for Geophysics: Current and Future Trends

Deep Learning for Geophysics: Current and Future Trends

An artificial neural network approach for the inversion of surface wave dispersion curves

A hybrid residual neural network–Monte Carlo approach to invert surface wave dispersion data

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