Paper Citation Record · LEDGER

Solving All Seismic Tomographic Problems using Deep Learning

As of 17 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2504.14830.

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pith.paper-citation-record.v1
2504.14830 v1

Coverage vector

measured 32 of 32 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Outbound references

Observation 6b361bbd-4e88-4f18-9569-ce9c6955b048 · outbound

This paper cites Determination of three-dimensional velocity anomalies under a seismic array using first P arrival times from local earthquakes: 1.

Solving All Seismic Tomographic Problems using Deep Learning Determination of three-dimensional velocity anomalies under a seismic array using first P arrival times from local earthquakes: 1

Reference 1

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Observation d15f1fb6-0f75-4a4d-9279-89f2ea6400ba · outbound

This paper cites Deep-learning tomography.The Leading Edge, 37(1):58–66, 2018.

Solving All Seismic Tomographic Problems using Deep Learning Deep-learning tomography.The Leading Edge, 37(1):58–66, 2018

Reference 2

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Observation bfab1982-28d9-45d3-bd28-652d5611a257 · outbound

This paper cites Parameter estimation and inverse problems.

Solving All Seismic Tomographic Problems using Deep Learning Parameter estimation and inverse problems

Reference 3

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Observation d14e1ab8-11f2-4388-b7cd-e5da024bfc5d · outbound

This paper cites Two crustal low-velocity channels beneath se tibet revealed by joint inversion of rayleigh wave dispersion and receiver functions.

Solving All Seismic Tomographic Problems using Deep Learning Two crustal low-velocity channels beneath se tibet revealed by joint inversion of rayleigh wave dispersion and receiver functions

Reference 4

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Observation 2e416024-8d59-44e7-828e-89c7b4409809 · outbound

This paper cites Pattern recognition and machine learning.

Solving All Seismic Tomographic Problems using Deep Learning Pattern recognition and machine learning

Reference 5

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Observation 6a6fd065-ee08-47f1-9fb2-990563adc14a · outbound

This paper cites An efficient, probabilistic neural network approach to solving inverse problems: Inverting surface wave velocities for Eurasian crustal thickness.

Solving All Seismic Tomographic Problems using Deep Learning An efficient, probabilistic neural network approach to solving inverse problems: Inverting surface wave velocities for Eurasian crustal thickness

Reference 6

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Observation d1a789f4-7ac0-4c4c-b83a-805062682fbb · outbound

This paper cites Probabilistic neural network-based 2d travel-time tomography.

Solving All Seismic Tomographic Problems using Deep Learning Probabilistic neural network-based 2d travel-time tomography

Reference 7

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Observation 5e839c4a-309f-4d2e-81e9-99363d4b17b0 · outbound

This paper cites Graph mixture density networks.

Solving All Seismic Tomographic Problems using Deep Learning Graph mixture density networks

Reference 8

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Observation 08b7a8fd-a7a3-4455-885e-64bef5008aaa · outbound

This paper cites Uncertainty loops in travel-time to- mography from nonlinear wave physics.

Solving All Seismic Tomographic Problems using Deep Learning Uncertainty loops in travel-time to- mography from nonlinear wave physics

Reference 9

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Observation ea807291-8f9f-481b-aa93-29b28e83a992 · outbound

This paper cites Transdimensional love-wave tomography of the British Isles and shear-velocity structure of the east Irish Sea Basin from ambient-noise inter- ferometry.

Solving All Seismic Tomographic Problems using Deep Learning Transdimensional love-wave tomography of the British Isles and shear-velocity structure of the east Irish Sea Basin from ambient-noise inter- ferometry

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:12.606013Z

Source-reported events for the cited work

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Observation 8a140169-84ef-47b4-9056-2b47e82d6150 · outbound

This paper cites Bayesian elastic full-waveform inversion using Hamilto- nian Monte Carlo.

Solving All Seismic Tomographic Problems using Deep Learning Bayesian elastic full-waveform inversion using Hamilto- nian Monte Carlo

Reference 11

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Observation 2eed410a-bdd2-4fb8-a9d9-10f43f81c541 · outbound

This paper cites An adaptive metropolis algorithm.

Solving All Seismic Tomographic Problems using Deep Learning An adaptive metropolis algorithm

Reference 12

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Observation 7719d8cd-e29d-4454-b301-5500732871c0 · outbound

This paper cites Regularisation of mixture density networks.

Solving All Seismic Tomographic Problems using Deep Learning Regularisation of mixture density networks

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:12.581760Z

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Observation cca460e7-afad-4faf-8056-9f53089aac13 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Solving All Seismic Tomographic Problems using Deep Learning Adam: A Method for Stochastic Optimization

Reference 14

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Observation 3096a88e-1add-4fca-8654-78b26d59908d · outbound

This paper cites Stein variational gradient descent: A general purpose Byesian inference algorithm.

Solving All Seismic Tomographic Problems using Deep Learning Stein variational gradient descent: A general purpose Byesian inference algorithm

Reference 15

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Observation 290ce8f7-5133-4269-986f-45178638047c · outbound

This paper cites The community velocity model v.

Solving All Seismic Tomographic Problems using Deep Learning The community velocity model v

Reference 16

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Observation a317eff5-957d-4685-ab68-3657f59cd311 · outbound

This paper cites The high-resolution community velocity model v2.

Solving All Seismic Tomographic Problems using Deep Learning The high-resolution community velocity model v2

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:12.552036Z

Source-reported events for the cited work

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Observation 83fc3164-e9dd-44ed-ab6b-a1d96a790473 · outbound

This paper cites Overcoming limitations of mixture density net- works: A sampling and fitting framework for multimodal future prediction.

Solving All Seismic Tomographic Problems using Deep Learning Overcoming limitations of mixture density net- works: A sampling and fitting framework for multimodal future prediction

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:12.540819Z

Source-reported events for the cited work

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Observation 96bdf665-2a6b-4bb7-87a3-355034ab7598 · outbound

This paper cites Mixture models: Inference and applications to clustering , vol- ume 38.

Solving All Seismic Tomographic Problems using Deep Learning Mixture models: Inference and applications to clustering , vol- ume 38

Reference 19

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Observation b11cdf9c-187b-473c-b30f-c7420069c1b9 · outbound

This paper cites Global crustal thickness from neural network inversion of surface wave data.

Solving All Seismic Tomographic Problems using Deep Learning Global crustal thickness from neural network inversion of surface wave data

Reference 20

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Observation 2dd27fef-42d0-4aa1-96b7-c3212d425b4b · outbound

This paper cites Monte Carlo sampling of solutions to inverse problems.

Solving All Seismic Tomographic Problems using Deep Learning Monte Carlo sampling of solutions to inverse problems

Reference 21

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Observation 1d0abe55-7cb4-412f-9107-4dcbaecfe291 · outbound

This paper cites Multiple reflection and transmission phases in complex layered media using a multistage fast marching method.

Solving All Seismic Tomographic Problems using Deep Learning Multiple reflection and transmission phases in complex layered media using a multistage fast marching method

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:12.505382Z

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Observation 03df6c8b-8fa5-447e-9150-ee0a9c06d2f5 · outbound

This paper cites Neural networks and inversion of seismic data.

Solving All Seismic Tomographic Problems using Deep Learning Neural networks and inversion of seismic data

Reference 23

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Source-reported events for the cited work

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Observation b55c2270-dcfb-40f2-b2f7-112ae4977f1e · outbound

This paper cites Probabilistic programming in python using pymc3.

Solving All Seismic Tomographic Problems using Deep Learning Probabilistic programming in python using pymc3

Reference 24

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Observation de156487-ba48-4b32-bbe9-5e7a1fb348da · outbound

This paper cites Inverse problem theory and methods for model parameter estimation, volume 89.

Solving All Seismic Tomographic Problems using Deep Learning Inverse problem theory and methods for model parameter estimation, volume 89

Reference 25

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Observation b7d4e337-7b4b-4824-a320-ed0fd37a92ea · outbound

This paper cites A new crustal shear-velocity model in southwest china from joint seismological inversion and its implications for regional crustal dynamics.

Solving All Seismic Tomographic Problems using Deep Learning A new crustal shear-velocity model in southwest china from joint seismological inversion and its implications for regional crustal dynamics

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:12.469587Z

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Observation 797e8261-3469-4b5d-a41f-8da33e4021d9 · outbound

This paper cites Deep learning for geophysics: Current and future trends.

Solving All Seismic Tomographic Problems using Deep Learning Deep learning for geophysics: Current and future trends

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:12.458793Z

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Observation 82708c72-655c-4524-a2f4-883aaf1d95a5 · outbound

This paper cites Seismic tomography using variational inference methods.Journal of Geophysical Research: Solid Earth, 125(4):e2019JB018589, 2020.

Solving All Seismic Tomographic Problems using Deep Learning Seismic tomography using variational inference methods.Journal of Geophysical Research: Solid Earth, 125(4):e2019JB018589, 2020

Reference 28

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Observation a6ac2475-16cf-41cf-bea3-e3db532cc13e · outbound

This paper cites Variational full-waveform inversion.

Solving All Seismic Tomographic Problems using Deep Learning Variational full-waveform inversion

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:12.441305Z

Source-reported events for the cited work

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Observation b7554e2e-671e-4717-b6a7-59b719594e61 · outbound

This paper cites Bayesian geophysical inversion using invertible neural networks.

Solving All Seismic Tomographic Problems using Deep Learning Bayesian geophysical inversion using invertible neural networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:12.430449Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b524277f-ffcd-47c1-b540-662147499cea · outbound

This paper cites 3-d bayesian variational full wave- form inversion.

Solving All Seismic Tomographic Problems using Deep Learning 3-d bayesian variational full wave- form inversion

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T11:45:12.418080Z

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Observation 7c0874f1-1229-4edd-8fae-4145abee4829 · outbound

This paper cites Rapid Bayesian Seismic Tomography using Graph Mixture Density Networks.

Solving All Seismic Tomographic Problems using Deep Learning Rapid Bayesian Seismic Tomography using Graph Mixture Density Networks

Reference 32

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Pith citing papers

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