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REVIEW 5 major objections 7 minor 128 references

Integrating Artificial Intelligence and Geophysical Insights for Earthquake Forecasting: A Cross-Disciplinary Review

T0 review · 5 major / 7 minor · reviewed 2026-08-08 · deepseek-v4-flash

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Pith's one-line read AI earthquake forecasting will not advance by swapping one neural architecture for another; it needs rigorous benchmarking against physics-based models such as ETAS and the injection of seismological knowledge into features, network…

desk verdict A useful, well-organized review of AI for earthquake forecasting whose core benchmarking message holds up; the multi-source non-seismic data promise is its weakest leg. read the letter →

arxiv 2502.12161 v1 pith:T32LGL3S submitted 2025-02-10 physics.geo-ph cs.AIcs.LG

Zhang Ying , Wen Congcong , Sornette Didier , Zhan Chengxiang This is my paper · ORCID
classification physics.geo-phcs.AIcs.LG
keywords StatisticalphysicsEarthquakeforecastingseismologyMachinelearningGeophysicsDeepETASbenchmarkPseudo-prospectivetesting
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This review argues that most AI-based earthquake forecasting research evaluates models against the wrong baselines. Across the 141 surveyed studies, roughly a third include no baseline comparison at all, and most that do compare only against other machine-learning methods or a Poisson null hypothesis, not against the best statistical-seismology benchmarks such as the ETAS model. The paper's central claim is that useful progress will come less from novel architectures than from benchmarking AI against physics-based forecasting models and from building seismological structure into inputs, network design, and loss functions. It presents a taxonomy of model outputs, inputs, loss functions, and evaluation metrics, and uses an 11-year pseudo-prospective comparison in Southern California to show that a fully convolutional network can match ETAS skill while running 2,000 to 4,000 times faster.

What carries the argument

The machinery carrying the argument is the comparison against the ETAS model, the epidemic-type aftershock sequence model from statistical seismology, together with a proposed pipeline of physics-informed design choices: feature engineering based on magnitude-frequency statistics and aftershock decay parameters, network architectures that embed the triggering structure of ETAS, and loss functions that reweight samples according to seismological priors such as magnitude-frequency balance. The paper uses its own Southern California benchmark, run on a 0.1-degree grid over 11 years with the ETAS model as reference, to demonstrate that a fully convolutional network reaches ETAS-level skill with far lower computational cost. That benchmark, along with the six comparable studies, carries the claim that AI contributes to earthquake forecasting mainly through speed parity, supremacy, or complementary discovery rather than through architectural novelty alone.

What would settle it

Run a prospective test under standard statistical-seismology protocols in California in which the same model is trained once on seismicity-only features and once with ionospheric, radon, electromagnetic, and thermal-infrared channels added, then scored with the Molchan area skill and the L-test against the inhomogeneous ETAS benchmark; if the augmented model does not beat the seismicity-only model beyond sampling noise over a multi-year window, the paper's premise about non-seismic precursors loses its empirical footing.

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Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the field's bottleneck is evaluation and integration, not AI capability. Only six of the reviewed studies compare an AI model with a reasonably strong statistical-seismology model, and those six show a consistent pattern: models that copy the mathematical structure of ETAS, or that learn a flexible neural point process, can match or exceed ETAS in forecasting skill while cutting computation time by orders of magnitude. The review uses these cases to argue that AI will earn its place in seismology in one of three ways: performing on par with leading geophysical models but far faster, surpassing them, or revealing new physical patterns. It also demonstrates that many studies treat earthquake forecasting as a plain binary classification or regression problem, ignoring the severe imbalance between earthquake and non-earthquake samples and the strong spatio-temporal clustering of seismic events.

Load-bearing premise

The proposal assumes that non-seismic and non-mechanical precursor signals, such as ionospheric total electron content, electromagnetic emissions, thermal infrared anomalies, radon, and groundwater levels, carry information about future earthquakes that is not already contained in the seismic catalog; if they do not, widening the input space will not improve forecasts.

Editorial extensions

If this is right

  • Future AI earthquake forecasts should be judged by whether they match or beat ETAS-class models, not by gains over Poisson nulls or other machine-learning baselines.
  • Standardized outputs, such as the probability of at least one earthquake above a magnitude threshold in a space-time bin, would make models directly comparable.
  • Loss functions that encode seismological weighting, balancing rare large events, background versus triggered events, and spatio-temporal clustering, will matter more than network depth.
  • Models that embed the triggering structure of ETAS can retain geophysical interpretability while running roughly a thousand times faster, making near-real-time operational forecasting more practical.
  • Pseudo-prospective testing must account for short-term catalog incompleteness; otherwise, comparisons between AI models and ETAS are unreliable.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable consequence the review leaves implicit is that if non-seismic precursors carry independent signal, the improvement should appear first in forecasting background and mainshock events, since ETAS and its AI imitators already capture triggered aftershocks well.
  • A meta-analysis of the surveyed studies might show that models using seismological feature engineering report smaller gains over strong baselines, because those baselines already exploit the same features; that would sharpen the claim that integration, not input diversity, is the bottleneck.
  • By the review's own standard, AI's most defensible immediate contribution is operational speed: even at prediction parity, replacing a slow ETAS calibration with a fast neural model makes real-time ensemble hazard mapping feasible.
  • The review does not assess whether transformer-based or foundation models trained on large multi-modal datasets would change its benchmark conclusions; that remains an open extension of its argument.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 7 minor

Summary. This review surveys 141 (or, inconsistently, 142/140/136) AI-based earthquake prediction and forecasting studies published from 1994 to late 2024 and assesses them from a seismological perspective. It catalogs model outputs, input types, loss functions, and evaluation metrics; argues that most surveyed studies oversimplify the forecasting problem and evaluate against weak baselines; and emphasizes that meaningful progress requires benchmarking against strong statistical-seismology models such as ETAS and injecting seismological structure into models and loss functions. The authors single out six AI studies that compare against geophysical or statistical-seismology benchmarks, and they present their own FCN-vs-ETAS comparison on California data as an additional reference framework. The paper concludes with recommendations for pseudo-prospective testing, awareness of catalog incompleteness, and seismologically informed loss-function and feature design.

Significance. The review fills a useful niche: it gives AI researchers a concrete, well-organized map of earthquake-forecasting output types, inputs, losses, and metrics (Sections 4.3-4.6), and it forcefully restates the benchmarking message of Mignan and Broccardo with additional 2023 examples. It also ships reproducible assets: the GitHub corpus list, the FCN benchmark code/data link, and the explicit acknowledgment of short-term catalog incompleteness in Section 5.3 are genuine strengths. However, the paper's headline promise—that adding 'data from many varied sources' (especially non-seismic precursors) will materially improve forecasting and reveal precursor mechanisms—is not actually supported by the survey evidence it presents. The six highlighted ETAS-comparison studies all use seismic inputs; none demonstrates that non-seismic variables add skill beyond a catalog-based baseline. The paper itself cites the lack of physical mechanism and of rigorous statistical testing for non-seismic precursors (Section 2.2). Thus the contribution is strongest as a critical methodology review and a physics-informed-AI agenda, but weaker as an evidence-based case for multi-source precursor integration.

major comments (5)
  1. [Section 1 and Section 2.3] The surveyed corpus size is stated inconsistently: Section 1 states 142 papers, Section 2.3 states 141, Section 3.3 refers to 'a review of 136 papers' and later '140 studies', and Section 4.4.3 says 'Out of 141 investigated works.' The percentages in Section 2.3 (66.9% compared to a baseline) and the claim that only six studies provide meaningful benchmark comparisons depend on a precisely defined corpus. Please reconcile these numbers and provide the survey selection protocol (databases, search terms, inclusion/exclusion criteria) so that the representativeness of the corpus can be assessed.
  2. [Section 2.3, paragraph 2] The sentence 'four studies from 2023 compared their models with reasonably strong geophysical models, six of which made comparisons with some versions of the general class of ETAS models' is internally contradictory: four and six cannot both be the denominator and the subset. The following paragraph in Section 3.3 then lists six highlighted studies, several of which are not from 2023. Please clarify which studies are being counted, with references and years, and align the two counts.
  3. [Section 4.2, Table 1] Table 1 is referred to as reporting Area Skill Scores for the ETAS and FCN models across 12 time-magnitude windows, plus a runtime comparison, but the table body appears empty in the manuscript: no numerical scores or speed values are given. Since the FCN-versus-ETAS pseudo-prospective experiment is presented as a central benchmark contribution and as evidence for 'performance parity with speed,' the absence of the actual numbers makes the claim unverifiable. Please include the full Table 1 with all Area Skill Scores and runtime measurements, and check that the GitHub link resolves to these results.
  4. [Sections 1, 4.4.2-4.4.3, and 5.5] The load-bearing claim that integrating 'data from many varied sources' (ionospheric TEC, electromagnetic emissions, thermal infrared, radon, groundwater, etc.) will enhance predictive accuracy and uncover earthquake-precursor mechanisms is not supported by the surveyed evidence. Section 2.2 itself notes the lack of a clear physical mechanism and of rigorous statistical testing for non-seismic precursors; Section 4.4.3 shows that only 27 of 141 studies used non-seismic inputs; and none of the six highlighted benchmark-comparison studies demonstrates that non-seismic variables improve forecast skill after conditioning on past seismicity. Stronger versions of this claim should be reframed as an open hypothesis, with explicit experimental tests proposed (e.g., ablation or Shapley-value analysis against an ETAS-conditioned baseline). The physics-informed-AI half of the proposal is independently supported by the six highlighted studies and should be separated from the multi-source-precursor half.
  5. [Section 2.2 and Section 3.3] The review states that 'none [of the 136 papers] had conducted prospective testing' and that the six highlighted studies were evaluated pseudo-prospectively. This is a strong negative claim about the entire corpus. Given the corpus-size inconsistencies noted above, please either verify this claim with a per-paper testing-mode table in the Supplement or soften the statement to 'none of the papers we could verify' with a clear audit trail.
minor comments (7)
  1. [Abstract] The abstract says 'precursors' and 'many varied sources' before the body has established the evidentiary status of those sources; consider aligning the abstract with the more cautious discussion in Section 2.2.
  2. [Section 3.3, paragraph on Zlydenko et al.] The text reads 'This ANN model performs on par with, or even surpasses, a standard ETAS model with isotropic spatial kernels ... in terms of the average information gain per earthquake'; please specify whether the comparison is on Japanese data for all magnitude thresholds and report the exact information-gain values that support 'on par' versus 'surpasses.'
  3. [Section 4.3.10, Figure 2] Figure 2 is described as a cumulative frequency of output types, but the axes and the exact cumulative definition are not explained in the caption. Please add a self-contained caption with axis labels and the counting convention.
  4. [Section 4.4, Input definitions] Several equations have notational typos that could confuse readers: Eq. (3) for Magnitude Density omits the volume factor in the text definition, Eq. (11) places the denominator inside the summation in the prose, and Eq. (18) appears to define eta as a sum rather than an average. Please proofread all input equations against the cited sources.
  5. [Section 4.6.3, Eq. (90)-(96)] The L-test, S-test, and N-test definitions use oi,j both for the predicted seismicity and later for the forecasted number; please rename the predicted rate to avoid confusion with the observed count omega.
  6. [Section 5.1] The phrase 'fast-food research' is informal for a Physics Reports review; consider replacing it with a neutral descriptive term such as 'superficial research practice.'
  7. [References] Some references are incomplete or carry placeholder text: the citation for 'Liu et al. (reference)' in the Input 14 definition and the truncated reference in Section 5.5 need to be completed.

Circularity Check

0 steps flagged · score 2.0 of 10

No derivation in this review reduces to fitted values or to a self-citation chain; the central recommendations are independent, though the paper cites the authors' own FCN and STC benchmarks as illustrative examples.

full rationale

This is a cross-disciplinary review rather than a derivation from first principles, so the usual circularity patterns (fitted input renamed as prediction, definitional equivalence, uniqueness imported from authors) do not arise. The paper's central empirical claim is a survey finding: among 141 AI forecasting studies, only a few compare against strong statistical seismology baselines such as ETAS, and six recent pseudo-prospective studies show that physics-informed AI can reach or exceed such baselines. That claim is supported by external benchmarks and by independent works (Mignan and Broccardo's 77-paper reanalysis, Dascher-Cousineau et al., Zlydenko et al., Stockman et al.), not solely by the authors' prior papers. The authors' own FCN model (Zhang et al. [53]) and ETAS-inspired STC model (Zhan et al. [54]) are cited and even used to build the proposed benchmark framework in Section 4.2, but they are presented as transparently labeled prior results with publicly available code and pseudo-prospective Molchan-diagram evaluations; the review does not derive its conclusion from those papers' success. The paper explicitly flags the weakness of the non-seismic precursor premise in Section 2.2 ('the lack of a clear physical mechanism... has led to widespread criticism' and 'lack of rigorous statistical testing methodology'), which means the multi-source-data recommendation rests on an unvalidated assumption; that is a correctness and evidence-quality concern, not a circular-derivation concern. Because the only notable issue is a cluster of self-citations that is not load-bearing, the circularity score is low.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

The review makes no derivation and fits no constants. Its conclusions rest on domain assumptions about precursor information and AI capability, plus a survey-selection assumption.

assumptions (3)
  • domain assumption Non-seismic precursors such as ionospheric, thermal, gas and hydrologic anomalies carry predictive information about earthquakes beyond what seismic catalogs provide.
    Invoked in Section 1 to motivate expanding inputs; the review cites mixed evidence but does not establish a validated precursor signal.
  • domain assumption AI methods can extract multidimensional, nonlinear relationships from heterogeneous geophysical data at a scale conventional statistics cannot.
    Section 1 states AI 'becomes indispensable'; no controlled demonstration is provided in the review itself.
  • ad hoc to paper The 141 surveyed papers are a representative corpus for concluding that only six provide meaningful comparisons to strong seismological benchmarks.
    Section 1 and Section 3.3 describe the corpus as collected via undisclosed search criteria; absolute claims about the field depend on this selection.

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Cite this review

Pith. "Pith review of Integrating Artificial Intelligence and Geophysical Insights for Earthquake Forecasting: A Cross-Disciplinary Review." pith.science (2026). https://pith.science/paper/T32LGL3S

@misc{pith2026250212161,
  author       = {Pith},
  title        = {Pith review of: Integrating Artificial Intelligence and Geophysical Insights for Earthquake Forecasting: A Cross-Disciplinary Review},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/T32LGL3S}},
  note         = {Machine review of arXiv:2502.12161}
}
read the original abstract

Earthquake forecasting remains a significant scientific challenge, with current methods falling short of achieving the performance necessary for meaningful societal benefits. Traditional models, primarily based on past seismicity and geomechanical data, struggle to capture the complexity of seismic patterns and often overlook valuable non-seismic precursors such as geophysical, geochemical, and atmospheric anomalies. The integration of such diverse data sources into forecasting models, combined with advancements in AI technologies, offers a promising path forward. AI methods, particularly deep learning, excel at processing complex, large-scale datasets, identifying subtle patterns, and handling multidimensional relationships, making them well-suited for overcoming the limitations of conventional approaches. This review highlights the importance of combining AI with geophysical knowledge to create robust, physics-informed forecasting models. It explores current AI methods, input data types, loss functions, and practical considerations for model development, offering guidance to both geophysicists and AI researchers. While many AI-based studies oversimplify earthquake prediction, neglecting critical features such as data imbalance and spatio-temporal clustering, the integration of specialized geophysical insights into AI models can address these shortcomings. We emphasize the importance of interdisciplinary collaboration, urging geophysicists to experiment with AI architectures thoughtfully and encouraging AI experts to deepen their understanding of seismology. By bridging these disciplines, we can develop more accurate, reliable, and societally impactful earthquake forecasting tools.

Figures

Figures reproduced from arXiv: 2502.12161 by the authors.

Figure 1
Figure 1. (a) Spatial distribution of earthquakes with magnitude larger than 0 that oc [PITH_FULL_IMAGE:figures/full_fig_p034_1.png] view at source ↗
Figure 2
Figure 2. Cumulative frequency of different types of outputs. [PITH_FULL_IMAGE:figures/full_fig_p043_2.png] view at source ↗
Figure 3
Figure 3. Number of times different types of inputs have been used in published papers [PITH_FULL_IMAGE:figures/full_fig_p112_3.png] view at source ↗

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Works this paper leans on

128 extracted references · 67 canonical work pages

  1. [1]

    Freund, G

    F. Freund, G. Ouillon, J. Scoville, D. Sornette, Earthquake precursors in the light of peroxy defects theory: Critical review of systematic observations, TheEuropean PhysicalJournal Special Topics 230(2021) 7–46. 95

    2021

  2. [2]

    Mearns, D

    E. Mearns, D. Sornette, The Great 1976 Tangshan Earthquake: Learning from the 1966-1976 Chinese Prediction Program, Cambridge Scholar Publishing, Lady Stephenson Library, Newcastle upon Tyne, NE6 2PA, UK, 2021

    1976

  3. [3]

    Huang, Forecasting the epicenter of a future major earthquake, Proceedings of the National Academy of Sciences 112 (2015) 944–945

    Q. Huang, Forecasting the epicenter of a future major earthquake, Proceedings of the National Academy of Sciences 112 (2015) 944–945

    2015

  4. [4]

    Gelfand, S

    I. Gelfand, S. A. Guberman, V. Keilis-Borok, L. Knopoff, F. Press, E. Y. Ranzman, I. Rotwain, A. Sadovsky, Pattern recognition applied to earthquake epicenters in california, Physics of the Earth and Plan- etary Interiors 11 (1976) 227–283

    1976

  5. [5]

    Healy, V

    J. Healy, V. Kossobokov, J. Dewey, A test to evaluate the earth- quake prediction algorithm, m8, U.S. Geol. Surv. Open-File Report 92- 401, 23 p. with 6 Appendices, 1992. https://doi.org/10.3133/ofr92401 ; https://pubs.er.usgs.gov/publication/ofr92401]. (1992)

    work page doi:10.3133/ofr92401 1992

  6. [6]

    Kossobokov, L

    V. Kossobokov, L. Romashkova, V. Keilis-Borok, J. Healy, Testing earthquake prediction algorithms: Statistically significant real-time prediction of the largest earthquakes in the circum-pacific, 1992-1997, Physics of the Earth and Planetary Interiors 111 (1999) 187–196

    1999

  7. [7]

    Ismail-Zadeh, Kossobokov, Earthquake Prediction, M8 Algorithm, In: Gupta H (ed) Encyclopedia of Solid Earth Geophysics

    A. Ismail-Zadeh, Kossobokov, Earthquake Prediction, M8 Algorithm, In: Gupta H (ed) Encyclopedia of Solid Earth Geophysics. Encyclope- dia of Earth Sciences Series. Springer, 2020

    2020

  8. [8]

    Kossobokov, A

    V. Kossobokov, A. Soloviev, Testing earthquake prediction algorithms, Journal Geological Society of India 97 (2021) 1514–1519

    2021

Show all 128 references
  1. [9]

    S. E. Hough, Predicting the unpredictable: the tumultuous science of earthquake prediction, Princeton University Press, 2010

    2010

  2. [10]

    Mignan, G

    A. Mignan, G. Ouillon, D. Sornette, F. Freund, Global earthquake forecastingsystem(GEFS):Thechallengesahead, Eur.Phys.J.Special Topics 230 (2021) 473–490

    2021

  3. [11]

    Mizrahi, I

    L. Mizrahi, I. Dallo, N. J. van der Elst, A. Christophersen, I. Spassiani, M. J. Werner, P. Iturrieta, J. A. Bayona, I. Iervolino, M. Schneider, et al., Developing, testing, and communicating earthquake forecasts: 96 Current practices and future directions, Reviews of Geophysi...

    2024

  4. [12]

    Mignan, M

    A. Mignan, M. Broccardo, Neural network applications in earthquake prediction (1994–2019): Meta-analytic and statistical insights on their limitations, Seismol. Res. Lett. 91 (2020) 2330–2342. URL: https: //doi.org/10.1785/0220200021. doi:10.1785/0220200021

    2020 doi

  5. [13]

    P. M. R. DeVries, F. Viégas, M. Wattenberg, B. J. Meade, Deep learning of aftershock patterns following large earthquakes, Nature 560 (2018) 632–634. URL: http://dx.doi.org/10.1038/ s41586-018-0438-y. doi:10.1038/s41586-018-0438-y

    2018 doi

  6. [14]

    Mignan, M

    A. Mignan, M. Broccardo, One neuron versus deep learning in after- shock prediction, Nature 574 (2019) E1–E3. URL:https://doi.org/ 10.1038/s41586-019-1582-8. doi:10.1038/s41586-019-1582-8

    2019 doi

  7. [15]

    Zhang, C

    Y. Zhang, C. Zhan, Q. Huang, D. Sornette, Seismically informed refer- ence models enhance ai-based earthquake prediction systems, Journal of Geophysical Research: Solid Earth 129 (2024) e2023JB028037

    2024

  8. [16]

    P. A. Reasenberg, L. M. Jones, Earthquake hazard after a mainshock in california, Science 243 (1989) 1173–1176

    1989

  9. [17]

    Ogata, Statistical models for earthquake occurrences and resid- ual analysis for point processes, J

    Y. Ogata, Statistical models for earthquake occurrences and resid- ual analysis for point processes, J. Am. Stat. Assoc. 83 (1988) 9–27. URL: https://www.tandfonline.com/doi/abs/10.1080/01621459. 1988.10478560. doi:10.1080/01621459.1988.10478560

    1988

  10. [18]

    Helmstetter, Y

    A. Helmstetter, Y. Y. Kagan, D. D. Jackson, Comparison of short- term and time-independent earthquake forecast models for southern california, Bulletin of the Seismological Society of America 96 (2006) 90–106

    2006

  11. [19]

    M. C. Gerstenberger, S. Wiemer, L. M. Jones, P. A. Reasenberg, Real- time forecasts of tomorrow’s earthquakes in california, Nature 435 (2005) 328–331

    2005

  12. [20]

    Woessner, A

    J. Woessner, A. Christophersen, J. D. Zechar, D. Monelli, Building self- consistent, short-term earthquake probability (step) models: improved 97 strategies and calibration procedures, Annals of Geophysics 53 (2010) 141–154

    2010

  13. [21]

    A. J. Michael, M. J. Werner, Preface to the Focus Sec- tion on the Collaboratory for the Study of Earthquake Pre- dictability (CSEP): New Results and Future Directions, Seismological Research Letters 89 (2018) 1226–1228. URL: https://doi.org/10.1785/0220180161. doi: 10.1785/02...

    2018 doi

  14. [22]

    Schorlemmer, M

    D. Schorlemmer, M. J. Werner, W. Marzocchi, T. H. Jordan, Y. Ogata, D. D. Jackson, S. Mak, D. A. Rhoades, M. C. Ger- stenberger, N. Hirata, M. Liukis, P. J. Maechling, A. Strader, M. Taroni, S. Wiemer, J. D. Zechar, J. Zhuang, The Collaboratory for the Study of Earthquake Pred...

    2018 doi

  15. [23]

    Kamer, S

    Y. Kamer, S. Nandan, G. Ouillon, S. Hiemer, D. Sornette, Democ- ratizing earthquake predictability research: introducing the richterx platform, The European Physical Journal Special Topics 230 (2021) 451–471. doi:10.1140/epjst/e2020-000260-2

    2021 doi

  16. [24]

    Nandan, Y

    S. Nandan, Y. Kamer, G. Ouillon, S. Hiemer, D. Sornette, Global models for short-term earthquake forecasting and predictive skill as- sessment, Eur. Phys. J. Special Topics 238 (2021) 425–449

    2021

  17. [25]

    Kossobokov, L

    V. Kossobokov, L. Romashkova, V. Keilis-Borok, J. Healy, Testing earthquake prediction algorithms: Statistically significant real-time prediction of the largest earthquakes in the circum-pacific, 1992-1997, Physics of the Earth and Planetary Interiors 111 (1999) 187–196. doi:1...

    1999 doi

  18. [26]

    Kossobokov, A

    V. Kossobokov, A. Soloviev, Testing earthquake prediction algorithms, Journal Geological Society of India 97 (2021) 1514–1519. doi:10.1007/ s12594-021-1907-8

    2021

  19. [27]

    Y.-Y. Song, L. Ying, Decision tree methods: applications for classifi- cation and prediction, Shanghai archives of psychiatry 27 (2015) 130. 98

    2015

  20. [28]

    Breiman, Random forests, Machine Learning 45 (2001) 5–32

    L. Breiman, Random forests, Machine Learning 45 (2001) 5–32

    2001

  21. [29]

    Xiangrong, Tong, Q

    L. Xiangrong, Tong, Q. Zhang, A. Sadka, L. Li, H. Zhou, Inverse boost- ing pruning trees for depression detection on twitter, arXiv preprint arXiv:1906.00398 (2019)

    2019 arXiv

  22. [30]

    J. H. Friedman, Greedy function approximation: A gradient boosting machine, Annals of Statistics 29 (2001) 1189–1232

    2001

  23. [31]

    M. A. Hearst, S. T. Dumais, E. Osuna, J. Platt, B. Scholkopf, Support vector machines, IEEE Intelligent Systems and their applications 13 (1998) 18–28

    1998

  24. [32]

    Kramer, O

    O. Kramer, O. Kramer, K-nearest neighbors, Dimensionality reduction with unsupervised nearest neighbors (2013) 13–23

    2013

  25. [33]

    K. Zhou, Q. Hou, R. Wang, B. Guo, Real-time kd-tree construction on graphics hardware, ACM Transactions on Graphics (TOG) 27 (2008) 1–11

    2008

  26. [34]

    Rish, et al., An empirical study of the naive bayes classifier, in: IJCAI 2001 workshop on empirical methods in artificial intelligence, volume 3, Seattle, WA, USA;, 2001, pp

    I. Rish, et al., An empirical study of the naive bayes classifier, in: IJCAI 2001 workshop on empirical methods in artificial intelligence, volume 3, Seattle, WA, USA;, 2001, pp. 41–46

    2001

  27. [35]

    Ostertagová, Modelling using polynomial regression, Procedia en- gineering 48 (2012) 500–506

    E. Ostertagová, Modelling using polynomial regression, Procedia en- gineering 48 (2012) 500–506

    2012

  28. [36]

    Wedel, W

    M. Wedel, W. A. Kamakura, M. Wedel, W. A. Kamakura, Mixture regression models, Springer, 2000

    2000

  29. [37]

    Menard, Applied logistic regression analysis, SAGE publications, 2001

    S. Menard, Applied logistic regression analysis, SAGE publications, 2001

    2001

  30. [38]

    Bebis, M

    G. Bebis, M. Georgiopoulos, Feed-forward neural networks, Ieee Po- tentials 13 (1994) 27–31

    1994

  31. [39]

    Z. Li, F. Liu, W. Yang, S. Peng, J. Zhou, A survey of convolutional neu- ral networks: analysis, applications, and prospects, IEEE transactions on neural networks and learning systems 33 (2021) 6999–7019

    2021

  32. [40]

    L. R. Medsker, L. Jain, et al., Recurrent neural networks, Design and Applications 5 (2001) 2. 99

    2001

  33. [41]

    X. Li, C. Wen, L. Wang, Y. Fang, Topology constrained shape corre- spondence, IEEE transactions on visualization and computer graphics 27 (2020) 3926–3937

    2020

  34. [42]

    C. Wen, J. Liang, S. Yuan, H. Huang, Y. Hao, H. Lin, Y.-S. Liu, Y. Fang, Goal-driven transformer for robot behavior learning from play data, in: International Conference on Pattern Recognition, Springer, 2025, pp. 346–359

    2025

  35. [43]

    Hospedales, A

    T. Hospedales, A. Antoniou, P. Micaelli, A. Storkey, Meta-learning in neural networks: A survey, IEEE transactions on pattern analysis and machine intelligence 44 (2021) 5149–5169

    2021

  36. [44]

    H. Lin, C. Zhang, D. Hong, K. Dong, C. Wen, Fedrsclip: Federated learning for remote sensing scene classification using vision-language models, arXiv preprint arXiv:2501.02461 (2025)

    2025 arXiv

  37. [45]

    Negarestani, S

    A. Negarestani, S. Setayeshi, M. Ghannadi-Maragheh, B. Akashe, Layered neural networks based analysis of radon concentration and environmental parameters in earthquake prediction, Journal of Environmental Radioactivity 62 (2002) 225–233. URL:https://www. sciencedirect.com/scie...

    2002 doi

  38. [46]

    J. Xue, S. Wu, Q. Huang, L. Zhao, N. V. Sarlis, P. A. Varotsos, Rase: A real-time automatic search engine for anomalous seismic electric signals in geoelectric data, IEEE Transactions on Geoscience and Remote Sensing 61 (2023) 1–11. doi:10.1109/TGRS.2023.3260202

    2023

  39. [47]

    J. Chen, H. Tang, W. Chen, Deep learning of the aftershock hystere- sis effect based on the elastic dislocation theory, Nat. Hazards Earth Syst. Sci. 20 (2020) 3117–3134. URL: https://nhess.copernicus. org/articles/20/3117/2020/. doi:10.5194/nhess-20-3117-2020

    2020 doi

  40. [48]

    Dascher-Cousineau, O

    K. Dascher-Cousineau, O. Shchur, E. E. Brodsky, S. Günnemann, Using deep learning for flexible and scalable earthquake forecast- ing, Geophys. Res. Lett. 50 (2023) e2023GL103909. URL: https: //doi.org/10.1029/2023GL103909. doi:https://doi.org/10.1029/ 2023GL103909. 100

    2023 doi

  41. [49]

    Zlydenko, G

    O. Zlydenko, G. Elidan, A. Hassidim, D. Kukliansky, Y. Ma- tias, B. Meade, A. Molchanov, S. Nevo, Y. Bar-Sinai, A neural encoder for earthquake rate forecasting, Sci. Rep. 13 (2023) 12350. URL: https://doi.org/10.1038/s41598-023-38033-9. doi:10.1038/s41598-023-38033-9

    2023 doi

  42. [50]

    Zhuang, Y

    J. Zhuang, Y. Ogata, D. Vere-Jones, Stochastic Declustering of Space- Time Earthquake Occurrences, Journal of the American Statistical Association 97 (2002) 369–380

    2002

  43. [51]

    Ogata, J

    Y. Ogata, J. Zhuang, Space–time ETAS models and an improved ex- tension, Tectonophysics 413 (2006) 13–23

    2006

  44. [52]

    Stockman, D

    S. Stockman, D. J. Lawson, M. J. Werner, Forecasting the 2016–2017 central apennines earthquake sequence with a neural point process, Earth’s Future 11 (2023) e2023EF003777. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/ 2023EF003777. doi:https://doi.org/10.1029...

    2023 doi

  45. [53]

    Zhang, C

    Y. Zhang, C. Zhan, Q. Huang, D. Sornette, Forecasting future earthquakes with deep neural networks: Application to California, Geophysical Journal International 240 (2024) 81–95. URL: https: //doi.org/10.1093/gji/ggae373. doi: 10.1093/gji/ggae373. arXiv:https://academic.oup.co...

    2024 doi

  46. [54]

    C. Zhan, S. Gao, Y. Zhang, J. Li, Q. Meng, Etas-inspired spatio- temporal convolutional (stc) model for next-day earthquake forecast- ing, IEEE Transactions on Geoscience and Remote Sensing 62 (2024) 1–14. doi:10.1109/TGRS.2024.3424881

    2024

  47. [55]

    M. H. A. Banna, K. A. Taher, M. S. Kaiser, M. Mahmud, M. S. Rah- man, A. S. M. S. Hosen, G. H. Cho, Application of artificial intelligence in predicting earthquakes: State-of-the-art and future challenges, IEEE Access 8 (2020) 192880–192923. doi:10.1109/ACCESS.2020.3029859

    2020

  48. [56]

    N. S. M. Ridzwan, S. H. M. Yusoff, Machine learning for earthquake prediction: a review (2017–2021), Earth Science Informatics 16 (2023) 1133–1149. URL: https://doi.org/10.1007/s12145-023-00991-z. doi:10.1007/s12145-023-00991-z. 101

    2023 doi

  49. [57]

    Hwang, J

    S. Hwang, J. Park, N. Kim, Y. Choi, I. So Kweon, Multispectral pedestrian detection: Benchmark dataset and baseline, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015

    2015

  50. [58]

    Schorlemmer, M

    D. Schorlemmer, M. Gerstenberger, Relm testing center, Seismological Research Letters 78 (2007) 30–36

    2007

  51. [59]

    Nandan, G

    S. Nandan, G. Ouillon, S. Wiemer, D. Sornette, Objective estimation of spatially variable parameters of epidemic type aftershock sequence model: Application to california, Journal of Geophysical Research: Solid Earth 122 (2017) 5118–5143

    2017

  52. [60]

    Clauset, C

    A. Clauset, C. R. Shalizi, M. E. J. Newman, Power- law distributions in empirical data, SIAM Review 51 (2009) 661–703. URL: https://doi.org/10.1137/070710111. doi:10.1137/ 070710111. arXiv:https://doi.org/10.1137/070710111

    2009 doi

  53. [61]

    Nandan, S

    S. Nandan, S. K. Ram, G. Ouillon, D. Sornette, Is seismicity operating at a critical point?, Physical Review Letters 126 (2021) 128501

    2021

  54. [62]

    J. Long, E. Shelhamer, T. Darrell, Fully convolutional networks for semantic segmentation, in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 3431–3440

    2015

  55. [63]

    Stockman, D

    S. Stockman, D. Lawson, M. Werner, Earthquakenpp: Benchmark datasets for earthquake forecasting with neural point processes, 2024. URL: https://arxiv.org/abs/2410.08226. arXiv:2410.08226

    2024

  56. [64]

    Hajikhodaverdikhan, M

    P. Hajikhodaverdikhan, M. Nazari, M. Mohsenizadeh, S. Shamshir- band, K.-w. Chau, Earthquake prediction with meteorological data by particle filter-based support vector regression, Engineering Applica- tions of Computational Fluid Mechanics 12 (2018) 679–688

    2018

  57. [65]

    Ikram, U

    A. Ikram, U. Qamar, Developing an expert system based on association rules and predicate logic for earthquake prediction, Knowledge-Based Systems 75 (2015) 87–103

    2015

  58. [66]

    I. M. Murwantara, P. Yugopuspito, R. Hermawan, Comparison of ma- chine learning performance for earthquake prediction in indonesia using 102 30 years historical data, TELKOMNIKA (Telecommunication Com- puting Electronics and Control) 18 (2020) 1331–1342

    2020

  59. [67]

    Jeyaraman, M

    R. Jeyaraman, M. Senthil Kumar, N. Venkatanathan, Analysis of pre- earthquake signals using ann: implication for short-term earthquake forecasting, in: Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication: Proceedings of MDCWC 2020, Springe...

    2020

  60. [68]

    Z. Bao, J. Zhao, P. Huang, S. Yong, X. Wang, A deep learning-based electromagnetic signal for earthquake magnitude prediction, Sensors 21 (2021) 4434

    2021

  61. [69]

    E. I. Alves, Earthquake forecasting using neural networks: results and future work, Nonlinear Dynamics 44 (2006) 341–349

    2006

  62. [70]

    Berhich, F.-Z

    A. Berhich, F.-Z. Belouadha, M. I. Kabbaj, LSTM-based earthquake prediction: enhanced time feature and data representation, Interna- tional Journal of High Performance Systems Architecture 10 (2021) 1–11

    2021

  63. [71]

    Berhich, F.-Z

    A. Berhich, F.-Z. Belouadha, M. I. Kabbaj, An attention-based lstm network for large earthquake prediction, Soil Dynamics and Earth- quake Engineering 165 (2023) 107663

    2023

  64. [72]

    Bahrami, M

    B. Bahrami, M. Shafiee, Fuzzy descriptor models for earthquake time prediction using seismic time series, International Journal of Uncer- tainty, Fuzziness and Knowledge-Based Systems 23 (2015) 505–519

    2015

  65. [74]

    Picozzi, A

    M. Picozzi, A. G. Iaccarino, Forecasting the preparatory phase of induced earthquakes by recurrent neural network, Forecasting 3 (2021) 17–36

    2021

  66. [75]

    Wijaya, A

    U. Wijaya, A. H. Muhammad, et al., Indonesian seismic mitigation using earthquake predicted artificial intelligence model, in: 2022 5th International Conference on Information and Communications Tech- nology (ICOIACT), IEEE, 2022, pp. 349–354. 103

    2022

  67. [76]

    J. Chen, H. Tang, W. Chen, Deep learning of the aftershock hysteresis effect based on the elastic dislocation theory, Natural Hazards and Earth System Sciences 20 (2020) 3117–3134

    2020

  68. [77]

    Karimzadeh, M

    S. Karimzadeh, M. Matsuoka, J. Kuang, L. Ge, Spatial prediction of aftershocks triggered by a major earthquake: A binary machine learning perspective, ISPRS International Journal of Geo-Information 8 (2019) 462

    2019

  69. [78]

    G.C.Fox, J.B.Rundle, A.Donnellan, B.Feng, Earthquakenowcasting with deep learning, Geohazards 3 (2022) 199–226

    2022

  70. [79]

    R. Kail, E. Burnaev, A. Zaytsev, Recurrent convolutional neural net- works help to predict location of earthquakes, IEEE Geoscience and Remote Sensing Letters 19 (2021) 1–5

    2021

  71. [80]

    Zhang, Y

    Z. Zhang, Y. Wang, A spatiotemporal model for global earthquake pre- diction based on convolutional lstm, IEEE Transactions on Geoscience and Remote Sensing (2023)

    2023

  72. [81]

    Buscema, G

    P. Buscema, G. Massini, G. Maurelli, Artificial adaptive systems to predict the magnitude of earthquakes., Bollettino di Geofisica Teorica ed Applicata 56 (2015)

    2015

  73. [82]

    Narayanakumar, K

    S. Narayanakumar, K. Raja, A bp artificial neural network model for earthquake magnitude prediction in himalayas, india, Circuits and Systems 7 (2016) 3456–3468

    2016

  74. [83]

    W. Y. Mario Maya, L. Telesca, Multi-step forecasting of earthquake magnitude using meta-learning based neural networks, Cybernetics and Systems 53 (2022) 563–580. URL: https://doi.org/10.1080/ 01969722.2021.1989170. doi: 10.1080/01969722.2021.1989170. arXiv:https://doi.org/10....

    2022

  75. [84]

    E. Amar, T. Khattab, F. Zada, Intelligent earthquake prediction sys- tem based on neural network, International Journal of Civil and Envi- ronmental Engineering 8 (2015) 874–878

    2015

  76. [85]

    Rojo Limón, , E

    G. Rojo Limón, , E. Kissling, G. Ouillon, D. Sornette, A consistent and high-quality M4+ earthquake catalogue for 104 Turkey 2007–2016 from two independent catalogues, Geophys- ical Journal International 225 (2021) 711–728. URL: https: //doi.org/10.1093/gji/ggaa616. doi: 10.10...

    2021 doi

  77. [86]

    C. G. Bufe, D. J. Varnes, Predictive modeling of the seis- mic cycle of the greater san francisco bay region, Journal of Geophysical Research: Solid Earth 98 (1993) 9871–9883. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/ 10.1029/93JB00357. doi: https://doi.org/10.1029...

    1993 doi

  78. [87]

    Sornette, C

    D. Sornette, C. G. Sammis, Complex critical exponents from renor- malization group theory of earthquakes: Implications for earthquake predictions, Journal de Physique I 5 (1995) 607–619

    1995

  79. [88]

    D. D. Bowman, G. Ouillon, C. G. Sammis, A. Sornette, D. Sornette, An observational test of the critical earthquake concept, Journal of Geophysical Research: Solid Earth 103 (1998) 24359–24372. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/ 10.1029/98JB00792. doi: https:...

    1998 doi

  80. [89]

    Gutenberg, C

    B. Gutenberg, C. F. Richter, Frequency of earthquakes in california, Bulletin of the Seismological society of America 34 (1944) 185–188

    1944

  81. [90]

    Kamer, S

    Y. Kamer, S. Hiemer, Data-driven spatial value estimation with applications to california seismicity: To b or not to b, Jour- nal of Geophysical Research: Solid Earth 120 (2015) 5191–5214. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/10. 1002/2014JB011510. doi: https:/...

    2015 doi

  82. [91]

    Bender, Maximum likelihood estimation of b values for magnitude grouped data, Bulletin of the Seismological Society of America 73 (1983) 831–851

    B. Bender, Maximum likelihood estimation of b values for magnitude grouped data, Bulletin of the Seismological Society of America 73 (1983) 831–851

    1983

  83. [92]

    Aki, Maximum likelihood estimate of b in the formula log n= a-bm and its confidence limits, Bull

    K. Aki, Maximum likelihood estimate of b in the formula log n= a-bm and its confidence limits, Bull. Earthquake Res. Inst., Tokyo Univ. 43 (1965) 237–239. 105

    1965

  84. [93]

    Pisarenko, A

    V. Pisarenko, A. Sornette, D. Sornette, M. Rodkin, New approach to the characterization of m max and of the tail of the distribution of earthquake magnitudes, Pure and Applied Geophysics 165 (2008) 847–888

    2008

  85. [94]

    V. F. Pisarenko, D. Sornette, M. V. Rodkin, Distribution of max- imum earthquake magnitudes in future time intervals: application to the seismicity of japan (1923–2007), Earth, planets and space 62 (2010) 567–578

    2010

  86. [95]

    Pisarenko, A

    V. Pisarenko, A. Sornette, D. Sornette, M. Rodkin, Characterization of the tail of the distribution of earthquake magnitudes by combining the gev and gpd descriptions of extreme value theory, Pure and Applied Geophysics 171 (2014) 1599–1624

    2014

  87. [96]

    M. V. Matthews, P. A. Reasenberg, Statistical methods for investi- gating quiescence and other temporal seismicity patterns, Pure and Applied Geophysics 126 (1988) 357–372

    1988

  88. [97]

    Spassov, C

    E. Spassov, C. Sinadinovski, K. McCue, Spatial and temporal variation of seismicity across australia, J. Balkan Geophys. Soc 5 (2002) 115–122

    2002

  89. [98]

    T. Utsu, Y. Ogata, et al., The centenary of the omori formula for a decay law of aftershock activity, Journal of Physics of the Earth 43 (1995) 1–33

    1995

  90. [99]

    T. C. Hanks, H. Kanamori, A moment magni- tude scale, Journal of Geophysical Research: Solid Earth 84 (1979) 2348–2350. URL: https://agupubs. onlinelibrary.wiley.com/doi/abs/10.1029/JB084iB05p02348. doi:https://doi.org/10.1029/JB084iB05p02348. arXiv:https://agupubs.onlinelibra...

    1979 doi

  91. [100]

    Kanamori, The energy release in great earthquakes, Journal of geophysical research 82 (1977) 2981–2987

    H. Kanamori, The energy release in great earthquakes, Journal of geophysical research 82 (1977) 2981–2987

    1977

  92. [101]

    Karimzadeh, M

    S. Karimzadeh, M. Matsuoka, J. Kuang, L. Ge, Spatial prediction of aftershocks triggered by a major earthquake: A binary machine learning perspective, ISPRS International Journal of Geo-Information 8 (2019) 462. 106

    2019

  93. [102]

    J. R. Henderson, D. J. Barton, G. R. Foulger, Fractal clus- tering of induced seismicity in The Geysers geothermal area, California, Geophysical Journal International 139 (1999) 317–324. URL: https://doi.org/10.1046/j.1365-246x. 1999.00939.x. doi: 10.1046/j.1365-246x.1999.0093...

    1999

  94. [103]

    I.Zaliapin, A.Gabrielov, V.Keilis-Borok, H.Wong, Clusteringanalysis of seismicity and aftershock identification, Phys. Rev. Lett. 101 (2008) 018501. URL: https://link.aps.org/doi/10.1103/PhysRevLett. 101.018501. doi:10.1103/PhysRevLett.101.018501

    2008 doi

  95. [104]

    Xiong, L

    P. Xiong, L. Tong, K. Zhang, X. Shen, R. Battiston, D. Ouzounov, R. Iuppa, D. Crookes, C. Long, H. Zhou, Towards advancing the earthquake forecasting by machine learning of satellite data, Science of The Total Environment 771 (2021) 145256. URL: https://www. sciencedirect.com/...

    2021

  96. [105]

    D. P. Bertsekas, Nonlinear programming, Jour- nal of the Operational Research Society 48 (1997) 334–334. URL: https://doi.org/10.1057/palgrave. jors.2600425. doi: 10.1057/palgrave.jors.2600425. arXiv:https://doi.org/10.1057/palgrave.jors.2600425

    1997 doi

  97. [106]

    T.-Y. Lin, P. Goyal, R. Girshick, K. He, P. Dollar, Focal loss for dense object detection, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017

    2017

  98. [107]

    Zhang, Y

    Z. Zhang, Y. Wang, A spatiotemporal model for global earthquake prediction based on convolutional lstm, IEEE Transactions on Geo- science and Remote Sensing 61 (2023) 1–12. doi:10.1109/TGRS.2023. 3302316

    2023 doi

  99. [108]

    J. Liu, T. Zhang, C. Gao, P. Wang, Forecasting earthquake magnitude and epicenter by incorporating spatiotemporal priors into deep neural networks, IEEE Transactions on Geoscience and Remote Sensing 61 (2023) 1–13. doi:10.1109/TGRS.2023.3281784. 107

    2023

  100. [109]

    Zhang, C

    Y. Zhang, C. Zhan, Q. Huang, D. Sornette, Seismically informed refer- ence models enhance AI-based earthquake prediction systems, Journal of Geophysical Research: Solid Earth 129 (2024) e2023JB028037. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/10. 1029/2023JB028037. ...

    2024 doi

  101. [110]

    Helmstetter, D

    A. Helmstetter, D. Sornette, Predictability in the ETAS Model of Interacting Triggered Seismicity, J. Geophys. Res. Solid Earth 108 (2003) 10.1029/2003JB002485

    2003 doi

  102. [111]

    Y.Malevergne, D.Sornette, ExtremeFinancialRisks(fromdependence to risk management, Springer, HeidelbergK, 2006

    2006

  103. [113]

    J. B. Rundle, D. L. Turcotte, A. Donnellan, L. Grant Lud- wig, M. Luginbuhl, G. Gong, Nowcasting earthquakes, Earth and Space Science 3 (2016) 480–486. URL: https: //agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/ 2016EA000185. doi: https://doi.org/10.1002/2016EA000185. arXiv...

    2016 doi

  104. [114]

    Helmstetter, Y

    A. Helmstetter, Y. Y. Kagan, D. D. Jackson, Compari- son of Short-Term and Time-Independent Earthquake Fore- cast Models for Southern California, Bulletin of the Seis- mological Society of America 96 (2006) 90–106. URL: https://doi.org/10.1785/0120050067. doi: 10.1785/01200500...

    2006 doi

  105. [115]

    Parsons, On the use of receiver operating characteris- tic tests for evaluating spatial earthquake forecasts, Geo- physical Research Letters 47 (2020) e2020GL088570

    T. Parsons, On the use of receiver operating characteris- tic tests for evaluating spatial earthquake forecasts, Geo- physical Research Letters 47 (2020) e2020GL088570. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/ 2020GL088570. doi: https://doi.org/10.1029/202...

    2020 doi

  106. [116]

    Zhang, S

    Y. Zhang, S. Didier, Q. Meng, A new 3-d error diagram for a more balanced assessment of binary alarms for predicting earthquakes: Ap- plication to tir anomalies in sichuan area, china, IEEE Transactions on Geoscience and Remote Sensing 61 (2023) 1–11. doi:10.1109/TGRS. 2023.3293132

    2023

  107. [117]

    Zhuang, Gambling scores for earthquake predictions and forecasts, Geophysical Journal International 181 (2010) 382–390

    J. Zhuang, Gambling scores for earthquake predictions and forecasts, Geophysical Journal International 181 (2010) 382–390. URL: https://doi.org/10.1111/j.1365-246X. 2010.04496.x. doi: 10.1111/j.1365-246X.2010.04496.x. arXiv:https://academic.oup.com/gji/article-pdf/181/1/382/59...

    2010 arXiv

  108. [118]

    S. Wu, Q. Huang, L. Zhao, Instantaneous inversion of air- borne electromagnetic data based on deep learning, Geo- physical Research Letters 49 (2022) e2021GL097165. URL: https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/ 2021GL097165. doi: https://doi.org/10.1029/2021GL0...

    2022 doi

  109. [119]

    Molchan, Strategies in strong earthquake prediction, Physics of the Earth and Planetary Interiors 61 (1990) 84–98

    G. Molchan, Strategies in strong earthquake prediction, Physics of the Earth and Planetary Interiors 61 (1990) 84–98. URL:https://www. sciencedirect.com/science/article/pii/003192019090097H. doi:https://doi.org/10.1016/0031-9201(90)90097-H

    1990

  110. [120]

    G. M. Molchan, Structure of optimal strategies in earthquake pre- diction, Tectonophysics 193 (1991) 267–276. URL: https://www. sciencedirect.com/science/article/pii/004019519190336Q. doi:https://doi.org/10.1016/0040-1951(91)90336-Q, earthquake prediction

    1991

  111. [121]

    J. D. Zechar, J. Zhuang, A parimutuel gambling perspec- tive to compare probabilistic seismicity forecasts, Geophys- ical Journal International 199 (2014) 60–68. URL: https: //doi.org/10.1093/gji/ggu137. doi: 10.1093/gji/ggu137. arXiv:https://academic.oup.com/gji/article-pdf/1...

    2014 doi

  112. [122]

    Kamer, S

    Y. Kamer, S. Nandan, G. Ouillon, S. Hiemer, D. Sornette, Democ- ratizing earthquake predictability research: introducing the RichterX platform, The European Physical Journal Special Topics 230 (2021) 451–471

    2021

  113. [123]

    Y. Y. Kagan, Short-term properties of earthquake cata- logs and models of earthquake source, Bulletin of the Seis- mological Society of America 94 (2004) 1207–1228. URL: https://doi.org/10.1785/012003098. doi: 10.1785/012003098. arXiv:https://pubs.geoscienceworld.org/ssa/bssa/...

    2004 doi

  114. [124]

    Zhang, S

    H. Zhang, S. Ke, W. Liu, Y. Zhang, A combining earth- quake forecasting model between deep learning and epidemic- type aftershock sequence (etas) model, Geophysical Jour- nal International 239 (2024) 1545–1556. URL: https: //doi.org/10.1093/gji/ggae349. doi: 10.1093/gji/ggae34...

    2024 doi

  115. [125]

    Mancini, M

    S. Mancini, M. Segou, M. J. Werner, T. Parsons, G. Beroza, L. Chiaraluce, On the use of high-resolution and deep-learning seismic catalogs for short-term earthquake forecasts: Poten- tial benefits and current limitations, Journal of Geophys- ical Research: Solid Earth 127 (202...

    2022 doi

  116. [126]

    G. C. Beroza, M. Segou, S. Mostafa Mousavi, Machine learning and earthquake forecasting—next steps, Nature communications 12 (2021) 4761

    2021

  117. [127]

    M.Raissi, G.E.Karniadakis, Hiddenphysicsmodels: Machinelearning of nonlinear partial differential equations, Journal of Computational Physics 357 (2018) 125–141

    2018

  118. [128]

    Raissi, P

    M. Raissi, P. Perdikaris, G. E. Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, Journal of Computational physics 378 (2019) 686–707. 110

    2019

  119. [129]

    Häfner, J

    D. Häfner, J. Gemmrich, M. Jochum, Machine-guided discovery of a real-world rogue wave model, Proceedings of the National Academy of Sciences 120 (2023) e2306275120. URL:https://www.pnas.org/doi/ abs/10.1073/pnas.2306275120. doi: 10.1073/pnas.2306275120. arXiv:https://www.pnas...

    2023 doi

  120. [130]

    E. S. Cochran, J. E. Vidale, S. Tanaka, Earth tides can trigger shallow thrust fault earthquakes, Science 306 (2004) 1164–1166. 111 Figure 3: Number of times different types of inputs have been used in published papers for different outputs. The histogram on the right shows th...

    2004

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