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Felipe Santibañez-Leal edited this page Jun 17, 2026 · 1 revision

References — consolidated bibliography

The complete, deduplicated bibliography for CAOS_SEISMIC. Every per-topic sub-page carries its own References section; this page collects them in one place, removes duplicates, and groups them by subject. All sources are canonical, peer-reviewed literature or official USGS / ISC / CSEP documentation. DOIs are given wherever registered; where a work is a preprint or conference paper, the arXiv / proceedings identifier is given instead.

How to read this list. Citations are grouped by theme. Many works are foundational across several pages (for example the ICEF report, Ogata 1988/1998, and Wiemer & Wyss 2000 recur throughout) — each appears once here, in its most natural group. The per-page References sections remain the authoritative local context for each equation.

Foundational framing — predictability and operational forecasting

  1. Jordan, T. H., Chen, Y.-T., Gasparini, P., Madariaga, R., Main, I., Marzocchi, W., Papadopoulos, G., Sobolev, G., Yamaoka, K. & Zschau, J. (2011). Operational Earthquake Forecasting: State of Knowledge and Guidelines for Utilization (ICEF Report). Annals of Geophysics 54(4), 315–391. doi:10.4401/ag-5350
  2. Geller, R. J., Jackson, D. D., Kagan, Y. Y. & Mulargia, F. (1997). Earthquakes cannot be predicted. Science 275(5306), 1616–1617. doi:10.1126/science.275.5306.1616
  3. Bak, P. & Tang, C. (1989). Earthquakes as a self-organized critical phenomenon. J. Geophys. Res. 94(B11), 15635–15637. doi:10.1029/JB094iB11p15635
  4. Mizrahi, L., Dallo, I., van der Elst, N. J., Christophersen, A., Spassiani, I., Werner, M. J., et al. (2024). Developing, Testing, and Communicating Earthquake Forecasts: Current Practices and Future Directions. Reviews of Geophysics 62. doi:10.1029/2023RG000823
  5. Spassiani, I., Falcone, G., Murru, M. & Marzocchi, W. (2023). Operational Earthquake Forecasting in Italy: validation after 10 yr of operativity. Geophys. J. Int. 234(3), 2501–2518. doi:10.1093/gji/ggad256
  6. Schneider, M., Marzocchi, W., et al. (2022). Bridging the gap between earthquake forecasts and uncertainty communication. Nat. Hazards Earth Syst. Sci. 22(4), 1499–1518. doi:10.5194/nhess-22-1499-2022

Magnitude–frequency and catalog completeness

  1. Gutenberg, B. & Richter, C. F. (1944). Frequency of earthquakes in California. Bull. Seismol. Soc. Am. 34(4), 185–188.
  2. Aki, K. (1965). Maximum likelihood estimate of $b$ in the formula $\log N = a - bM$ and its confidence limits. Bull. Earthq. Res. Inst. 43, 237–239.
  3. Utsu, T. (1965). A method for determining the value of $b$ in a formula $\log n = a - bM$. Geophys. Bull. Hokkaido Univ. 13, 99–103.
  4. Tinti, S. & Mulargia, F. (1987). Confidence intervals of $b$ values for grouped magnitudes. Bull. Seismol. Soc. Am. 77(6), 2125–2134.
  5. Shi, Y. & Bolt, B. A. (1982). The standard error of the magnitude–frequency $b$ value. Bull. Seismol. Soc. Am. 72(5), 1677–1687.
  6. Wiemer, S. & Wyss, M. (2000). Minimum magnitude of completeness in earthquake catalogs: examples from Alaska, the western United States, and Japan. Bull. Seismol. Soc. Am. 90(4), 859–869. doi:10.1785/0119990114
  7. Woessner, J. & Wiemer, S. (2005). Assessing the quality of earthquake catalogues: estimating the magnitude of completeness and its uncertainty. Bull. Seismol. Soc. Am. 95(2), 684–698. doi:10.1785/0120040007
  8. Kagan, Y. Y. (2002). Seismic moment distribution revisited: I. Magnitude distribution. Geophys. J. Int. 148(3), 520–541. doi:10.1046/j.1365-246x.2002.01594.x

Aftershock decay, clustering and ETAS

  1. Omori, F. (1894). On the aftershocks of earthquakes. J. Coll. Sci. Imp. Univ. Tokyo 7, 111–200.
  2. Utsu, T. (1961). A statistical study on the occurrence of aftershocks. Geophys. Mag. 30, 521–605.
  3. Utsu, T., Ogata, Y. & Matsu'ura, R. S. (1995). The centenary of the Omori formula for a decay law of aftershock activity. J. Phys. Earth 43(1), 1–33. doi:10.4294/jpe1952.43.1
  4. Ogata, Y. (1983). Estimation of the parameters in the modified Omori formula for aftershock frequencies by the maximum likelihood procedure. J. Phys. Earth 31(2), 115–124. doi:10.4294/jpe1952.31.115
  5. Shcherbakov, R., Turcotte, D. L. & Rundle, J. B. (2004). A generalized Omori's law for earthquake aftershock decay. Geophys. Res. Lett. 31, L11613. doi:10.1029/2004GL019808
  6. Ogata, Y. (1988). Statistical models for earthquake occurrences and residual analysis for point processes. J. Am. Stat. Assoc. 83(401), 9–27. doi:10.1080/01621459.1988.10478560
  7. Ogata, Y. (1998). Space–time point-process models for earthquake occurrences. Ann. Inst. Statist. Math. 50(2), 379–402. doi:10.1023/A:1003403601725
  8. Zhuang, J., Ogata, Y. & Vere-Jones, D. (2002). Stochastic declustering of space–time earthquake occurrences. J. Am. Stat. Assoc. 97(458), 369–380. doi:10.1198/016214502760046925

Operational aftershock and short-term hybrid models

  1. Reasenberg, P. A. & Jones, L. M. (1989). Earthquake hazard after a mainshock in California. Science 243(4895), 1173–1176. doi:10.1126/science.243.4895.1173
  2. Reasenberg, P. A. & Jones, L. M. (1994). Earthquake aftershocks: update. Science 265(5176), 1251–1252. doi:10.1126/science.265.5176.1251
  3. Gerstenberger, M. C., Wiemer, S., Jones, L. M. & Reasenberg, P. A. (2005). Real-time forecasts of tomorrow's earthquakes in California (STEP). Nature 435, 328–331. doi:10.1038/nature03622
  4. Page, M. T., van der Elst, N., Hardebeck, J., Felzer, K. & Michael, A. J. (2016). Three ingredients for improved global aftershock forecasts: tectonic region, time-dependent catalog incompleteness, and intersequence variability. Bull. Seismol. Soc. Am. 106(5), 2290–2301. doi:10.1785/0120160073

Medium-term and smoothed-seismicity background models

  1. Rhoades, D. A. & Evison, F. F. (2004). Long-range earthquake forecasting with every earthquake a precursor according to scale. Pure Appl. Geophys. 161(1), 47–72. doi:10.1007/s00024-003-2434-9
  2. Evison, F. F. & Rhoades, D. A. (2004). Demarcation and scaling of long-term seismogenesis. Pure Appl. Geophys. 161(1), 21–45. doi:10.1007/s00024-003-2433-x
  3. Rhoades, D. A. & Evison, F. F. (2006). The EEPAS forecasting model and the probability of moderate-to-large earthquakes in central Japan. Tectonophysics 417(1–2), 119–140. doi:10.1016/j.tecto.2005.05.051
  4. Rhoades, D. A. (2007). Application of the EEPAS model to forecasting earthquakes of moderate magnitude in Southern California. Seismol. Res. Lett. 78(1), 110–115. doi:10.1785/gssrl.78.1.110
  5. Helmstetter, A., Kagan, Y. Y. & Jackson, D. D. (2007). High-resolution time-independent grid-based forecast for $M \ge 5$ earthquakes in California. Seismol. Res. Lett. 78(1), 78–86. doi:10.1785/gssrl.78.1.78
  6. Werner, M. J., Helmstetter, A., Jackson, D. D. & Kagan, Y. Y. (2011). High-resolution long-term and short-term earthquake forecasts for California. Bull. Seismol. Soc. Am. 101(4), 1630–1648. doi:10.1785/0120090340
  7. Kagan, Y. Y. (2017). Worldwide earthquake forecasts. Geophys. J. Int. 211(1), 335–345. doi:10.1093/gji/ggx300

Renewal, recurrence and long-term hazard

  1. Reid, H. F. (1910). The Mechanics of the Earthquake (The California Earthquake of April 18, 1906, Report of the State Earthquake Investigation Commission, Vol. 2). Carnegie Institution of Washington.
  2. Schwartz, D. P. & Coppersmith, K. J. (1984). Fault behavior and characteristic earthquakes: examples from the Wasatch and San Andreas fault zones. J. Geophys. Res. 89(B7), 5681–5698. doi:10.1029/JB089iB07p05681
  3. Ellsworth, W. L., Matthews, M. V., Nadeau, R. M., Nishenko, S. P., Reasenberg, P. A. & Simpson, R. W. (1999). A physically-based earthquake recurrence model for estimation of long-term earthquake probabilities. U.S. Geological Survey Open-File Report 99-522.
  4. Matthews, M. V., Ellsworth, W. L. & Reasenberg, P. A. (2002). A Brownian model for recurrent earthquakes. Bull. Seismol. Soc. Am. 92(6), 2233–2250. doi:10.1785/0120010267
  5. Working Group on California Earthquake Probabilities (2003). Earthquake probabilities in the San Francisco Bay Region: 2002–2031. U.S. Geological Survey Open-File Report 03-214.
  6. Field, E. H., Biasi, G. P., Bird, P., Dawson, T. E., Felzer, K. R., Jackson, D. D., et al. (2015). Long-term, time-dependent probabilities for the third Uniform California Earthquake Rupture Forecast (UCERF3). Bull. Seismol. Soc. Am. 105(2A), 511–543. doi:10.1785/0120140093

Rate-and-state friction, Coulomb stress and tidal triggering

  1. Dieterich, J. H. (1979). Modeling of rock friction: 1. Experimental results and constitutive equations. J. Geophys. Res. 84(B5), 2161–2168. doi:10.1029/JB084iB05p02161
  2. Ruina, A. (1983). Slip instability and state variable friction laws. J. Geophys. Res. 88(B12), 10359–10370. doi:10.1029/JB088iB12p10359
  3. Dieterich, J. (1994). A constitutive law for rate of earthquake production and its application to earthquake clustering. J. Geophys. Res. 99(B2), 2601–2618. doi:10.1029/93JB02581
  4. King, G. C. P., Stein, R. S. & Lin, J. (1994). Static stress changes and the triggering of earthquakes. Bull. Seismol. Soc. Am. 84(3), 935–953. doi:10.1785/BSSA0840030935
  5. Stein, R. S. (1999). The role of stress transfer in earthquake occurrence. Nature 402, 605–609. doi:10.1038/45144
  6. Heimisson, E. R. & Segall, P. (2018). Constitutive law for earthquake production based on rate-and-state friction: Dieterich 1994 revisited. J. Geophys. Res. Solid Earth 123(5), 4141–4156. doi:10.1029/2018JB015656
  7. Toda, S., Stein, R. S., Sevilgen, V. & Lin, J. (2011). Coulomb 3.3 graphic-rich deformation and stress-change software. U.S. Geological Survey Open-File Report 2011-1060.
  8. Beeler, N. M. & Lockner, D. A. (2003). Why earthquakes correlate weakly with the solid Earth tides: effects of periodic stress on the rate and probability of earthquake occurrence. J. Geophys. Res. Solid Earth 108(B8), 2391. doi:10.1029/2001JB001518
  9. Tanaka, S., Ohtake, M. & Sato, H. (2002). Evidence for tidal triggering of earthquakes. J. Geophys. Res. Solid Earth 107(B10), 2211. doi:10.1029/2001JB001577
  10. Cochran, E. S., Vidale, J. E. & Tanaka, S. (2004). Earth tides can trigger shallow thrust fault earthquakes. Science 306(5699), 1164–1166. doi:10.1126/science.1103961
  11. Métivier, L., de Viron, O., Conrad, C. P., Renault, S., Diament, M. & Patau, G. (2009). Evidence of earthquake triggering by the solid earth tides. Earth Planet. Sci. Lett. 278, 370–375. doi:10.1016/j.epsl.2008.12.024
  12. Rubinstein, J. L., La Rocca, M., Vidale, J. E., Creager, K. C. & Wech, A. G. (2008). Tidal modulation of nonvolcanic tremor. Science 319, 186–189. doi:10.1126/science.1150558
  13. Houston, H. (2015). Low friction and fault weakening revealed by rising sensitivity of tremor to tidal stress. Nat. Geosci. 8, 409–415. doi:10.1038/ngeo2419
  14. Ide, S., Yabe, S. & Tanaka, Y. (2016). Earthquake potential revealed by tidal influence on earthquake size–frequency statistics. Nat. Geosci. 9, 834–837. doi:10.1038/ngeo2796
  15. van der Elst, N. J., Delorey, A. A., Shelly, D. R. & Johnson, P. A. (2016). Fortnightly modulation of San Andreas tremor and low-frequency earthquakes. Proc. Natl. Acad. Sci. 113(31), 8601–8606. doi:10.1073/pnas.1524316113
  16. Scholz, C. H., Tan, Y. J. & Albino, F. (2019). The mechanism of tidal triggering of earthquakes at mid-ocean ridges. Nat. Commun. 10, 2526. doi:10.1038/s41467-019-10605-2

Point-process theory and neural temporal point processes

  1. Hawkes, A. G. (1971). Spectra of some self-exciting and mutually exciting point processes. Biometrika 58(1), 83–90. doi:10.1093/biomet/58.1.83
  2. Daley, D. J. & Vere-Jones, D. (2003). An Introduction to the Theory of Point Processes, Vol. I: Elementary Theory and Methods (2nd ed.). Springer. doi:10.1007/b97277
  3. Ogata, Y. (1981). On Lewis' simulation method for point processes (the thinning algorithm). IEEE Trans. Inf. Theory 27(1), 23–31. doi:10.1109/TIT.1981.1056305
  4. Schoenberg, F. P. (2003). Multidimensional residual analysis of point process models for earthquake occurrences. J. Am. Stat. Assoc. 98(464), 789–795. doi:10.1198/016214503000000710
  5. Reinhart, A. (2018). A review of self-exciting spatio-temporal point processes and their applications. Statistical Science 33(3), 299–318. doi:10.1214/17-STS629
  6. Rasmussen, J. G. (2018). Lectures on the Poisson Process and temporal point processes. arXiv:1806.00221
  7. Du, N., Dai, H., Trivedi, R., Upadhyay, U., Gomez-Rodriguez, M. & Song, L. (2016). Recurrent Marked Temporal Point Processes: Embedding Event History to Vector. KDD 2016, 1555–1564. doi:10.1145/2939672.2939875
  8. Mei, H. & Eisner, J. (2017). The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process. NeurIPS 2017. arXiv:1612.09328
  9. Zuo, S., Jiang, H., Li, Z., Zhao, T. & Zha, H. (2020). Transformer Hawkes Process. ICML 2020, PMLR v119, 11692–11702. proceedings.mlr.press/v119/zuo20a.html
  10. Zhang, Q., Lipani, A., Kirnap, O. & Yilmaz, E. (2020). Self-Attentive Hawkes Process. ICML 2020, PMLR v119. arXiv:1907.07561
  11. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł. & Polosukhin, I. (2017). Attention Is All You Need. NeurIPS 2017. arXiv:1706.03762
  12. Shchur, O., Türkmen, A. C., Januschowski, T. & Günnemann, S. (2021). Neural Temporal Point Processes: A Review. IJCAI 2021 (Survey Track). arXiv:2104.03528
  13. Zaheer, M., Kottur, S., Ravanbakhsh, S., Póczos, B., Salakhutdinov, R. & Smola, A. (2017). Deep Sets. NeurIPS 2017. arXiv:1703.06114

Neural / deep-learning earthquake forecasting and the benchmark evidence

  1. DeVries, P. M. R., Viegas, F., Wattenberg, M. & Meade, B. J. (2018). Deep learning of aftershock patterns following large earthquakes. Nature 560, 632–634. doi:10.1038/s41586-018-0438-y
  2. Mignan, A. & Broccardo, M. (2019). One neuron versus deep learning in aftershock prediction. Nature 575, E1–E3. doi:10.1038/s41586-019-1582-8
  3. DeVries, P. M. R., Viegas, F., Wattenberg, M. & Meade, B. J. (2019). Reply to: One neuron versus deep learning in aftershock prediction. Nature 575, E4–E5. doi:10.1038/s41586-019-1583-7
  4. Mignan, A. & Broccardo, M. (2020). Neural network applications in earthquake prediction (1994–2019): meta-analytic and statistical insights on their limitations. Seismol. Res. Lett. 91(4), 2330–2342. doi:10.1785/0220200021
  5. Zlydenko, O., Elidan, G., Hassidim, A., Kukliansky, D., Matias, Y., Meade, B., Molchanov, A., Nevo, A. & Bar-Sinai, Y. (2023). A neural encoder for earthquake rate forecasting (FERN). Sci. Rep. 13, 12350. doi:10.1038/s41598-023-38033-9
  6. Dascher-Cousineau, K., Shchur, O., Brodsky, E. E. & Günnemann, S. (2023). Using deep learning for flexible and scalable earthquake forecasting (RECAST). Geophys. Res. Lett. 50, e2023GL103909. doi:10.1029/2023GL103909
  7. Schultz, R. (2026). Forecasting the Rate of Induced Seismicity as a Neural Temporal Point Process. JGR: Machine Learning and Computation. doi:10.1029/2025JH001052
  8. Stockman, S., Lawson, D. & Werner, M. J. (2026, accepted). EarthquakeNPP: A Benchmark for Earthquake Forecasting with Neural Point Processes. Transactions on Machine Learning Research (TMLR). arXiv:2410.08226

General deep-learning architectures and deep-learning seismology

  1. Hochreiter, S. & Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation 9(8), 1735–1780. doi:10.1162/neco.1997.9.8.1735
  2. Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H. & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. EMNLP 2014. arXiv:1406.1078
  3. Kipf, T. N. & Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. ICLR 2017. arXiv:1609.02907
  4. Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E. (2017). Neural Message Passing for Quantum Chemistry. ICML 2017. arXiv:1704.01212
  5. Mousavi, S. M. & Beroza, G. C. (2022). Deep-learning seismology. Science 377, eabm4470. doi:10.1126/science.abm4470
  6. McBrearty, I. W. & Beroza, G. C. (2023). Earthquake phase association with graph neural networks. Bull. Seismol. Soc. Am. 113(2), 524–547. doi:10.1785/0120220182

Detection, phase-picking and waveform foundation models

  1. Zhu, W. & Beroza, G. C. (2019). PhaseNet: a deep-neural-network-based seismic arrival-time picking method. Geophys. J. Int. 216(1), 261–273. doi:10.1093/gji/ggy423
  2. Mousavi, S. M., Ellsworth, W. L., Zhu, W., Chuang, L. Y. & Beroza, G. C. (2020). Earthquake Transformer — an attentive deep-learning model for simultaneous earthquake detection and phase picking. Nat. Commun. 11, 3952. doi:10.1038/s41467-020-17591-w
  3. Woollam, J., Münchmeyer, J., Tilmann, F., Rietbrock, A., Lange, D., Bornstein, T., Diehl, T., Giunchi, C., Haslinger, F., Jozinović, D., Michelini, A., Saul, J. & Soto, H. (2022). SeisBench — A Toolbox for Machine Learning in Seismology. Seismol. Res. Lett. 93(3), 1695–1709. doi:10.1785/0220210324
  4. Sun, H., Ross, Z. E., Zhu, W. & Azizzadenesheli, K. (2023). Phase Neural Operator for Multi-Station Picking of Seismic Arrivals (PhaseNO). Geophys. Res. Lett. 50, e2023GL106434. doi:10.1029/2023GL106434
  5. Liu, T., Münchmeyer, J., Laurenti, L., Marone, C., de Hoop, M. V. & Dokmanić, I. (2024). SeisLM: a Foundation Model for Seismic Waveforms. arXiv:2410.15765

Declustering and clustering analysis

  1. Gardner, J. K. & Knopoff, L. (1974). Is the sequence of earthquakes in Southern California, with aftershocks removed, Poissonian? Bull. Seismol. Soc. Am. 64(5), 1363–1367.
  2. Baiesi, M. & Paczuski, M. (2004). Scale-free networks of earthquakes and aftershocks. Phys. Rev. E 69, 066106. doi:10.1103/PhysRevE.69.066106
  3. Zaliapin, I., Gabrielov, A., Keilis-Borok, V. & Wong, H. (2008). Clustering analysis of seismicity and aftershock identification. Phys. Rev. Lett. 101, 018501. doi:10.1103/PhysRevLett.101.018501
  4. Zaliapin, I. & Ben-Zion, Y. (2020). Earthquake declustering using the nearest-neighbor approach in space-time-magnitude domain. J. Geophys. Res. Solid Earth 125, e2018JB017120. doi:10.1029/2018JB017120

Forecast evaluation, scoring rules and CSEP

  1. Brier, G. W. (1950). Verification of forecasts expressed in terms of probability. Monthly Weather Review 78(1), 1–3. doi:10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2
  2. Murphy, A. H. (1973). A new vector partition of the probability score. J. Appl. Meteorol. 12(4), 595–600. doi:10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2
  3. Bradley, A. P. (1997). The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition 30(7), 1145–1159. doi:10.1016/S0031-3203(96)00142-2
  4. Gneiting, T. & Raftery, A. E. (2007). Strictly Proper Scoring Rules, Prediction, and Estimation. J. Am. Stat. Assoc. 102(477), 359–378. doi:10.1198/016214506000001437
  5. Field, E. H. (2007). Overview of the Working Group for the Development of Regional Earthquake Likelihood Models (RELM). Seismol. Res. Lett. 78(1), 7–16. doi:10.1785/gssrl.78.1.7
  6. Schorlemmer, D., Gerstenberger, M. C., Wiemer, S., Jackson, D. D. & Rhoades, D. A. (2007). Earthquake Likelihood Model Testing. Seismol. Res. Lett. 78(1), 17–29. doi:10.1785/gssrl.78.1.17
  7. Schorlemmer, D. & Gerstenberger, M. C. (2007). RELM Testing Center. Seismol. Res. Lett. 78(1), 30–36. doi:10.1785/gssrl.78.1.30
  8. Zechar, J. D. & Jordan, T. H. (2008). Testing alarm-based earthquake predictions. Geophys. J. Int. 172(2), 715–724. doi:10.1111/j.1365-246X.2007.03676.x
  9. Zechar, J. D., Gerstenberger, M. C. & Rhoades, D. A. (2010). Likelihood-based tests for evaluating space–rate–magnitude earthquake forecasts. Bull. Seismol. Soc. Am. 100(3), 1184–1195. doi:10.1785/0120090192
  10. Zechar, J. D. & Jordan, T. H. (2010). The Area Skill Score Statistic for Evaluating Earthquake Predictability Experiments. Pure Appl. Geophys. 167, 893–906. doi:10.1007/s00024-010-0086-0
  11. Rhoades, D. A., Schorlemmer, D., Gerstenberger, M. C., Christophersen, A., Zechar, J. D. & Imoto, M. (2011). Efficient testing of earthquake forecasting models. Acta Geophysica 59(4), 728–747. doi:10.2478/s11600-011-0013-5
  12. Zechar, J. D., Schorlemmer, D., Werner, M. J., Gerstenberger, M. C., Rhoades, D. A. & Jordan, T. H. (2013). Regional Earthquake Likelihood Models I: First-order results. Bull. Seismol. Soc. Am. 103(2A), 787–798. doi:10.1785/0120120186
  13. Kagan, Y. Y. (2017). Earthquake number forecasts testing. Geophys. J. Int. 211(1), 335–345. doi:10.1093/gji/ggx300
  14. Savran, W. H., Werner, M. J., Marzocchi, W., Rhoades, D. A., Jackson, D. D., Milner, K., Field, E. & Michael, A. (2020). Pseudoprospective Evaluation of UCERF3-ETAS Forecasts during the 2019 Ridgecrest Sequence. Bull. Seismol. Soc. Am. 110(4), 1799–1817. doi:10.1785/0120200026
  15. Savran, W. H., Bayona, J. A., Iturrieta, P., Bayliss, K., Werner, M. J., et al. (2022). pyCSEP: A Python Toolkit for Earthquake Forecast Developers. Seismol. Res. Lett. 93(5), 2858–2870. doi:10.1785/0220220033; JOSS doi:10.21105/joss.03658
  16. Serafini, F., Bayona, J. A., Silva, F., Savran, W., Stockman, S., Maechling, P. J. & Werner, M. J. (2025). A benchmark database of ten years of prospective next-day earthquake forecasts in California from CSEP. Sci. Data 12, 1501. doi:10.1038/s41597-025-05766-3

Catalogs, geophysical models and geodesy

  1. Dziewonski, A. M., Chou, T.-A. & Woodhouse, J. H. (1981). Determination of earthquake source parameters from waveform data for studies of global and regional seismicity. J. Geophys. Res. 86, 2825–2852. doi:10.1029/JB086iB04p02825
  2. Ekström, G., Nettles, M. & Dziewoński, A. M. (2012). The global CMT project 2004–2010: centroid-moment tensors for 13,017 earthquakes. Phys. Earth Planet. Inter. 200–201, 1–9. doi:10.1016/j.pepi.2012.04.002
  3. Di Giacomo, D., Engdahl, E. R., Storchak, D. A., et al. ISC-GEM Global Instrumental Earthquake Catalogue (v12.1). International Seismological Centre. doi:10.31905/d808b825
  4. Bird, P. (2003). An updated digital model of plate boundaries (PB2002). Geochem. Geophys. Geosyst. 4(3), 1027. doi:10.1029/2001GC000252
  5. Hayes, G. P., Moore, G. L., Portner, D. E., Hearne, M., Flamme, H., Furtney, M. & Smoczyk, G. M. (2018). Slab2, a comprehensive subduction zone geometry model. Science 362, 58–61. doi:10.1126/science.aat4723
  6. Blewitt, G., Hammond, W. C. & Kreemer, C. (2016). MIDAS robust trend estimator for accurate GPS station velocities without step detection. J. Geophys. Res. Solid Earth 121. doi:10.1002/2015JB012552

Official documentation and data services

  • CSEP / pyCSEP — Collaboratory for the Study of Earthquake Predictability. cseptesting.org · theory: docs.cseptesting.org/getting_started/theory.html · code: github.com/SCECcode/pycsep
  • USGS Operational Aftershock Forecasting (OAF) — earthquake.usgs.gov/data/oaf/
  • USGS FDSN-event web service — earthquake.usgs.gov/fdsnws/event/1/
  • ISC web services — isc.ac.uk/iscbulletin/search/webservices/catalogue/
  • ObsPy FDSN client — docs.obspy.org/packages/obspy.clients.fdsn.html
  • GeoNet (New Zealand) FDSN — geonet.org.nz/data/access/FDSN
  • EMSC SeismicPortal web services — seismicportal.eu/webservices.html
  • USGS Slab2 — earthquake.usgs.gov/slab2/
  • GEM Global Active Faults — github.com/GEMScienceTools/gem-global-active-faults

See also: Home · Models-Classical · Models-ML · Models-Employed · Methodology-History · Evaluation-and-Tests · Data-Sources · Glossary

⚠️ Disclaimer — read this. CAOS_SEISMIC produces probabilistic forecasts, not predictions. It is an independent research and education tool. It is NOT an official earthquake early-warning or civil-protection system, it does NOT predict when, where, or how large an earthquake will be, and it must NOT be used for life-safety, emergency, or evacuation decisions. Every number it publishes is a bounded, calibrated probability conditioned on the present state of seismicity — never an alarm, a countdown, or a "safe" state. A single outcome neither confirms nor refutes a probabilistic forecast.

It complements, and does not replace or speak for, official agencies — always follow your national seismological and civil-protection authorities (e.g. USGS, INGV, CSN (Chile, SENAPRED for civil protection), GeoNet, JMA). The software is provided "as is", without warranty of any kind (MIT License); the authors accept no liability for its use. Data are courtesy of their providers (USGS/ANSS, ISC/ISC-GEM, Global CMT, EMSC, CSN, and others) under their respective licenses and attribution terms. See Honest-Limits for the full epistemic context.

CAOS_SEISMIC · seismic.fasl-work.com · source · MIT

CAOS_SEISMIC

Conditional probabilistic seismic forecasting — forecasts, never predictions.

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