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Predicting the magnitude of injection-induced earthquakes using machine learning techniques

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Abstract

Predicting the magnitude of induced earthquakes by underground injection is a critical strategy for risk assessment. This paper proposes the application of three machine learning techniques—support vector machine, probabilistic neural network, and AdaBoost algorithm—to predict the magnitude of the largest injection-induced earthquake (M) within a predetermined period. These machine learning techniques are used to model the relationships between ten input parameters—six seismicity indicators and four inputs related to injection wells—and earthquake magnitude classes (M < 3, 3 ≤ M < 4, and M ≥ 4). Models are applied to the earthquake and injection data for the Central Oklahoma region in the USA, and their input data are balanced using the data-level approach. The performance of each model is measured using the average recall of earthquake magnitude classes. The results show that balancing the training data improves the performance of the models, and the magnitude of induced earthquakes depends on the injection volume in the nine months before the earthquake prediction period. The parametric analysis of each model’s input reveals that induced earthquake magnitudes are more likely to occur when there are shorter distances between the bottom of injection wells and the crystalline basement. Among the investigated models, the support vector machine model trained on the data balanced using synthetic minority oversampling technique performed best by predicting an average of 72% of earthquake magnitude classes. Overall, the findings of this study will allow for predicting the magnitude of induced earthquakes and the development of an early warning system for policymakers and residents living in areas prone to injection-induced earthquakes.

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References

  • Adeli H, Panakkat A (2009) A probabilistic neural network for earthquake magnitude prediction. Neural Netw 22(7):1018–1024

    Article  Google Scholar 

  • Asim K, Martínez-Álvarez F, Basit A, Iqbal T (2017) Earthquake magnitude prediction in the Hindukush region using machine learning techniques. Nat Hazards 85(1):471–486

    Article  Google Scholar 

  • Asim K, Idris A, Iqbal T, Martinez-Alvarez F (2018) Seismic indicators based earthquake predictor system using Genetic Programming and AdaBoost classification. Soil Dyn Earthq Eng 111:1–7

    Article  Google Scholar 

  • Asim K, Moustafa S, Niaz I, Elawadi E, Iqbal T, Martinez-Alvarez F (2020) Seismicity analysis and machine learning models for short-term low magnitude seismic activity predictions in Cyprus. Soil Dyn Earthq Eng 130:105932

    Article  Google Scholar 

  • Barua S, Islam MM, Yao X, Murase K (2012) MWMOTE--majority weighted minority oversampling technique for imbalanced data set learning. IEEE Trans Knowl Data Eng 26(2):405–425

    Article  Google Scholar 

  • Batista G, Prati R, Monard M (2004) A study of the behavior of several methods for balancing machine learning training data. ACM SIGKDD Explor Newsl 6(1):20–29

    Article  Google Scholar 

  • Batista G, Bazzan A, Monard, M (2003) Balancing training data for automated annotation of keywords: a case study. WOB, pp 10–18

  • Bufe C, Varnes D (1993) Predictive modeling of seismic cycle in the greater San-Fransisco bay region. J Geophys Res Solid Earth 98(B6):9871–9883

    Article  Google Scholar 

  • Chawla N, Bowyer K, Hall L, Kegelmeyer W (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357

    Article  Google Scholar 

  • Dieterich J, Okubo P (1996) An unusual pattern of seismic quiescence in Kalapana. Geophys Res Lett 18(9):154–165

    Google Scholar 

  • Ellsworth WL (2013) Injection-induced earthquakes. Science 341(6142):1225942

    Article  Google Scholar 

  • Fernandez A, Garcia S, Galar M, Prati RC, Krawczyk B, Herrera F (2018) Learning from imbalanced data sets. Springer

    Book  Google Scholar 

  • Freund Y, Schapire RE (1995) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139

    Article  Google Scholar 

  • Frohlich C, Ellsworth W, Brown WA, Brunt M, Luetgert J, MacDonald T, Walter S (2014) The 17 May 2012 M 4.8 earthquake near Timpson, East Texas: an event possibly triggered by fluid injection. J Geophys Res 119(1):581–593

    Article  Google Scholar 

  • Gutenberg B, Richter CF (1956) Earthquake magnitude, intensity, energy and acceleration. Bull Seismol Soc Am 46(1):105–145

    Article  Google Scholar 

  • Han H, Wang W, Mao B (2005) Borderline-smote: a new over-sampling method in imbalanced data sets learning. In: International conference on intelligent computing, pp 878–887

  • Hart P (1968) The condensed nearest neighbor rule. IEEE Trans Inf Theory 14(3):515–516

    Article  Google Scholar 

  • Hastie T, Tibshirani R, Friedman J (2001) The elements of statistical learning. Springer

    Book  Google Scholar 

  • He H, Bai Y, Garcia E, Li S (2008) ADASYN: adaptive synthetic sampling approach for imbalanced learning. In: IEEE international joint conference on neural networks (IEEE World Congress on Computational Intelligence), pp 1322–1328

  • Horton S (2012) Disposal of hydrofracking waste fluid by injection into subsurface aquifers triggers earthquake swarm in central Arkansas with potential for damaging earthquake. Seismol Res Lett 83(2):250–260

    Article  Google Scholar 

  • James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning with applications in R. Springer

    Google Scholar 

  • Kecman V, Huang T, Vogt M (2005) Iterative single data algorithm for training kernel machines from huge data sets: theory and performance. Stud Fuzziness Soft Comput 177:255–274

    Article  Google Scholar 

  • Kubat M, Matwin S (1997) Addressing the curse of imbalanced training sets: one-sided selection. IcmI 97:179–186

    Google Scholar 

  • Lakkos S, Hadjiprocopis A, Comley R, Smith P (1994) A neural network scheme for earthquake prediction based on the seismic electric signals. In: Proceedings of IEEE signal workshop on neural networks for signal processing, pp 681–689

  • Langenbruch C, Weingarten M, Zoback MD (2018) Physics-based forecasting of man-made earthquake hazards in Oklahoma and Kansas. Nat Commun 9(1):1–10

    Article  Google Scholar 

  • Laurikkala J (2001) Improving identification of difficult small classes by balancing class distribution. In: Proceedings of the 8th conference on AI in medicine in Europe, pp 63–66

  • Leach R, Dowla F (1996) Earthquake early warning system using real-time signal processing, neural networks for signal processing. In: Proceedings of the 1996 IEEE signal processing society workshop, pp 463–472

  • Lemaitre G, Nogueira F, Aridas CK (2017) Imbalanced-learn: a python toolbox to tackle the curse of imbalanced datasets in machine learning. J Mach Learn Res 18(1):559–563

    Google Scholar 

  • Li Z, Meier M, Hauksson E, Zha Z, Andrews J (2018) Machine learning seismic wave discrimination: application to earthquake early warning. Geophys Res Lett 45(10):4773–4779

    Article  Google Scholar 

  • Lubbers N, Bolton DC, Mohd-Yusof J, Marone C, Barros K, Johnson PA (2018) Earthquake catalog-based machine learning identification of laboratory fault states and the effects of magnitude of completeness. Geophys Res Lett 45(24):13269–13276

    Article  Google Scholar 

  • Ma X, Chen T (2022) Small seismic events in Oklahoma detected and located by machine learning-based models. Bull Seismol Soc Am 112(6):2859

    Article  Google Scholar 

  • Ma L, Zhu L, Shi Y (1999) Attempts at using seismicity indicators for the prediction of large earthquakes by Genetic Algorithm-Neural Network method. Asia-Pac Econ Coop Earthq Simul Brisb Aust 31:483–489

    Google Scholar 

  • Mallouhy R, Abou Jaoude C, Guyeux C, Makhoul A (2019) Major earthquake event prediction using various machine learning algorithms. In: International conference on information and communication technologies for disaster management IEEE, pp 1–7

  • Marsan D (2003) Relative absence of large seismic quiescence following large earthquakes. Geophys Res Abstr 5(1):2876

    Google Scholar 

  • McGarr A, Simpson D, Seeber L (2002) Case histories of induced and triggered seismicity. Int Geophys Ser 81(A):647–664

    Article  Google Scholar 

  • Mehrabifard A, Eberhardt E, Yousefabadi Z (2021) Investigation of stress-, pore pressure-& depth dependence of induced seismicity b-value using supervised machine learning. In: 55th US Rock mechanics/geomechanics symposium

  • Mousavi SM, Beroza GC (2020) A machine-learning approach for earthquake magnitude estimation. Geophys Res Lett 47(1):e2019GL085976

    Article  Google Scholar 

  • Nguyen H, Cooper E, Kamei K (2011) Borderline over-sampling for imbalanced data classification. Int J Knowl Eng Soft Data Paradig 3(1):4–21

    Article  Google Scholar 

  • Ohtake M (1974) Seismic activity induced by water injection at Matsushiro, Japan. J Phys Earth 22(1):163–176

    Article  Google Scholar 

  • Ostad-Ali-Askari K, Shayan M (2021) Subsurface drain spacing in the unsteady conditions by HYDRUS-3D and artificial neural networks. Arab J Geosci 14(18):1–14

    Article  Google Scholar 

  • Panakkat A, Adeli H (2007) Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int J Neural Syst 17(1):13–33

    Article  Google Scholar 

  • Panakkat A, Adeli H (2008) Recent efforts in earthquake prediction (1990–2007). Nat Hazard Rev 9(2):70–80

    Article  Google Scholar 

  • Panakkat A, Adeli H (2009) Recurrent neural network for approximate earthquake time and location prediction using multiple seismicity indicators. Comput Aided Civ Infrastruct Eng 24(4):280–292

    Article  Google Scholar 

  • Pawley S, Schultz R, Playter T, Corlett H, Shipman T, Lyster S, Hauck T (2018) The geological susceptibility of induced earthquakes in the Duvernay play. Geophys Res Lett 45(4):1786–1793

    Article  Google Scholar 

  • Perol T, Gharbi M, Denolle M (2018) Convolutional neural network for earthquake detection and location. Sci Adv 4(2):e1700578

    Article  Google Scholar 

  • Platt J (1998) Sequential minimal optimization: a fast algorithm for training support vector machines. Microsoft Research, Technical Report MSR-TR-98-14

  • Rafiei MH, Adeli H (2017) NEEWS: a novel earthquake early warning model using neural dynamic classification and neural dynamic optimization. Soil Dyn Earthq Eng 100:417–427

    Article  Google Scholar 

  • Samui P, Kim D (2013) Determination of reservoir-induced earthquake using support vector machine and Gaussian process regression. Appl Geophys 10:229–234

    Article  Google Scholar 

  • Sharma M, Arora M (2005) Prediction of seismicity cycles in the Himalayas using artificial neural networks. Acta Geophys Pol 53(3):299–309

    Google Scholar 

  • Tiampo K, Rundle J, McGinnis S, Gross S, Klein W (2002) Mean-field threshold systems and phase dynamics: an application to earthquake fault systems. Europhys Lett 60(3):481–487

    Article  Google Scholar 

  • Tomek I (1976a) An experiment with the edited nearest-neighbor rule. IEEE Trans Syst Man Cybern 6(6):448–452

    Google Scholar 

  • Tomek I (1976b) Two modifications of CNN. IEEE Trans Syst Man Commun 6:769–772

    Google Scholar 

  • Walsh FR, Zoback MD (2015) Oklahoma’s recent earthquakes and saltwater disposal. Sci Adv 1(5):e1500195

    Article  Google Scholar 

  • Walsh FR, Zoback MD (2016) Probabilistic assessment of potential fault slip related to injection-induced earthquakes: application to north-central Oklahoma, USA. Geology 44(12):991–994

    Article  Google Scholar 

  • Wang P, Pozzi M, Small MJ, Harbert W (2015) Statistical method for early detection of changes in seismic rate associated with wastewater injection. Bull Seismol Soc Am 105(6):2852–2862

    Article  Google Scholar 

  • Wang P, Small MJ, Harbert W, Pozzi M (2016) A Bayesian approach for assessing seismic transitions associated with wastewater injections. Bull Seismol Soc Am 106(3):832–845

    Article  Google Scholar 

  • Wang Y, Han P, Lu X, Wu R, Huang J (2006) The performance comparison of AdaBoost and SVM applied to SAR ATR. In: CIE international conference on radar IEEE, pp 1–4

  • Weingarten M, Ge S, Godt JW, Bekins BA, Rubinstein JL (2015) High-rate injection is associated with the increase in U.S. mid-continent seismicity. Science 348(6241):1336–13406

    Article  Google Scholar 

  • Wilson D (1972) Asymptotic properties of nearest neighbor rules using edited data. IEEE Trans Syst Man Cybern 2(3):408–421

    Article  Google Scholar 

  • Zhang G, Zhu L (1999) Prediction of the 1997 strong earthquakes in Jiashi Xinjiang, China. Bull Seismol Soc Am 89(5):1171–1183

    Article  Google Scholar 

  • Zhang X, Zhang J, Yuan C, Liu S, Chen Z, Li W (2020) Locating induced earthquakes with a network of seismic stations in Oklahoma via a deep learning method. Sci Rep 10(1):1–12

    Google Scholar 

  • Zhang J, Mani I (2003) KNN approach to unbalanced data distributions: a case study involving information extraction. In: Proceedings of workshop on learning from imbalanced datasets

  • Zhou Z (2019) Ensemble methods: foundations and algorithms. Chapman and Hall/CRC

    Google Scholar 

  • Zhu B, Gao Z, Zhao J, Vanden Broucke S (2019) IRIC: an R library for binary imbalanced classification. SoftwareX 10:100341

    Article  Google Scholar 

  • Zoback MD, Harjes HP (1997) Injection-induced earthquakes and crustal stress at 9 km depth at the KTB deep drilling site, Germany. J Geophys Res 102(B8):18477–18491

    Article  Google Scholar 

  • Zoller G, Hainzel S, Kurths J (2002) A systematic test on precursory seismic quiescence in Armenia. Nat Hazards 26(3):245–263

    Article  Google Scholar 

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  1. School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran

    Javad N. Rashidi & Mehdi Ghassemieh

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  1. Javad N. Rashidi
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Data analysis was performed by JR. The first draft of the manuscript was written by JR and MG. All authors commented on the manuscript draft. Also, both authors read and approved the final draft of the manuscript.

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Correspondence to Mehdi Ghassemieh.

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Rashidi, J.N., Ghassemieh, M. Predicting the magnitude of injection-induced earthquakes using machine learning techniques. Nat Hazards 118, 545–570 (2023). https://doi.org/10.1007/s11069-023-06018-6

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