Machine Learning Model Catches Every Major Earthquake in Test on Turkey’s North Anatolian Fault

Earthquake scientists have long dreamed of a tool that could tell them which stretch of a major fault is most likely to rupture next. A new study published in Earth Science Informatics brings that ambition a step closer to practical reality. Eray Ozkan of Manisa Celal Bayar University has built a machine learning framework that evaluates the seismic hazard potential of individual fault segments along the North Anatolian Fault Zone, one of the most dangerous strike-slip fault systems in the world. The framework was trained on 539 seismic events and designed to forecast major destructive earthquakes of magnitude 5.6 or greater, a threshold that marks the transition from nuisance shaking to genuinely life-threatening events.

The North Anatolian Fault Zone is a natural laboratory for this kind of work. It slices across northern Turkey for roughly 1,200 kilometers, accommodating the westward extrusion of the Anatolian plate as it grinds between the Eurasian and African-Arabian plate systems. The fault has a well-documented history of sequential ruptures, with large earthquakes migrating along its length over the past century, including the devastating 1999 Izmit and Duzce events. For the new study, the fault was divided into nine predefined segments, each treated as an analytical unit with its own seismic history and hazard profile. This fault-specific approach departs from broad regional hazard maps, which average risk over large areas and can obscure the concentrated danger posed by a particular locked segment.

At the heart of the framework is a dataset of 539 seismic events drawn from Turkish earthquake catalogs, including records maintained by the Turkish Accelerometric Database and Analysis System operated by AFAD, the country’s disaster and emergency management authority. From this catalog, Ozkan engineered a set of predictive features designed to capture what he describes as short-to-medium-term seismic memory indicators. These are statistical fingerprints of recent earthquake activity on a segment, such as patterns in the timing and magnitude of preceding events, that encode how the fault remembers its own recent behavior. The idea builds on decades of seismological thinking, from the classic Gutenberg-Richter frequency-magnitude relationship to the seismic gap hypothesis of McCann and colleagues in 1979, which holds that segments that have not ruptured recently accumulate the greatest pending hazard.

The machine learning core of the study is the Extreme Gradient Boosting algorithm, widely known as XGBoost. This ensemble technique builds a hierarchy of decision trees in sequence, with each new tree trained to correct the residual errors of its predecessors. Gradient boosting has become a workhorse of tabular data science because it handles heterogeneous features well, resists overfitting through regularization, and often outperforms deep neural networks on structured datasets of modest size. In head-to-head comparison within this study, XGBoost demonstrated superior predictive capability over alternative approaches, ultimately achieving a final test Area Under the Receiver Operating Characteristic Curve score of 0.8930. An AUC of that magnitude indicates that the model ranks a randomly chosen future major earthquake above a randomly chosen quiet period nearly nine times out of ten, a level of discrimination that is considered strong for a problem as notoriously difficult as earthquake forecasting.

Perhaps the most consequential part of the work is not the classifier itself but the calibration layer wrapped around it. Raw machine learning outputs are probabilities in name only; without calibration, a stated 40 percent chance may not correspond to any meaningful real-world frequency. Ozkan performed a high-resolution probability calibration and imposed what he calls a strict Zero Miss operational strategy. The logic is blunt: for disaster management, failing to warn of a major earthquake is far more costly than issuing occasional false alarms. The optimization therefore searched for the decision boundary that would capture every major event in the test set, even at the price of reduced precision. That optimal boundary was formally established at a 35 percent probability threshold.

The results at that threshold are striking. When the calibrated framework was evaluated, it successfully captured 100 percent of the major earthquakes in the test data, meaning no event of magnitude 5.6 or greater slipped through unflagged. At the same time, the model achieved a balanced accuracy of 87.3 percent, a metric that averages the detection rate for hazardous events and the correct rejection rate for quiet periods, thereby preventing a large class of negatives from inflating the score. In practical terms, the framework behaved like a smoke detector tuned to be extremely sensitive: it rarely stays silent when danger is present, while still avoiding the constant crying wolf that would erode institutional trust in any operational warning system.

With the classifier calibrated, the final step was deployment. Ozkan applied the framework to evaluate future hazard probabilities across each of the nine predefined fault segments for their upcoming statistical cycles. The output is a quantitative, segment-by-segment ranking of hazard potential, expressed as probabilities rather than vague qualitative categories. This kind of dynamic, data-driven assessment could give disaster management authorities a defensible basis for deciding where to position search-and-rescue capacity, pre-stage emergency supplies, prioritize retrofitting programs, and focus public preparedness campaigns. Static hazard maps, updated on decadal timescales, cannot offer that level of temporal responsiveness.

The study sits within a rapidly expanding field. Machine learning approaches to seismology have progressed from early artificial neural networks trained on magnitude time series and seismic electric signals, through radon monitoring models, to modern deep learning architectures such as LSTM networks applied to seismicity analysis and satellite data streams. Recent work has extended these methods to ground motion modeling for probabilistic seismic hazard analysis in subduction zones and to real-time prediction of spectral acceleration for earthquake early warning. What distinguishes the new framework is its explicit marriage of classification performance with operational threshold calibration, an element that much of the academic literature leaves as an afterthought but that determines whether a model can actually be used in an emergency management context.

Important caveats remain. The framework was developed and tested on a single fault system, and its 539-event dataset, while substantial for a fault-specific analysis, reflects the catalog completeness and magnitude homogeneity of the Turkish record. Earthquake prediction in the strict sense, meaning the deterministic specification of the time, place, and magnitude of a future event, remains beyond any existing method, and this study does not claim otherwise. What the framework offers is a probabilistic hazard potential, a statement about relative likelihood over statistical cycles, which is a fundamentally more modest and more defensible objective. The author also reports that no external datasets were generated or analyzed beyond the study itself, and the work was conducted without dedicated funding, underscoring the individual, exploratory nature of the research.

Even with those qualifications, the implications are considerable. Turkey’s population is concentrated along corridors that the North Anatolian Fault threads like a fuse, and the Marmara region in particular is regarded by many seismologists as overdue for a major rupture. A tool that can translate decades of cataloged seismicity into calibrated, segment-specific probabilities offers exactly the kind of quantitative guidance that logistical planners need, converting abstract seismological knowledge into actionable resource allocation. If the framework’s performance holds under independent validation and in other tectonic settings, threshold-calibrated machine learning could become a standard component of the seismic risk toolkit, sitting alongside probabilistic seismic hazard analysis and operational forecasting systems rather than replacing them. The study demonstrates that the bottleneck in earthquake forecasting may no longer be the raw predictive power of the algorithms, but the careful, operationally minded engineering that turns a good classifier into a trustworthy decision aid.

Subject of Research: Machine learning-based fault-specific seismic hazard assessment along the North Anatolian Fault Zone

Article Title: A threshold-calibrated machine learning framework for evaluating fault-specific seismic hazard potentials

Article References: Ozkan, E. (2026). A threshold-calibrated machine learning framework for evaluating fault-specific seismic hazard potentials. Earth Science Informatics, 19(11), Article 210. https://doi.org/10.1007/s12145-026-02265-w

Image Credits: AI Generated

DOI: 10.1007/s12145-026-02265-w

Keywords: seismic hazard, machine learning, North Anatolian Fault, XGBoost, earthquake forecasting, threshold calibration, probability calibration, fault segments, Turkey, disaster management, balanced accuracy, seismicity indicators