Multi-Parameter Controls on Megathrust Earthquakes Revealed by Explainable Artificial Intelligence in a Complex Orogenic System

The North-Eastern Himalaya, Indo-Burma Ranges, and Andaman-Nicobar together form one of the most seismically active and structurally intricate tectonic regions in the world, hosting numerous Mw ≥ 6.5 earthquakes throughout recorded history. Understanding the physical controls for the occurrence of such high-magnitude events is vital for improving hazard assessment and the prediction of possible regions of future earthquakes. Conventional methods often struggle to integrate a large number of geological, geodetic, and geophysical factors that influence earthquake generation, as all these factors together play a role in masking and amplifying the effects of one another. In this study, we address this challenge by developing a multi-parameter, explainable artificial intelligence (XAI)-based approach to identify the dominant factors influencing megathrust earthquakes in this region. We have used a clustering technique to compile 16 different parameters, like gravity anomalies, plate convergence rate, accumulated strain, sediment cover, slab geometry, crustal thickness, slab age, and seismic attenuation factor, to form a comprehensive input to the model. Since the study region represents two different tectonic setups- continent-continent collision zone in the Himalayan and the Andaman Arakan ocean-continent subduction zone in the Indo-Burmese ranges, therefore the dataset was separated based on their tectonic characteristics. A Fully Connected Neural Network (FCN) has been trained and deployed to classify earthquakes into Class 1 (Mw ≥ 6.5) and Class 0 (Mw < 6.5). An XAI technique, Layerwise Relevance Propagation (LRP), was applied to determine which of the parameters are heavily influencing the classification or model's predictions. LRP is an XAI method that traces a model’s prediction backward through the network and redistributes the output score with respect to input features to show which parts contributed the most.

LRP research reveals distinct and geologically consistent elements that determine the major players for the occurrence of earthquakes in the two tectonic regimes. In the continent–continent collision zone, composite strain, composite plate convergence velocity, gravity anomalies (Bouguer and free-air), and slab depth emerge as the dominant parameters influencing earthquake classification. Conversely, the oceanic subduction regime is primarily controlled by sediment thickness, gravity gradient, slab age, along with composite velocity and composite strain. Notably, higher values of composite velocity and composite strain are consistently associated with the occurrence of megathrust earthquakes in both tectonic settings, highlighting their fundamental role in strain accumulation and seismic rupture processes. The significance of sediment thickness may be understood by its influence on the roughness of the subduction interface. A thicker sediment cover makes subduction smoother by making the slab bathymetric imperfections less noticeable, whereas a thinner sediment cover makes the interface rougher, which causes more strain to build up along the megathrust. This process aligns with the frequent occurrence of megathrust earthquakes in the area, such as the 2004 Great Sumatra earthquake. The proposed model successfully captures this relationship between sediment thickness, strain accumulation, and seismic potential.

This first-order study demonstrates that combining XAI with multi-parameter tectonic datasets establishes a robust framework for identifying and understanding the primary causes of seismicity in complex orogenic/geodynamic systems.