3D Inversion of Underground Concealed Coal Fire Sources Based on Self-Potential Data in Xinjiang, China

Accurate localization of concealed fire sources is the fundamental prerequisite for effective coal fire control and mitigation. The self-potential method, as a passive, cost-effective geophysical technique for large-area surveys, exhibits unique advantages in coal fire detection, yet 3D inversion of self-potential data for high-precision coal fire source localization remains insufficiently studied. In this paper, a 3D iterative compact inversion algorithm based on the minimum support functional is adopted to invert self-potential data for 3D localization of underground concealed coal fire sources. The algorithm’s reliability and robustness are verified systematically via numerical simulations at both laboratory and field scales, followed by sandbox experiments with artificial battery sources and burning coal specimens, and finally validated its engineering applicability in the Sandaoba Coal Fire Area in Xinjiang. Numerical results show that the algorithm achieves high-precision inversion of source current density across multiple orders of magnitude, with a relative error below 10%. Sandbox experiments confirm that coal combustion generates a typical dipole self-potential field with distinct negative surface anomalies, whose amplitude is positively correlated with combustion intensity. Field inversion results successfully delineate the 3D boundary of the concealed fire source (buried at 40–140 m), which is highly consistent with borehole temperature measurement data. This study provides a robust technical framework for 3D detection of concealed coal fires, offering strong technical support for precise coal fire governance and hazard mitigation.