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Modeling of magnetic anomalies is a fundamental tool in exploration geophysics. Since the appearance of early electronic computers, calculation of the magnetic field from models of the subsurface and the related inverse problem have played a major role in the geological interpretation of magnetic anomalies.An early mathematical formulation for anomalies due to 2D polygonal structures of uniform polarization is found in Talwani and Heirtzler (1962, 1964). Their algorithm remains the most used and cited to date. Thanks to its wide applicability, Talwani and Heirtzler's approach has become popular, both for expeditious interpretation of magnetic data and as a forward engine for inverse methods. Moreover, the aforementioned 2D formulation can be extended to 3D bodies (Plouff 1975(Plouff , 1976Talwani, 1965). More recently, Won and Bevis (1987) proposed an evolution of the original formulation by Talwani and Heirtzler which avoids the use of trigonometric functions, achieving a speed up of the calculation of magnetic anomaly.

Since the '60s of the last century, the calculation of the magnetic anomalies caused by 2D uniformly polarized bodies with polygonal cross‐section has been mainly performed using the popular algorithm of Talwani and Heirtzler (1962, 1964). Recently, Kravchinsky et al. (2019, https://doi.org/10.1029/2019GL082767) claimed errors in the above algorithm formulation, proposing new corrective formulas and questioning the effectiveness of almost 60 years of magnetic calculations. Here we show that the two approaches are equivalent and Kravchinsky et al.'s formulas simply represent an algebraic variant of those of Talwani and Heirtzler. Moreover, we analyze a large amount of random magnetic scenarios, involving both changing‐shape polygons and a realistic geological model, showing a complete agreement among the magnetic responses of the two discussed algorithms and the one proposed by Won and Bevis (1987, https://doi.org/10.1190/1.1442298). We release the source code of the algorithms in Julia and Python languages.

Potential field data in the form of gravity and magnetic anomalies have been used for long to characterize the structure of the subsurface, ranging from applications to the small scale as in the context of exploration geophysics (e.g.,

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