Indian Ocean geoid low, situated at the south of the Indian peninsula, is an intriguing feature. Hitherto, several interpretations have been put forth to explain the genesis and causative depths of the anomaly. In this work, we investigate the scaling spectral inversion approach utilizing gravity data to estimate the causative depth and nature of the source from this region. In practice, crustal or sub-surface heterogeneities follow scaling or fractal characteristics, and comparative studies across various regions suggest that the scaling power spectrum method provides better insights into the sub-surface source distribution. An optimum fitting and comparison between the modeled and observed power spectrum have been attained by utilizing two norm penalties e.g. L₁ and L₂ norms for various combinations of depth and scaling exponents. Equally important, the L₁ norm misfit function yields better results when compared to the L₂ norm function. We propose that estimated source depths (̴80 km and 120 km, respectively) are perfect examples of a half-space model with a source following scaling or fractal characteristics (scaling exponent value of ̴1.78) along the upper mantle boundary. However, the scaling spectrum windowing analysis of the studied region reveals a variable depth to long wavelength sources as 1014 km, 431 km, and 94 km, which conform well with the recently published depth range. Further, the weight of low and high wavenumbers indicates that the causative source depths might be due to the above-stated depth range rather than a single model fit. The study has demonstrated a good case for wider use of L₁ norm-based power spectrum inversion for source depth estimation from potential field data. We further suggest that the causative source for the Indian Ocean geoid low may extend deeper than upper mantle discontinuities.

印度洋大地水准面低地位于印度半岛南部，是一个有趣的特征。迄今为止，已经提出了几种解释来解释异常的成因和成因深度。在这项工作中，我们研究了利用重力数据来估计该区域源的成因深度和性质的缩放光谱反演方法。在实践中，地壳或地下非均质性遵循标度或分形特征，不同地区的比较研究表明，标度功率谱方法可以更好地了解地下源分布。通过利用两个范数罚分（例如 L ₁和 L ₂深度和缩放指数的各种组合的规范。同样重要的是，与L _{2相比，L 1}范数失配函数产生更好的结果规范函数。我们提出估计的源深度（分别为̴80 km和120 km）是半空间模型的完美示例，其源沿上地幔边界具有缩放或分形特征（缩放指数值̴1.78）。然而，研究区域的缩放光谱窗口分析显示，长波长源的深度可变，分别为 1014 公里、431 公里和 94 公里，这与最近公布的深度范围非常吻合。此外，低波数和高波数的权重表明，原因源深度可能是由于上述深度范围而不是单一模型拟合。该研究为更广泛地使用 L _{1提供了一个很好的案例}基于范数的功率谱反演，用于根据势场数据进行源深度估计。我们进一步认为印度洋大地水准面低压的成因可能比上地幔不连续面更深。