Several studies report observations of orbital cyclicity in seismic reflection data as distinct power spectral peaks that align with Milanković periodicities. It remains unclear, however, if hypothesis testing for orbital forcing using seismic data can be performed with statistical power comparable to directly sampled data, such as outcrop, drill core or borehole logs. In this study we aim to quantify this using Monte Carlo ensemble modelling to compare seismic and borehole log cyclostratigraphy. We develop a method for spectral background estimation that accounts for some of the amplitude and frequency effects inherent to seismic data. We then forward model the seismic response of an ensemble of models where the acoustic impedance approximates red noise, with and without an injected orbital signal from an astronomical solution. We demonstrate two examples: i) a simplified model with constant background velocity, constant sedimentation rate and a parametric seismic source wavelet, and ii) a real-world example based on ODP Site 1084 (Cape Basin). We observe that the sensitivity and specificity for the seismic case are strongly frequency-dependent, compared to the largely frequency-independent results for the borehole log cyclostratigraphy. For the real-world data example, we observe a spectral peak corresponding to 95 kyr eccentricity cyclicity with an uncalibrated confidence level of >95 %. Our Monte Carlo ensemble modelling, however, shows that the false positive rate at this frequency and confidence level is around 25 %, compared to around 5 % for the equivalent borehole log cyclostratigraphy. We also demonstrate short-period eccentricity modulation and bundling analysis applied to the seismic data, which is able to successfully invert for the model sedimentation rate for the simplified synthetic example. These results suggest that reliably identifying Milanković cycles from seismic reflection data is strongly dependent on the site geology, the geophysical parameters and the spectral frequency in question. Seismic examples should ideally be “ground truthed” against positive evidence of orbital cyclicity from a nearby borehole. In such cases, seismic data can be used to extrapolate borehole cyclostratigraphy data both laterally between boreholes and vertically beyond the maximum drilled depth. We suggest that sediment drifts are the sedimentary environment that is most promising for the detection of orbital cyclicity in seismic reflection images, and similar principles could also be applied to other geophysical reflection methods such as sub-bottom profilers.

中文翻译：

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地震旋回地层学：使用地震反射数据对轨道周期性进行假设检验


几项研究报告称，在地震反射数据中对轨道周期性的观察是与 Milanković 周期一致的不同功率谱峰值。然而，目前尚不清楚是否可以使用地震数据进行轨道强迫的假设检验，其统计功效与直接采样数据（如露头、钻芯或钻孔测井）相当。在这项研究中，我们旨在使用 Monte Carlo 集成建模来量化这一点，以比较地震和钻孔测井回旋地层学。我们开发了一种频谱背景估计方法，可以解释地震数据固有的一些振幅和频率效应。然后，我们对一组模型的地震响应进行正演建模，其中声阻抗近似于红噪声，有和没有来自天文解决方案的注入轨道信号。我们演示了两个示例：i） 具有恒定背景速度、恒定沉降速率和参数地震源小波的简化模型，以及 ii） 基于 ODP Site 1084（开普盆地）的真实示例。我们观察到，与钻孔测井回旋地层学的结果在很大程度上与频率无关的结果相比，地震情况的灵敏度和特异性与频率密切相关。对于实际数据示例，我们观察到一个对应于 95 kyr 偏心率周期性的光谱峰值，未校准的置信水平为 >95 %。然而，我们的蒙特卡洛集成模型表明，在这个频率和置信水平下的假阳性率约为 25%，而等效的钻孔测井回旋地层学的假阳性率约为 5%。 我们还演示了应用于地震数据的短周期偏心率调制和捆绑分析，它能够成功地反转简化合成示例的模型沉积速率。这些结果表明，从地震反射数据中可靠地识别米兰科维奇周期在很大程度上取决于现场地质、地球物理参数和所讨论的光谱频率。理想情况下，地震示例应根据附近钻孔的轨道周期性的积极证据进行“地面实况验证”。在这种情况下，地震数据可用于推断钻孔之间的横向和垂直超出最大钻孔深度的钻孔旋回地层学数据。我们认为沉积物漂移是最有希望检测地震反射图像中轨道周期性的沉积环境，类似的原理也可以应用于其他地球物理反射方法，例如海底剖面仪。