如何估计噪声层后面的高斯分布？

I would approach this using Kernel Density Estimation. I allows you to estimate the probability density directly from data, without too many assumptions about the underlying distribution. By changing the kernel bandwidth you can control how much smoothing you apply, which I assume could be tuned manually by visual inspection until you get something that meets your expectations. An example of KDE implementation in python using scikit-learn can be found here.

Example:

import numpy as np
from sklearn.neighbors import KernelDensity

# x is your original data
x = ...
# Adjust bandwidth to get the smoothness to your liking
bandwidth = ...

kde = KernelDensity(kernel='gaussian', bandwidth=bandwidth).fit(x)
support = np.linspace(min(x), max(x), 1000)
density = kde.score_samples(support)

一旦过滤分布估计，你可以分析和使用类似识别峰此。

from scipy.signal import find_peaks

# You can tweak with the other arguments of the 'find_peaks' function
# in order to fine-tune the extracted peaks according to your PDF
peaks = find_peaks(density)

免责声明：这是一个或多或少的高级答案，因为您的问题也很高级。我假设您知道自己在执行代码方面的工作，并且只是在寻找想法。但是，如果您需要任何具体的帮助，请向我们展示一些代码以及到目前为止您已经尝试过的内容，以便我们更加具体。