STEAD subsample 4 CDiffSD

STEAD Subsample Dataset for CDiffSD Training

Overview

This dataset is a subsampled version of the STEAD dataset, specifically tailored for training our CDiffSD model (Cold Diffusion for Seismic Denoising). It consists of four HDF5 files, each saved in a format that requires Python's `h5py` method for opening.

Dataset Files

The dataset includes the following files:

train: Used for both training and validation phases (with validation train split). Contains earthquake ground truth traces.

noise_train: Used for both training and validation phases. Contains noise used to contaminate the traces.

test: Used for the testing phase, structured similarly to train.

noise_test: Used for the testing phase, contains noise data for testing.

Each file is structured to support the training and evaluation of seismic denoising models.

Data

The HDF5 files named noise contain two main datasets:

traces: This dataset includes N number of events, with each event being 6000 in size, representing the length of the traces. Each trace is organized into three channels in the following order: E (East-West), N (North-South), Z (Vertical).

metadata: This dataset contains the names of the traces for each event.

Similarly, the train and test files, which contain earthquake data, include the same traces and metadata datasets, but also feature two additional datasets:

p_arrival: Contains the arrival indices of P-waves, expressed in counts.

s_arrival: Contains the arrival indices of S-waves, also expressed in counts.

Usage

To load these files in a Python environment, use the following approach:

```python

import h5pyimport numpy as np

# Open the HDF5 file in read modewith h5py.File('train_noise.hdf5', 'r') as file:    # Print all the main keys in the file    print("Keys in the HDF5 file:", list(file.keys()))

    if 'traces' in file:        # Access the dataset        data = file['traces'][:10]  # Load the first 10 traces

    if 'metadata' in file:        # Access the dataset        trace_name = file['metadata'][:10]  # Load the first 10 metadata entries```

Ensure that the path to the file is correctly specified relative to your Python script.

Requirements

To use this dataset, ensure you have Python installed along with the Pandas library, which can be installed via pip if not already available:

```bashpip install numpypip install h5py```

用于CDiffSD训练的STEAD子采样数据集

概述
本数据集为STEAD数据集的子采样版本，专为CDiffSD模型（Cold Diffusion for Seismic Denoising）训练定制。数据集包含四个HDF5文件，所有文件均需通过Python的`h5py`库读取。

数据集文件
数据集包含以下文件：
- train：用于训练与验证阶段（采用训练集验证集拆分方式），包含地震真值波形轨迹。
- noise_train：用于训练与验证阶段，包含用于污染波形轨迹的噪声数据。
- test：用于测试阶段，结构与train文件一致。
- noise_test：用于测试阶段，包含测试用噪声数据。

所有文件均适配地震降噪模型的训练与评估。

数据结构
命名含noise的HDF5文件包含两个核心数据集：
1. traces：该数据集包含N个事件，每个事件对应长度为6000的波形轨迹，每个轨迹按东-西（E）、北-南（N）、垂向（Z）三个通道组织。
2. metadata：该数据集包含每个事件对应的轨迹名称。

同理，包含地震数据的train与test文件，除上述traces与metadata数据集外，还额外包含两个数据集：
1. p_arrival：以采样点数为单位，存储P波的到达索引。
2. s_arrival：以采样点数为单位，存储S波的到达索引。

使用方法
在Python环境中加载该数据集的示例代码如下：
python
import h5py
import numpy as np

# 以只读模式打开HDF5文件
with h5py.File('train_noise.hdf5', 'r') as file:
# 打印文件内的所有主键
print("HDF5文件内的键列表:", list(file.keys()))

if 'traces' in file:
# 读取数据集
data = file['traces'][:10] # 加载前10条波形轨迹

if 'metadata' in file:
# 读取元数据集
trace_name = file['metadata'][:10] # 加载前10条元数据条目

请确保Python脚本中指定的文件路径正确无误。

使用要求
使用该数据集前，请确保已安装Python及Pandas库，若未安装可通过pip命令安装：
bash
pip install numpy
pip install h5py