SEISAMUSE

文献阅读(27)

发表于 2025-04-23 分类于 work 本文字数： 1.5k 阅读时长 ≈ 1 分钟

Coherence-Based Characterization of a Long-Period Monochromatic Seismic Signal

去仪器响应练习

发表于 2025-04-23 分类于 seismology 本文字数： 1.3k 阅读时长 ≈ 1 分钟

用我的脚本下载了一堆的miniseed放在seed下，仪器响应文件放在resp文件夹下，利用obspy去除仪器响应，还保存为miniseed。

import os
from glob import glob
from obspy import read, read_inventory
# 设置路径
seed_folder = "seed"
resp_folder = "resp"
output_folder = "seed_corrected"
os.makedirs(output_folder, exist_ok=True)
seed_files = glob(os.path.join(seed_folder, "*.seed"))   # 遍历所有 seed 文件
for seed_file in seed_files:
    print(f"Processing {os.path.basename(seed_file)}")
    try:
        st = read(seed_file)   # 读取 MiniSEED 数据
        for tr in st:
            net = tr.stats.network
            sta = tr.stats.station
            loc = tr.stats.location
            cha = tr.stats.channel
            resp_file = os.path.join(resp_folder, f"RESP.{net}.{sta}.{loc}.{cha}") # 构造 RESP 文件路径
            if not os.path.exists(resp_file):
                print(f"RESP file not found: {resp_file}, skipping trace.")
                continue
            inventory = read_inventory(resp_file)   # 读取 RESP 文件作为 inventory
            # 去除仪器响应
            tr.remove_response(inventory=inventory, output="VEL", pre_filt=[0.001, 0.002, 0.3, 0.45 ], zero_mean=True, taper=True)
        output_file = os.path.join(output_folder, os.path.basename(seed_file)) # 输出文件名
        st.write(output_file, format="MSEED")
        print(f"Saved corrected data to {output_file}\n")
    except Exception as e:
        print(f"Error processing {seed_file}: {e}")

如何下载Fnet数据

发表于 2025-04-22 分类于 work 本文字数： 1.8k 阅读时长 ≈ 2 分钟

感谢Seisman写了FnetPy,可以很方便地下载数据。
安装：

1	pip install https://github.com/seisman/FnetPy/archive/master.zip

例子：

import os
from obspy.io.xseed import Parser
from FnetPy import Client
from datetime import datetime,timedelta
from obspy import read_inventory,read,Trace
from datetime import datetime
client = Client('用户名', '密码')

current_date=datetime(2013,1,1,0,0)
#current_date=datetime(2004,12,30,0,0)
end_date=datetime(2025,1,1,0,0)
output_dir='./seed'
raw_dir='./raw'
resp_dir='./resp'
os.makedirs(output_dir, exist_ok=True)
os.makedirs(raw_dir, exist_ok=True)
os.makedirs(resp_dir, exist_ok=True)
while (current_date<end_date):
    start_time = current_data
    start_str = current_date.strftime("%Y_%m_%d_%H_%M_%S")
    name=start_str+".seed"
    fname=os.path.join(raw_dir,name)
    output_file=os.path.join(output_dir, name)
    if os.path.exists(output_file):
        print(f"[跳过] 文件已存在: {output_file}")
        current_date+=timedelta(days=1)
        continue
    open(output_file, 'wb').close()  # 创建空文件
    if not os.path.exists(fname):
        print('download data from '+str(start_time)+' to '+str(end_time))
        client.get_waveform(start_time, duration_in_seconds=86400+3600,component="LHZ",filename=fname) # download
        print('download '+fname+' down!')
    current_date+=timedelta(days=1)
    st = read(fname) # read
    parser = Parser(fname) #parse
    parser.write_resp(resp_dir, zipped=False)
    for tr in st:
        resp_file = f"RESP.{tr.get_id()}"
        inv = read_inventory(os.path.join(resp_dir, resp_file))
        tr.remove_response(inventory=inv, output="VEL")
        #print(tr.stats)
    st.write(output_file, format="MSEED")
print('Finished')

这个脚本可以下载2013到2025年Fnet所有台站的LHZ连续波形数据，数据长度为25小时，overlap 1小时。去除仪器响应后的速度记录存放在seed文件夹中。每天的数据名称为%Y_%m_%d_%H_%M_%S.seed。账户需要去NIED去申请，当然用我的也可以。

obspy下载连续背景噪声数据

发表于 2025-04-21 分类于 work 本文字数： 1.6k 阅读时长 ≈ 1 分钟

利用obspy的MassDownloader下载连续的波形示例。

import obspy
from obspy.clients.fdsn.mass_downloader import RectangularDomain, CircularDomain, \
    Restrictions, MassDownloader
# Rectangular domain containing parts of southern Germany.
#domain = RectangularDomain(minlatitude=-15, maxlatitude=18,
#                           minlongitude=10, maxlongitude=22)
domain = CircularDomain(
        latitude=0,
        longitude=0,
        minradius=0.0,
        maxradius=12.0
        )
restrictions = Restrictions(
    # Get data for a whole year.
    starttime=obspy.UTCDateTime(2005, 1, 15),
    endtime=obspy.UTCDateTime(2007, 10, 15),
    # Chunk it to have one file per day.
    chunklength_in_sec=86400,
    # Considering the enormous amount of data associated with continuous
    # requests, you might want to limit the data based on SEED identifiers.
    # If the location code is specified, the location priority list is not
    # used; the same is true for the channel argument and priority list.
    network="??", station="????", location="*", channel="LHZ",
    # The typical use case for such a data set are noise correlations where
    # gaps are dealt with at a later stage.
    reject_channels_with_gaps=False,
    # Same is true with the minimum length. All data might be useful.
    minimum_length=0.0,
    # Guard against the same station having different names.
    minimum_interstation_distance_in_m=100.0)
# Restrict the number of providers if you know which serve the desired
# data. If in doubt just don't specify - then all providers will be
# queried.
#mdl = MassDownloader(providers=["IRIS","USGS","GFZ"])
mdl = MassDownloader()
mdl.download(domain, restrictions, mseed_storage="waveforms",
             stationxml_storage="stations")

有趣的程序和项目（二）

发表于 2025-04-17 更新于 2025-04-19 分类于 work 本文字数： 1.2k 阅读时长 ≈ 1 分钟

Fast analysis of the seismic rupture SSA2py is an emergent python based software that allows fast analysis of the seismic rupture, making possible the near-realtime identification of the rupture characteristics after a significant seismic event.
Fast matching filter Seismic matched-filter search for both CPU and GPU architectures.
MUSIC Teleseismic Back-Projection Perform the Back-Projection Imaging on the seismograms of large earthquakes recorded by large-scale dense arrays.
Seismic_BPMF Fully automated workflow for earthquake detection and location with the backprojection and matched filtering methods.
ARRU_seismic_backprojection ARRU Phase Picker: Attention Recurrent‐Residual U‐Net for Picking Seismic P‐ and S‐Phase Arrivals.
earthquake_back_projection Back-projection of high-frequency radiation from earthquake source using multiple arrays. Methodology is based on Ishii (2012).
earthquake_detection Codes used in the earthquake detection and location method presented in Beauce et al. 2019, DOI: 10.1029/2019JB018110. A real data example is also provided.
beampower Fast routines for seismic backprojection/beamforming for both CPU and GPU architectures.
Iterative Linear Stress Inversion (ILSI) Python package for stress tensor inversion.
Pyrocko A Python framework for efficient use of pre-computed Green’s functions in seismological and other physical forward and inverse source problems.