A general method for extracting the amplitude spectrum of the seismic wavelet from the seismic traces

小波 数学 振幅 光谱(功能分析) 地震反演 地震学 地质学 几何学 计算机科学 人工智能 物理 方位角 量子力学
作者
Haoqi Zhao,Jinghuai Gao,Junxiong Jia
出处
期刊:Inverse Problems [IOP Publishing]
卷期号:39 (6): 065015-065015 被引量:2
标识
DOI:10.1088/1361-6420/accf0b
摘要

Abstract When solving geophysical problems using reflection seismology, extracting the amplitude spectrum of the seismic wavelet (ASSW) from a seismic trace is the basis for many downstream works, such as deconvolution and sparse reflection coefficient inversion. This paper pertains to statistical methods for estimating ASSW, that is, separating the scattering effect of the formation from the amplitude spectrum of a seismic trace to obtain a band-limited ASSW. Common methods assume the reflection coefficient sequence (RCS) is white, making them unsatisfactory in many cases. Gao et al (2017 Inverse Problems 33 085005) proposed an ASSW extraction method based on contraction operator mapping (COM method) and experimentally proved it effective for non-white RCSs. However, this method is only valid for unimodal ASSW, which makes it unsuitable for most sources. We propose a general method for estimating ASSW which does not require the whiteness assumption of the RCS and does not restrict the specific form of ASSW. We show that the COM method has function representation limitations and redefines a new operator, proving that it is also a contractive mapping. Our method can be regarded as a general form of the COM method, filling the theoretical gap that existing methods cannot be applied to non-white RCS and non-unimodal ASSW. It provides a general method for the estimation of ASSW. We thoroughly verify this generality by simulating different source types while considering wavelet attenuation. Compared with the COM method, the proposed method has absolute advantages.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
3秒前
FYF完成签到 ,获得积分10
4秒前
量子星尘发布了新的文献求助10
5秒前
sad发布了新的文献求助10
5秒前
负责的山水完成签到,获得积分10
5秒前
6秒前
orixero应助珺儿采纳,获得10
6秒前
ZDddd发布了新的文献求助10
9秒前
上官若男应助MRM采纳,获得10
10秒前
linxi发布了新的文献求助10
11秒前
11秒前
123完成签到,获得积分10
12秒前
14秒前
14秒前
17秒前
17秒前
红叶发布了新的文献求助10
18秒前
CodeCraft应助jade采纳,获得10
18秒前
mjnrhw完成签到,获得积分20
18秒前
美丽完成签到 ,获得积分0
19秒前
老实莫言完成签到,获得积分20
19秒前
珺儿发布了新的文献求助10
20秒前
MRM发布了新的文献求助10
22秒前
小马甲应助愤怒的面包采纳,获得10
22秒前
JamesPei应助沉默的凝荷采纳,获得10
23秒前
量子星尘发布了新的文献求助10
23秒前
香蕉觅云应助mjnrhw采纳,获得10
24秒前
小蘑菇应助威武白桃采纳,获得10
24秒前
26秒前
迅速曲奇应助ClancyJacky采纳,获得60
27秒前
ytli发布了新的文献求助30
28秒前
大模型应助科研通管家采纳,获得10
28秒前
学术通zzz应助科研通管家采纳,获得10
28秒前
28秒前
31秒前
文艺谷蓝完成签到,获得积分10
31秒前
31秒前
yyy发布了新的文献求助10
32秒前
汉堡包应助许嘘嘘嘘嘘采纳,获得30
33秒前
33秒前
高分求助中
【提示信息,请勿应助】请使用合适的网盘上传文件 10000
Continuum Thermodynamics and Material Modelling 2000
The Oxford Encyclopedia of the History of Modern Psychology 1500
Green Star Japan: Esperanto and the International Language Question, 1880–1945 800
Sentimental Republic: Chinese Intellectuals and the Maoist Past 800
The Martian climate revisited: atmosphere and environment of a desert planet 800
Learning to Listen, Listening to Learn 520
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3867218
求助须知:如何正确求助?哪些是违规求助? 3409471
关于积分的说明 10663754
捐赠科研通 3133679
什么是DOI,文献DOI怎么找? 1728348
邀请新用户注册赠送积分活动 832968
科研通“疑难数据库(出版商)”最低求助积分说明 780510