1 问题

Forecasting earthquakes is one of the most important problems in Earth science because of their devastating consequences. Current scientific studies related to earthquake forecasting focus on three key points: when the event will occur, where it will occur, and how large it will be.

地震预测是地球科学中的一个极为重要的问题，因为其具有毁灭性的后果。与地震预测相关的现代科学研究关注三个关键点：什么时候地震？什么地方地震？多大的地震？

In this competition, you will address when the earthquake will take place. Specifically, you’ll predict the time remaining before laboratory earthquakes occur from real-time seismic data.

在本竞赛中，需要指出何时发生地震。尤其是需要从实时地震数据中预测离地震发生还剩下多少时间。

If this challenge is solved and the physics are ultimately shown to scale from the laboratory to the field, researchers will have the potential to improve earthquake hazard assessments that could save lives and billions of dollars in infrastructure.

如果本竞赛被解决，并且可以将解决方法应用到实际当中，那么研究者可以提高地震预测水平，进而挽救无数生命和财产。

This challenge is hosted by Los Alamos National Laboratory which enhances national security by ensuring the safety of the U.S. nuclear stockpile, developing technologies to reduce threats from weapons of mass destruction, and solving problems related to energy, environment, infrastructure, health, and global security concerns.

Los Alamos National Laboratory举办了本次比赛。该实验室通过保证美国的核储备安全来加载国家安全，开发技术来降低大规模杀伤性武器的威胁，并且解决与能源、环境、基础设施、健康和全球安全相关的问题。

2 数据

The goal of this competition is to use seismic signals to predict the timing of laboratory earthquakes. The data comes from a well-known experimental set-up used to study earthquake physics. The acoustic_data input signal is used to predict the time remaining before the next laboratory earthquake (time_to_failure).

本竞赛的目标是使用地政信号来预测实验地震的时间。数据来源于著名的实验，其主要用于研究地震。输入信号acoustic_data被用来预测在下次实验地震（time_to_failure）之前还剩下多少时间。

The training data is a single, continuous segment of experimental data. The test data consists of a folder containing many small segments. The data within each test file is continuous, but the test files do not represent a continuous segment of the experiment; thus, the predictions cannot be assumed to follow the same regular pattern seen in the training file.

训练数据是一个单独的连续的实验数据块。测试数据在一个文件夹中，包含许多小数据块。每个测试文件的数据是连续的，但是测试文件并不表示是一个实验的连续数据块；因此测试集的预测结果不会与训练集保持相同的一般性模式。

For each seg_id in the test folder, you should predict a single time_to_failure corresponding to the time between the last row of the segment and the next laboratory earthquake.

对于测试文件夹中的每个seg_id，你可以预测一个单独的time_to_failure，其对应数据块最后一行和下次实验地震之间的时间。

File descriptions
train.csv - A single, continuous training segment of experimental data.
test - A folder containing many small segments of test data.
sample_sumbission.csv - A sample submission file in the correct format.

文件描述
train.csv - 一个单独的连续的训练实验数据块
test - 一个文件夹，包含许多小的测试数据块
sample_sumbission.csv - 上传结果文件示例

Data fields
acoustic_data - the seismic signal [int16]
time_to_failure - the time (in seconds) until the next laboratory earthquake [float64]
seg_id - the test segment ids for which predictions should be made (one prediction per segment)

数据域
acoustic_data - 地震信号 [int16]
time_to_failure - 到下次实验地震的剩余时间[float64]，单位是秒
seg_id - 测试数据块索引号，每个测试数据块对应着一个预测结果